Research activity information

• Oct. 2023 Information Processing Society of Japan, CSS2023 Incentive award, Towards Modeling the Visual Recognition for Human Security Countermeasures Using Large-Scale Language Models

  鍛冶佳佑, 中野瑠人, 山田 明, 小澤 誠一


• Oct. 2022 Information Processing Society of Japan, CSEC Excellent Research Award, 機械学習を用いた悪性TLS通信の検知と通信特徴の推移に関する考察

  Kaisei Fujiwara, Seiichi Ozawa, Hiroyuki Kaisei, Chanho Park


• Oct. 2020 Information Processing Society of Japan, CSS2020 Concept Research Award, Darknet Scan Packet Analysis Using Port Embedding Vector

  Shintaro Ishikawa, Tao Ban, Seiichi Ozawa

  共同研究・競争的資金等の業績IDIRI


• Dec. 2019 Asia Pacific Neural Network Society, APNNS Excellent Service Award

  Seiichi Ozawa


• Apr. 2011 IEEE Computational Intelligence Society, EAIS 2011 Outstanding Paper Award, "Incremental Recursive Fisher Linear Discriminant for Online Feature Extraction"に関する研究

  OZAWA Seiichi, OHTA Ryohei

• Assessment of first-touch skills in robotic surgical training using hi-Sim and the hinotori surgical robot system among surgeons and novices.

  Takeshi Urade, Nobuaki Yamasaki, Munenori Uemura, Junichiro Hirata, Yasuyoshi Okamura, Yuki Mitani, Tatsuya Hattori, Kaito Nanchi, Seiichi Ozawa, Yasuo Chihara, Kiyoyuki Chinzei, Masato Fujisawa, Takumi Fukumoto

  PURPOSE: Surgeons' adaptability to robotic manipulation remains underexplored. This study evaluated the participants' first-touch robotic training skills using the hinotori surgical robot system and its simulator (hi-Sim) to assess adaptability. METHODS: We enrolled 11 robotic surgeons (RS), 13 laparoscopic surgeons (LS), and 15 novices (N). After tutorial and training, participants performed pegboard tasks, camera and clutch operations, energizing operations, and suture sponge tasks on hi-Sim. They also completed a suture ligation task using the hinotori surgical robot system on a suture simulator. Median scores and task completion times were compared. RESULTS: Pegboard task scores were 95.0%, 92.0%, and 91.5% for the RS, LS, and N groups, respectively, with differences between the RS group and LS and N groups. Camera and clutch operation scores were 93.1%, 49.7%, and 89.1%, respectively, showing differences between the RS group and LS and N groups. Energizing operation scores were 90.9%, 85.2%, and 95.0%, respectively, with a significant difference between the LS and N groups. Suture sponge task scores were 90.6%, 43.1%, and 46.2%, respectively, with differences between the RS group and LS and N groups. For the suture ligation task, completion times were 368 s, 666 s, and 1095 s, respectively, indicating differences among groups. Suture scores were 12, 10, and 7 points, respectively, with differences between the RS and N groups. CONCLUSION: First-touch simulator-based robotic skills were partially influenced by prior robotic surgical experience, while suturing skills were affected by overall surgical experience. Thus, robotic training programs should be tailored to individual adaptability.

  Nov. 2024, Langenbeck's archives of surgery, 409(1) (1), 332 - 332, English, International magazine

  Scientific journal


• eFedGauss: A Federated Approach to Fuzzy Multivariate Gaussian Clustering.

  Miha Ozbot, Seiichi Ozawa, Igor Skrjanc

  Jul. 2024, Proc. of 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1 - 8, English

  [Refereed]

  International conference proceedings


• Detection of Abnormal Regions in a Field by Semantic Segmentation Using Remote Sensing Data

  村中建太, 小澤誠一

  2024, システム制御情報学会論文誌, 37(11) (11)


• Securing Code With Context: Enhancing Vulnerability Detection Through Contextualized Graph Representations

  Muhammad Fakhrur Rozi, Tao Ban, Seiichi Ozawa, Akira Yamada, Takeshi Takahashi, Daisuke Inoue

  2024, IEEE Access, English

  [Refereed]

  Scientific journal


• Frameworks for Privacy-Preserving Federated Learning.

  Le Trieu Phong, Tran Thi Phuong, Lihua Wang, Seiichi Ozawa

  Last, Jan. 2024, IEICE Trans. Inf. Syst., 107(1) (1), 2 - 12, Japanese

  [Refereed]

  Scientific journal


• Researcher Network Visualization Using Matrix Researcher2vec

  Enna Hirata, Takahiro Yamashita, Seiichi Ozawa

  Last, Jul. 2023, Journal of Advanced Computational Intelligence and Intelligent Informatics, 27(4) (4), 603 - 608

  [Refereed][Invited]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Computational intelligence in cyber-physical systems and the Internet of Things

  Cesare Alippi, Seiichi Ozawa

  The emergence of nontrivial embedded sensor units and cyber-physical systems and the Internet of Things has made possible the design and implementation of sophisticated applications where large amounts of real-time data are collected, possibly to constitute a big data picture as time passes. Within this framework, intelligence mechanisms based on machine learning, neural networks, and brain computing approaches play a key role to provide systems with advanced functionalities. Intelligent mechanisms are needed to guarantee appropriate performances within an evolving, time-variant environment, optimally harvest the available and manage the residual energy, reduce the energy consumption of the whole system, identify and mitigate the occurrence of faults, and provide shields against cyber-attacks. The chapter introduces the above aspects of intelligence, whose functionalities are needed to boost the next generation of cyber-physical and Internet of Things applications, a smart world generation whose footprint is already around us.

  Jan. 2023, Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition, 251 - 267, English

  [Refereed][Invited]

  In book


• Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation.

  Muhammad Fakhrur Rozi, Tao Ban, Seiichi Ozawa, Akira Yamada, Takeshi Takahashi, Sangwook Kim, Daisuke Inoue

  2023, IEEE Access, 11, 102727 - 102745, English

  [Refereed]

  Scientific journal


• Understanding the Influence of AST-JS for Improving Malicious Webpage Detection

  Muhammad Fakhrur Rozi, Seiichi Ozawa, Tao Ban, Sangwook Kim, Takeshi Takahashi, Daisuke Inoue

  Corresponding, Dec. 2022, Applied Sciences, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Permissioned Blockchain-Based XGBoost for Multi Banks Fraud Detection.

  Septiviana Savitri Asrori, Lihua Wang, Seiichi Ozawa

  Corresponding, Dec. 2022, ICONIP (3), 683 - 692, English

  [Refereed]

  International conference proceedings

  Link to Kobe Univ. Repository (Kernel)


• eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees.

  Fuki Yamamoto, Seiichi Ozawa, Lihua Wang 0001

  Privacy protection has attracted increasing attention, and privacy concerns often prevent flexible data utilization. In most industries, data are distributed across multiple organizations due to privacy concerns. Federated learning (FL), which enables cross-organizational machine learning by communicating statistical information, is a state-of-the-art technology that is used to solve this problem. However, for gradient boosting decision tree (GBDT) in FL, balancing communication efficiency and security while maintaining sufficient accuracy remains an unresolved problem. In this paper, we propose an FL scheme for GBDT, i.e., efficient FL for GBDT (eFL-Boost), which minimizes accuracy loss, communication costs, and information leakage. The proposed scheme focuses on appropriate allocation of local computation (performed individually by each organization) and global computation (performed cooperatively by all organizations) when updating a model. It is known that tree structures incur high communication costs for global computation, whereas leaf weights do not require such costs and are expected to contribute relatively more to accuracy. Thus, in the proposed eFL-Boost, a tree structure is determined locally at one of the organizations, and leaf weights are calculated globally by aggregating the local gradients of all organizations. Specifically, eFL-Boost requires only three communications per update, and only statistical information that has low privacy risk is leaked to other organizations. Through performance evaluation on public data sets (ROC AUC, Log loss, and F1-score are used as metrics), the proposed eFL-Boost outperforms existing schemes that incur low communication costs and was comparable to a scheme that offers no privacy protection.

  Corresponding, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Apr. 2022, IEEE Access, 10, 43954 - 43963, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks

  Sachiko Kanamori, Taeko Abe, Takuma Ito, Keita Emura, Lihua Wang, Shuntaro Yamamoto, Le Trieu Phong, Kaien Abe, Sangwook Kim, Ryo Nojima, Seiichi Ozawa, Shiho Moriai

  Information Processing Society of Japan, 2022, Journal of Information Processing, 30, 789 - 795, English

  [Refereed][Invited]

  Scientific journal


• Frequency-Based Enhancement Network for Efficient Super-Resolution.

  Parichehr Behjati, Pau Rodríguez, Carles Fernández Tena, Armin Mehri, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzàlez 0001

  2022, IEEE Access, 10, 57383 - 57397, English

  [Refereed]

  Scientific journal


• An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis

  Nitta, Akihiro, Yuya Chonan, Satoshi Hayashi, Takuji Nakamura, Hiroyuki Tsuji, Noriyuki Murakami, Ryo Nishide, Takenao Ohkawa, Seiichi Ozawa

  MDPI, Nov. 2021, Engineering Proceedings, 9(1) (1), 1 - 4, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Logo Detection With No Priors

  Diego A. Velazquez, Josep M. Gonfaus, Pau Rodríguez, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzàlez

  In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.

  IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Jul. 2021, IEEE Access, 9, 106998 - 107011, English

  [Refereed]

  Scientific journal


• Outlier Detection by Privacy-Preserving Ensemble Decision Tree U sing Homomorphic Encryption.

  Kengo Itokazu, Lihua Wang, Seiichi Ozawa

  Corresponding, IEEE, Jul. 2021, International Joint Conference on Neural Networks(IJCNN), 1 - 7, English

  [Refereed]

  International conference proceedings


• JStrack: Enriching Malicious JavaScript Detection Based on AST Graph Analysis and Attention Mechanism.

  Muhammad Fakhrur Rozi, Tao Ban, Seiichi Ozawa, Sangwook Kim, Takeshi Takahashi, Daisuke Inoue

  Corresponding, Jul. 2021, Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science, 13109, 669 - 680, English

  [Refereed]

  International conference proceedings

  Link to Kobe Univ. Repository (Kernel)


• Port-Piece Embedding for Darknet Traffic Features and Clustering of Scan Attacks

  Shintaro Ishikawa, Seiichi Ozawa, Tao Ban

  Corresponding, Springer International Publishing, Dec. 2020, In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science, vol 12533. Springer, Cham., 593 - 603

  [Refereed]

  In book


• New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis

  Fuki Yamamoto, Lihua Wang, Seiichi Ozawa

  Corresponding, Springer International Publishing, Dec. 2020, In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science, vol 12533. Springer, Cham., 558 - 569, English

  [Refereed]

  In book


• Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images

  Pau Rodriguez, Diego Velazquez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzalez

  Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions.

  MDPI, Nov. 2020, APPLIED SCIENCES-BASEL, 10(22) (22), English

  [Refereed]

  Scientific journal


• Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

  Samuel Ndichu, Sangwook Kim, Seiichi Ozawa

  Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis. Therefore, accurate detection of JS codes that masquerade as innocuous scripts is vital. The existing deobfuscation methods assume that a specific tool can recover an original JS code entirely. For a multi-layer obfuscation, general tools realize a formatted JS code, but some sections remain encoded. For the detection of such codes, this study performs Deobfuscation, Unpacking, and Decoding (DUD-preprocessing) by function redefinition using a Virtual Machine (VM), a JS code editor, and a python int_to_str() function to facilitate feature learning by the FastText model. The learned feature vectors are passed to a classifier model that judges the maliciousness of a JS code. In performance evaluation, the authors use the Hynek Petrak's dataset for obfuscated malicious JS codes and the SRILAB dataset and the Majestic Million service top 10,000 websites for obfuscated benign JS codes. They then compare the performance to other models on the detection of DUD-preprocessed obfuscated malicious JS codes. Their experimental results show that the proposed approach enhances feature learning and provides improved accuracy in the detection of obfuscated malicious JS codes.

  Corresponding, INST ENGINEERING TECHNOLOGY-IET, Sep. 2020, CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 5(3) (3), 184 - 192, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences

  Muhammad Fakhrur Rozi, Sangwook Kim, Seiichi Ozawa

  JavaScript is a dynamic computer programming language that has been used for various cyberattacks on client-side web applications. Malicious behaviors in JavaScript are injected on purpose as the outputs of web applications, such as redirection and pop-up texts or images. It exploits vulnerabilities by using a variety of methods such as drive-by download or cross-site scripting. To protect users from such cyberattacks, we propose a deep neural network for detecting malicious JavaScript codes by examining their bytecode sequences. We use the V8 JavaScript compiler to generate a bytecode sequence, which corresponds to an abstract form of machine codes. The benefit of using bytecode representation is that we can easily break complex obfuscation in JavaScript. To identify the attacker's malicious intention, We adopt a deep pyramid convolutional neural network (DPCNN) combining with recurrent neural network models, which can handle long-range associations in a bytecode sequence. In our experiment, various recurrent networks are testified to encode temporal features of code behaviors, and our results show that the proposed approach provides high accuracy in detection of malicious JavaScript.

  Corresponding, IEEE, Jul. 2020, 2020 International Joint Conference on Neural Networks (IJCNN), 1 - 8, English

  [Refereed]

  International conference proceedings


• Deep Learning-based Object Detection for Crop Monitoring in Soybean Fields

  Muhammad Taufiq Pratama, Sangwook Kim, Seiichi Ozawa, Takenao Ohkawa, Yuya Chona, Hiroyuki Tsuji, Noriyuki Murakami

  In this paper, a soybean flower/seedpod detection system is built for collecting growing state information by introducing convolutional neural networks, aiming that observed plant states (e.g., #flowers and #seedpods) are used to predict the crop yields of soybeans by combining the environment information in future. To predict the crop yields (i.e., quantity of seedpods) precisely, it is considered important to know how the number of flowers are translated over time and how such flower transients can affect the final yields of soybeans. However, there has not existed a way to measure the number of flowers in real environments. For this purpose, We propose a deep learning approach to automatically detect flower and seedpod regions from images which are taken in real soybean fields without environmental control. Various object detection methods are compared, including RetinaNet, Faster R-CNN, and Cascade R-CNN. Ablation studies are provided to analyze how these methods perform on both flower and seedpod across different parameters. In our experimental results, Cascade R-CNN gives the best average precision (AP) of 89.6, while RetinaNet and Faster R-CNN give AP of 83.3 and 88.7, respectively. Cascade RCNN also attains the highest accuracy in detecting small objects, which are not easily detected by other models. With accurate detection, the system is expected to contribute to constructing high-performance measurement for soybean flowers and seedpods, which ultimately leads to better pipeline in evaluating plant status.

  Corresponding, IEEE, Jul. 2020, 2020 International Joint Conference on Neural Networks (IJCNN), 1 - 7, English

  [Refereed]

  International conference proceedings


• A study of IoT malware activities using association rule learning for darknet sensor data

  Seiichi Ozawa, Tao Ban, Naoki Hashimoto, Junji Nakazato, Jumpei Shimamura

  Along with the proliferation of Internet of Things (IoT) devices, cyberattacks towards these devices are on the rise. In this paper, we present a study on applying Association Rule Learning to discover the regularities of these attacks from the big stream data collected on a large-scale darknet. By exploring the regularities in IoT-related indicators such as destination ports, type of service, and TCP window sizes, we succeeded in discovering the activities of attacking hosts associated with well-known classes of malware programs. As a case study, we report an interesting observation of the attack campaigns before and after the first source code release of the well-known IoT malware Mirai. The experiments show that the proposed scheme is effective and efficient in early detection and tracking of activities of new malware on the Internet and hence induces a promising approach to automate and accelerate the identification and mitigation of new cyber threats.

  SPRINGER, Feb. 2020, INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 19(1) (1), 83 - 92, English, International magazine

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• t-Distributed Stochastic Neighbor Embedding Spectral Clustering using higher order approximations.

  Nicoleta Rogovschi, Sarah Zouinina, Basarab Matei, Issam Falih, Nistor Grozavu, Seiichi Ozawa

  Dec. 2019, Aust. J. Intell. Inf. Process. Syst., 17(1) (1), 78 - 86, English

  [Refereed]

  Scientific journal


• Exploring and Identifying Malicious Sites in Dark Web Using Machine Learning

  Yuki Kawaguchi, Seiichi Ozawa

  Corresponding, Springer International Publishing, Dec. 2019, In: Gedeon T., Wong K., Lee M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science, vol 11955. Springer, Cham., 319 - 327, English

  [Refereed]

  In book


• A Fast Privacy-Preserving Multi-Layer Perceptron Using Ring-LWE-Based Homomorphic Encryption

  Takehiro Tezuka, Lihua Wang, Takuya Hayashi, Seiichi Ozawa

  Corresponding, IEEE, Nov. 2019, 2019 International Conference on Data Mining Workshops (ICDMW), 37 - 44, English

  [Refereed]

  International conference proceedings


• A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

  Samuel Ndichu, Sangwook Kim, Seiichi Ozawa, Takeshi Misu, Kazuo Makishima

  Websites attract millions of visitors due to the convenience of services they offer, which provide for interesting targets for cyber attackers. Most of these websites use JavaScript (JS) to create dynamic content. The exploitation of vulnerabilities in servers, plugins, and other third-party systems enables the insertion of malicious codes into websites. These exploits use methods such as drive-by-downloads, pop up ads, and phishing attacks on news, porn, piracy, torrent or free software websites, among others. Many of the recent cyber-attacks exploit JS vulnerabilities, in some cases employing obfuscation to hide their maliciousness and evade detection. It is, therefore, primal to develop an accurate detection system for malicious JS to protect users from such attacks. This study adopts Abstract Syntax Tree (AST) for code structure representation and a machine learning approach to conduct feature learning called Doc2vec to address this issue. Doc2vec is a neural network model that can learn context information of texts with variable length. This model is a well-suited feature learning method for JS codes, which consist of text content ranging among single line sentences, paragraphs, and full-length documents. Besides, features learned with Doc2Vec are of low dimensions which ensure faster detections. A classifier model judges the maliciousness of a JS code using the learned features. The performance of this approach is evaluated using the D3M dataset (Drive-by-Download Data by Marionette) for malicious JS codes and the JSUNPACK plus Alexa top 100 websites datasets for benign JS codes. We then compare the performance of Doc2Vec on plain JS codes (Plain-JS) and AST form of JS codes (AST-JS) to other feature learning methods. Our experimental results show that the proposed AST features and Doc2Vec for feature learning provide better accuracy and fast classification in malicious JS codes detection compared to conventional approaches and can flag malicious JS codes previously identified as hard-to-detect. (C) 2019 The Authors. Published by Elsevier B.V.

  Corresponding, ELSEVIER, Nov. 2019, APPLIED SOFT COMPUTING, 84, English, International magazine

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Extraction of Soil Moisture Change Involved in Soybean Yield by Similarity Evaluation Encompassing Time Series Data

  Kurumi Higashiyama, Ryo Nishide, Takenao Ohkawa, Yuya Chonan, Satoshi Hayashi, Takuji Nakamura, Hiroyuki Tsuji, Noriyuki Murakami, Seiichi Ozawa

  Sep. 2019, ICISIP 2019 : The 7th IIAE International Conference on Intelligent Systems and Image Processing 2019, 278 - 285, English

  [Refereed]

  International conference proceedings


• Feature Selection and Grouping of Cultivation Environment Data to Extract High/Low Yield Inhibition Factor of Soybeans

  Katsuhiro Nagata, Midori Namba, Seiichi Ozawa, Yuya Chonan, Satoshi Hayashi, Takuji Nakamura, Hiroyuki Tsuji, Noriyuki Murakami, Ryo Nishide, Takenao Ohkawa

  Jun. 2019, 12th European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) International Conference, English

  [Refereed]

  International conference proceedings


• An Image Sensing Method to Capture Soybean Growth State for Smart Agriculture Using Single Shot MultiBox Detector

  OMURA Kazuki, YAHATA So, OZAWA Seiichi, OHKAWA Takenao, CHONAN Yuya, TSUJI Hiroyuki, MURAKAMI Noriyuki

  Recently, smart agriculture, a new approach to farming using ICT, has been received great attention. To control cultivate condition precisely, it is important to capture the growth state of plants as well as environmental factors such as temperature, moisture, solar radiation, etc. In this paper, we propose an image sensing method to detect soy flowers and seedpods as growth fa

  IEEE SMC, Oct. 2018, Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics, 1 - 6, English

  [Refereed]

  International conference proceedings


• Detection of Malicious JavaScript Contents Using Doc2vec Feature Learning

  WANGAR Samuel Ndichu, OZAWA Seiichi, MUSU Takesh, OKADA Kouichirou

  To add more functionality and enhance usability of web applications, JavaScript (JS) is frequently used. Even with many advantages and usefulness of JS, an annoying fact is that many recent cyberattacks such as drive-by-download attacks exploit vulnerability of JS codes. In general, malicious JS codes are not easy to detect, because they sneakily exploit vulnerabilities of brow

  IEEE, Jul. 2018, Proc. of 2018 International Joint Conference on Neural Networks, 1 - 7, English

  [Refereed]

  International conference proceedings


• Optimal pattern Discovery to Reveal the High Yield Inhibition Factor of Soybeans

  NAMBA Midori, UMEJIMA Kohei, NISHIDE Ryo, OHKAWA Takenao, OZAWA Seiichi, MURAKAMI Noriyuki, TSUJI Hiroyuki

  Our research group is working on soybeans which the quantity of yielding is difficult to predict. We focus on the common characteristics observed at multiple cultivation points, in order to examine methods to acquire new knowledge in deciding the work based on the amount of yields. Our previous study has examined a method to discover optimal patterns using qualitative value of

  Apr. 2018, Journal of the Institute of Industrial Applications Engineers (Web), 6(2) (2), 66‐72 (WEB ONLY), English

  [Refereed][Invited]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Computational intelligence in the time of cyber-physical systems and the internet of things

  Cesare Alippi, Seiichi Ozawa

  The emergence of nontrivial embedded sensor units and cyber-physical systems and the Internet of Things has made possible the design and implementation of sophisticated applications where large amounts of real-time data are collected, possibly to constitute a big data picture as time passes. Within this framework, intelligence mechanisms based on machine learning, neural networks, and brain computing approaches play a key role to provide systems with advanced functionalities. Intelligent mechanisms are needed to guarantee appropriate performances within an evolving, time-variant environment, optimally harvest the available energy and manage the residual energy, reduce the energy consumption of the whole system, identify and mitigate occurrence of faults, and provide shields against cyberattacks.

  Elsevier, Jan. 2018, Artificial Intelligence in the Age of Neural Networks and Brain Computing, 245 - 263, English

  In book


• A Machine Learning Approach to Malicious JavaScript Detection using Fixed Length Vector Representation

  Samuel Ndichu, Seiichi Ozawa, Takeshi Misu, Kouichirou Okada

  To add more functionality and enhance usability of web applications, JavaScript (JS) is frequently used. Even with many advantages and usefulness of JS, an annoying fact is that many recent cyberattacks such as drive-by-download attacks exploit vulnerability of JS codes. In general, malicious JS codes are not easy to detect, because they sneakily exploit vulnerabilities of browsers and plugin software, and attack visitors of a web site unknowingly. To protect users from such threads, the development of an accurate detection system for malicious JS is soliciting. Conventional approaches often employ signature and heuristic-based methods, which are prone to suffer from zero-day attacks, i.e., causing many false negatives and/or false positives. For this problem, this paper adopts a machinelearning approach to feature learning called Doc2Vec, which is a neural network model that can learn context information of texts. The extracted features are given to a classifier model (e.g., SVM and neural networks) and it judges the maliciousness of a JS code. In the performance evaluation, we use the D3M Dataset (Drive-by-Download Data by Marionette) for malicious JS codes and JSUPACK for benign ones for both training and test purposes. We then compare the performance to other feature learning methods. Our experimental results show that the proposed Doc2Vec features provide better accuracy and fast classification in malicious JS code detection compared to conventional approaches.

  Corresponding, IEEE, 2018, 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018-July, 1 - 8, English

  [Refereed]

  International conference proceedings


• Large-scale cyber attacks monitoring using Evolving Cauchy Possibilistic Clustering

  Igor Skrjanc, Seiichi Ozawa, Tao Ban, Dejan Dovzan

  We are living in an information age where all our personal data and systems are connected to the Internet and accessible from more or less anywhere in the world. Such systems can be prone to cyber-attacks; therefore the monitoring and identification of cyber-attacks play a significant role in preventing the abuse of our data and systems. The majority of such systems proposed in the literature are based on a model/classifiers built with the help of classical/off-line learning methods on a learning data set. Since cyber-attacks evolve over time such models or classifiers sooner or later become outdated. To keep a proper system functioning the models need to be updated over a period of time. When dealing with models/classifiers learned by classical off-line methods, this is an expensive and time-consuming task. One way to keep the models updated is to use evolving methodologies to learn and adapt the models in an on-line manner. Such methods have been developed, extensively studied and implemented for regression problems. The presented paper introduces a novel evolving possibilistic Cauchy clustering (eCauchy) method for classification problems. The given method is used as a basis for large-scale monitoring of cyber-attacks. By using the presented method a more flexible system for detection of attacks is obtained. The approach was tested on a database from 1999 KDD intrusion detection competition. The obtained results are promising. The presented method gives a comparable degree of accuracy on raw data to other methods found in the literature; however, it has the advantage of being able to adapt the classifier in an on-line manner. The presented method also uses less labeled data to learn the classifier than classical methods presented in the literature decreasing the costs of data labeling. The study is opening a new possible application area for evolving methodologies. In future research, the focus will be on implementing additional data filtering and new algorithms to optimize the classifier for detection of cyber-attacks. (C) 2017 Elsevier B.V. All rights reserved.

  ELSEVIER, Jan. 2018, APPLIED SOFT COMPUTING, 62, 592 - 601, English

  [Refereed]

  Scientific journal


• An Image Sensing Method to Capture Soybean Growth State for Smart Agriculture Using Single Shot MultiBox Detector

  Kazuki Omura, So Yahata, Seiichi Ozawa, Takenao Ohkawa, Yuya Chonan, Hiroyuki Tsuji, Noriyuki Murakami

  Recently, smart agriculture, a new approach to farming using ICT, has been received great attention. To control cultivate condition precisely, it is important to capture the growth state of plants as well as environmental factors such as temperature, moisture, solar radiation, etc. In this paper, we propose an image sensing method to detect soy flowers and seedpods as growth factors using a state-of-the-art deep learning architecture called Single Shot MultiBox Detector (SSD). Images of soybeans were taken at Hokkaido Agricultural Research Center from Year 2015 to 2017 and we carry out the performance test for our system using a dataset of soybean images. The detection accuracy for seedpods and flowers are 0.586 and 0.646 in F-measure, respectively.

  IEEE, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 1693 - 1698, English

  [Refereed]

  International conference proceedings


• A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning

  Naoki Hashimoto, Seiichi Ozawa, Tao Ban, Junji Nakazato, Jumpei Shimamura

  In this paper, we report an interesting observation of the darknet traffic before the source code of IoT malware Mirai was first opened on September 7th 2016. In our darknet analysis, the frequent pattern mining and the association rule learning were performed to a large set of TCP SYN packets collected from July 1st 2016 to September 15th 2016 with the NICT /16 darknet sensor. The number of collected packets is 1,840,973,403 packets in total which were sent from 17,928,006 unique hosts. In this study, we focus on the frequently appeared combinations of "window sizes" in TCP headers. We successfully extracted a certain number of frequent patters and association rules on window sizes, and we specified source hosts that sent out SYN packets matched with either of the extracted rules. In addition, we show that almost all such hosts sent SYN packets satisfying the three conditions known from the source code of Mirai. Such hosts started their scan activities from August 2nd 2016, and ended on September 4th 2016 (i.e., 3 days before the source code was opened). (C) 2018 The Authors. Published by Elsevier Ltd.

  Corresponding, ELSEVIER SCIENCE BV, 2018, INNS CONFERENCE ON BIG DATA AND DEEP LEARNING, 144(144) (144), 118 - 123, English

  [Refereed]

  International conference proceedings

  Link to Kobe Univ. Repository (Kernel)


• Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption

  Sangwook Kim, Masahiro Omori, Takuya Hayashi, Toshiaki Omori, Lihua Wang, Seiichi Ozawa

  Many services for data analysis require customer's data to be exposed and privacy issues are critical in related fields. To address this problem, we propose a Privacy-Preserving Naive Bayes classifier (PP-NBC) model which provides classification results without leaking privacy information in data sources. Through classification process in PP-NBC, the operations are evaluated using encrypted data by applying fully homomorphic encryption scheme so that service providers are able to handle customer's data without knowing their actual values. The proposed method is implemented with a homomorphic encryption library called HElib and we carry out a primitive performance evaluation for the proposed PP-NBC.

  Corresponding, SPRINGER INTERNATIONAL PUBLISHING AG, 2018, NEURAL INFORMATION PROCESSING (ICONIP 2018), PT IV, 11304, 349 - 358, English

  [Refereed]

  International conference proceedings


• Optimal Pattern Discovery based on Cultivation Data for Elucidation of High Yield Inhibition Factor of Soybean

  NAMBA Midori, UMEJIMA Kohei, NISHIDE Ryo, OHKAWA Takenao, OZAWA Seiichi, MURAKAMI Noriyuki, TSUJI Hiroyuki

  Sep. 2017, Proceedings of IIAE International Conference on Intelligent Systems and Image Processing (Web), 5th, 209‐216 (WEB ONLY), English

  [Refereed]

  International conference proceedings


• Optimal Pattern Discovery based on Cultivation Data for Elucidation of High Yield Inhibition Factor of Soybean

  Midori Namba, Kohei Umejima, Ryo Nishide, Takenao Ohkawa, Seiichi Ozawa, Noriyuki Murakami, Hiroyuki Tsuji

  There is a high demand to promote efficiency in agriculture, as the number of workers engaging in agriculture has been decreasing and aging in recent years. Our research group is working on soybeans which the quantity of yielding is difficult to predict. We focus on the common characteristics observed at multiple cultivation points, in order to examine methods to acquire new kn

  The Institute of Industrial Applications Engineers, Jul. 2017, The 5th IIAE International Conference on Intelligent Systems and Image Processing 2017 (ICISIP2017), 209 - 216, English

  [Refereed]

  International conference proceedings


• t-Distributed Stochastic Neighbor Embedding Spectral Clustering

  Nicoleta Rogovschi, Jun Kitazono, Nistor Grozavu, Toshiaki Omori, Seiichi Ozawa

  This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method and spectral clustering. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [1] which are computational expensive and is not easy to apply on large-scale data sets. One of the issue of this problem is to reduce the dimensionality befor to cluster the dataset. The t-SNE method which performs good results for visulaization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t-SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.

  IEEE, 2017, 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017-May, 1628 - 1632, English

  [Refereed]

  International conference proceedings


• A Hybrid Machine Learning Approach to Automatic Plant Phenotyping for Smart Agriculture

  So Yahata, Tetsu Onishi, Kanta Yamaguchi, Seiichi Ozawa, Jun Kitazono, Takenao Ohkawa, Takeshi Yoshida, Noriyuki Murakami, Hiroyuki Tsuji

  Recently, a new ICT approach to agriculture called "Smart Agriculture" has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.

  IEEE, 2017, 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 1787 - 1793, English

  [Refereed]

  International conference proceedings


• AI Web-Contents Analyzer for Monitoring Underground Marketplace

  Yuki Kawaguchi, Akira Yamada, Seiichi Ozawa

  It is well known that products for cyber-attacks such as exploits and malware codes are illegally traded on hidden web services called Dark Web that are not indexed by conventional search engines we usually use. In general, it is not easy to capture the whole picture of trade activities on Dark Web because special browsers and tools are needed to visit such dark market sites and forums. And they usually require us to make a registration and/or to pass a qualification test. However, to understand the trends of cyber-attacks, there is no doubt that Dark Web is one of the useful information sources. In this paper, we try to understand the sales trends of illegal products for cyber-attacks from the largest marketplace called AlphaBay, which is relatively easier to collect information without passing any qualification tests, To monitor business trades on Dark Web, we develop an AI web-contents analyzer, which consists of a Tor crawler to collect the product information and a topic analyzer to capture the trends of what people are interested in and popular products of cyber-attacks. For this purpose, we use a topic model called Latent Dirichlet Allocation (LDA) and we show that the topic analysis would be helpful for predicting new cyber-attacks.

  SPRINGER INTERNATIONAL PUBLISHING AG, 2017, NEURAL INFORMATION PROCESSING, ICONIP 2017, PT V, 10638(5) (5), 888 - 896, English

  [Refereed]

  International conference proceedings


• Privacy Preserving Extreme Learning Machine Using Additively Homomorphic Encryption

  Shohei Kuri, Takuya Hayashi, Toshiaki Omori, Seiichi Ozawa, Yoshinori Aono, Le Trieu Phong, Lihua Wang, Shiho Moriai

  Recently, computational outsourcing using cloud services is getting popular for big data analysis, and many cloud sourcing providers provide machine learning platforms where we can perform various prediction and classification tasks very easily. On the other hand, there still remains a big hurdle to analyze personal big data on cloud services because the leakage of personal information is a critical issue. As a remedy for this, we propose a privacy preserving machine learning algorithm for Extreme Learning Machine (PP-ELM), which can learn from data encrypted with an additively homomorphic encryption. In the proposed outsourcing method, we consider a three-participants model consisting of data contributors, outsourced server, and data analyst. A data contributor preprocesses and encrypts data, and an outsourced server receives encrypted data and calculate hidden layer outputs using additive operations. Then, a data analyst receives the hidden outputs of ELM from the outsourced server and they are used to obtain ELM connection weights. Since the proposed outsourcing model can learn ELM over encrypted data, it is expected to mitigate a hurdle to deal with personal data on cloud services. In addition, the proposed PP-ELM allows us to learn multiple sources of personal data in a secure way, and this might lead to a better solution for a practical problem than before.

  IEEE, 2017, 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2017(2) (2), 1350 - 1357, English

  [Refereed]

  International conference proceedings


• Evolving Cauchy Possibilistic Clustering and Its Application to Large-Scale Cyberattack Monitoring

  Igor Skrjanc, Seiichi Ozawa, Dejan Dovzan, Ban Tao, Junji Nakazato, Jumpei Shimamura

  This paper gives the idea of large-scale monitoring for cyberattacks using evolving Cauchy possibilistic clustering (eCauchy). The idea of density based clustering is appealing when the data samples are highly noisy and when also the outliers appears frequently. The basic measure of density in recursive form can be modified in a way to be applied on classification problems such as large-scale monitoring for cyberattacks. The algorithm is in on-line form to deal with the data streams and is therefore appropriate for dealing with big-data problems. The development of density as a measure of similarity follows from Cauchy density and is similar to the typicality defined in the possibilistic clustering approach. The described eCauchy clustering deals with just few tuning parameters, such as maximal density. The algorithm evolves the structure during operation by adding and removing the clusters. This is appropriate for data granulation which is of great importance in the case of the clusters which are of different sizes and shapes. In the proposed large-scale monitoring system, darknet sensor packets within a certain period are transformed into 17 traffic features and they are categorized by eCauchy in an on-line fashion. To evaluate the proposed darknet monitoring system, a large set of TCP and UDP packets collected from January 2nd 2016 to March 1st 2016 (60 days) with the NICT /16 darknet sensor are used for evaluation. Our experimental results demonstrate that the proposed monitoring system can detect DDoS backscatter with more than 98% accuracy for TCP packets and non-DDoS backscatter with 72.8% accuracy for UDP packets. The proposed system can learn and predict quite fast, 12.6 sec. for TCP and 312.6 sec. for UDP.

  Corresponding, IEEE, 2017, 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2018-January, 1 - 7, English

  [Refereed]

  International conference proceedings


• Optimal pattern mining from time-series cultivation data of soybeans for knowledge discovery

  Kohei Umejima, Noriyuki Murakami, Fumihito Arimitsu, Hiroyuki Tsuji, Seiichi Ozawa, Takenao Ohkawa

  Recently, we can analyze and store big data by high performance computers. In this paper, we present a method of optimal pattern mining from soybean cultivation data for knowledge discovery by introducing an evaluation function based on differences in the frequency of high-yields and lowyields. We can discover factors affecting the growth of soybeans by analyzing optimal patterns extracted using evaluation functions. In our proposed method, optimal patterns are enumerated by eliminating elements that decrease the value of evaluation functions from frequent closed patterns. As a result, our experiment showed the efficiency of the proposed method. In addition, we can observe both general and new knowledge by analyzing extracted optimal pattern groups.

  Association for Computing Machinery, Dec. 2016, ACM International Conference Proceeding Series, 19 - 24, English

  [Refereed]

  International conference proceedings


• A fast online learning algorithm of radial basis function network with locality sensitive hashing

  Siti Hajar Aminah Ali, Kiminori Fukase, Seiichi Ozawa

  In this paper, we propose a new incremental learning algorithm of radial basis function (RBF) Network to accelerate the learning for large-scale data sequence. Along with the development of the internet and sensor technologies, a time series of large data chunk are continuously generated in our daily life. Thus it is usually difficult to learn all the data within a short period. A remedy for this is to select only essential data from a given data chunk and provide them to a classifier model to learn. In the proposed method, only data in untrained regions, which correspond to a region with a low output margin, are selected. The regions are formed by grouping the data based on their near neighbor using locality sensitive hashing (LSH), in which LSH has been developed to search neighbors quickly in an approximated way. As the proposed method does not use all training data to calculate the output margins, the time of the data selection is expected to be shortened. In the incremental learning phase, in order to suppress catastrophic forgetting, we also exploit LSH to select neighbor RBF units quickly. In addition, we propose a method to update the hash table in LSH so that the data selection can be adaptive during the learning. From the performance of nine datasets, we confirm that the proposed method can learn large-scale data sequences fast without sacrificing the classification accuracies. This fact implies that the data selection and the incremental learning work effectively in the proposed method.

  SPRINGER HEIDELBERG, Sep. 2016, EVOLVING SYSTEMS, 7(3) (3), 173 - 186, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• A fast online learning algorithm of radial basis function network with locality sensitive hashing

  Siti Hajar Aminah Ali, Kiminori Fukase, Seiichi Ozawa

  In this paper, we propose a new incremental learning algorithm of radial basis function (RBF) Network to accelerate the learning for large-scale data sequence. Along with the development of the internet and sensor technologies, a time series of large data chunk are continuously generated in our daily life. Thus it is usually difficult to learn all the data within a short period. A remedy for this is to select only essential data from a given data chunk and provide them to a classifier model to learn. In the proposed method, only data in untrained regions, which correspond to a region with a low output margin, are selected. The regions are formed by grouping the data based on their near neighbor using locality sensitive hashing (LSH), in which LSH has been developed to search neighbors quickly in an approximated way. As the proposed method does not use all training data to calculate the output margins, the time of the data selection is expected to be shortened. In the incremental learning phase, in order to suppress catastrophic forgetting, we also exploit LSH to select neighbor RBF units quickly. In addition, we propose a method to update the hash table in LSH so that the data selection can be adaptive during the learning. From the performance of nine datasets, we confirm that the proposed method can learn large-scale data sequences fast without sacrificing the classification accuracies. This fact implies that the data selection and the incremental learning work effectively in the proposed method.

  Springer Verlag, Sep. 2016, Evolving Systems, 7(3) (3), 173 - 186, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Stochastic Collapsed Variational Bayesian Inference for Biterm Topic Model

  Narutaka Awaya, Jun Kitazono, Toshiaki Omori, Seiichi Ozawa

  It is useful for many applications to find out meaningful topics from short texts, such as tweets and comments on websites. Since directly applying conventional topic models (e.g., LDA) to short texts often produces poor results, as a general approach to short texts, a biterm topic model (BTM) was recently proposed. However, the original BTM implementation uses collapsed Gibbs sampling (CGS) for its inference, which requires many iterations over the entire dataset. On the other hand, for LDA, there have been proposed many fast inference algorithms throughout the decade. Among them, a recently proposed stochastic collapsed variational Bayesian inference (SCVBO) is promising because it is applicable to an online setting and takes advantage of the collapsed representation, which results in an improved variational bound. Applying the idea of SCVBO, we develop a fast one-pass inference algorithm for BTM, which can be used to analyze large-scale general short texts and is extensible to an online setting. To evaluate the performance of the proposed algorithm, we conducted several experiments using short texts on Twitter. Experimental results showed that our algorithm found out meaningful topics significantly faster than the original algorithm.

  Corresponding, IEEE, Jul. 2016, 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 60th, 3364 - 3370, English

  [Refereed]

  International conference proceedings


• Online feature extraction based on accelerated kernel principal component analysis for data stream

  Annie Anak Joseph, Takaomi Tokumoto, Seiichi Ozawa

  Kernel principal component analysis (KPCA) is known as a nonlinear feature extraction method. Takeuchi et al. have proposed an incremental type of KPCA (IKPCA) that can update an eigen-space incrementally for a sequence of data. However, in IKPCA, the eigenvalue decomposition should be carried out for every single data, even though a chunk of data is given at one time. To reduce the computational costs in learning chunk data, this paper proposes an extended IKPCA called Chunk IKPCA (CIKPCA) where a chunk of multiple data is learned with single eigenvalue decomposition. For a large data chunk, to reduce further computation time and memory usage, it is first divided into several smaller chunks, and only useful data are selected based on the accumulation ratio. In the proposed CIKPCA, a small set of independent data are first selected from a reduced set of data so that eigenvectors in a high-dimensional feature space can be represented as a linear combination of such independent data. Then, the eigenvectors are incrementally updated by keeping only an eigenspace model that consists of the sextuplet such as independent data, coefficients, eigenvalues, and mean information. The proposed CIKPCA can augment an eigen-feature space based on the accumulation ratio that can also be updated without keeping all the past data, and the eigen-feature space is rotated by solving an eigenvalue problem once for each data chunk. The experiment results show that the learning time of the proposed CIKPCA is greatly reduced as compared with KPCA and IKPCA without sacrificing recognition accuracy.

  Corresponding, SPRINGER HEIDELBERG, Mar. 2016, EVOLVING SYSTEMS, 7(1) (1), 15 - 27, English

  [Refereed]

  Scientific journal


• Improving the Accuracy of Sentiment Analysis of SNS Comments Using Transfer Learning and Its Application to Flaming Detection

  吉田舜, 北園淳, 小澤誠一, 菅原貴弘, 芳賀達也

  In recent years, along with the popularization of SNS, the incidents, which are called flaming, that the number of negative comments surges are on the increase. This becomes a problem for companies because flamings hurt companies' reputation. In order to minimalize the damage of reputation, we propose the method that detects flamings by estimating the sentiment polarities of SNS comments. Because of the unique SNS characteristics such as repetition of same comments, the polarities of words are sometimes wrongly estimated. To alleviate this problem, transfer learning is introduced. In this research, the sentiment polarities of words are trained in every domain. This will enable to extract the words that are domain-specific and dictate the polarity of comments. These words are occurred in retweets. Transfer learning is implemented to non-extracted words by averaging the occurrence probabilities in other domains. These processes keep the polarities of important words that dictate the polarity of comments and modify the wrongly estimated polarities of words. The experimental results show that the proposed method improves the performance of estimating the sentiment polarity of comments. Moreover, flamings can be detected without missing by monitoring time course of the number of negative comments.

  Corresponding, The Institute of Electrical Engineers of Japan, Mar. 2016, 電気学会論文誌 C, 136(3) (3), 340 - 347, Japanese

  [Refereed]

  Scientific journal


• A Neural Network Model for Detecting DDoS Attacks Using Darknet Traffic Features

  Siti Hajar Aminah Ali, Seiichi Ozawa, Tao Ban, Junji Nakazato, Jumpei Shimamura

  This paper presents a fast and large-scale monitoring system for detecting one of the major cyber-attacks, Distributed Denial of Service (DDoS). The proposed system monitors the packet traffic on a subnet of unused IPs called darknet. Almost all darknet packets are originated from malicious activities. However, it is not obvious what traffic patterns DDoS attacks have. Therefore, we adopt a classifier and train it with traffic features of known DDoS attacks using 80/TCP and 53/UDP packets which can be labeled based on the header information and payloads. The proposed system consists of the two parts: pre-processing and classifier. In the pre-processing part, darknet packets for 30 seconds are transformed into a feature vector which consists of 17 traffic features on darknet traffic. As for the classifier part, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) in which data to be trained are selected by using LSH and fast online learning is actualized by training only selected data. The learning of RAN-LSH is carried out not only with the training data for 80/TCP and 53/UDP packets but also with new training data labeled by a supervisor. The performance of the proposed detection system is evaluated for 9,968 training data obtained from 80/TCP and 53/UDP packets and 5,933 test data obtained from darknet packets with other protocols and source/destination ports. The results indicate that the proposed system detects backscatter packets caused by DDoS attacks accurately and adapts to new attacks quickly.

  Corresponding, IEEE, 2016, 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016-, 2979 - 2985, English

  [Refereed]

  International conference proceedings


• t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom

  Jun Kitazono, Nistor Grozavu, Nicoleta Rogovschi, Toshiaki Omori, Seiichi Ozawa

  One of the dimension reduction (DR) methods for data-visualization, t-distributed stochastic neighbor embedding (t-SNE), has drawn increasing attention. t-SNE gives us better visualization than conventional DR methods, by relieving so-called crowding problem. The crowding problem is one of the curses of dimensionality, which is caused by discrepancy between high and low dimensional spaces. However, in t-SNE, it is assumed that the strength of the discrepancy is the same for all samples in all datasets regardless of ununiformity of distributions or the difference in dimensions, and this assumption sometimes ruins visualization. Here we propose a new DR method inhomogeneous t-SNE, in which the strength is estimated for each point and dataset. Experimental results show that such pointwise estimation is important for reasonable visualization and that the proposed method achieves better visualization than the original t-SNE.

  SPRINGER INTERNATIONAL PUBLISHING AG, 2016, NEURAL INFORMATION PROCESSING, ICONIP 2016, PT III, 9949, 119 - 128, English

  [Refereed]

  International conference proceedings


• Optimal Pattern Mining from Time-Series Cultivation Data of Soybeans for Knowledge Discovery

  Kohei Umejima, Fumihito Arimitsu, Seiichi Ozawa, Noriyuki Murakami, Hiroyuki Tsuji, Takenao Ohkawa

  Recently, we can analyze and store big data by high performance computers. In this paper, we present a method of optimal pattern mining from soybean cultivation data for knowledge discovery by introducing an evaluation function based on differences in the frequency of high-yields and low-yields. We can discover factors affecting the growth of soybeans by analyzing optimal patterns extracted using evaluation functions. In our proposed method, optimal patterns are enumerated by eliminating elements that decrease the value of evaluation functions from frequent closed patterns. As a result, our experiment showed the efficiency of the proposed method. In addition, we can observe both general and new knowledge by analyzing extracted optimal pattern groups.

  ASSOC COMPUTING MACHINERY, 2016, PROCEEDINGS OF THE WORKSHOP ON TIME SERIES ANALYTICS AND APPLICATIONS (TSAA'16), 19 - 24, English

  [Refereed]

  International conference proceedings


• A Sentiment Polarity Prediction Model Using Transfer Learning and Its Application to SNS Flaming Event Detection

  Seiichi Ozawa, Shun Yoshida, Jun Kitazono, Takahiro Sugawara, Tatsuya Haga

  In recent years, with the popularization of SNS, the incidents called flaming, in which a large number of negative comments are retweeted and spread to many followers on SNS, are increasing. Since a flaming event sometimes causes severe criticism by public people, it is becoming a great thread to companies and therefore it is important for companies to protect their reputation from such flaming events. In order to protect companies from serious damages in reputation, we propose a machine learning approach to the detection of flaming events by monitoring the sentiment polarity of SNS comments. From the nature of SNS comments such as the spread of a large number of retweets with the same content for a short time, the word distributions are often strongly biased and it leads to poor performance in sentiment polarity prediction. To alleviate this problem, we introduce transfer learning into the conventional Naive Bayes classifier. More concretely, in the Naive Bayes classifier, the occurrence probabilities of words on a target domain are recalculated using those on other domains, where a domain corresponds to a company to be protected. The experimental results demonstrate that the proposed transfer learning contribute to the improvement in the sentiment polarity prediction for SNS comments. In addition, we show that the proposed system can detect flaming events correctly by monitoring the number of negative comments.

  IEEE, 2016, PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 1 - 7, English

  [Refereed]

  International conference proceedings


• Development of Multidimensional Unfolding Based on Stochastic Neighbor Relationship

  Naoki Murata, Jun Kitazono, Seiichi Ozawa

  Multi-dimensional Unfolding (MU) is a method to visualize relevance data between two sets (e.g., preference data) as a single scatter plot. Usually, in the analysis of relevance data, users are interested in which elements are strongly related to each other (e.g., how much an individual likes an item), and not in which elements are irrelevant to each other. However, the convent

  ACM, 2016, ICMLC 2017 Proceedings of the 9th International Conference on Machine Learning and Computing, 60th, 4p - 252, Japanese


• An autonomous online malicious spam email detection system using extended RBF network

  Siti-Hajar-Aminah Ali, Seiichi Ozawa, Junji Nakazato, Tao Ban, Jumpei Shimamura

  In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1st, 2013 to August 29th, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on average over the testing period.

  Institute of Electrical and Electronics Engineers Inc., Sep. 2015, Proceedings of the International Joint Conference on Neural Networks, 2015-, 1 - 7, English

  [Refereed]

  International conference proceedings


• A Non-Destructive Measurement Method for Agricultural Plants Using Image Sensing

  ARAKAWA Shuhei, YOSHIDA Takeshi, OZAWA Seiichi, FUKAO Takanori, OHKAWA Takenao, MURAKAMI Noriyuki, TSUJI Hiroyuki

  This paper presents a non-destructive image sensing method to estimate the height of agricultural plants. In this method, several images are taken by moving a digital camera attached to a single-axis robot, and the two consecutive images with a plant tip are automatically matched using SIFT keypoints. Then, the plant height is estimated from the two images based on the triangul

  Sep. 2015, Proc. of Int. Symposium on Applied Electromagnetics and Mechanics, 1 - 2, English

  [Refereed]

  International conference proceedings


• Distributed Denial of Service (DDoS) Backscatter Detection System Using Resource Allocating Network with Data Selection

  Ali Siti-Hajar-Aminah, Furutani Nobuaki, Ozawa Seiichi, Nakazato Junji, Ban Tao, Shimamura Jumpei

  神戸大学大学院工学研究科, 2015, Memoirs of the Graduate Schools of Engineering and System Informatics Kobe University, 7, 8 - 13, English

  Link to Kobe Univ. Repository (Kernel)


• An Autonomous Online Malicious Spam Email Detection System Using Extended RBF Network

  Siti-Hajar-Aminah Ali, Seiichi Ozawa, Junji Nakazato, Tao Ban, Jumpei Shimamura

  In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam em ails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a mal ware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1st, 2013 to August 29th, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on average over the testing period.

  IEEE, 2015, 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), English

  [Refereed]

  International conference proceedings


• An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

  ALI Siti, Hajar Aminah, OZAWA Seiichi, NAKAZATO Junji, BAN Tao, SHIMAMURA Jumpei

  In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collecte

  Scientific Research Publishing, 2015, Journal of Intelligent Learning Systems and Applications,, 7, 42 - 57, English

  [Refereed]

  Scientific journal


• Large-Scale Monitoring for Cyber Attacks by Using Cluster Information on Darknet Traffic Features

  Hironori Nishikaze, Seiichi Ozawa, Jun Kitazono, Tao Ban, Junji Nakazato, Jumpei Shimamura

  This paper presents a machine learning approach to large-scale monitoring for malicious activities on Internet. In the proposed system, network packets sent from a subnet to a darknet (i.e., a set of unused IPs) are collected, and they are transformed into 27-dimensional TAP (Traffic Analysis Profile) feature vectors. Then, a hierarchical clustering is performed to obtain clusters for typical malicious behaviors. In the monitoring phase, the malicious activities in a subnet are estimated from the closest TAP feature cluster. Then, such TAP feature clusters for all subnets are visualized on the proposed monitoring system in real time. In the experiment, we use a big data set of 303,733,994 darknet packs collected from February 1st to February 28th, 2014 (28 days) for monitoring. As a result, we can successfully detect an indication of the pandemic of a new malware, which attacked to the vulnerability of Synology NAS (port 5,000/TCP).

  ELSEVIER SCIENCE BV, 2015, INNS CONFERENCE ON BIG DATA 2015 PROGRAM, 53, 175 - 182, English

  [Refereed]

  International conference proceedings


• Adaptive DDoS-Event Detection from Big Darknet Traffic Data

  Nobuaki Furutani, Jun Kitazono, Seiichi Ozawa, Tao Ban, Junji Nakazato, Jumpei Shimamura

  This paper presents an adaptive large-scale monitoring system to detect Distributed Denial of Service (DDoS) attacks whose backscatter packets are observed on the darknet (i.e., unused IP space). To classify DDoS backscatter, 17 features of darknet traffic are defined from IPs/ports information for source and destination hosts. To adapt to the change of DDoS attacks, we newly implement an online learning function in the proposed monitoring system, where an SVM classifier is continuously trained with darknet features transformed from packets during a certain period. In the performance evaluation, we use the MWS Dataset 2014 that consists of darknet packets collected from 1st January 2014 to 28th February 2014 (8 weeks). We demonstrate that the proposed system keeps good test performance in the detection of DDoS backscatter (0.98 in F-measure).

  SPRINGER INTERNATIONAL PUBLISHING AG, 2015, NEURAL INFORMATION PROCESSING, ICONIP 2015, PT IV, 9492, 376 - 383, English

  [Refereed]

  International conference proceedings


• Incremental two-dimensional kernel principal component analysis

  Yonghwa Choi, Seiichi Ozawa, Minho Lee

  In this paper, we propose a new online non-linear feature extraction method, called the incremental two-dimensional kernel principal component analysis (I2DKPCA), not only to reduce the computational cost but also to provide good feature representation. Batch type feature extraction methods such as principal component analysis (PCA) and two-dimensional PCA (2DPCA) require more computational time and memory usage, as they collect the entire training data to extract the basis vectors. Also, these linear feature extraction methods could not effectively represent the non-linear distribution of input data. Therefore, by adopting a non-linear kernel approach with chunk concept, the KPCA and 2DKPCA can effectively address the non-linear feature representation problem by adaptively changing the feature spaces. However, this kernel approach requires more computational time for processing images with high dimensional input data. In order to solve these problems, we combined the 2DKPCA with incremental learning for (1) solving the non-linear problem and (2) reducing the memory usage with computational time. In order to evaluate the performance of I2DKPCA, several experiments have been performed using well-known face and object image databases. (C) 2014 Elsevier B.V. All rights reserved.

  ELSEVIER, Jun. 2014, NEUROCOMPUTING, 134, 280 - 288, English

  [Refereed]

  Scientific journal


• An Incremental Linear Discriminant Analysis for Data Streams under Non-stationary Environment

  JOSEPH Anak Annie, JANG Young-Min, OZAWA Seiichi, LEE Minho

  In real life, data are not always generated under stationary environments. However, traditional learning systems have normally assumed that the property of data streams is stationary over time, and this sometimes leads to the degradation in the system performance when there are some hidden contexts changes (e.g. changes in class boundaries and temporal trends in time series). S

  Institute of Systems, Control and Information Engineers, Apr. 2014, Trans. of Institute of Systems, Control and Information Engineers, 27(4) (4), 133 - 140, English

  [Refereed][Invited]

  Scientific journal


• A Fast Incremental Learning Algorithm for Feed-forward Neural Networks Using Resilient Propagation

  Joseph Annie anak, Ozawa Seiichi

  神戸大学大学院工学研究科, 2014, Memoirs of the Graduate Schools of Engineering and System Informatics Kobe University, 6, 13 - 17, English

  Link to Kobe Univ. Repository (Kernel)


• A Neural Network Model for Semi-supervised Sequential Multi-task Learning in Multi-label Pattern Recognition Problems

  Daisuke Higuchi, Seiichi Ozawa

  In this paper, we propose an incremental neural network model for a general class of sequential multi-task classification problems where a training data of a task may not only have multiple class labels but also have task information. Such data property originates from the uncertainty of teaching signals given by a supervisor. To handle this type of classification problems, the proposed model consists of a three-layer feedforward neural network with long-term/short-term memories, and it has the following functions: one-pass incremental learning, task allocation, handling multi-label data, task consolidation, and knowledge transfer. We newly introduce the following two types of task consolidation functions other than the conventional error-based one: the task consolidation based on the co-occurrence relation of class labels and task information. In the experiments, we evaluate the proposed model for various kinds of data sets. The experimental results demonstrate that the proposed model has good performance in both classification and task categorization even if the task information is not always given.

  IOS PRESS, 2014, SMART DIGITAL FUTURES 2014, 262, 402 - 411, English

  [Refereed]

  International conference proceedings


• A Fast Incremental Kernel Principal Component Analysis for Data Streams

  Annie Anak Joseph, Seiichi Ozawa

  Kernel Principal Component Analysis (KPCA) is widely used feature extraction as it have been proven that KPCA is powerful in many areas in pattern recognition. Considering that the conventional KPCA should decompose a kernel matrix of all training data, this would be an unrealistic assumption for data streams in real-world applications. Therefore, in this paper, we propose an online feature extraction called Chunk Incremental Kernel Principal Component Analysis (CIKPCA) that can handle data streams in an incremental mode. In the proposed method, the training data are assumed to be given in a chunk of multiple data at one time. In CIKPCA, an eigen-feature space is updated by solving the eigenvalue decomposition once whenever a chunk of data is given. However, if a chunk size is large, a kernel matrix to be decomposed is also large, resulting in high computational time. Considering that not all the data are useful for the eigen-feature space learning, the data in a chunk are first selected based on the importance. Several benchmark data sets in the UCI Machine Learning Repository are used to evaluate the performance of the proposed method. The experimental results show that our proposed method can accelerate the learning of the eigen-feature space compared to Takeuchi et al.'s IKPCA without reducing the recognition accuracy.

  IEEE, 2014, PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 3135 - 3142, English

  [Refereed]

  International conference proceedings


• Detection of DDoS Backscatter Based on Traffic Features of Darknet TCP Packets

  Nobuaki Furutani, Tao Ban, Junji Nakazato, Jumpei Shimamura, Jun Kitazono, Seiichi Ozawa

  In this work, we propose a method to discriminate backscatter caused by DDoS attacks from normal traffic. Since DDoS attacks are imminent threats which could give serious economic damages to private companies and public organizations, it is quite important to detect DDoS backscatter as early as possible. To do this, 11 features of port/IP information are defined for network packets which are sent within a short time, and these features of packet traffic are classified by Suppurt Vector Machine (SVM). In the experiments, we use TCP packets for the evaluation because they include control flags (e.g. SYN-ACK, RST-ACK, RST, ACK) which can give label information (i.e. backscatter or non-backscatter). We confirm that the proposed method can discriminate DDoS backscatter correctly from unknown darknet TCP packets with more than 90% accuracy.

  IEEE, 2014, 2014 NINTH ASIA JOINT CONFERENCE ON INFORMATION SECURITY (ASIA JCIS), 39 - 43, English

  [Refereed]

  International conference proceedings


• Detecting Backscatter of DDoS Attacks from Darknet Traffic

  古谷暢章, BAN Tao, 中里純二, 島村隼平, 北園淳, 小澤誠一

  In this work, we propose a method to quickly discriminate DDoS backscatter packets from those of other traffic observed by darknet sensors (i.e., backscatter or non-backscatter). Upon the packets that are sent by a host towards the monitored darknet during a short time-window, we define 12 descriptive features, which are then input to an SVM classifier for classification.In the experiments, we use TCP packets sent from port 80 and UDP packets sent from port 53 as the training and testing data, because of the easiness to label them based on domain knowledge. Experiments showed promising results on these two ports.

  The Institute of Electronics, Information and Communication Engineers, 2014, 電子情報通信学会技術研究報告, 114(340(ICSS2014 51-62)) (340(ICSS2014 51-62)), 49 - 53, Japanese


• Sentiment Analysis for Various SNS Media Using Naive Bayes Classifier and Its Application to Flaming Detection

  Shun Yoshida, Jun Kitazono, Seiichi Ozawa, Takahiro Sugawara, Tatsuya Haga, Shogo Nakamura

  SNS is one of the most effective communication tools and it has brought about drastic changes in our lives. Recently, however, a phenomenon called flaming or backlash becomes an imminent problem to private companies. A flaming incident is usually triggered by thoughtless comments/actions on SNS, and it sometimes ends up damaging to the company's reputation seriously. In this paper, in order to prevent such unexpected damage to the company's reputation, we propose a new approach to sentiment analysis using a Naive Bayes classifier, in which the features of tweets/comments are selected based on entropy-based criteria and an empirical rule to capture negative expressions. In addition, we propose a semi-supervised learning approach to relabeling noisy training data, which come from various SNS media such as Twitter, Facebook, blogs and a Japanese textboard called '2-channel'. In the experiments, we use four data sets of users' comments, which were posted to different SNS media of private companies. The experimental results show that the proposed Naive Bayes classifier model has good performance for different SNS media, and a semi-supervised learning effectively works for the data consisting of long comments. In addition, the proposed method is applied to detect flaming incidents, and we show that it is successfully detected.

  IEEE, 2014, 2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIG DATA (CIBD), 20 - 25, English

  [Refereed]

  International conference proceedings


• Detecting Malicious Spam Mails: An Online Machine Learning Approach

  Yuli Dai, Shunsuke Tada, Tao Ban, Junji Nakazato, Jumpei Shimamura, Seiichi Ozawa

  Malicious spam is one of the major problems of the Internet nowadays. It brings financial damage to companies and security threat to governments and organizations. Most recent spam emails contain URLs that redirect spam receivers to malicious Web servers. In this paper, we propose an online machine learning based malicious spam email detection system. The term-weighting scheme represents each spam email. These feature vectors are then used as the input of the classifier. The learning is periodically performed to update the classifier so that the system provides increased adaptability to take account of spam emails whose contents change from time to time. A real data set is labeled by the SPIKE system which is developed by NICT. Evaluation experiments show that the detection system is efficient and accurate to identify malicious spam emails.

  SPRINGER-VERLAG BERLIN, 2014, NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III, 8836, 365 - 372, English

  [Refereed]

  International conference proceedings


• An improvement of incremental recursive fisher linear discriminant for online feature extraction

  Ryohei Ohta, Seiichi Ozawa

  This paper proposes a new online feature extraction method called the Incremental Recursive Fisher Linear Discriminant (IRFLD), whose batch learning algorithm, referred to as RFLD, was proposed by Xiang and colleagues. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class covariance matrix. However, RFLD and the proposed IRFLD can break this limit; that is, an arbitrary number of discriminant vectors can be obtained. In the proposed IRFLD, the Incremental Linear Discriminant Analysis (ILDA) of Pang and colleagues is extended in such a way that effective discriminant vectors are recursively searched for in the complementary space of a conventional discriminant subspace. In addition, to estimate a suitable number of effective discriminant vectors, the classification accuracy is evaluated using the cross-validation method in an online manner. For this purpose, validation data are obtained by performing k-means clustering on incoming training data and the previous validation data. The performance of IRFLD is evaluated for 16 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also show that this performance improvement is attained by adding discriminant vectors in a complementary LDA subspace. (c) 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(4): 2940, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10430

  WILEY, Apr. 2013, ELECTRONICS AND COMMUNICATIONS IN JAPAN, 96(4) (4), 29 - 40, English

  [Refereed]

  Scientific journal


• A Robust Incremental Principal Component Analysis for Feature Extraction from Stream Data with Missing Values

  Daijiro Aoki, Toshiaki Omori, Seiichi Ozawa

  In this paper, we propose a robust incremental principal component analysis (IPCA) for stream data that can handle missing values on an ongoing basis. In the proposed IPCA, a missing value is substituted with the value estimated from a conditional probability density function. The conditional probability density functions are incrementally updated when new data are given. In the experiments, we evaluate the performance for both artificial and real data sets through the comparison with the two conventional approaches to handing missing values. We first investigate the estimation errors of missing values. The experimental results demonstrate that the proposed IPCA gives lower estimation errors compared to the other approaches. Next, we investigate the approximation accuracy of eigenvectors. The results show that the proposed IPCA has relatively good accuracy of eigenvectors not only for major components but also for minor components.

  IEEE, 2013, 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 1 - 8, English

  [Refereed]

  International conference proceedings


• A Robust Incremental Principal Component Analysis for Feature Extraction from Stream Data with Missing Values

  Daijiro Aoki, Toshiaki Omori, Seiichi Ozawa

  In this paper, we propose a robust incremental principal component analysis (IPCA) for stream data that can handle missing values on an ongoing basis. In the proposed IPCA, a missing value is substituted with the value estimated from a conditional probability density function. The conditional probability density functions are incrementally updated when new data are given. In the experiments, we evaluate the performance for both artificial and real data sets through the comparison with the two conventional approaches to handing missing values. We first investigate the estimation errors of missing values. The experimental results demonstrate that the proposed IPCA gives lower estimation errors compared to the other approaches. Next, we investigate the approximation accuracy of eigenvectors. The results show that the proposed IPCA has relatively good accuracy of eigenvectors not only for major components but also for minor components.

  IEEE, 2013, 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 1 - 8, English

  [Refereed]

  International conference proceedings


• A Neural Network Model for Online Multi-Task Multi-Label Pattern Recognition

  Daisuke Higuchi, Seiichi Ozawa

  This paper presents a new sequential multi-task learning model with the following functions: one-pass incremental learning, task allocation, knowledge transfer, task consolidation, learning of multi-label data, and active learning. This model learns multi-label data with incomplete task information incrementally. When no task information is given, class labels are allocated to appropriate tasks based on prediction errors; thus, the task allocation sometimes fails especially at the early stage. To recover from the misallocation, the proposed model has a backup mechanism called task consolidation, which can modify the task allocation not only based on prediction errors but also based on task labels in training data (if given) and a heuristics on multi-label data. The experimental results demonstrate that the proposed model has good performance in both classification and task categorization.

  SPRINGER-VERLAG BERLIN, 2013, ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2013, 8131, 162 - 169, English

  [Refereed]

  International conference proceedings


• A neural network model for large-scale stream data learning using locally sensitive hashing

  Aminah Ali Siti Hajar, Kiminori Fukase, Seiichi Ozawa

  Recently, mining knowledge from stream data such as access logs of computer, commodity distribution data, sales data, and human lifelog have been attracting many attentions. As one of the techniques suitable for such an environment, active learning has been studied for a long time. In this work, we propose a fast learning technique for neural networks by introducing Locality Sensitive Hashing (LSH) and a local learning algorithm with LSH in RBF networks. © Springer-Verlag 2013.

  Springer, 2013, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8226(1) (1), 369 - 376, English

  [Refereed]

  International conference proceedings


• Fast Incremental Principal Component Analysis and Its Application to Face Image Recognition

  AOKI Daijiro, OZAWA SEIICHI

  In the conventional Incremental Principal Component Analysis (IPCA), an eigenvalue problem has to be solved whenever one or a small number of training data are given in sequence. Since the eigenvalue decomposition requires high computational costs in general, solving the eigenvalue problem repeatedly results in the deterioration in the real-time learning property of IPCA. Hence

  THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS, Jun. 2012, TECHNICAL REPORT OF IEICE, 112(108) (108), 1 - 6, Japanese

  Symposium


• Online Feature Extraction Algorithms for Data Streams -Incremental Principal Component Analysis-

  小澤誠一

  Along with the development of the network technology and high-performance small devices such as surveillance cameras and smart phones, various kinds of multimodal information (texts, images, sound, etc.) are captured real-time and shared among systems through networks. Such information is given to a system as a stream of data. In a person identification system based on face recognition, for example, image frames of a face are captured by a video camera and given to the system for an identification purpose. Those face images are considered as a stream of data. Therefore, in order to identify a person more accurately under realistic environments, a high-performance feature extraction method for streaming data, which can be autonomously adapted to the change of data distributions, is solicited. In this review paper, we discuss a recent trend on online feature extraction for streaming data. There have been proposed a variety of feature extraction methods for streaming data recently. Due to the space limitation, we here focus on the incremental principal component analysis.

  The Institute of Electrical Engineers of Japan, 2012, 電気学会論文誌 C, 132(1) (1), 6 - 13, Japanese


• A property of learning chunk data using incremental kernel principal component analysis

  Takaomi Tokumoto, Seiichi Ozawa

  An incremental learning algorithm of Kernel Principal Component Analysis (KPCA) called Chunk Incremental KPCA (CIKPCA) has been proposed for online feature extraction in pattern recognition. CIKPCA can reduce the number of times to solve the eigenvalue problem compared with the conventional incremental KPCA when a small number of data are simultaneously given as a stream of data chunks. However, our previous work suggests that the computational costs of the independent data selection in CIKPCA could dominate over those of the eigenvalue decomposition when a large chunk of data are given. To verify this, we investigate the influence of the chunk size to the learning time in CIKPCA. As a result, CIKPCA requires more learning time than IKPCA unless a large chunk of data are divided into small chunks (e.g., less than 50). © 2012 IEEE.

  IEEE, 2012, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2012 - Proceedings, 7 - 10, English

  [Refereed]

  International conference proceedings


• Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments

  Annie Anak Joseph, Young-Min Jang, Seiichi Ozawa, Minho Lee

  In this paper, a new approach to an online feature extraction under nonstationary environments is proposed by extending Incremental Linear Discriminant Analysis (ILDA). The extended ILDA not only detect so-called "concept drifts" but also transfer the knowledge on discriminant feature spaces of the past concepts to construct good feature spaces. The performance of the extended ILDA is evaluated for the benchmark datasets including sudden changes and reoccurrence in concepts.

  SPRINGER-VERLAG BERLIN, 2012, NEURAL INFORMATION PROCESSING, ICONIP 2012, PT II, 7664, 640 - 647, English

  [Refereed]

  International conference proceedings


• A Sequential Multitask Learning Algorithm for Pattern Recognition

  Tomoyasu Takata, Daisuke Higuchi, Seiichi Ozawa

  In this work, we extend the sequential multitask learning model called Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR) by introducing the following new learning functions: multi-label recognition, semi-supervised task learning and active learning. The extended RAN-MTPR can learn a training data with multiple class labels, can handle a semi-supervised setting for task learning, and can actively request class labels for unsure inputs. We evaluate the performance of the extended RAN-MTPR, and we know that the above three functions work well to enhance the generalization performance for pattern recognition problems.

  IEEE, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING AND EPIGENETIC ROBOTICS (ICDL), 1 - 2, English

  [Refereed]

  International conference proceedings


• A Sequential Multi-task Learning Neural Network with Metric-Based Knowledge Transfer

  Simeng Yue, Seiichi Ozawa

  In this paper, we propose a new sequential multitask pattern recognition model called Resource Allocating Network for Multi-Task Learning with Metric Learning (RAN-MTLML). RAN-MTLML has the following five functions: one-pass incremental learning, task-change detection, memory/retrieval of task knowledge, reorganization of classifier, and knowledge transfer. The knowledge transfer is actualized by transferring the metrics of all source tasks to a target task based on the task relatedness. Experimental results demonstrate the effectiveness of introducing the metric learning and the knowledge transfer on metric in the proposed RAN-MTLML.

  IEEE, 2012, 2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1, 671 - 674, English

  [Refereed]

  International conference proceedings


• Guest editorial: Evolving autonomous systems under realistic environments

  Seiichi Ozawa, Hisashi Handa

  Dec. 2011, Evolving Systems, 2(4) (4), 215 - 217, English

  [Refereed]

  Scientific journal


• A real-time personal authentication system based on incremental feature extraction and classification of audiovisual information

  Young-Min Jang, Minho Lee, Seiichi Ozawa

  We propose a new approach to a real-time personal authentication system based on incrementally updated visual (face) and audio (voice) features of persons. The proposed system consists of real-time face detection, incremental audiovisual feature extraction, and incremental neural classifier model with long-term memory. The face detection part, a biologically motivated face-color preferable selective attention model first localizes face candidate regions in natural scenes, and then the Adaboost-based face detection identifies human faces from the localized face-candidate regions. The mel-frequency cepstral coefficient is used for vocal feature extraction of speakers. Moreover, incremental principal component analysis (IPCA) is used to reduce the dimensions of audiovisual features and to update them incrementally. The features extracted by IPCA is fed to the resource allocating network with long-term memory which learns facial and vocal features incrementally and recognizes faces in real time. Experimental results show that the proposed system can enhance the test performance incrementally without serious forgetting. In addition, a multi-modal (facial and vocal) feature effectively increases the robustness of the personal authentication system in noisy environments. © 2011 Springer-Verlag.

  Dec. 2011, Evolving Systems, 2(4) (4), 261 - 272, English

  [Refereed]

  Scientific journal


• Radial Basis Function Network for Multitask Pattern Recognition

  Hitoshi Nishikawa, Seiichi Ozawa

  In this paper, a novel type of radial basis function network is proposed for multitask pattern recognition. We assume that recognition tasks are switched sequentially without notice to a learner and they have relatedness to some extent. We further assume that training data are given to learn one by one and they are discarded after learning. To learn a recognition system incrementally in such a multitask environment, we propose Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR). There are five distinguished functions in RAN-MTPR: one-pass incremental learning, task change detection, task categorization, knowledge consolidation, and knowledge transfer. The first three functions enable RAN-MTPR not only to acquire and accumulate knowledge of tasks stably but also to allocate classes to appropriate tasks unless task labels are not explicitly given. The fourth function enables RAN-MTPR to recover the failure in task categorization by minimizing the conflict in class allocation to tasks. The fifth function, knowledge transfer from one task to another, is realized by sharing the internal representation of a hidden layer with different tasks and by transferring class information of the most related task to a new task. The experimental results show that the recognition performance of RAN-MTPR is enhanced by introducing the two types of knowledge transfer and the consolidation works well to reduce the failure in task change detection and task categorization if the RBF width is properly set.

  SPRINGER, Jun. 2011, NEURAL PROCESSING LETTERS, 33(3) (3), 283 - 299, English

  [Refereed]

  Scientific journal


• Incremental recursive fisher linear discriminant for online feature extraction

  Seiichi Ozawa, Ryohei Ohta

  In this paper, we propose a new online feature extraction algorithm called Incremental Recursive Fisher Linear Discriminant (IRFLD). In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of a between-class covariance matrix. However, the proposed IRFLD can remove this limitation. That is, an arbitrary number of discriminant vectors up to input dimensions can be obtained to construct a feature space. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant space. In addition, a suitable number of effective discriminant vectors are automatically determined using a cross-validation method, where several representative training data are held as validation data and they are updated using the k-means clustering whenever a chunk of new training data are given. The performance of IRFLD is evaluated for 5 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary discriminant space. © 2011 IEEE.

  2011, IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems, 70 - 76, English

  [Refereed]

  International conference proceedings


• INCREMENTAL TWO-DIMENSIONAL TWO-DIRECTIONAL PRINCIPAL COMPONENT ANALYSIS (I(2D)(2)PCA) FOR FACE RECOGNITION

  Yonghwa Choi, Takaomi Tokumoto, Minho Lee, Seiichi Ozawa

  In this paper, we propose a new incremental two-directional two-dimensional principal component analysis (I(2D)(2)PCA) to efficiently recognize human faces. For implementing a real time face recognition system in an embedded system, the reduction of computational load as well as memory of a feature extraction algorithm is very important issue. The (2D)(2)PCA is faster than the conventional PCA. From memory capacity point of view, the incremental PCA is very efficient algorithm by adapting the eigensapce only using a new incoming sample data without memorizing all of previous trained data. In order to construct an efficient algorithm with less memory and small computational load, we propose a new feature extraction method by combining the IPCA and the (2D)(2)PCA. To evaluate the performance of the proposed (I(2D)(2)PCA), a series of experiments were performed on two face image databases: ORL and Yale face databases. The experimental results show that the proposed feature extraction method is efficient by reducing the memory while computational load is nearly similar to I(2D)(2)PCA.

  IEEE, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, pp. 1493 - 1496, 1493 - 1496, English

  [Refereed]

  International conference proceedings


• An Improvement of Incremental Recursive Fisher Linear Discriminant for Online Feature Extraction

  太田良平, 小澤誠一

  This paper proposes a new online feature extraction method called Incremental Recursive Fisher Linear Discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class covariance matrix. However, RFLD and the proposed IRFLD can break this limit; that is, an arbitrary number of discriminant vectors can be obtained. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant subspace. In addition, to estimate a suitable number of effective discriminant vectors, the classification accuracy is evaluated with a cross-validation method in an online manner. For this purpose, validation data are obtained by performing the k-means clustering against incoming training data and previous validation data. The performance of IRFLD is evaluated for 16 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary LDA subspace.

  The Institute of Electrical Engineers of Japan, 2011, 電気学会論文誌 C, 131(7) (7), 1368 - 1376, Japanese


• Incremental 2-Directional 2-Dimensional Linear Discriminant Analysis for Multitask Pattern Recognition

  Chunyu Liu, Young-Min Jang, Seiichi Ozawa, Minho Lee

  In this paper, we propose an incremental 2-directional 2-dimensional linear discriminant analysis (I-(2D)(2)LDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. In I-(2D) 2LDA, a discriminant space of the current task spanned by 2 types of discriminant vectors is augmented with effective discriminant vectors that are selected from other tasks based on the class separability. We call the selective augmentation of discriminant vectors knowledge transfer of feature space. In the experiments, the proposed I-(2D)(2)LDA is evaluated for the three tasks using the ORL face data set: person identification (Task 1), gender recognition (Task 2), and young-senior discrimination (Task 3). The results show that the knowledge transfer works well for Tasks 2 and 3; that is, the test performance of gender recognition and that of young-senior discrimination are enhanced.

  IEEE, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), pp. 2911-2916, 2911 - 2916, English

  [Refereed]

  International conference proceedings


• A Fast Incremental Kernel Principal Component Analysis for Learning Stream of Data Chunks

  Takaomi Tokumoto, Seiichi Ozawa

  In this paper, a new incremental learning algorithm of Kernel Principal Component Analysis (KPCA) is proposed for online feature extraction in pattern recognition problems. The proposed algorithm is derived by extending the Takeuchi et al.'s Incremental KPCA (T-IKPCA) that can learn a new data incrementally without keeping past training data. However, even if more than two data are given in a chunk, T-IKPCA should learn them individually; that is, in order to update the eigen-feature space, the eigenvalue decomposition should be performed for every data in the chunk. To alleviate this problem, we extend T-IKPCA such that an eigen-feature space learning is conducted by performing the eigenvalue decomposition only once for a chunk of given data. In the proposed IKPCA, whenever a new chunk of training data are given, linearly independent data are first selected based on the cumulative proportion. Then, the eigenspace augmentation is conducted by calculating the coefficients for the selected linearly independent data, and the eigen-feature space is rotated based on the rotation matrix that can be obtained by solving a kernel eigenvalue problem. To verify the effectiveness of the proposed IKPCA, the learning time and the accuracy of eigenvectors are evaluated using the three UCI benchmark data sets. From the experimental results, we confirm that the proposed IKPCA can learn an eigen-feature space very fast without sacrificing the recognition accuracy.

  IEEE, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), pp. 2881-2888, 2881 - 2888, English

  [Refereed]

  International conference proceedings


• A neural network model for learning data stream with multiple class labels

  Tomoyasu Takata, Seiichi Ozawa

  In this paper, we extend the sequential multitask learning model called Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR) proposed by Nishikawa et al. such that it can learn a training sample with multiple class labels which are originated from different lassification tasks. Here, we assume that no task information is given for training samples. Therefore, the extended RAN-MTPR has to allocate multiple class labels to appropriate tasks under unsupervised settings. This is carried out based on the prediction errors in the output sections, and the most probable task is selected from the output section with a minimum error. Through the computer simulations using the ORL face dataset, we show that the extended RAN-MTPR works well as a multitask learning model. © 2011 IEEE.

  2011, Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, 2, 35 - 40, English

  [Refereed]

  International conference proceedings


• Fast Variable Selection by Block Addition and Block Deletion

  Nagatani Takashi, Ozawa Seiichi, Abe Shigeo

  Dec. 2010, Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 4, pp. 200-211, English

  [Refereed]

  Scientific journal


• An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks

  Ozawa Seiichi, Tabuchi Toshihisa, Nakasaka Sho, Roy Asim

  Dec. 2010, Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 4, pp. 179-189, English

  [Refereed]

  Scientific journal


• Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems

  Masayuki Hisada, Seiichi Ozawa, Kau Zhang, Nikola Kasabov

  In this paper, we propose a new incremental linear discriminant analysis (ILDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. The Pang et al.'s ILDA is extended such that a discriminant space of the current task is augmented with effective discriminant vectors that are selected from other tasks based on the class separability. We call this selective augmentation of discriminant vectors knowledge transfer of feature space. In the experiments, the proposed ILDA is evaluated for seven MTPR problems, each of which consists of three recognition tasks. The results demonstrate that the proposed ILDA with knowledge transfer outperforms the conventional ILDA and its naive extension to MTPR problems with regard to both class separability and recognition accuracy. We confirm that the proposed knowledge transfer works well to evolve effective feature spaces online in MTPR problems. © Springer-Verlag 2010.

  Aug. 2010, Evolving Systems, 1(1) (1), 17 - 27, English

  [Refereed]

  Scientific journal


• A REINFORCEMENT LEARNING MODEL USING DETERMINISTIC STATE-ACTION SEQUENCES

  Makoto Murata, Seiichi Ozawa

  This paper presents a new approach to reinforcement learning in which an optimal action policy is learned not only for primitive actions but also for deterministic state-action sequences called macro-actions. To control the exploration and exploitation of macro-actions, the temperature parameter defined by the state values and the frequency of visiting states are added to representative state-action. pairs called memory items, which are stored in the long-term memory of the proposed Actor-Critic neural model. In the proposed model, no explicit form of macro-actions is defined. A macro-action is defined as a sequence of memory items with low temperature. By applying the softmax action selection to each of the memory items, an agent takes a series of actions in a deterministic way, resulting in the exploitation of a macro-action. The experimental results demonstrate that the proposed model can learn quite faster than the conventional Actor-Critic neural models in which no macro-action is introduced.

  ICIC INTERNATIONAL, Feb. 2010, INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 6(2) (2), 577 - 590, English

  [Refereed]

  Scientific journal


• Online Feature Extraction by Incremental Kernel Principal Component Analysis

  OZAWA Seiichi, TAKEUCHI Yohei, ABE Shigeo

  本論文では,初期データにのみ教師情報が与えられる「準教師付き学習タスク」において,ストリーミングデータからオンラインで非線形な特徴を抽出できる追加学習型カーネル主成分分析(IKPCA)を提案する.提案するIKPCAでは,学習データが入力されるたびにカーネル主成分分析の固有値問題を更新し,それを解くことで固有ベクトルの更新を行う.特徴固有空間で一次独立なデータを選択して固有ベクトルを表現するため,データの一次独立性を判定する必要がなく,追加学習時に保持するデータ数が少なくなって学習が高速化される.ベンチマークデータを用いた評価実験において,主成分分析(PCA)と追加学習型主成分分析(IPCA),更にカーネル主成分分析(KPCA)と比較し,IKPCAで得られる特徴量の評価を行った.その結果,IKPCAによってバッチ学習のKPCAと同等の認識性能が得られ,安定した追加学習が行われることを示した.このことは,IKPCAとKPCAにおいて,固有ベクトルや固有値の一致度を調べた実験からも確認された.また,多くの評価データでPCAやIPCAよりも,認識性能の優れた特徴が得られることを示した.

  The Institute of Electronics, Information and Communication Engineers, 2010, The IEICE transactions on information and systems, J93-D(6) (6), 826 - 836, Japanese


• An Autonomous Incremental Learning Algorithm of Resource Allocating Network for Online Pattern Recognition

  Seiichi Ozawa, Sho Nakasaka, Asim Roy

  In this paper, we propose a new autonomous incremental learning algorithm for radial basis function networks called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN). The proposed AL-RAN can carried out the following operations autonomously: (1) data collection for initial learning, (2) data normalization, (3) allocation of RBFs, (4) setting and adjusting RBF widths, and (5) incremental learning. In this paper, we mainly improve the first four functions in the initial learning phase where a convergence criterion based on the class separability of collected data is adopted in order to reduce the computational costs. In AL-RAN, training data are first collected until the class separability is converged or the recognition accuracies for normalized and unnormalized data have a significant difference. Then, an initial structure of AL-RAN is autonomously determined from the collected data, and AL-RAN is trained with them. After the initial learning, the incremental learning of AL-RAN is conducted whenever a new training data is given. In the experiments, we evaluate AL-RAN using five benchmark datasets. The experimental results demonstrate that the above autonomous functions work well and the number of collected data in the proposed AL-RAN is significantly decreased without sacrificing the final recognition accuracy as compared with the previous version of AL-RAN.

  IEEE, 2010, 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, pp. 706-71, English

  [Refereed]

  International conference proceedings


• A Fast Incremental Learning for Radial Basis Function Networks Using Local Linear Regression

  小澤誠一, 岡本圭介

  To avoid the catastrophic interference in incremental learning, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only new training data but also some memory items stored in long-term memory are trained either by a gradient descent algorithm or by solving a linear regression problem. In the latter approach, radial basis function (RBF) centers are not trained but selected based on output errors when connection weights are updated. The proposed incremental learning algorithm belongs to the latter approach where the errors not only for a training data but also for several retrieved memory items and pseudo training data are minimized to suppress the catastrophic interference. The novelty of the proposed algorithm is that connection weights to be learned are restricted based on RBF activation in order to improve the efficiency in learning time and memory size. We evaluate the performance of the proposed algorithm in one-dimensional and multi-dimensional function approximation problems in terms of approximation accuracy, learning time, and average memory size. The experimental results demonstrate that the proposed algorithm can learn fast and have good performance with less memory size compared to memory-based learning methods.

  The Institute of Electrical Engineers of Japan, 2010, 電気学会論文誌 C, 130(9) (9), 1667 - 1673, Japanese


• A Real-Time Personal Authentication System with Selective Attention and Incremental Learning Mechanism in Feature Extraction and Classifier

  Young-Min Jang, Seiichi Ozawa, Minho Lee

  We propose a new approach for a real-time personal authentication system, which consists of a selective face attention model, incremental feature extraction, and an incremental neural classifier model with long-term memory. In this paper, a face-color preferable selective attention combined with the Adaboost algorithm is used to detect human faces, and incremental principal component analysis (IPCA) and resource allocating network with long-term memory (RAN-LTM) are effectively combined to implement real-time personal authentication systems. The biologically motivated face-color preferable selective attention model localizes face candidate regions in a natural scene, and then the Adaboost based face detection process identifies human faces from the localized face-candidate regions. IPCA updates an eigen- space incrementally by rotating eigen-axes and adaptively increasing the eigen-space dimensions. The features extracted by projecting inputs to the eigen-space are given to RAN-LTM which learns facial features incrementally without unexpected forgetting and recognizes faces in real time. The experimental results show that the proposed model successfully recognizes 200 human faces through incremental learning without serious forgetting.

  SPRINGER-VERLAG BERLIN, 2010, PRICAI 2010: TRENDS IN ARTIFICIAL INTELLIGENCE, 6230, 445 - +, English

  [Refereed]

  International conference proceedings


• A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction

  Seiichi Ozawa, Yohei Takeuchi, Shigeo Abe

  In this paper, we present a modified version of incremental Kernel Principal Component Analysis (IKPCA) which was originally proposed by Takeuchi et al. as an online nonlinear feature extraction method. The proposed IKPCA learns a high-dimensional feature space incrementally by solving an eigenvalue problem whose matrix size is given by the power of the number of independent data. In the proposed IKPCA, independent data are used for calculating eigenvectors in a feature space, but they are selected in a low-dimensional eigen-feature space. Hence, the size of an eigenvalue problem is usually small, and this allows IKPCA to learn eigen-feature spaces very fast even though the eigenvalue decomposition has to be carried out at every learning stage. The proposed IKPCA. consists of two learning phases: initial learning phase and incremental learning phase. In the former, some parameters are optimized and an initial eigen-feature space is computed by applying the conventional KPCA. In the latter, the eigen-feature space is incrementally updated whenever a new data is given. In the experiments, we evaluate the learning time and the approximation accuracies of eigenvectors and eigenvalues. The experimental results demonstrate that the proposed IKPCA learns eigen-feature spaces very fast with good approximation accuracy.

  SPRINGER-VERLAG BERLIN, 2010, PRICAI 2010: TRENDS IN ARTIFICIAL INTELLIGENCE, 6230, 487 - 497, English

  [Refereed]

  International conference proceedings


• A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

  津守研二, 小澤誠一

  When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

  The Institute of Electrical Engineers of Japan, 2010, 電気学会論文誌 C, 130(1) (1), 21 - 28, Japanese


• A Multitask Learning Model for Online Pattern Recognition

  Seiichi Ozawa, Asim Roy, Dmitri Roussinov

  This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy'. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased.

  IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Mar. 2009, IEEE TRANSACTIONS ON NEURAL NETWORKS, 20(3) (3), 430 - 445, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• A reinforcement learning model with function of generating macro-actions in Grid-World maze problems and a study on its learning property

  Hiroshi Onda, Seiichi Ozawa

  A macro-action is a typical series of useful actions that brings high expected rewards to an agent. Murata et al. have proposed an Actor-Critic model which can generate macro-actions automatically based on the information on state values and visiting frequency of states. However, their model has not assumed that generated macro-actions are utilized for leaning different tasks. In this paper, we extend the Murata's model such that generated macro-actions can help an agent learn an optimal policy quickly in multi-task Grid-World (MTGW) maze problems. The proposed model is applied to two MTGW problems, each of which consists of six different maze tasks. Prom the experimental results, it is concluded that the proposed model could speed up learning if macro-actions are generated in the so-called correlated regions. © 2009 The Institute of Electrical Engineers of Japan.

  Institute of Electrical Engineers of Japan, 2009, IEEJ Transactions on Electronics, Information and Systems, 129(4) (4), 21 - 743, English

  [Refereed]

  Scientific journal


• Curiosity Driven Incremental LDA Agent Active Learning

  Shaoning Pang, Seiichi Ozawa, Nik Kasabov

  This paper presented a novel active linear discriminant analysis (LDA) learning method in the form of curiosity-driven incremental LDA (cILDA) and multiple cILDA agents cooperative learning (mcILDA). The curiosity in psychology here is modelled mathematically as a discriminability residue in-between instance space and its corresponding eigenspace. As the learning proceeds, the curiosity of an individual agent updates over time by two incremental learning processes: One updates the characterization of eigenspace and another re-calculates the curiosity. In the multi-agent scenario, individual agent communicates and cooperates with each other at every learning stage to discover the discriminant characterization of the whole pattern. In the experiment, we described how the discriminative instances could be significantly selected based on the curiosity with, at most, minor sacrifices in learning rate and classification accuracy. The experimental results show that the proposed curiosity learning performs gracefully under different level of redundancy, and the proposed cILDA/mcILDA learning system is capable of learning less instances, but has more often an improved discrimination performance.

  IEEE, 2009, IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, pp. 2401-2408, 1616 - +, English

  [Refereed]

  International conference proceedings


• An Incremental Learning Algorithm of Recursive Fisher Linear Discriminant

  Ryohei Ohta, Seiichi Ozawa

  This paper presents an online feature extraction method called Incremental Recursive Fisher Linear Discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class scatter matrix. RFLD and the proposed IRFLD can eliminate this limitation. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional ILDA subspace. In addition, to estimate a suitable number of effective discriminant vectors, we also propose a convergence criterion for the recursive computations which is defined by using the class separability of discriminant features projected on the complementary subspace. The experimental results suggest that the recognition accuracies of IRFLD is improved as the learning proceeds. For several datasets, we confirm that the proposed IRFLD outperforms ILDA in terms of the recognition accuracy. However, the advantage of IRFLD against ILDA depends on datasets.

  IEEE, 2009, IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, pp. 2310-2315, 2671 - 2676, English

  [Refereed]

  International conference proceedings


• Adaptive Incremental Principal Component Analysis in Nonstationary Online Learning Environments

  Seiichi Ozawa, Yuki Kawashima, Shaoning Pang, Niko La Kasabov

  In this paper, we propose a new Chunk IPCA algorithm in which an optimal threshold of accumulation ratio is adaptively selected such that the classification accuracy is maximized for a validation data set. In order to obtain a proper set of validation data, an online clustering method called Evolving Clustering Method (ECM) is introduced into Chunk IPCA. In the proposed Chunk IPCA called CIPCA-ECM, training data are first separated into the subsets of every class; then, ECM is applied to each subset to update the validation data set. In the experiments, the evaluation of the proposed Chunk IPCA algorithm is carried out using the four UCI data sets and the effectiveness of updating the threshold is discussed. The results suggest that the incremental learning of an eigenspace in the proposed CIPCA-ECM is stably carried out, and a compact and effective eigenspace is obtained over the entire learning stages. The recognition accuracy of CIPCA-ECM is almost equal to the best performance of CIPCA-FIX in which an optimal threshold is manually predetermined.

  IEEE, 2009, IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, pp. 2394-2400, 2889 - +, English

  [Refereed]

  International conference proceedings


• Incremental Principal Component Analysis Based on Adaptive Accumulation Ratio

  Seiichi Ozawa, Kazuya Matsumoto, Shaoning Pang, Nikola Kasabov

  We have proposed all online feature extraction method called Chunk Incremental Principal Component Analysis (Chunk IPCA) where a chunk of data is trained at a time to update an eigenspace model. In this paper, we propose an extended version of Chunk IPCA in which a proper threshold for the accumulation ratio is adaptively determined such that the highest classification accuracy is maintained for a validation data set. Whenever a new chunk of training data is given, the validation set is updated in all online fashion by using the k-means clustering or through the prototype selection based oil the classification results. The experimental results show that the extended version of Chunk IPCA call determine a proper threshold oil an ongoing basis, resulting in keeping higher classification accuracy than the original Chunk IPCA..

  SPRINGER-VERLAG BERLIN, 2009, ADVANCES IN NEURO-INFORMATION PROCESSING, PT I, 5506, 1196 - +, English

  [Refereed]

  International conference proceedings


• A Novel Incremental Linear Discriminant Analysis for Multitask Pattern Recognition Problems

  Masayuki Hisada, Seiichi Ozawa, Kau Zhang, Shaoning Pang, Nikola Kasabov

  hi this paper, we propose a new incremental linear discriminant analysis (ILDA) for multitask pattern recognition (MTPR) problems in which training samples of a specific recognition task are given one after another for a certain period of time and the task is switched to another related task in turn. The Pang et al.'s ILDA is extended such that a discriminant space of the current task is augmented with effective discriminant vectors that are selected from other related tasks based on the class separability. We call the selection and augmentation of such discriminant vectors knowledge transfer of feature subspaces. Tit the experiments, the proposed ILDA is evaluated for the four MTPR, problems, each of which consists of three multi-class recognition tasks. The results demonstrate that the proposed ILDA outperforms the ILDA without the knowledge transfer with regard to both the class separability and recognition accuracy. Front the experimental results, we confirm that the proposed knowledge transfer mechanism works well to construct effective discriminant feature spaces incrementally.

  SPRINGER-VERLAG BERLIN, 2009, ADVANCES IN NEURO-INFORMATION PROCESSING, PT I, 5506, 1163 - +, English

  [Refereed]

  International conference proceedings


• A Neural Network Model for Sequential Multitask Pattern Recognition Problems

  Hitoshi Nishikawa, Seiichi Ozawa, Asim Roy

  In this paper, we propose a new multitask learning (MTL) model which can learn a series of multi-class pattern recognition problems stably. The knowledge transfer in the proposed MTL model is implemented by the following mechanisms: (1) transfer by sharing the internal representation of RBFs and (2) transfer of the information on class subregions from the related tasks. The proposed model can detect task changes on its own based on the output errors even though no task information is given by the environment. It also learn training samples of different tasks that are given one after another. In the experiments, the recognition performance is evaluated for the eight MTPR problems which are defined from the four UCI data sets. The experimental results demonstrate that the proposed MTL model outperforms a single-task learning model in terms of the final classification accuracy. Furthermore, we show that the transfer of class subregion contributes to enhancing the generalization performance of a new task with less training samples.

  SPRINGER-VERLAG BERLIN, 2009, ADVANCES IN NEURO-INFORMATION PROCESSING, PT I, 5506, 821 - +, English

  [Refereed]

  International conference proceedings


• Tuning membership functions of kernel fuzzy classifiers by maximizing margins

  Kazuya Morikawa, Seiichi Ozawa, Shigeo Abe

  We propose two methods for tuning membership functions of a kernel fuzzy classifier based on the idea of SVM (support vector machine) training. We assume that in a kernel fuzzy classifier a fuzzy rule is defined for each class in the feature space. In the first method, we tune the slopes of the membership functions at the same time so that the margin between classes is maximized under the constraints that the degree of membership to which a data sample belongs is the maximum among all the classes. This method is similar to a linear all-at-once SVM. We call this AAO tuning. In the second method, we tune the membership function of a class one at a time. Namely, for a class the slope of the associated membership function is tuned so that the margin between the class and the remaining classes is maximized under the constraints that the degrees of membership for the data belonging to the class are large and those for the remaining data are small. This method is similar to a linear one-against-all SVM. This is called OAA tuning. According to the computer experiment for fuzzy classifiers based on kernel discriminant analysis and those with ellipsoidal regions, usually both methods improve classification performance by tuning membership functions and classification performance by AAO tuning is slightly better than that by OAA tuning. © 2009 Springer-Verlag.

  2009, Memetic Computing, 1(3) (3), 221 - 228, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• A Reinforcement Learning Model Using Macro-actions in Multi-task Grid-World Problems

  Hiroshi Onda, Seiichi Ozawa

  A macro-action is a typical series of useful actions that brings high expected rewards to an agent. Murata et al. have proposed an Actor-Critic model which can generate macro-actions automatically based on the information on state values and visiting frequency of states. However, their model has not assumed that generated macro-actions are utilized for leaning different tasks. In this paper, we extend the Murata's model such that generated macro-actions can help an agent learn an optimal policy quickly in multi-task Grid-World (MTGW) maze problems. The proposed model is applied to two MTGW problems, each of which consists of six different maze tasks. From the experimental results, it is concluded that the proposed model could speed up learning if macro-actions are generated in the so-called correlated regions.

  IEEE, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, 6 pages, 3088 - 3093, English

  [Refereed]

  International conference proceedings


• An Autonomous Learning Algorithm of Resource Allocating Network

  Toshihisa Tabuchi, Seiichi Ozawa, Asim Roy

  Selecting proper parameters of RBF networks has been a puzzling problem even for batch learning. The parameter selection is usually carried out by an external supervisor. To exclude the intervention by an external supervisor from the parameter selection, we propose a new learning scheme called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN). AL-RAN is an incremental learning algorithm which consists of the following functions: automated data, normalization and automated adjustment of RBF widths. In the experiments, we evaluate AL-RAN using nine benchmark datasets in terms of the decision accuracy of data normalization and the final classification accuracy. The experimental results demonstrate that the above two functions in AL-RAN work well and the final classification accuracy of AL-RAN is almost the same as that, of a non-autonomous model whose parameters are manually tuned by an external supervisor.

  SPRINGER-VERLAG BERLIN, 2009, INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING, PROCEEDINGS, 5788, 134 - +, English

  [Refereed]

  International conference proceedings


• An Incremental Learning Algorithm for Resource Allocating Networks Based on Local Linear Regression

  Seiichi Ozawa, Keisuke Okamoto

  To learn things incrementally without the catastrophic interference, we have proposed Resource Allocating Network with Long-Term Memory (RAN-LTM). In RAN-LTM, not only training data but also memory items stored in long-term memory are trained. In this paper, we propose an extended RAN-LTM called Resource Allocating Network by Local Linear Regression (RAN-LLR), in which its centers are not trained but selected based on output errors and the connections are updated by solving a linear regression problem. To reduce the computation and memory costs, the modified connections are restricted based on RBF activity. In the experiments, we first apply RAN-LLR to a one-dimensional function approximation problem to see how the negative interference is effectively suppressed. Then, the performance of RAN-LLR is evaluated for a real-world prediction problem. The experimental results demonstrate that the proposed RAN-LLR can learn fast and accurately with less memory costs compared with the conventional models.

  SPRINGER-VERLAG BERLIN, 2009, NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS, 5863, 562 - 569, English

  [Refereed]

  International conference proceedings


• Incremental learning of chunk data for online pattern classification systems

  Seiichi Ozawa, Shaoning Pang, Nikola Kasabov

  This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where training samples are presented only once. For this purpose, we have extended incremental principal component analysis (IPCA) and some classifier models were effectively combined with it. However, there was a drawback in this approach that training samples must be learned one by one due to the limitation of IPCA. To overcome this problem, we propose another extension of IPCA called chunk IPCA in which a chunk of training samples is processed at a time. In the experiments, we evaluate the classification performance for several large-scale data sets to discuss the scalability of chunk IPCA under one-pass incremental learning environments. The experimental results suggest that chunk IPCA can reduce the training time effectively as compared with IPCA unless the number of input attributes is too large. We study the influence of the size of initial training data and the size of given chunk data on classification accuracy and learning time. We also show that chunk IPCA can obtain major eigenvectors with fairly good approximation.

  IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Jun. 2008, IEEE TRANSACTIONS ON NEURAL NETWORKS, 19(6) (6), 1061 - 1074, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• An Incremental Principal Component Analysis Based on Dynamic Accumulation Ratio

  Seiichi Ozawa, Kazuya Matsumoto, Shaoning Pang, Nikola Kasabov

  We have proposed an online feature extraction method called Chunk Incremental Principal Component Analysis (CIPCA) where a chunk of data is trained at a time to update an eigenspace model. This paper presents an extended version in which the threshold for accumulation ratio is adaptively determined so that the classification accuracy for validation data is always maximized. To define the validation set online, the prototypes are selected from given training samples by k-means clustering or nearest neighbor classifier. The experimental results show that the proposed CIPCA can update the threshold properly so as to maintain high classification accuracy.

  IEEE, 2008, 2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, 2370 - +, English

  [Refereed]

  International conference proceedings


• Adaptive face recognition system using fast Incremental Principal Component Analysis

  Seiichi Ozawa, Shaoning Pang, Nikola Kasabov

  In this paper, a novel face recognition system is presented in which not only a classifier but also a feature space is learned incrementally to adapt to a chunk of incoming training samples. A distinctive feature of the proposed system is that the selection of useful features and the learning of an optimal decision boundary are conducted in an online fashion. In the proposed system, Chunk Incremental Principal Component Analysis (CIPCA) and Resource Allocating Network with Long-Term Memory are effectively combined. In the experiments, the proposed face recognition system is evaluated for a self-compiled face image database. The experimental results demonstrate that the test performance of the proposed system is consistently improved over the learning stages, and that the learning speed of a feature space is greatly enhanced by CIPCA.

  SPRINGER-VERLAG BERLIN, 2008, NEURAL INFORMATION PROCESSING, PART II, 4985, 396 - +, English

  [Refereed]

  International conference proceedings


• Incremental Learning for Multitask Pattern Recognition Problems

  Seiichi Ozawa, Asim Roy

  This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector Of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes by itself and to utilize the knowledge on the previous tasks for learning of new tasks. In addition, the learning model must acquire knowledge of multiple tasks incrementally without unexpected forgetting under the condition that not only tasks but also training samples are sequentially given. The proposed model is evaluated for two artificial MTPR problem. In the experiments, we verify that the proposed model can acquire and accumulate task knowledge very stably and the speed of knowledge acquisition for new tasks is enhanced by transferring knowledge.

  IEEE COMPUTER SOC, 2008, SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, pp. 747- 751, 747 - +, English

  [Refereed]

  International conference proceedings


• Feature extraction by supervised independent component analysis based on category information

  Hiroki Takabatake, Manabu Kotani, Seiichi Ozawa

  Independent component analysis (ICA) is a technique of transforming observation signals into their unknown independent components; hence, ICA has often been applied to blind signal separation problems. In this application, it is expected that tile obtained independent components extract essential information of independent signal sources from input data in an unsupervised fashion. Based on Such characteristics, ICA is currently utilized as a feature extraction method for images and sounds for recognition purposes. However, since ICA is an unsupervised learning, the obtained independent components are not always useful in recognition. To overcome this problem, we propose a supervised approach to ICA using category information. The proposed method is implemented in a conventional three-layered neural network, but its objective function to be minimized is defined not only for the output layer but also for the hidden layer. The objective function consists of the following two terms: one evaluates the kurtosis of hidden unit outputs and the other evaluates the error between Output signals and their teacher signals. The experiments are performed using several standard datasets to evaluate performance of the proposed algorithm. It is confirmed that a higher recognition accuracy is attained by the proposed method as compared with a conventional ICA algorithm. (c) 2007 Wiley Periodicals, Inc.

  SCRIPTA TECHNICA-JOHN WILEY & SONS, Nov. 2007, ELECTRICAL ENGINEERING IN JAPAN, 161(2) (2), 25 - 32, English

  [Refereed]

  Scientific journal


• A learning algorithm of boosting kernel discriminant analysis for pattern recognition

  Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, Shigeo Abe

  In this paper, we present a new method to enhance classification performance of a multiple classifier system by combining a boosting technique called AdaBoost.M2 and Kernel Discriminant Analysis (KDA). To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in a different feature space, which is obtained by applying KDA to a small set of hard-to-classify training samples. The training of the system is conducted based on AdaBoot.M2, and the classifiers are implemented by Radial Basis Function networks. To perform KDA at every boosting round in a realistic time scale, a new kernel selection method based on the class separability measure is proposed. Furthermore, a new criterion of the training convergence is also proposed to acquire good classification performance with fewer boosting rounds. To evaluate the proposed method, several experiments are carried out using standard evaluation datasets. The experimental results demonstrate that the proposed. method can select an optimal kernel parameter more efficiently than the conventional cross-validation method, and that the training of boosting classifiers is terminated with a fairly small number of rounds to attain good classification accuracy. For multi-class classification problems, the proposed method outperforms both Boosting Linear Discriminant Analysis (BLDA) and Radial-Basis Function Network (RBFN) with regard to the classification accuracy. On the other hand, the performance evaluation for 2-class problems shows that the advantage of the proposed BKDA against BLDA and RBFN depends on the datasets.

  IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, Nov. 2007, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, E90D(11) (11), 1853 - 1863, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• An efficient incremental kernel principal component analysis for online feature selection

  Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

  In this paper, a feature extraction method for online classification problems is proposed by extending Kernel Principal Component Analysis (KPCA). In our previous work, we proposed an incremental KPCA algorithm which could learn a new input incrementally without keeping all the past training data. In this algorithm, eigenvectors are represented by a linear sum of linearly independent data which are selected from given training data. A serious drawback of the previous IKPCA is that many independent data are prone to be selected during learning and this causes large computation and memory costs. For this problem, we propose a novel approach to the selection of independent data that is, they are not selected in the high-dimensional feature space but in the low-dimensional eigenspace spanned by the current eigenvectors. Using this method, the number of independent data is restricted to the number of eigenvectors. This restriction makes the learning of the modified IKPCA (M-IKPCA) very fast without loosing the approximation accuracy against true eigenvectors. To verify the effectiveness of M-IKPCA, the learning time and the accuracy of eigenspaces are evaluated using two UCI benchmark datasets. As a result, we confirm that the learning of M-IKPCA is at least 5 times faster than the previous version of IKPCA. ©2007 IEEE.

  2007, IEEE International Conference on Neural Networks - Conference Proceedings, 2346 - 2351, English

  [Refereed]

  International conference proceedings


• An online face recognition system with incremental learning ability

  Seiichi Ozawa, Michiro Hirai, Shigeo Abe

  In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space is learned incrementally to adapt to a chunk of training samples. A benefit of this type of incremental learning is that the search for useful features and the learning of an optimal decision boundary are carried out in an online fashion. To implement this idea, Chunk Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory are effectively combined. Using Chunk IPCA, a feature space is updated by rotating its eigen-axes and increasing the dimensions to adapt to a chunk of given training samples. In the experiments, the proposed incremental learning model is evaluated over a self-compiled face image database. As the result, we verify that the proposed model works well without serious forgetting and the test performance is improved as the learning stages proceed.

  IEEE, 2007, PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8, 1963 - 1966, English

  [Refereed]

  International conference proceedings


• An efficient incremental Kernel Principal Component Analysis for online feature selection

  Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

  In this paper, a feature extraction method for online classification problems is proposed by extending Kernel Principal Component Analysis (KPCA). In our previous work, we proposed an incremental KPCA algorithm which could learn a new input incrementally without keeping all the past training data. In this algorithm, eigenvectors are represented by a linear sum of linearly independent data which are selected from given training data. A serious drawback of the previous IKPCA is that many independent data are prone to be selected during learning and this causes large computation and memory costs. For this problem, we propose a novel approach to the selection of independent data; that is, they are not selected in the high-dimensional feature space but in the low-dimensional eigenspace spanned by the current eigenvectors. Using this method, the number of independent data is restricted to the number of eigenvectors. This restriction makes the learning of the modified IKPCA (M-IKPCA) very fast without loosing the approximation accuracy against true eigenvectors. To verify the effectiveness of M-IKPCA, the learning time and the accuracy of eigenspaces are evaluated using two UCI benchmark datasets. As a result, we confirm that the learning of M-IKPCA is at least 5 times faster than the previous version of IKPCA.

  IEEE, 2007, 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, CD-ROM (6 pages), 2346 - 2351, English

  [Refereed]

  International conference proceedings


• Boosting Kernel Discriminant Analysis for pattern classification

  Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, Shigeo Abe

  This paper presents a new boosting algorithm called Boosting Kernel Discriminant Analysis (BKDA) in which the feature selection and the classifier training are conducted by Kernel Discriminant Analysis (KDA) and AdaBoost.M2, respectively. To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in the different feature space which is obtained by applying KDA to a small set of hard-to-classify training samples. The proposed BKDA is evaluated using standard benchmark datasets. The experimental results demonstrate that BKDA outperforms both Boosting Linear Discriminant Analysis (BLDA) and Support Vector Machine (SVM) for multi-class classification problems. On the other hand, the performance evaluation for 2-class problems shows that the advantage of the proposed BKDA against BLDA and SVM depends on the datasets.

  IEEE, 2007, 2007 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS, VOLS 1 AND 2, CD-ROM (4 pages), 674 - +, English

  [Refereed]

  International conference proceedings


• Feature Extraction by Supervised Independent Component Analysis Based on Category Information

  TAKABATAKE Hiroki, KOTANI Manabu, OZAWA Seiichi

  Independent component analysis (ICA) is a technique of transforming observation signals into their unknown independent components; hence ICA has been often applied to blind signal separation problems. In this application, it is expected that the obtained independent components extract essential information of independent signal sources from input data in an unsupervised fashion. Based on such characteristics, ICA is recently utilized as a feature extraction method for images and sounds for recognition purposes. However, since ICA is an unsupervised learning, the obtained independent components are not always useful in recognition. To overcome this problem, we propose a supervised approach to ICA using category information. The proposed method is implemented in a conventional three-layered neural network, but its objective function to be minimized is defined for not only the output layer but also the hidden layer. The objective function consists of the following two terms: one evaluates the kurtosis of hidden unit outputs and the other evaluates the error between output signals and their teacher signals. The experiments are performed for some standard datasets to evaluate the proposed algorithm. It is verified that higher recognition accuracy is attained by the proposed method as compared with a conventional ICA algorithm.

  The Institute of Electrical Engineers of Japan, Apr. 2006, IEEJ Transactions on Electronics, Information and Systems, 126(4) (4), 542 - 547, Japanese


• On-line feature selection for adaptive evolving connectionist systems

  Seiichi Ozawa, Shaoning Pang, Nikola Kasabov

  A new concept for pattern classification systems is proposed in which the feature selection and the learning classifier are simultaneously carried out on-line. An advantage of this concept is that classification systems can improve their performance constantly even if insufficient training samples are given when the learning starts, often resulting in inappropriate feature selection and poor classifier performance. To implement this concept, we propose an adaptive evolving connectionist model in which Incremental Principal Component Analysis and Evolving Clustering Method are effectively combined. The proposed on-line learning scheme has two major desirable properties. First, the performance is improved as the learning proceeds and it converges to an acceptable level from any initial conditions. Second, the learning is sequentially carried out without retaining all the training data given so far; thus, the learning is conducted efficiently in term of the computation and memory costs. To evaluate the proposed model, the recognition performance is investigated using three standard datasets in the UCI machine learning repository. From the experimental results, we verify that the proposed scheme possesses the above two characteristics.

  ICIC INTERNATIONAL, Feb. 2006, INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2(1) (1), 181 - 192, English

  [Refereed]

  Scientific journal


• An incremental learning algorithm of ensemble classifier systems

  Takuya Kidera, Seiichi Ozawa, Shigeo Abe

  In this paper, we propose an incremental learning model for ensemble classifier systems. In the proposed model, the number of classifiers is predetermined and fixed during the learning, and all classifiers are updated at every learning stage based on an extended algorithm of AdaBoost.M1. A neural network model called Resource Allocating Network with Long-Term Memory (RAN-LTM), which has been developed to realize stable incremental learning, is adopted as a classifier. We also propose a new method to update the classifier weights in the weighted majority voting under the one-pass incremental learning situations. In the experiments, first we verify that the proposed model can learn incrementally without serious forgetting and that the performance is not influenced seriously by the size of a training subset given at every learning stage. Then, through a comparison with Resource Allocating Network (RAN), RAN-LTM, and AdaBoost.M1, we demonstrate that the proposed incremental ensemble classifier system has comparable performance with a batch-learning ensemble classifier system, and that it outperforms both batch-learning and incremental-learning single-classifier systems. © 2006 IEEE.

  Institute of Electrical and Electronics Engineers Inc., 2006, IEEE International Conference on Neural Networks - Conference Proceedings, 3421 - 3427, English

  International conference proceedings


• Incremental kernel PCA for online learning of feature space

  Shosuke Kimura, Seiichi Ozawa, Shigeo Abe

  In this paper, a feature extraction method for online classification problems is presented by extending Kernel Principal Component Analysis (KPCA). ne proposed incremental KPCA (IKPCA) constructs a nonlinear high-dimensional feature space incrementally by not only updating eigen-axes but also adding new eigen-axes. The augmentation of a new eigen-axis is carried out when the accumulation ratio falls below a threshold value. We mathematically derive the incremental update equations of eigen-axes and the accumulation ratio without keeping all training samples. From the experimental results, we conclude that the proposed IKPCA works well as an incremental learning algorithm of a feature space in the sense that a minimum number of axes are augmented to maintain a designated accumulation ratio, and that the eigenvectors with major eigenvalues can converge closely to those of the batch type of KPCA. In addition, the recognition accuracy of IKPCA is similar to or slightly better than that of KPCA.

  IEEE, 2006, INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 1, PROCEEDINGS, Vol. 1, pp. 595-600, 595 - +, English

  [Refereed]

  International conference proceedings


• Incremental Learning of Feature Space and Classifier for On-Line Pattern Recognition

  OZAWA Seiichi, PANG Shaoning, KASABOV Nikola

  Jan. 2006, International Journal of Knowledge-Based & Intelligent Engineering Systems, Vol. 10, No. 1, pp. 57-65, English

  [Refereed]

  Scientific journal


• An incremental principal component analysis for chunk data

  Seiichi Ozawa, Shaoning Pang, Nikola Kasabov

  This paper presents a new algorithm of dynamic feature selection by extending the algorithm of Incremental Principal Component Analysis (IPCA), which has been originally proposed by Hall and Martin. In the proposed IPCA, a chunk of training samples can be processed at a time to update the eigenspace of a classification model without keeping all the training samples given so far. Under the assumption that L of training samples are given in a chunk, first we derive a new eigenproblem whose solution gives us a rotation matrix of eigen-axes, then we introduce a new algorithm of augmenting eigen-axes based on the accumulation ratio. We also derive the one-pass incremental update formula for the accumulation ratio. The experiments are carried out to verify if the proposed IPCA works well. Our experimental results demonstrate that it works well independent of the size of data chunk, and that the eigenvectors for major components are obtained without serious approximation errors at the final learning stage. In addition, it is shown that the proposed IPCA can maintain the designated accumulation ratio by augmenting new eigen-axes properly. This property enables a learning system to construct an informative eigenspace with minimum dimensionality.

  IEEE, 2006, 2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, pp. 10493-10500, 2278 - +, English

  [Refereed]

  International conference proceedings


• An incremental learning algorithm of ensemble classifier systems

  Takuya Kidera, Seiichi Ozawa, Shigeo Abe

  In this paper, we propose an incremental learning model for ensemble classifier systems. In the proposed model, the number of classifiers is predetermined and fixed during the learning, and all classifiers are updated at every learning stage based on an extended algorithm of AdaBoost.M1. A neural network model called Resource Allocating Network with Long-Term Memory (RAN-LTM), which has been developed to realize stable incremental learning, is adopted as a classifier. We also propose a new method to update the classifier weights in the weighted majority voting under the one-pass incremental learning situations. In the experiments, first we verify that the proposed model can learn incrementally without serious forgetting and that the performance is not influenced seriously by the size of a training subset given at every learning stage. Then, through a comparison with Resource Allocating Network (RAN), RAN-LTM, and AdaBoost.M1, we demonstrate that the proposed incremental ensemble classifier system has comparable performance with a batch-learning ensemble classifier system, and that it outperforms both batch-learning and incremental-learning single-classifier systems.

  IEEE, 2006, 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, pp. 6453-6459, 3421 - +, English

  [Refereed]

  International conference proceedings


• Incremental linear discriminant analysis for classification of data streams

  S Pang, S Ozawa, N Kasabov

  This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number A classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.

  IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Oct. 2005, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 35(5) (5), 905 - 914, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• Feature extraction using independent components of each category

  M Kotani, S Ozawa

  We describe an application of independent component analysis (ICA) to pattern recognition in order to evaluate the effectiveness of features extracted by ICA. We propose a recognition method suitable for independent components that consists of modules for each category. A module has two parts: feature extraction and classification. Features are independent components estimated by ICA and outputs of modules are candidates for categories. These candidates are combined and categories are decided with a majority rule. This recognition method is applied to two tasks: hand-written digits in the MNIST database and acoustic diagnosis for a compressor as real-world tasks. A FastICA algorithm is applied to extracting independent features in the proposed method. Through recognition experiments, we demonstrate that the ICA of each category extracts useful features for these tasks and the independent components are superior to the principal components in recognition accuracy.

  SPRINGER, Oct. 2005, NEURAL PROCESSING LETTERS, 22(2) (2), 113 - 124, English

  [Refereed]

  Scientific journal


• Incremental learning of feature space and classifier for face recognition

  S Ozawa, SL Toh, S Abe, SN Pang, N Kasabov

  We have proposed a new approach to pattern recognition in which not only a classifier but also a feature space of input variables is learned incrementally. In this paper, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined to implement this idea. Since IPCA updates a feature space incrementally by rotating the eigen-axes and increasing the dimensions, the inputs of a neural classifier must also change in their values and the number of input variables. To solve this problem, we derive an approximation of the update formula for memory items, which correspond to representative training samples stored in the long-term memory of RAN-LTM. With these memory items, RAN-LTM is efficiently reconstructed and retrained to adapt to the evolution of the feature space. This function is incorporated into our face recognition system. In the experiments, the proposed incremental learning model is evaluated over a self-compiled video clip of 24 persons. The experimental results show that the incremental learning of a feature space is very effective to enhance the generalization performance of a neural classifier in a realistic face recognition task. (c) 2005 Elsevier Ltd. All rights reserved.

  PERGAMON-ELSEVIER SCIENCE LTD, Jun. 2005, NEURAL NETWORKS, 18(5-6) (5-6), 575 - 584, English

  [Refereed]

  Scientific journal


• Pattern Recognition Method Using Independent Components for Each Class

  KOTANI Manabu, KINUKAWA Shuhei, OZAWA Seiichi

  Applications of independent component analysis (ICA) to feature extraction have been a topic of research interest. However, the effectiveness of pattern features extracted by conventional ICA algorithms greatly depends on datasets in general. As one of the reasons, we have pointed out that conventional ICA features are obtained by increasing only their independence even if class information is available. In this paper, we propose a supervised learning approach to ICA to extract useful and robust features. The proposed method consists of several modules, each of which is responsible for extracting features for each class and identifying the class labels using the k nearest neighbor classifier. All the module outputs are combined to identify final results based on a majority rule. We evaluate the performance of the proposed method in several recognition tasks. From these results, we confirm the effectiveness of the recognition method using independent components for each class.

  The Institute of Electrical Engineers of Japan, May 2005, IEEJ Transactions on Electronics, Information and Systems, 125(5) (5), 807 - 812, Japanese


• Boosting Kernel discriminant analysis with adaptive Kernel selection

  S Kita, S Maekawa, S Ozawa, S Abe

  In this paper, we present a new method to enhance classification performance based on Boosting by introducing nonlinear discriminant analysis as feature selection. To reduce the dependency between hypotheses, each hypothesis is constructed in a different feature space formed by Kernel Discriminant Analysis (KDA). Then, these hypotheses are integrated based on AdaBoost. To conduct KDA in each Boosting iteration within realistic time, a new method of kernel selection is also proposed. Several experiments are carried out for the blood cell data and thyroid data to evaluate the proposed method. The result shows that it is almost the same as the best performance of Support Vector Machine without any time-consuming parameter search.

  SPRINGER-VERLAG WIEN, 2005, Adaptive and Natural Computing Algorithms, 429 - 432, English

  [Refereed]

  International conference proceedings


• Chunk incremental LDA computing on data streams

  S Pang, S Ozawa, N Kasabov

  This paper presents a constructive method for deriving an updated discriminant eigenspace for classification, when bursts of new classes of data is being added to an initial discriminant eigenspace in the form of random chunks. The proposed Chunk incremental linear discriminant analysis (I-LDA) can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.

  SPRINGER-VERLAG BERLIN, 2005, ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS, 3497, 51 - 56, English

  [Refereed]

  Scientific journal


• Incremental learning for online face recognition

  S Ozawa, SL Toh, S Abe, SN Pang, N Kasabov

  In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space of input variables is learned incrementally to adapt to incoming training samples. A benefit of this type of incremental learning is that the search for useful features and the learning of an optimal decision boundary are carried out in an online fashion. To implement this idea, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined. Using IPCA, a feature space is updated by rotating its eigen-axes and increasing the dimensions to adapt to a new training sample. In RAN-LTM, a small number of training samples called memory items are selected and they are utilized for retraining a classifier to realize an excellent incremental ability. To accommodate the classifier to the evolution of the feature space, we present a way to reconstruct the neural classifier without keeping all of the training samples given previously. In the experiments, the proposed incremental learning model is evaluated over a self-compiled face image database. As the result, we verify that the proposed model works well without serious forgetting and the test performance is improved as the learning stages proceed.

  IEEE, 2005, Proceedings of the International Joint Conference on Neural Networks (IJCNN), 5, 3174 - 3179, English

  [Refereed]

  International conference proceedings


• Detection of gas leakage sound using modular neural networks for unknown environments

  M Kotani, M Katsura, S Ozawa

  It is important to detect flammable or poisonous gas leaked from the cracks in pipes of petroleum refining plants or chemical plants. We applied a novel strategy of construction of neural network to the acoustic diagnosis technique for the gas leakage. An example of the modular neural network to realize the strategy is able to adapt its structure according to the dynamic environment. Experiments were performed for an artificial gas leakage device under various experimental conditions over about 18 months in a petroleum refining plant. Experimental results showed that the proposed network could adapt the structure to changes in environments and its performance was superior to that of feed-forward networks with the re-training strategy. From these results, we confirmed the effectiveness of the modular neural network for practical use. (C) 2004 Elsevier B.V. All rights reserved.

  ELSEVIER SCIENCE BV, Dec. 2004, NEUROCOMPUTING, 62, 427 - 440, English

  [Refereed]

  Scientific journal


• A Fast Incremental Learning Algorithm for Radial Basis Function Networks

  OKAMOTO Keisuke, OZAWA Seiichi, ABE Shigeo

  When training samples are given incrementally, neural networks often suffer from the catastrophic interference, which results in forgetting input-output relationships acquired in the past. To avoid the catastrophic interference, we have proposed Resource Allocating Network with Long-Term Memory (RAN-LTM). In RAN-LTM, not only a new training sample but also some memory items stored in long-term memory are used for training based on a gradient descent algorithm. In general, the gradient descent algorithm is usually slow and can be easily fallen into local minima. In this paper, to alleviate these problems, we introduce a linear regression approach into the learning of RAN-LTM, in which its centers are not trained but selected based on output errors in an incremental fashion. In this approach, the regression is carried out for not only a training sample and memory items but also pseudodata that are selected around the centers of hidden units based on the complexity of an approximated function. This selection reduces the total number of pseudodata at each learning step; as a result, fast incremental learning is realized in RAN-LTM. Since only memory items are stored in memory, the proposed RAN-LTM does not need so much memory capacity when the incremental learning is carried out. This property is useful especially for small-scale systems. To verify these characteristics of RAN-LTM, we apply it to several function approximation problems, in which the performance in approximation accuracy, learning time, and needed memory capacity are investigated by comparison with some conventional models. Moreover, when extending the learning domain with time, the increase trends in learning time and needed memory capacity are investigated. From the experimental results, it is verified that the proposed model can learn fast and accurately, and that it needs rather small memory capacity so far as the learning domain is not too large.

  The Society of Instrument and Control Engineers, 2004, Transactions of the Society of Instrument and Control Engineers, 40(12) (12), 1227 - 1235, Japanese


• Feature Extraction Using Supervised Independent Component Analysis by Maximizing Class Distance

  SAKAGUCHI Yoshinori, OZAWA Seiichi, KOTANI Manabu

  Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of pattern features extracted by conventional ICA algorithms depends on pattern sets; that is, how patterns are distributed in the feature space. As one of the reasons, we have pointed out that ICA features are obtained by increasing only their independence even if the class information is available. In this context, we can expect that more high-performance features can be obtained by introducing the class information into conventional ICA algorithms.
  In this paper, we propose a supervised ICA (SICA) that maximizes Mahalanobis distance between features of different classes as well as maximize their independence. In the first experiment, two-dimensional artificial data are applied to the proposed SICA algorithm to see how maximizing Mahalanobis distance works well in the feature extraction. As a result, we demonstrate that the proposed SICA algorithm gives good features with high separability as compared with principal component analysis and a conventional ICA. In the second experiment, the recognition performance of features extracted by the proposed SICA is evaluated using the three data sets of UCI Machine Learning Repository. From the results, we show that the better recognition accuracy is obtained using our proposed SICA. Furthermore, we show that pattern features extracted by SICA are better than those extracted by only maximizing the Mahalanobis distance.

  The Institute of Electrical Engineers of Japan, Jan. 2004, IEEJ Transactions on Electronics, Information and Systems, 124(1) (1), 157 - 163, Japanese


• A Memory-based Reingorcement Learning Model Utilizing Macro-Actions

  MURATA Makoto, OZAWA Seiichi

  2004, Proc.of 7th International Conference on Adaptive and Natural Computing Algorithm, English

  [Refereed]

  International conference proceedings


• A memory-based neural network model for efficient adaptation to dynamic environments

  S Ozawa, K Tsumori

  When environments are dynamically varied for agents. the knowledge acquired from an environment would be useless in the future environments. Thus, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However. modifying all acquired Knowledge is not always efficient. Because the knowledge once acquired may be useful again when the same (or similar) environment reappears. Moreover, some of the knowledge can be shared among different environments. To learn efficiently in such a situation, we propose a neural network model that consists of the following four modules: resource allocating network, long-term memory, association buffer. and environmental change detector. We apply this model to a simple dynamic environment in which several target functions to be approximated are varied in turn.

  IEEE, 2004, 2004 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, PROCEEDINGS, 437 - 442, English

  [Refereed]

  International conference proceedings


• Supervised independent component analysis with class information

  M Kotani, H Takabatake, S Ozawa

  Independent Component Analysis (ICA) is a method to transform from mixed signals into independent components. ICA has been so far applied to blind signal separation problems such as sound, speech, images, and biological signals. Recently, ICA is applied to feature extraction for face, speech, and image recognitions. Since ICA is an unsupervised learning, extracted independent components are not always useful for recognition purposes. In this paper, we propose a new supervised learning approach to ICA using class information to enhance the separability of features. The proposed method is implemented by a three-layered feedforward network in which target signals are given to the output units. The defined objective function is composed of the following two terms: one is for evaluating independency of hidden outputs and the other is for evaluating errors between output signals and their targets. Simulations are performed for some datasets in the UCI repository to evaluate the effectiveness of the proposed method. In the proposed method, we obtain higher recognition accuracies as compared with a conventional unsupervised ICA algorithm.

  SPRINGER-VERLAG BERLIN, 2004, NEURAL INFORMATION PROCESSING, 3316, 1052 - 1057, English

  [Refereed]

  Scientific journal


• One-pass incremental membership authentication by face classification

  SN Pang, S Ozawa, N Kasabov

  Real membership authentication applications require machines to learn from stream data while making a decision as accurately as possible whenever the authentication is needed. To achieve that, we proposed a novel algorithm which authenticated membership by a one-pass incremental principle component analysis(IPCA) learning. It is demonstrated that the proposed algorithm involves an useful incremental feature construction in membership authentication, and the incremental learning system works optimally due to its performance is converging to the performance of a batch learning system.

  SPRINGER-VERLAG BERLIN, 2004, BIOMETRIC AUTHENTICATION, PROCEEDINGS, 3072, 155 - 161, English

  [Refereed]

  Scientific journal


• A modified incremental principal component analysis for on-line learning of feature space and classifier

  S Ozawa, SN Pang, N Kasabov

  We have proposed a new concept for pattern classification systems in which feature selection and classifier learning are simultaneously carried out on-line. To realize this concept, Incremental Principal Component Analysis (IPCA) and Evolving Clustering Method (ECM) was effectively combined in the previous work. However, in order to construct a desirable feature space, a threshold value to determine the increase of a new feature should be properly given in the original IPCA. To alleviate this problem, we can adopt the accumulation ratio as its criterion. However, in incremental situations, the accumulation ratio must be modified every time a new sample is given. Therefore, to use this ratio as a criterion, we also need to develop a one-pass update algorithm for the ratio. In this paper, we propose an improved algorithm of IPCA in which the accumulation ratio as well as the feature space can be updated online without all the past samples. To see if correct feature construction is carried out by this new IPCA algorithm, the recognition performance is evaluated for some standard datasets when ECM is adopted as a prototype learning method in Nearest Neighbor classifier.

  SPRINGER-VERLAG BERLIN, 2004, PRICAI 2004: TRENDS IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 3157, 231 - 240, English

  [Refereed]

  Scientific journal


• Study on Detection of Leakage Sound Using Independent Component Analysis

  KOTANI Manabu, ARIMOTO Takahiko, OZAWA Seiichi

  It is important to detect gas leakage sounds from pipes in petroleum refining plants and chemical plants, as often the gas used in these plants are flammable or poisonous. In order to detect the leakage accurately, we should select a feature extraction method for sounds properly. The purpose of this paper is to examine whether independent component analysis (ICA) is useful as a feature extraction method. Several experiments are performed in a plant using an artificial gas leakage device under various experimental conditions. A separating matrix that separates the independent components from collected leakage sounds and background noises is trained by an ICA algorithm. Through several simulations, we find that most basis functions acquired from this training are localized in frequency. Furthermore, there are remarkable differences in amplitude of some independent components between leakage sounds and background noises. From these results, we confirm that the feature extraction using the ICA algorithm is very useful for detecting gas leakage sounds.

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE), Oct. 2003, Transactions of the Institute of Systems, Control and Information Engineers, 16(10) (10), 539 - 547, Japanese

  Link to Kobe Univ. Repository (Kernel)


• A Fast Incremental Learning Algorithm of RBF Networks with Long-Term Memory

  Keisuke Okamoto, Seiichi Ozawa, Shigeo Abe

  To avoid the catastrophic interference in incremental learning, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only a new training sample but also some memory items stored in Long-Term Memory are trained based on a gradient descent algorithm. In general, the gradient descent algorithm is usually slow and can be easily fallen into local minima. To solve these problems, we propose a fast incremental learning algorithm of RAN-LTM, in which its centers are not trained but selected based on output errors. This model does not need so much memory capacity and it also realizes robust incremental learning ability. To verify these characteristics of RAN-LTM, we apply it to two function approximation problems: one-dimensional function approximation and prediction of Mackey-Glass time series. From the experimental results, it is verified that the proposed RAN-LTM can learn fast and accurately without large main memory unless incremental learning is conducted over a long period of time.

  Sep. 2003, Proceedings of the International Joint Conference on Neural Networks, 1, 102 - 107


• Reinforcement Learning Using Feedforward Neural Network with Memory Mechanism

  OZAWA Seiichi, SHIRAGA Naoto

  In reinforcement learning problems, the agent learns what to do so as to maximize numerical rewards. In many cases, the agent learns its proper actions through the estimation of an action-value function. When the agent's states are continuous, the action-value function cannot be represented by a lookup table in general. A solution for this problem is that a neural network is utilized for approximating it. However, when neural networks are trained incrementally, input-output relationships that are trained formerly tend to be collapsed by given new data. This phenomenon is called “interference”. Since the rewards are incrementally given from the environment, the interference could be also serious in reinforcement learning problems. To solve this problem, we propose a memory-based reinforcement learning model that is composed of Resource Allocating Network and memory. The distinctive feature of the proposed model is that it needs quite a small main memory to execute the accurate learning of action-value functions. To examine this feature, the proposed model is applied to the two conventional problems: Random Walk Task and Extended Mountain-Car Task. In these tasks, the learning domains are temporally expanded in order to evaluate the incremental learning ability. In the simulations, we verify that the proposed model can approximate proper action-value functions with quite a small main memory as compared with the conventional approaches.

  The Society of Instrument and Control Engineers, 2003, Transactions of the Society of Instrument and Control Engineers, 39(12) (12), 1129 - 1135, Japanese


• Incremental Learning in Dynamic Environments Using Neural Network with Long-term Memory

  Tsumori Kenji, Ozawa Seiichi, Abe Shigeo

  When the environment is dynamically changed for agents, knowledge acquired from an environment might be useless in the future environments. Therefore, agents should not only acquire new knowledge but also modify or delete old knowledge. However, this modification and deletion are not always efficient in learning. Because the knowledge once acquired in the past can be useful again in the future when the same environment reappears. To learn efficiently in this situation, agents should have memory to store old knowledge. In this paper, we propose an agent architecture that consists of four modules: resource allocating network (RAN), long-term memory (LTM), association buffer (A-Buffer), and environmental change detector (ECD). To evaluate the adaptability in a class of dynamic environments, we apply this model to a simple problem that some target functions to be approximated are changed in turn.

  The Institute of Systems, Control and Information Engineers, 2003, Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, 3(0) (0), 5506 - 5506, English


• A Face Recognition System Using Neural Networks with Incremental Learning Ability.

  Soon Lee Toh, OZAWA Seiichi

  2003, Proc. ANZIIS 2003, 155-161, English

  [Refereed]

  International conference proceedings


• Training of Support Vector Machines by the Steepest Ascent Method

  ABE Shigeo, HIROKAWA Youichi, OZAWA Seiichi

  Since the training of support vector machines needs to solve the dual problem with the number of variables equal to the number of training data, the training becomes slow when the number of training data is large. To speed up training the Sequential Minimal Optimization (SMO) technique has been proposed, in which two data are optimized simultaneously. In this paper, we propose to extend SMO so that more than two data are optimized simultaneously. Namely, we select a working set including variables, solve the equality constraint for one variable included in the working set, and substitute it into the objective function. Then we solve the subproblem related to the working set by calculating the inverse of the Hessian matrix. We evaluate our method for the five benchmark data sets and show the speed-up of training over SMO.

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE), Nov. 2002, Transactions of the Institute of Systems, Control and Information Engineers, 15(11) (11), 607 - 614, Japanese

  Link to Kobe Univ. Repository (Kernel)


• A reinforcement learning algorithm for neural networks with incremental learning ability

  N. Shiraga, S. Ozawa, S. Abe

  © 2002 Nanyang Technological University. When neural networks are used for approximating action-values of Reinforcement Learning (RL) agents, the "interference" caused by incremental learning can be serious. To solve this problem, in this paper, a neural network model with incremental learning ability was applied to RL problems. In this model, correctly acquired input-output relations are stored into long-term memory, and the memorized data are effectively recalled in order to suppress the interference. In order to evaluate the incremental learning ability, the proposed model was applied to two problems: Extended Random-Walk Task and Extended Mountain-Car Task. In these tasks, the working space of agents is extended as the learning proceeds. In the simulations, we certified that the proposed model could acquire proper action-values as compared with the following three approaches to the approximation of action-value functions: tile coding, a conventional neural network model and the previously proposed neural network model.

  Jan. 2002, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, 5, 2566 - 2570


• Feature Extraction of Digit Patterns Utilizing Independent Component Analysis

  Kotani Manabu, Ozawa Seiichi

  In this paper, an approach to feature extraction utilizing independent component analysis (ICA) is pro-posed. In our approach, input patterns are transformed into feature vectors using ICA-bases that are obtained through two-layer neural network learning. A k-NN classifier is applied to these ICA feature vectors when the recognition accuracy is evaluated. Hand-written digits in MNIST database are used as target characters. Fast ICA algorithm is applied to these images in order to learn ICA-bases. In recognition experiments, we demonstrate that the ICA approach realizes a potential feature extraction method for hand-written digits. Furthermore, we show the addition of noise patterns to training data is effective for elimination of redundant basis functions.

  The Institute of Electrical Engineers of Japan, 2002, IEEJ Transactions on Electronics, Information and Systems, 122(3) (3), 465 - 470, Japanese


• Incremental Learning Algorithm for Feedforward Neural Network with Long-Term Memory

  KOBAYASHI Masataka, OZAWA Seiichi, ABE Shigeo

  When neural networks are trained incrementally, input-output relations that are trained formerly tend to be collapsed by the learning of new data. This phenomenon is often called interference. To suppress the interference efficiently, we propose an incremental learning model, in which Long-Term Memory (LTM) is introduced into Resource Allocating Network (RAN) proposed by Platt. This type of memory is utilized for storing useful training data (called LTM data) that are generated adaptively in the learning phase. When a new training datum is given, the proposed system searches several LTM data that are useful for suppressing the interference. The retrieved LTM data as well as the new training datum are trained simultaneously in RAN. In the simulations, the proposed model is applied to various incremental learning problems to evaluate the function approximation accuracy and the learning speed. From the simulation results, we certify that the proposed model can attain good approximation accuracy with small computation costs.

  The Society of Instrument and Control Engineers, 2002, Transactions of the Society of Instrument and Control Engineers, 38(9) (9), 792 - 799, Japanese


• Learning action-value functions using neural networks with incremental learning ability

  N Shiraga, S Ozawa, S Abe

  When the distribution of given training data is biased and temporally varied, it is well known that the learning of neural networks becomes difficult in general. In Reinforcement Learning (RL) problems, such situations often arise. In this paper, an incremental learning system, which has been devised for supervised learning, is implemented as an RL agent that can acquire an action-value function properly even in the above difficult situations. The proposed RL agent is applied to an extended mountain-car task in which learning domains axe temporally expanded. Through computer simulations, we demonstrate that the proposed agent can acquire a right policy in this task.

  I O S PRESS, 2001, KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, 69, 22 - 26, English

  [Refereed]

  International conference proceedings


• Reducing computations in incremental learning for feedforward neural network with long-term memory

  M. Kobayashi, A. Zamani, S. Ozawa, S. Abe

  When neural networks are trained incrementally, input-output relationships that are trained formerly tend to be collapsed by the learning of new training data. This phenomenon is called "interference". To suppress the interference, we have proposed an incremental learning system (called RAN-LTM), in which Long-Term Memory (LTM) is introduced into Resource Allocating Network (RAN). Since RAN-LTM needs to train not only new data but also some LTM data to suppress the interference, if many LTM data are retrieved, large computations are required. Therefore, it is important to design appropriate procedures for producing and retrieving LTM data in RAN-LTM. In this paper, these procedures in the previous version of RAN-LTM are improved. In simulations, the improved RAN-LTM is applied to the approximation of a one-dimensional function, and the approximation error and the training speed are evaluated as compared with RAN and the previous RAN-LTM.

  Jan. 2001, Proceedings of the International Joint Conference on Neural Networks, 3, 1989 - 1994


• Performance Improvement fo Dynamical Associative Memories by Solving Linear Inequalities

  Park Jungpil, Ozawa Seiichi, Abe Shigeo

  A main problem with dynamical associative memories (DAMs) is that when memory patterns are stored, pseudo-memories (false fixed points and limit cycles) are also generated and they hinder proper association of input patterns. To overcome this problem, Hassoun proposed a heuristic method of reducing pseudo-memories. In this method, DAMs are constructed such that a zero vector called “ground state” as well as stored patterns is stabilized and sparsely activated states (sparse patterns) converge to the ground state. Such dynamical properties of neural networks can be described with linear inequalities, and connection weights of networks are obtained by solving these inequalities using the Ho-Kashyap algorithm. In this paper, we propose an extended Hassoun model in which network dynamics are modified such that dense patterns, mix-ture patterns and inhibition patterns are also converged to the ground state. In simulations, we compare association performance of this extended Hassoun model with conventional associative memory models, and demonstrate the usefulness of our proposed model as a dynamical associative memory.

  The Institute of Electrical Engineers of Japan, 2001, IEEJ Transactions on Electronics, Information and Systems, 121(5) (5), 899 - 905, Japanese


• Acoustic Diagnosis with Modular Neural Networks to Adapt Dynamic Environment

  KOTANI Manabu, MIYATA Takeo, OZAWA Seiichi, AKAZAWA Kenzo

  The detection of gas leakage sound from pipes is important in petroleum refining plants and chemical plants, as often the gas used in these plants are flammable or poisonous. In order to establish the acoustic diagnosis technique for the leakage sound, we examined the application of modular neural networks to the stable detection. The modular neural network has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions to imitate the change of environment for a long term. The discrimination accuracy with the proposed network was observed to be about 93%. From the results, we confirmed the effectiveness for the application of the modular neural network to the detection of the leakage sound for the practical use.

  The Society of Instrument and Control Engineers, Sep. 2000, Transactions of the Society of Instrument and Control Engineers, 36(9) (9), 797 - 803, Japanese


• Fast Training of Three-Layered Neural Network Classifiers by Solving Inequalities

  TSUCHIYA Naoki, OZAWA Seiichi, ABE Shigeo

  In this paper we discuss training of three-layered neural network classifiers by solving inequalities. Namely, first we represent each class by the center of the training data belonging to the class, and determine the set of hyperplanes that separate each class (i.e., each center) into a single region. Then according to whether the center is on the positive or negative side of the hyperplane, we determine the target values of each class for the hidden neurons (i.e., hyperplanes). Since the convergence condition of the neural network classifier is now represented by the two sets of inequalities, we solve the sets successively by the Ho-Kashyap algorithm. We demonstrate the advantage of our method over the backpropagation algorithm using several benchmark data sets.

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE), Jun. 2000, Transactions of the Institute of Systems, Control and Information Engineers, 13(6) (6), 276 - 283, Japanese


• Training three-layer neural network classifiers by solving inequalities

  N Tsuchiya, S Ozawa, S Abe

  In this paper we discuss training of three-layer neural network classifiers by solving inequalities. Namely, first we represent each class by the center of the training data belonging to the class, and determine the set of hyperplanes that separate each class into a single region. Then according to whether the center is on the positive or negative side of the hyperplane, we determine the target values of each class for the hidden neurons. Since the convergence condition of the neural network classifier is now represented by the two sets of inequalities, we solve the sets successively by the Ho-Kashyap algorithm. We demonstrate the advantage of our method over the BP using three benchmark data sets.

  IEEE COMPUTER SOC, 2000, IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL III, 3, 555 - 560, English

  International conference proceedings


• An Architecture Design Method of Modular Dynamical Neural Networks Using Genetic Algorithms

  OZAWA Seiichi, TSUTSUMI Kazuyoshi, BABA Norio

  In this paper, we propose an evolutionary approach to architecture design of modular dynamical neural networks. As one of modular dynamical neural networks, we adopt Cross-Coupled Hopfield Nets (CCHN) in which plural Hopfield networks are coupled to each other. The architecture of CCHN is represented by some structural-parameters such as the number of modules, the numbers of units per module, the module connectivity, and so forth. In the proposed design method, these structural-parameters are treated as phenotype of an individual, and suitable modular architecture is searched through the evolution of its genetic representation (genotype) by using genetic algorithms. Based on a simple direct coding method, the order of length of genetic representation for the structural-parameters can be estimated to be O(N²) where N is the total number of units. On the other hand, the order of genetic representation proposed here is O(N). To verify the usefulness of proposed method, we apply a CCHN to associative memories. Here, the fitness of an individual is defined so as to be larger when a CCHN has a simpler architecture as well as when the association performance is higher. As the result of simulations, we certify that the proposed design method can find high-performance CCHN with simple modular architectures.

  The Society of Instrument and Control Engineers, 2000, Transactions of the Society of Instrument and Control Engineers, 36(3) (3), 298 - 305, Japanese


• Signal Processing of Speech Using Independent Component Analysis Based on Information Maximization Algorithm

  KOTANI Manabu, MAEKAWA Satoshi, OZAWA Seiichi, AKAZAWA Kenzo

  We describe what characteristics an independent component analysis can extract from Japanese continuous speech. Speech data was selected from ATR database uttered by a female speaker. The data was recorded at 20kHz sampling frequency and was pre-processed with a whitening filter. The learning algorithm of a network was an information-maximization approach proposed by Bell and Sejnowski. After the learning, most of the basis functions that are columns of a mixing matrix were localized in both time and frequency. Furthermore, we confirmed that there were some basis functions to extract the acoustic feature such as the pitch and the formant of each vowel.

  The Society of Instrument and Control Engineers, 2000, Transactions of the Society of Instrument and Control Engineers, 36(5) (5), 456 - 458, Japanese


• Representations of Speech by Sparse Coding Algorithm

  Manabu Kotani, Yasunobu Shirata, Maekawa Satoshi, Seiichi Ozawa, Kenzo Akazawa

  It was reported that a sparse coding algorithm produced a set of basis functions being spatially localized, oriented, and bandpass for natural images. The application of Independent Component Analysis (ICA) to the natural images has shown to be similar results to the sparse coding's result. However, the ICA can be applied in the case of basis function matrices to be non-singular and invertible. There are not such limitations in the sparse coding algorithm. This property allows that the code is overcomplete, that is, the number of code elements is greater than the effective dimensionality of the input space. The purpose of this paper is to examine what characteristics of speech the sparse coding algorithm extracts from natural sounds. Speech data was Japanese five vowels uttered by a female speaker during about 1sec. Most of the basis functions were localized in frequency after the training. Some basis functions only shifted in time and resembled each other. Each basis function was compared with the speech data and the result was that some basis functions responded selectively to each vowel. The frequency analysis for the basis function showed that some basis functions extracted the pitch frequency and the formant of each vowel.

  The Institute of Electrical Engineers of Japan, 2000, IEEJ Transactions on Electronics, Information and Systems, 120(12) (12), 1996 - 2002, Japanese


• Evolution of a dynamical modular neural network and its application to associative memories

  Seiichi Ozawa, Kazuyoshi Tsutsumi, Norio Baba

  This paper presents an evolutionary approach to architecture design of dynamical modular neural networks. As one of the modular neural networks, we adopt Cross-Coupled Hopfield Nets (CCHN) in which plural Hopfield networks are coupled to each other. The architecture of CCHN is represented by some structural parameters such as the number of modules, the numbers of module units, module connectivity, and so forth. In this paper, these structural parameters are treated as phenotype of an individual, and suitable modular architecture is searched by using genetic algorithms. To verify the usefulness of the proposed architecture design algorithm, we apply CCHN to associative memories.

  IEEE, 1999, International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES, 145 - 148, English

  Scientific journal


• Application of independent component analysis to feature extraction of speech

  Manabu Kotani, Yasunobu Shirata, Satoshi Maekawa, Seiichi Ozawa, Kenzo Akazawa

  We describe what characteristics an independent component analysis can extract from Japanese continuous speech. Speech data was selected from ATR database uttered by a female speaker. The data was recorded at 20 kHz sampling frequency and was pre-processed with a whitening filter. The learning algorithm of a network was an information-maximization approach proposed by Bell and Sejnowski. After the learning, most of the basis functions that are columns of a mixing matrix were localized in both time and frequency. Furthermore, we confirmed that there were some basis functions to extract the acoustic feature such as the pitch and the formant of each vowel.

  IEEE, 1999, Proceedings of the International Joint Conference on Neural Networks, 5, 2981 - 2984, English

  International conference proceedings


• Emergence of feature extraction function using genetic programming

  Manabu Kotani, Seiichi Ozawa, Masaki Nakai, Kenzo Akazawa

  A novel method of feature extraction to improve the performance of pattern recognition is proposed. It is assumed that the feature consists of a polynomial expression of the original patterns. The term of polynomial expressions are searched by the genetic programming. In order to evaluate the effectiveness of the proposed method, we apply k nearest neighbor classifier as the classification algorithm. Experiments were performed for an artificial task and an acoustic diagnosis for compressors as the real world task. From these results, we confirmed that the proposed method was effective for the feature extraction.

  IEEE, 1999, International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES, 149 - 152, English

  Scientific journal


• Application of independent component analysis to hand-written Japanese character recognition

  Seiichi Ozawa, Toshihide Tsujimoto, Manabu Kotani, Norio Baba

  We explore an approach to recognizing Japanese Hiragana characters utilizing independent components of input images (we call this method ICA-matching). These components are extracted by Fast ICA algorithm proposed by Hyvarinen and Oja. We propose several formats of inputs, which are different in how a character image is transformed into time sequences. From recognition experiments, we show that ICA-matching outperforms conventional methods in some cases. However, in order to realize high performance, we have to pay attention to the following parameters: dimensions of feature vectors and rate of noise added to training data. In discussions, we try to study how these parameters are related to the performance of ICA-matching.

  IEEE, 1999, Proceedings of the International Joint Conference on Neural Networks, 4, 2867 - 2871, English

  International conference proceedings


• Continuous-time model of Autoassociative Neural Memories utilizing the noise-subspace dynamics

  Seiichi Ozawa, Kazuyoshi Tsutsumi, Norio Baba

  This paper presents a continuous-time model of Autoassociative Neural Memories (ANMs) which correspond to a modified version of pseudoinverse-type ANMs. This ANM model is derived from minimizing the energy function for a modular neural network. Through the eigendecomposition of the connection matrix, we show that the dynamical properties of the ANM are qualitatively different in the two state subspaces: a pattern-subspace and a noise-subspace. The proposed ANM has a distinctive feature in the noise-subspace dynamics. The size of basins of attraction can be varied by controlling the contribution of the noise-subspace dynamics to the whole network. The first simulation confirms this attractive feature. In the second simulation, we investigate the performance robustness of the ANM for several kinds of correlated pattern sets. These simulation results confirm the usefulness of the proposed ANM.

  Kluwer Academic Publishers, 1999, Neural Processing Letters, 10(2) (2), 97 - 109, English

  [Refereed]

  Scientific journal

  Link to Kobe Univ. Repository (Kernel)


• An associative memory model derived from cross-coupled hopfield nets and its role in noise-space dynamics

  Seiichi Ozawa, Kazuyoshi Tsutsumi, Norio Baba

  In this paper, the association characteristics of cross-coupled Hopfield nets (CCHN) proposed as a modular neural network model are discussed analytically. In a CCHN, an arbitrary number of modules (Hopfield networks) can be mutually connected via feedforward networks called internetworks, whose output generates interactions among module networks. To evaluate the CCHN as a modular neural network, it has previously been applied to associative memory. Although its excellent association performance is supported by many simulation results, it is still difficult to compute the memory capacity exactly or to examine the dynamic properties rigorously, because CCHN information processing includes strong nonlinearity. Hence, as the first step to an analytical approach, this paper focuses on a single-module CCHN whose interaction is realized by a two-layered feedforward internetwork. In this case, the connection matrix of the CCHN degenerates into a single square-matrix, as does a conventional auto-association type of associative memory. Using eigenvalue analysis for the connection matrix, we reveal that the essential differences between the association characteristics of a CCHN and a conventional autocorrelation associative memory originate from dynamics in the noise-space that is the orthogonal complement of the subspace generated from memory patterns. ©1998 Scripts Technica.

  John Wiley and Sons Inc., 1998, Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), 125(2) (2), 27 - 34, English

  Scientific journal


• An artificial modular neural network and its basic dynamical characteristics

  Seiichi Ozawa, Kazuyoshi Tsutsumi, Norio Baba

  This work contains a proposition of an artificial modular neural network (MNN) in which every module network exchanges input/output information with others simultaneously. It further studies the basic dynamical characteristics of this network through both computer simulations and analytical considerations. A notable feature of this model is that it has generic representation with regard to the number of composed modules, network topologies, and classes of introduced interactions. The information processing of the MNN is described as the minimization of a total-energy function that consists of partial-energy functions for modules and their interactions, and the activity and weight dynamics are derived from the total-energy function under the Lyapunov stability condition. This concept was realized by Cross-Coupled Hopfield Nets (CCHN) that one of the authors proposed. In this paper, in order to investigate the basic dynamical properties of CCHN, we offer a representative model called Cross-Coupled Hopfield Nets with Local And Global Interactions (CCHN-LAGI) to which two distinct classes of interactions - local and global interactions - are introduced. Through a conventional test for associative memories, it is confirmed that our energy-function-based approach gives us proper dynamics of CCHN-LAGI even if the networks have different modularity. We also discuss the contribution of a single interaction and the joint contribution of the two distinct interactions through the eigenvalue analysis of connection matrices.

  Springer Verlag, 1998, Biological Cybernetics, 78(1) (1), 19 - 36, English

  [Refereed]

  Scientific journal


• An Autoassociative Memory Model Derived from a Modular Neural Network and a Diversity of the Association Properties

  OZAWA Seiichi, TSUTSUMI Kazuyoshi, BABA Norio

  In this paper, we propose a new autoassociative memory model which is derived from Cross-Coupled Hopfield Nets (CCHN). The CCHN is a modular neural network in which plural Hopfield networks are mutually connected via feedforward neural networks. The CCHN's architecture is determined by the following structural parameters : the number of modules, the numbers of units in the modules, the contribution of the module information processings and the interactions to the whole network information processing, and the module connectivity. If these parameters are changed, the network dynamics are also changed; therefore, it may be possible to implement a great number of autoassociative memories with different nature. Through some computer simulations, we will discuss a diversity of association properties in the proposed model.

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE), Dec. 1997, Transactions of the Institute of Systems, Control and Information Engineers, 10(12) (12), 668 - 678, Japanese

  Link to Kobe Univ. Repository (Kernel)


• An Associative Memory ModelI Derived from Cross-Coupled Hopfield Nets and The Roll of Noise-Space Dynamics

  Ozawa Seiichi, Tsutsumi Kazuyoshi, Baba Norio

  In this paper, the association characteristics of Cross-Coupled Hopfield Nets (CCHN) proposed as a modular neural network model are discussed in an analytical way. In the CCHN, an arbitrary number of modules (Hopfield networks) can be mutually connected via feedforward networks called “internetworks”, whose outputs generate the interactions among module networks. To evaluate the CCHN as a modular neural network, it has been applied to associative memories so far. Although its excellent association performance is supported by many simulation results, it is still difficult to compute the memory capacity exactly and examine the dynamical properties rigorously, because the information processing of the CCHN includes strong nonlinearity. Hence, as the first step to the analytical approach, this paper focuses on a 1-module CCHN whose interaction is realized by a two-layered feedforward internetwork. In this case, the connection matrix of the CCHN degenerates into a single square-matrix like a conventional auto-association type of associative memory. Through the eigenvalue analysis for the connection matrix, we reveal that the essential differences between the association characteristics of the CCHN and a conventional auto-correlation associative memory originate from the dynamics in the noise-space which is the orthogonal complement of the subspace generated from memory patterns.

  The Institute of Electrical Engineers of Japan, 1997, IEEJ Transactions on Electronics, Information and Systems, 117(9) (9), 1253 - 1258, Japanese


• A Multi-Module Neural Network Model and the Estimate of Its Nature as Associative Memory

  OZAWA Seiichi, TSUTSUMI Kazuyoshi

  本論文では,モジュール構造をもつニューラルネット(モジュール化ニューラルネット)のモデル化手法として,情報処理様式をエネルギー関数で記述する方法を採用する.この一モデルとして,モジュールネットの情報処理とモジュールネット間の相互作用に対するエネルギー関数を線形に加算し,更にモジュールネットの状態間に多対多の写像関係がある場合でも適用可能としたCross-Coupled Nets With Many-to-Many Mapping Internetworks(CCHN-MMMI)を提案する.また,CCHN-MMMIのネットワークダイナミックスを導出し,モジュール数が2であるCCHN-MMMIの想起特性とその連想記億能力をシミュレーション実験により調べる.シミューレーション実験では,モジュール構造が明示的に与えられることによる効果を,その想起過程から従来の自己相関型連想記億モデルとの比較により考察する.次に,文字パターン対の連想を例にとり,モジュールネットの状態間に多対多の写像関係がある場合でも正しく動作することを確認する.また,基本記憶をランダムに選んだとき,その数の増加に伴う想起ダイナミックスの劣化を定量的に調べ,連想記憶モデルとしての評価を行う.その結果,さまざまな基本記憶に対し,CCHN-MMMIはモジュールネットの状態間に多対多の写像関係がある場合でも正しく動作し,その相互作用は偽記憶の想起を妨げるよう機能することがわかった.特に,モジュールネット間の写像関係を多層ネットワークで学習するCCHN-MMMIでは,自己相関型連想記憶モデルに比べ大幅に連想能力が改善され,モジュールネット間に非線形な相互作用をもつことの効果が確認された.

  The Institute of Electronics, Information and Communication Engineers, Jun. 1994, The Transactions of the Institute of Electronics,Information and Communication Engineers., 77(6) (6), 1135 - 1145, Japanese

  Link to Kobe Univ. Repository (Kernel)

• Data Generation Model for Evasion of Fraud Detection in Financial Transactions

  Akitaka TSUJITA, Hiroaki INOUE, Chawthetza, Yusuke MARUYAMA, Seiichi OZAWA

  Last, 07 Mar. 2025, IEICE Technical Report, 124(422) (422), 375 - 382, Japanese

  Technical report


• Label Correction for Machine Learning-Based Cyber Attack Detection Assuming Uncertainty in Data Labels

  浦川, 遥輝, 山田, 明, Joo, Suwon, Vestin, Simon, Wang, Hui, Park, Chanho, 小澤, 誠一, Haruki, Urakawa, Akira, Yamada, Seiich, Ozawa

  機械学習は，さまざまな課題においてデータに基づいてモデル構築を実現しているが，サイバー攻撃検知において正確なラベルが付与されないため高い精度を実現できない問題がある．本稿では，不確実なデータラベルを前提とした機械学習によるサイバー攻撃検知のための誤ラベル訂正手法を提案する．従来手法であるConfident Learningは，画像分類など汎用的なタスクにおいてクラスの組ごとに独立に誤ラベルが発生する場合に対応できる．しかしながら，サイバー攻撃検知においては，正例と負例の均衡がとれていない場合が多い．また，インシデントに基づいて運用者がラベル付けを行うため，時刻がずれることによって発生する誤ラベルが多い．本稿では，Confident Learningを拡張して，データセットの不均衡性と時刻のずれに対して頑強な誤ラベル訂正手法を提案する．提案手法の有効性を検証するために，公開されているCICIDS2017データセットおよび，企業ネットワークに設置された侵入検知システムのログを用いて評価した．その結果，提案手法は，従来手法に比べて高い精度で誤ラベルを訂正できることが分かった．また，侵入検知システムのログにおいて，ラベル時刻のずれを訂正できることを確認した． Machine learning has enabled model development based on data across various domains. However, in cyber-attack detection, the lack of accurate labels hinders high accuracy. This paper proposes a method for correcting mislabeled data in cyber-attack detection using machine learning, assuming uncertain data labels. Confident Learning, a conventional method, can handle situations where label errors occur independently within each class in general tasks such as image classification. However, in cyber-attack detection, there is often a significant imbalance between positive and negative labels. Additionally, since labels are assigned by operators based on incidents, mislabeling regarding time discrepancies frequently occurs. This paper proposes an extension of Confident Learning that provides a robust method for correcting mislabeled data, addressing both dataset imbalance and time discrepancies. To validate the effectiveness of the proposed method, we evaluated it using the publicly available CICIDS2017 dataset and logs from an IDS(Intrusion Detection System) deployed in an enterprise network. The results demonstrate that the proposed method can correct mislabeled data with higher accuracy compared to conventional methods. Furthermore, we confirmed that the method can correct time discrepancies in the labels within the IDS logs.

  15 Oct. 2024, コンピュータセキュリティシンポジウム2024論文集, 76 - 83, Japanese


• 組織間水平連合学習による社会実装

  小澤誠一

  Lead, 01 May 2024, オペレーションズ・リサーチ, 69(5) (5), 241 - 246, Japanese

  [Refereed][Invited]

  Introduction scientific journal


• Efficient Replay Data Selection in Continual Federated Learning Model

  北野優斗, WANG Lihua, 小澤誠一

  Last, Mar. 2024, 電子情報通信学会技術研究報告(Web), 123(423(IT2023 117-135)) (423(IT2023 117-135)), Japanese

  Technical report


• Towards Modeling the Visual Recognition for Human Security Countermeasures Using Large-Scale Language Models

  鍛冶, 佳佑, 中野, 瑠人, 山田, 明, 小澤, 誠一

  フィッシングのようなサイバー攻撃では，ユーザ自身による対策が求められる．セキュリティ教育において，フィッシングの場合は，攻撃の特徴を記憶し，それらとWebサイトを比較することによって攻撃を判断するように教育される．しかし，日々進化するサイバー攻撃への対策として，新しい攻撃手法を学び直し続けることはユーザの大きな負担となる．本稿では，大規模言語モデル（LLM：Large Largeage Models）によるセキュリティ対策における視覚的認知メカニズムのモデル化に向けた分析方式を提案する．近年，LLMは，人間のフィードバックによるファインチューニングによって因果推論タスクが可能になってきている．しかし，LLMは，言語によって記述されていないタスクの取り扱いが難しい．そこで，提案方式は，視覚的認知の情報を言語化することによって，LLMによる視覚的認知メカニズムのモデル化を目指す．提案方式の有効性を検証するために，フィッシングサイトと正規サイトをそれぞれ110件用いて評価を行った．その結果，大規模言語モデルと視覚情報だけを用いて，適合率98.2\%，再現率83.7\%の精度でフィッシングサイトを検知できた．さらに，フィッシング対策の文書をLLMに与えて判定過程を観察することによって，人間の認知メカニズムとLLMの振る舞いの関連性を調査した．また，フィッシング判定以外の複数のセキュリティ判定タスクに対するLLMの有効性を明らかにした．今後は，ユーザを狙うさまざまなサイバー攻撃に本手法を拡大して，視覚的認知メカニズムをモデル化することによってセキュリティ対策および教育への応用を検討する． Cyber attacks such as phishing require users to take their own countermeasures. In security education, in the case of phishing, users are taught to memorize the characteristics of the attack and to judge the attack by comparing the attack with the website. However, it is a heavy burden for users to keep learning and relearning new attack methods to counter cyber attacks that are evolving day by day. In this paper, we propose an analysis method for modeling visual cognitive mechanisms in security countermeasures using large language model. Recently, large language model have become capable of performing causal inference tasks through fine tuning with human feedback. However, large language model have difficulty in handling tasks that are not described by language. Therefore, the proposed method aims at modeling visual cognition mechanisms using language model by converting visual cognition information into language. To verify the effectiveness of the proposed method, we conducted an evaluation using 110 phishing sites and legitimate sites, respectively. The results showed that the proposed method was able to detect phishing sites with an accuracy of 98.2\% and 83.7\% using only a large language model and visual information.Furthermore, we investigated the relevance to human cognitive mechanisms by qualitatively comparing the phishing decision process with a large language model given an anti-phishing document. We also clarified the effectiveness of the model in phishing attacks other than phishing sites. In the future, we will apply the model to security attacks other than phishing to realize security countermeasures and education based on cognitive mechanisms.

  Last, 23 Oct. 2023, コンピュータセキュリティシンポジウム2023論文集, 1536 - 1543, Japanese

  Summary national conference


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part III

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, Apr. 2023, Lecture Notes in Computer Science, 13625, English

  [Invited]

  Summary international conference


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part II

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, Apr. 2023, Lecture Notes in Computer Science, 13624, English

  [Refereed]


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer International Publishing, Apr. 2023, Lecture Notes in Computer Science, 13624, English

  [Refereed]


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part VII

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, Apr. 2023, Lecture Notes in Computer Science, 1794, English

  [Refereed]


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part V

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, Apr. 2023, Lecture Notes in Computer Science, 1792, English

  [Refereed]


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part IV

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, Apr. 2023, Lecture Notes in Computer Science, 1791, English

  [Refereed]


• ESG Topic Analysis of News Articles Using ChatGPT.

  小杉樹来, 小澤誠一, 廣瀬勇秀, 池田佳弘, 中川憲保, 飯塚正昭, 西田大輔

  2023, 人工知能学会第二種研究会資料(Web), 2023(FIN-031) (FIN-031)


• Instrument Detection and Segmentation in Suturing Task with the Surgical Robot hinotori Using YOLOv8 Fine-Tuning

  服部達也, 南地海音, 三谷祐貴, 植村宗則, 鎮西清行, 福本巧, 小澤誠一, 福本巧, 小澤誠一, 小澤誠一

  2023, 日本コンピュータ外科学会誌(Web), 25(3) (3)


• Skill Evaluation by Utilizing the Logs of the Medical Robot Hinotori

  三谷祐貴, 服部達也, 南地海音, 植村宗則, 鎮西清行, 福本巧, 福本巧, 小澤誠一, 小澤誠一, 小澤誠一

  2023, 日本コンピュータ外科学会誌(Web), 25(3) (3)


• Time Synchronization of Procedure Videos and Operation Logs in Robot-Assisted Surgery

  南地海音, 服部達也, 三谷祐貴, 植村宗則, 鎮西清行, 福本巧, 小澤誠一, 福本巧, 小澤誠一, 小澤誠一

  2023, 日本コンピュータ外科学会誌(Web), 25(3) (3)


• Scandalous Article Classification with Contrastive Learning BERT and Study of Sentence Embedded Representation

  高須悠一朗, 小澤誠一, 廣瀬勇秀, 池田佳弘, 中川憲保, 飯塚正昭, 西田大輔

  2023, 人工知能学会全国大会論文集(Web), 37th


• Neural Information Processing - 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part VI

  Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatow

  Springer, 2023, Lecture Notes in Computer Science, 1793, English

  [Refereed]


• Detecting Malicious TLS Communications Using Machine Learning and Considerations on the Transition of Communication Characteristics

  藤原魁成, 小澤誠一, 春木博行, PARK Chanho

  2022, 情報処理学会研究報告(Web), 2022(DPS-190) (DPS-190)


• プライバシー保護連合学習による組織間ビッグデータ解析とその応用

  小澤誠一, 小澤誠一

  2022, インテリジェント・システム・シンポジウム(CD-ROM), 30th


• Federated Continuous Learning of Gradient Boosting Decision Trees Using Dynamic Sampling

  三浦啓吾, 井上広明, KIM S., WANG L., 小澤誠一

  2022, インテリジェント・システム・シンポジウム(CD-ROM), 30th


• Analyst Reports Analysis And Investment Decision Rating Prediction Using Machine Learning

  鈴木章悟, 小澤誠一, 小澤誠一, 渡辺一男, 廣瀬勇秀, 池田佳弘, 飯塚正昭, 西田大輔

  2022, 人工知能学会第二種研究会資料(Web), 2022(FIN-028) (FIN-028)


• ESG-related text extraction from news articles for investment support

  吉田朋弘, 小澤誠一, 渡辺一男, 廣瀬勇秀, 池田佳弘, 飯塚正昭, 西田大輔

  2022, 人工知能学会第二種研究会資料(Web), 2022(FIN-028) (FIN-028)


• Light-weight Deep Learning Model for Implementation of Super Security Gate

  村中建太, 中谷透大, 小澤誠一, 西村祐太郎, 坂倉涼太, 鈴木章吾, 木村建次郎, 美馬勇輝, 木村憲明

  2021, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 65th


• Scan Packet Analysis by Port-number Embedding Vector Considering Large-scale Survey Packets in Darknet

  石川真太郎, 中藤大暉, 班涛, 小澤誠一

  2021, 情報処理学会研究報告(Web), 2021(CSEC-92) (CSEC-92)


• Development of Flower Counting System for Soybeans Using Object Detection and Tracking

  織部慧次朗, 小澤誠一

  2021, 知能システムシンポジウム講演資料(CD-ROM), 48th (Web)


• Extraction of Soil Moisture Environment Affecting Soybean Yield Based on Co-occurrence of Time Series Patterns

  逸見聡, 東山久瑠実, 長南友也, 林怜史, 中村卓司, 辻博之, 村上則幸, 西出亮, 大川剛直, 小澤誠一

  2021, 情報科学技術フォーラム講演論文集, 20th


• Advancement of Magnetic Field Distribution Image Analysis for Super Security Gate Using Deep Learning

  中谷透大, 美馬勇輝, 鈴木章吾, 坂倉涼太, 木村建次郎, 小澤誠一

  2020, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 64th


• Privacy-Preserving Technologies in Data Analysis and Its Applications

  小澤誠一

  2020, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 64th


• Masquerade Detection Based on Users’ Command Logs Using Deep Learning Models

  安達貴洋, 小澤誠一, 春木博行

  2020, 情報処理学会研究報告(Web), 2020(CSEC-88) (CSEC-88)


• Discovering Malicious Websites from Access Logs of URLs Using Deep Learning Model

  前橋祐斗, 小澤誠一, 山田明

  2020, 情報処理学会研究報告(Web), 2020(CSEC-88) (CSEC-88)


• AI×セキュリティの現状と期待

  小澤誠一

  2019, 計測自動制御学会制御部門マルチシンポジウム(CD-ROM), 6th


• Advancement of Network Scanning Monitoring Using Association Rule Mining and Darknet Analysis

  橋本直輝, BAN Tao, 島村隼平, 小澤誠一

  2019, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 63rd


• Automatic Soybean Growth Factor Acquisition Using RetinaNet and Object Tracking Method

  大村和暉, 小澤誠一, 大川剛直, 長南友也, 辻博之, 村上則幸

  2019, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 63rd


• Extraction of Important Sentences in Financial Documents Based on Business Confidence Information Using LSTM with Self-Attention Mechanism

  山岡周平, 小澤誠一, 小澤誠一, 廣瀬勇秀, 飯塚正昭

  2019, 人工知能学会全国大会(Web), 33rd


• Efficient Privacy-Preserving Prediction for Three-Layer Feedforward Neural Networks Using Ring-LWE-based Homomorphic Encryption

  手塚雄大, WANG Lihua, WANG Lihua, 林卓也, KIM Sangwook, 為井智也, 大森敏明, 小澤誠一

  2019, 人工知能学会全国大会(Web), 33rd


• Business Confidence Prediction for Analyst Report using Convolutional Neural Networks

  高山将丈, 小澤誠一, 小澤誠一, 廣瀬勇秀, 飯塚正昭

  2019, 人工知能学会全国大会(Web), 33rd


• Exploring Malicious URL in Dark Web Using Tor Crawler

  川口雄己, 小澤誠一

  2019, 電子情報通信学会技術研究報告, 118(478(ISEC2018 81-134)) (478(ISEC2018 81-134))


• Advancement of DDoS Monitoring Using Machine Learning and Darknet Analysis

  畑中拓哉, 小澤誠一, BAN Tao, 島村隼平

  2019, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 63rd


• Editorial: Booming of Neural Networks and Learning Systems.

  Akira Hirose, Alessio Micheli, Artur S. d'Avila Garcez, Choon Ki Ahn, Gang Pan, Hamid Reza Karimi, Jianbing Shen, José de Jesús Rubio, Lei Zhang, Lingjia Liu 0001, Lorenzo Livi, Nian Zhang, Nishchal K. Verma, Pedro Antonio Gutiérrez, Qi Tian 0001, Qinglai Wei, Seiichi Ozawa, Stuart H. Rubin, Wei-Neng Chen, Xi Li, Xiaofeng Liao, Youmin Zhang 0001, Zhen Ni, Haibo He

  2019, IEEE Trans. Neural Networks Learn. Syst., 30(1) (1), 2 - 10

  [Refereed]


• Reviewing the special issue of "Privacy Protection Data Mining"

  Seiichi Ozawa

  Lead, 一般社団法人 システム制御情報学会, 2019, システム／制御／情報, 63(2) (2), 84 - 84, Japanese

  [Invited]

  Book review


• 北海道における薬害によるダイズの分枝発達抑制

  辻博之, 村上則幸, 中村卓司, 長南友也, 小澤誠一, 大川剛直

  2018, 日本作物学会講演会要旨集, 245th


• ダークネットトラフィックデータの頻出パターン解析

  橋本直輝, 小澤誠一, BAN Tao, 中里純二, 島村隼平

  2017, 情報処理学会シンポジウムシリーズ(CD-ROM), 2017(2) (2)


• Development of an Image Sensing Method to Automatically Obtain Soybean Growth Condition

  八幡壮, 小澤誠一, 吉田武史, 大川剛直, 村上則幸, 辻博之

  2017, 人工知能学会全国大会論文集(CD-ROM), 31st


• 匿名ネットワークTorにおけるマーケット商品とセキュリティ事件との関連性に関する考察

  川口雄己, 山田彰, 小澤誠一

  2017, 情報処理学会シンポジウムシリーズ(CD-ROM), 2017(2) (2)


• ダークネットトラフィックの可視化とオンライン更新によるモニタリング

  畑中拓哉, 北園淳, 小澤誠一, 班涛, 中里純二, 島村隼平

  2016, 情報処理学会シンポジウムシリーズ(CD-ROM), 2016(2) (2)


• An Autonomous DDoS Backscatter Detection System from Darknet Traffic

  宇川雄樹, 北園淳, 小澤誠一, BAN Tao, 中里純二, 島村隼平

  2016, 情報処理学会研究報告(Web), 2016(SPT-17) (SPT-17)


• Online Learning of Unstructured Data Streams and Attack Detection in Cybersecurity(Deepening Machine Learning-Advanced Paradigm of Algorithms and their Applications)

  OZAWA Seiichi

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS, 2016, SYSTEMS, CONTROL AND INFORMATION, 60(3) (3), 120 - 125, Japanese


• ダークネットトラフィックに基づく学習型DDoS攻撃監視システムの開発

  古谷暢章, 北園淳, 小澤誠一, BAN Tao, 中里純二, 島村隼平

  2015, 情報処理学会シンポジウムシリーズ(CD-ROM), 2015(3) (3)


• ダークネットトラフィックに基づいたDDoSバックスキャッタ判定

  古谷暢章, BAN Tao, 中里純二, 島村隼平, 北園淳, 小澤誠一

  2015, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 59th


• A Neural Network Model for Incremental Learning of Large-Scale Stream Data

  Aminah Ali Siti Hajar, Fukase Kiminori, Ozawa Seiichi

  システム制御情報学会, 21 May 2014, システム制御情報学会研究発表講演会講演論文集, 58, 6p, English


• Fast Online Feature Extraction Using Chunk Incremental Kernel Principal Component Analysis

  Joseph Annie anak, Ozawa Seiichi

  システム制御情報学会, 21 May 2014, システム制御情報学会研究発表講演会講演論文集, 58, 4p, English


• ダークネットパケットに対するDDoS攻撃によるバックスキャッター判定に関する研究

  古谷暢章, BAN Tao, 中里純二, 島村隼平, 小澤誠一

  2014, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 58th


• SCI'13-The 57th Annual Conference of ISCIE

  OZAWA Seiichi

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS, 2014, SYSTEMS, CONTROL AND INFORMATION, 58(1) (1), 46 - 46, Japanese


• Handling Concept Drift Using Incremental Linear Discriminant Analysis with Knowledge Transfer in Non-stationary Data Streams

  Joseph Annie anak, Ozawa Seiichi

  システム制御情報学会, 15 May 2013, システム制御情報学会研究発表講演会講演論文集, 57, 3p, English


• ダークネットトラフィックデータの解析によるサブネットの脆弱性判定に関する研究

  西風宗典, BAN Tao, 小澤誠一

  2013, 情報処理学会シンポジウムシリーズ(CD-ROM), 2013(4) (4)


• An Incremental Learning Model for Multitask Pattern Recognition

  Ozawa Seiichi

  複数のパター認識を同時または逐次的に学習する問題は，マルチタスクパターン認識問題と呼ばれる．この問題では，同一の入力に対して複数のクラスラベルが割り当てられ，システムは訓練データを学習しながら，複数の認識概念を自律的に獲得することを求められる．本研究では，タスクおよび訓練データがどちらも逐次的に与えられる追加学習の設定で，タスク変動検知機能，知識移転機能，タスクの誤分類訂正機能をもつ追加学習型ニューラルネットモデルを紹介する．

  The Institute of Systems, Control and Information Engineers, 2010, Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, SCI10, 301 - 301, Japanese


• Online Feature Selection and Incremental Learning of Neural Networks

  Ozawa Seiichi

  インターネットの発達により，高次元かつ大量のデータが時々刻々と蓄積されるようになった．このような環境で認識，予測，診断などを効率よく行っていくには，時間的に変化するデータ群に適応して次元削減を行い，システムの追加学習が行われる必要がある．次元削減にはオンライン型の主成分分析アルゴリズムなどが提案されている．本発表では，オンライン型主成分分析と追加学習型ニューラルネットを組み合わせた学習モデルを紹介する．

  The Institute of Systems, Control and Information Engineers, 2007, Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, SCI07, 164 - 164, Japanese


• The Knowledge Engineering and Discovery Research Institute, The Auckland University of Technology

  OZAWA Seiichi

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS, 2005, SYSTEMS, CONTROL AND INFORMATION, 49(10) (10), 424 - 425, Japanese


• 本総合特集号を企画して(<特集>創発システム研究の現状そして今後の展開総合)

  椹木 哲夫, 小澤 誠一, 諏訪 晴彦

  一般社団法人 システム制御情報学会, 2005, システム／制御／情報, 49(12) (12), 488 - 488, Japanese


• On the Special Issue of the 2003 Kansai-Section Joint Convention of Institutes of Electrical Engineering Japan

  OZAWA Seiichi

  01 Nov. 2004, 電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society, 124(11) (11), 2201 - 2201, Japanese


• The 31st Intelligent Systems Symposium

  OZAWA Seiichi

  The Society of Instrument and Control Engineers, 10 Sep. 2004, Journal of The Society of Instrument and Control Engineers, 43(9) (9), 723 - 723, Japanese


• 独立成分分析を用いた個人性抽出に関する基礎研究

  小澤 誠一

  2003, 実吉奨学会研究報告集, 20,27-30, Japanese

  Others


• Research Trend on Modular Neural Networks

  OZAWA Seiichi

  The Society of Instrument and Control Engineers, 10 Dec. 2002, Journal of The Society of Instrument and Control Engineers, 41(12) (12), 888 - 893, Japanese


• Independent Component Analysis

  小澤 誠一, 小谷 学

  10 Aug. 2000, 計測と制御 = Journal of the Society of Instrument and Control Engineers, 39(8) (8), 522 - 522, Japanese


• Neural Network Approaches to Robot Control

  BABA Norio, OZAWA Seiichi

  The Robotics Society of Japan, 15 Jan. 1993, Journal of the Robotics Society of Japan, 11(1) (1), 44 - 48, Japanese


• ニューラルネットワーク応用の最新動向

  小谷 学, 小澤 誠一

  システム制御情報学会, 15 Oct. 1992, システム/制御/情報 : システム制御情報学会誌 = Systems, control and information, 36(10) (10), 669 - 677, Japanese


• 「ニューロメール」 について

  小澤 誠一

  15 Oct. 1992, システム/制御/情報 : システム制御情報学会誌 = Systems, control and information, 36(10) (10), 679 - 679, Japanese


• Dynamical Equations of Multi-Module Neural Networks

  Ozawa Seiichi

  Nara National College of Technology, 1992, Research reports of Nara Technical College, (28) (28), p41 - 44, Japanese


• The Self-Organized Neural Network with an Ability of Regularizing The Temporal Elasticity

  Ozawa seiichi

  Nara National College of Technology, 1990, Research reports of Nara Technical College, (26) (26), p61 - 67, Japanese


• The CV Syllable Recognition Using Multi-Layered Kohonen Net

  Ozawa Seiichi, Tsutsumi Kazuyoshi, Matsumoto Haruya

  Nara National College of Technology, 1989, Research reports of Nara Technical College, (25) (25), p57 - 62, Japanese

• データサイエンスの考え方 : 社会に役立つAI×データ活用のために

  小澤, 誠一, 齋藤, 政彦

  オーム社, Nov. 2021, Japanese, ISBN: 9784274227974


• データサイエンス基礎

  齋藤, 政彦, 小澤, 誠一, 羽森, 茂之, 南, 知惠子, 平田, 燕奈, 光明, 新, 小川, 賢, 為井, 智也, 上田, 修功, 森永, 聡, 本村, 陽一

  培風館, Mar. 2021, Japanese, ISBN: 9784563016104


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part VII

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030042394

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part VI

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030042240

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part V

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030042219

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part IV

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030042127

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part III

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030041823

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part II

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030041793

  Others


• Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I

  CHENG Long, LEUNG Andrew Chi-Sing, OZAWA Seiichi

  Joint editor, Springer, Dec. 2018, English, ISBN: 9783030041670

  Others


• INNS Conference on Big Data and Deep Learning

  OZAWA Seiichi, TAN Ah-Hwee, ANGELOV P. Plamen, ROY Asim, PRATAMA Mahardhika

  Joint editor, Elsevier, Dec. 2018, English

  Others


• Artificial Intelligence in the Age of Neural Networks and Brain Computing (Chapter 12 - Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things)

  ALIPPI Cesare, OZAWA Seiichi

  Others, Elsevier, Nov. 2018, English, The emergence of non-trivial embedded sensor units and cyber-physical systems and the Internet of Things has made possible the design and implementation of sophisticated applications where large amounts of real-time data are collected, possibly to constitute a big data picture as time passes. Within this framework, in-telligence mechanisms based on machine learning, neural netw, ISBN: 9780128154809

  Scholarly book


• Neural Information Processing

  HIROSE AKIRA, OZAWA SEIICHI, DOYA Kenji, IKEDA Kazushi, LEE Minho, LIU Derong

  Joint editor, Springer International Publishing AG, Oct. 2016, English, The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement, ISBN: 9783319466712

  Scholarly book

  DOI


• Neural information processing : 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings

  International Conference on Neural Information Processing, 廣瀬, 明, 小澤, 誠一, 銅谷, 賢治, 池田, 和司, Lee, Minho, Liu, Derong

  Springer, 2016, English, ISBN: 9783319466743


• State of the Art in Face Recognition

  Julio Ponce, Adem Karahoca, Seiichi Ozawa

  Joint work, IN-TECH, Jan. 2009, English

  Scholarly book


• 人工知能学辞典

  OZAWA Seiichi

  Joint work, 共立出版, Dec. 2005, Japanese

  Scholarly book


• Neural Networks Applications in Information Technology and Web Engineering

  OZAWA Seiichi, ABE Shigeo

  Joint work, Borneo Publishing Co., 2005, English

  Scholarly book


• Neural Information Processing: Research and Development

  OZAWA Seiichi

  Joint work, Springer-Verlag, 2004, English

  Scholarly book


• Dynamic Systems Approach for Embodiment and Sociality

  OZAWA Seiichi

  Joint work, Advanced Knowledge International, 2003, English

  Scholarly book


• ニューラルネットと計測制御

  NISHIKAWA Yoshikazu, KITAMURA Shinzo, OZAWA Seiichi

  Joint work, 朝倉書店, 1995, Japanese

  Scholarly book


• ニューラルネットの基礎と応用

  BABA Norio, KOJIMA Fumio, OZAWA Seiichi

  Joint work, 共立出版, 1994, Japanese, ISBN: 4320027140

  Scholarly book

• AIセキュリティと安全性評価への課題

  小澤誠一

  電子情報通信学会 総合大会, Mar. 2025, Japanese

  [Invited]

  Nominated symposium


• Proposal of a Continual Learning Model for Privacy-Preserving Federated Learning

  Yuto Kitano, Hiroaki Inoue, Lihua Wang, Seiichi Ozawa

  2025 Symposium on Cryptography and Information Security, Jan. 2025, Japanese

  Public symposium


• Navigating Responsible AI: Opportunities, Threats, and Ethical Boundaries

  Irwin King, Giovanni Russello, Seiichi Ozawa, Kaizhu Huang, Alexander Sumich

  The 2024 International Conference on Neural Information Processing (ICONIP2024), Dec. 2024, English

  [Invited]

  Nominated symposium


• AIを安全に利用する技術的課題と対策

  小澤誠一

  コンピュータセキュリティシンポジウム2024, AWS/PWS共同企画「AIガバナンスに向けた政策と技術の動向について」, Oct. 2024, Japanese

  [Invited]

  Nominated symposium


• Label Correction for Machine Learning-Based Cyber Attack Detection Assuming Uncertainty in Data Labels

  Haruki Urakawa, Akira Yamada, Joo Suwon, Vestin Simon, Wang Hui, Park Chanho, Seiichi Ozawa

  Computer Security Symposium 2024, Oct. 2024, Japanese

  Oral presentation


• ime Series Data Augmentation Using State-Space Model and Its Application to Cyber Attack Data Generation

  Seiichi Ozawa

  4th CIC-NICT Workshop, Canadian Institute for Cybersecurity, University of New Brunswick, Sep. 2024, English

  [Invited]

  Public discourse


• Time Series Data Augmentation Using State-Space Model and Its Application to Cyber Attack Data Generation

  Seiichi Ozawa

  Workshop on Cyber Defense and Resilience at Behaviour-Centric Cybersecurity Center (BCCC), York University, Sep. 2024, English

  [Invited]

  Public discourse


• AIの社会実装とセキュリティ

  小澤誠一

  情報処理学会 連続セミナー2024「情報技術の新たな地平：AIと量子が導く社会変革」, Sep. 2024, Japanese

  [Invited]

  Public discourse


• 組織間連合学習による社会課題への取組み

  小澤誠一

  EAGLE DAY 2024, Apr. 2024, Japanese

  [Invited]

  Public discourse


• Challenging social issues using inter-organizational federated learning AI – Efforts to detect fraud bank transactions

  Seiichi Ozawa

  NICT Cyber ​​Security Symposium 2024, Feb. 2024, Japanese

  [Invited]

  Invited oral presentation


• Scaling Estimation of Malware Infected IoT Devices through ASFP: Activescan Fingerprint

  Kazusa Miyatake, Yukiko Endo, Akira Yamada, Tao Ban, Takeshi Takahashi, Seiichi Ozawa

  2024 Symposium on Cryptography and Information Security, Jan. 2024, Japanese

  Public symposium


• Cloak-Bench: Proposal of Quantitative Evaluation Methods in Security Analysis Using Large Language Models - Application to Cloaking Detection in Phishing Kits

  Ryuto Nakano, Akira Yamada, Tao Ban, Takeshi Takahashi, Seiichi Ozawa

  2024 Symposium on Cryptography and Information Security, Jan. 2024, Japanese

  Public symposium


• Special fraud monitoring using AI

  Seiichi Ozawa

  2023年度日本OR学会関西支部シンポジウム, Dec. 2023, Japanese

  [Invited]

  Invited oral presentation


• The Impact of Professional Societies in Shaping Disruptive Technologies such as Generative AI

  Irwin King (moderator), Jonathan, H. Chan, Kenji Doya, Włodzisław Duch, Seiichi Ozawa

  The 2023 International Conference on Neural Information Processing (ICONIP2023), Nov. 2023, English

  [Invited]

  Nominated symposium


• Privacy-Preserving Machine Learning for Big Data Analysis – How can we solve social issues using AI? -

  Seiichi Ozawa

  Chitose International Forum on Science & Technology, Sep. 2023, Japanese

  [Invited]

  Invited oral presentation


• Data science and use case in agriculture - Sensing soybean growth information using deep learning -

  Seiichi Ozawa

  兵庫県立農林水産技術総合センター講演, Aug. 2023, Japanese

  [Invited]

  Public discourse


• Privacy-Preserving Machine Learning for Big Data Analysis - How can we solve social issues using AI? –“

  Seiichi Ozawa

  Lecture 2 at the University of Ljubljana, Mar. 2023, English

  [Invited]

  Public discourse


• Cyber Security and Its Countermeasures in AI Systems

  Seiichi Ozawa

  Lecture 1 at the University of Ljubljana, Mar. 2023, English

  [Invited]

  Public discourse


• 機械学習とOSSのセキュリティ

  小澤誠一

  情報セキュリティ・シンポジウム（日本銀行金融研究所・情報技術研究センター）, Mar. 2023, Japanese

  [Invited]

  Nominated symposium


• BFL-Boost: Blockchain-based Federated Learning for Gradient Boosting to Enhance Security in Model Training

  Septiviana Savitri Asrori, Lihua Wang, Seiichi Ozawa

  in 2023 Symposium on Cryptography and Information Security (SCIS), Jan. 2023, English

  Public symposium


• Cyber Security and Its Countermeasures in AI Systems

  Seiichi Ozawa

  2022 5th Artificial Intelligence and Cloud Computing Conference, Dec. 2022, English

  [Invited]

  Keynote oral presentation


• データサイエンスの考え方 : 社会に役立つAI×データ活用のために

  小澤誠一

  第10回オープンテクノフォーラム（神奈川県支部第116回 CPD講座, Nov. 2022, Japanese

  [Invited]

  Public discourse


• 動的サンプリングを用いた連合学習型勾配ブースティング決定木の継続学習

  三浦啓吾, 井上広明, 金 相旭, 王 立華, 小澤誠一

  第30回インテリジェント・システム・シンポジウム（FAN2022）, Sep. 2022, Japanese

  Oral presentation


• プライバシー保護連合学習による組織間ビッグデータ解析とその応用

  小澤誠一

  第30回インテリジェント・システム・シンポジウム,, Sep. 2022, Japanese

  [Invited]

  Keynote oral presentation


• Researcher2Vecによる研究者ネットワーク可視化システムの開発 － 神戸大学における研究DXの取組

  平田燕奈, 小澤誠一

  第30回インテリジェント・システム・シンポジウム（FAN2022）, Sep. 2022, Japanese

  Oral presentation


• 人工知能システムにおけるサイバーセキュリティリスクとその対策

  小澤誠一

  MS&ADサイバーワークショップ, Aug. 2022, Japanese

  [Invited]

  Public discourse


• プライバシー保護連合学習技術を活用した銀行不正送金検知

  盛合 志帆, 小澤 誠一

  NVIDIA AI DAYS 2022, Jun. 2022, Japanese

  [Invited]

  Public discourse


• Future deep learning machines inspired by the human brain,

  Nikola Kasabov, c, Soo-Young Lee, Zeng-Guang Hou, Taro Toyoizumi, Seiichi Ozawa, Jonathan, H. Chan

  The APNNS/IEEE-CIS Education Forum series on Deep Learning and Artificial Intelligence Summer School 2022 (DLAI6), Jun. 2022, English

  [Invited]

  Public discourse


• 投資支援のためのニュース記事からのESG関連文抽出

  吉田朋弘, 小澤誠一, 渡辺一男, 廣瀬勇秀, 池田佳弘, 飯塚正昭, 西田大輔

  第28回人工知能学会 金融情報学研究会（SIG-FIN）, Mar. 2022, Japanese

  Oral presentation


• 機械学習を用いたアナリストレポート分析と投資判断レーティング予測

  鈴木章悟, 小澤誠一, 渡辺一男, 廣瀬勇秀, 池田佳弘, 飯塚正昭, 西田大輔

  第28回人工知能学会 金融情報学研究会（SIG-FIN）, Mar. 2022, Japanese

  Oral presentation


• 機械学習を用いた悪性TLS通信の検知と通信特徴の推移に関する考察

  藤原魁成, 小澤誠一, 春木博行, Park Chanho

  第96回コンピュータセキュリティ合同研究発表会 (CSEC2022), Mar. 2022, Japanese

  Oral presentation


• 動的サンプリングを使用した勾配ブースティング決定木の連合追加学習

  三浦 啓吾, 王 立華, 小澤 誠一

  2022年 暗号と情報セキュリティシンポジウム（SCIS2022), Jan. 2022, Japanese

  Oral presentation


• Privacy-Preserving Machine Learning for Big Data Analysis and its potential applications

  Seiichi Ozawa

  2021 4th Artificial Intelligence and Cloud Computing Conference (AICCC 2021), Dec. 2021, English

  [Invited]

  Keynote oral presentation


• HTMLタグの構造に着目したグラフ畳み込みネットワークによる悪性サイト判定

  山本貴巳, Kim Sangwook, 班 涛, 高橋健志, 小澤誠一

  コンピュータセキュリティシンポジウム 2021, Oct. 2021, Japanese

  Oral presentation


• Detecting Malicious Websites Based onJavaScript Content Analysis

  Muhammad Fakhrur Rozi, Tao Ban, Sangwook Kim, Seiichi Ozawa, Takeshi Takahashi, Daisuke Inoue

  Computer Security Symposium 2021(CSS2021), Oct. 2021, English

  Oral presentation


• 深層学習モデルと勾配ブースティング決定木モデルを用いたユーザなりすまし検知

  土屋 寛途, 小澤 誠一, 春木 博行, Park Chanho

  コンピュータセキュリティシンポジウム 2021, Oct. 2021, Japanese

  Oral presentation


• 時系列パターンの共起性に基づく大豆の収量に関与する土壌水分環境の抽出

  逸見 聡, 東山 久瑠実, 長南 友也, 林 怜史, 中村 卓司, 辻 博之, 村上 則幸, 西出 亮, 大川 剛直, 小澤 誠一

  情報科学技術フォーラム講演論文集 (FIT), Aug. 2021, Japanese

  Oral presentation


• スーパーセキュリティゲートの実用化に向け た深層学習モデルの軽量化

  村中 建太, 中谷 透大, 小澤 誠一, 西村 祐太朗, 坂倉 涼太, 鈴木 章吾, 木村建次郎, 美馬 勇輝, 木村 憲明

  第65回システム制御情報学会研究発表講演会 (SCI’21), May 2021, Japanese

  Oral presentation


• Scan Packet Analysis by Port-number Embedding Vector Considering Large-scale Survey Packets in Darknet

  Shintaro Ishikawa, Daiki Nakafuji, Tao Ban, Seiichi Ozawa

  第186回マルチメディア通信と分散処理・第92回コンピュータセキュリティ合同研究発表会, Mar. 2021, Japanese

  Oral presentation


• Development of Flower Counting System for Soybeans Using Object Detection and Tracking

  Keijiro Oribe, Seiichi Ozawa

  計測自動制御学会 第48回知能システムシンポジウム, Mar. 2021, Japanese

  Oral presentation


• Role of AI Required in Digital Transformation

  Seiichi Ozawa

  日本テクノセンターAI基礎研修, Feb. 2021, Japanese

  [Invited]

  Invited oral presentation


• Outlier Detection by Privacy-Preserving Ensemble Decision Tree Using Homomorphic Encryption

  Kengo Itokazu, Lihua Wang, Seiichi Ozawa

  2021年 暗号と情報セキュリティシンポジウム（SCIS2021), Jan. 2021, Japanese

  Oral presentation


• Advancement of Character-Level Convolutional Neural Networks for Malicious Site Detection Based on URL Word Frequency

  Takami Yamamoto, Shintaro Ishikawa, Akira Yamada, Seiichi Ozawa

  2021 Symposium on Cryptography and Information Security, Jan. 2021, Japanese

  Oral presentation


• Deep Pyramid Convolutional Neural Networks for Detecting Obfuscated Malicious JavaScript Codes Using Bytecode Sequence Features

  Rozi Muhammad Fakhrur, Sangwook Kim, Seiichi Ozawa

  Computer Security Symposium 2020 (CSS2020), Oct. 2020, English

  Oral presentation


• Darknet Scan Packet Analysis Using Port Embedding Vector

  Shintaro Ishikawa, Seiichi Ozawa, Tao Ban

  コンピュータセキュリティシンポジウム 2020, Oct. 2020, Japanese

  Oral presentation


• Privacy-Preserving XGBoost Introducing Federated Learning Scheme

  Fuki Yamamoto, Lihua Wang, Seiichi Ozawa

  コンピュータセキュリティシンポジウム 2020, Oct. 2020, Japanese

  Oral presentation


• An Introduction to Privacy-Preserving Machine Learning for Big Data Analysis

  Seiichi Ozawa

  Deep Learning and Artificial Intelligence Summer School 2020 (DLAI3), Jun. 2020, Japanese

  [Invited]

  Invited oral presentation


• Seminar on AI Foundations - AI for Data Analysis -

  Seiichi Ozawa

  日本テクノセンターAI基礎研修, Jun. 2020, Japanese

  [Invited]

  Invited oral presentation


• Privacy-Preserving Technology in Data Analysis and Its Applications

  Seiichi Ozawa

  第64回システム制御情報学会研究発表講演会 (SCI’20), May 2020, Japanese

  [Invited]

  Invited oral presentation


• Advancement of Magnetic Field Distribution Image Analysis for Super Security Gate Using Deep Learning

  Tota Nakatani, Yuki Mima, Syogo Suzuki, Ryota Sakakura, Kenjiro Kimura, Seiichi Ozawa

  システム制御情報学会研究発表講演会講演論文集(CD-ROM), May 2020, Japanese

  Oral presentation


• Extraction of Keyword Related Sentences in Analyst Reports and Its Application to Observation of Business Confidence

  Shota TAKAYAMA, Seiichi OZAWA, Takehide HIROSE, Masaaki IIZUKA, Kazuo WATANABE, Ryuta HENMI

  人工知能学会 第24回金融情報学研究会, Mar. 2020, Japanese

  Oral presentation


• Discovering Malicious Websites from Access Logs of URLs Using Deep Learning Model

  前橋祐斗, 小澤誠一, 山田明

  情報処理学会研究報告(Web), Mar. 2020, Japanese

  Oral presentation


• Masquerade Detection Based on Users’ Command Logs Using Deep Learning Models

  Takahiro Adachi, Seichi Ozawa, Hiroyuki Haruki

  情報処理学会研究報告(Web), Mar. 2020


• デジタルトランスフォーメーションがもたらす社会変革（１）『いまさら聞けないデジタル化

  小澤 誠一

  KOBE×DXプロジェクト2019 ＤＸミドルマネジメント向け講座, Jan. 2020, Japanese, デジタルトランスフォーメーション研究機構, 神戸学院大学 神戸三宮サテライト, 変化の激しいビジネス環境や顧客ニーズが多様化する現在の社会では、あらゆる場面でデータの活用やデジタル技術が不可欠です。こうしたデータやデジタル技術やデータの活用については、かつてのようにITの専門家であるベンダや企業・自治体内の情報システム部門だけでなく、事業・サービスを主導するミドルマネジメント層が理解していなければ、事業の効率化やイノベーションを期待することはできません。今や業務、経営とデジタルは完全に一体となっています。 本講座は、まずデジタル技術について、その各技術の概要について簡単に触れた後、データの利活用について、事業を推進するリーダー役であるミドル層として最低限、理解しておかなければならない知識について、活用事例やデータ取得の難しさ等も含めて紹介します。特にこれからの社会やビジネスを大きく変えるであろうVRについて実際に体感し、デジタル技術を自分事として捉えられるようにします。この講座を受講することによりデジタルを活用した未来を想像し、自社ビジネスとデジタルのつながりを考えることができるようになることが期待できます。 また、本講座受講後はデータ利活用のための技術を、深い知見を有する社員や委託先とスムーズなコミュニケーションができるようになり、また、経営層に対しては意思決定に必要なデータを提供し、それを説得力のある形で説明できるレベルになることが期待できます。

  [Invited]

  Public discourse


• Machine Learning Approach to Detection of Malicious URLs and JavaScript

  Seiichi Ozawa

  2019 International Conference on Neural Information Processing, Dec. 2019, English, Asia Pacific Neural Network Society (APNNS), Manly, Sydney, Australia, International conference

  [Invited]

  Invited oral presentation


• Brain-Inspired Neural Network Architectures for Brain Inspired AI

  Nikola Kasabov (convener), Zeng-Guang Hou, Minho Lee, Seiichi Ozawa, Jie Yang, David Powers

  International Conference on Neural Information Processing, Dec. 2019, English, Asia Pacific Neural Network Society (APNNS), Manly, Sydney, Austria, International conference

  [Invited]

  Nominated symposium


• DXと人工知能のメカニズムと活用

  小澤 誠一

  DX実務者入門講座(第3回), Dec. 2019, Japanese, デジタルトランスフォーメーション研究機構, 神戸学院大学 神戸三宮サテライト(神戸市), Japan, デジタル技術やデータ利活用で業務プロセスを変革し、新しいビジネスモデルを創造していくデジタルトランスフォーメーション（ＤＸ）が、いま、社会全体で求められています。現在進行しているサービスや、顧客管理におけるＤＸの活用事例、人工知能技術、情報セキュリティ、そしてそれらを支える統計解析や数学の基礎について、コンパクトに学べるＤＸ入門講座を神戸三宮で開催します。６回全てご参加いただいた受講者には「受講認定証」を授与いたします。奮ってご参加ください。

  [Invited]

  Public discourse


• 人工知能のメカニズムと活用

  小澤 誠一

  日本総研セミナー, Nov. 2019, Japanese

  [Invited]

  Public discourse


• Privacy-Preserving Decision Tree Classification Using Ring-LWE-Based Homomorphic Encryption

  Satoshi Fukui, Lifue Wang, Seiichi Ozawa

  コンピュータセキュリティシンポジウム2019論文集, Oct. 2019, Japanese

  Oral presentation


• Enhancement in DDoS Backscatter Detection Using Visualization Images of Darknet UDP Communication and Traffic Statistics

  Tatsuya Nomura, Seiichi Ozawa, Tao Ban, Junpei Shimamura

  コンピュータセキュリティシンポジウム2019論文集, Oct. 2019, Japanese

  Oral presentation


• Privacy-Preserving Data Mining and Digital Transformation

  Seiichi Ozawa

  Society5.0実現のためのセンシングソリューション技術分科会，JEITA，電子情報技術産業協会, Aug. 2019, Japanese

  [Invited]

  Invited oral presentation


• Efficient Privacy-Preserving Prediction for Three-Layer Feedforward Neural Networks Using Ring-LWE-based Homomorphic Encryption

  手塚雄大, WANG Lihua, WANG Lihua, 林卓也, KIM Sangwook, 為井智也, 大森敏明, 小澤誠一

  人工知能学会全国大会(Web), Jun. 2019, Japanese, The Japanese Society for Artificial Intelligence,

  Concerns about privacy of data prevent from making good use of a huge amount of data. Data analysis while preserving privacy is a very important task. In this research, we propose a Privacy-Preserving Machine Learning that can efficiently compute inner product in a three-layered neural network using Ring-LWE-based Homomorphic Encryption. We propose a two-party model consisting of client and server: the former encrypts input data and receives a classification result from a server and the latter performs predicting process over the encrypted data using a trained classification model. This enables that the client acquires the inference result without revealing the privacy of their data and the server protects their model from exposing it. The proposed method costs 10.549 [ms] per one class for prediction process and performed keeping its accuracy close to the case of sigmoid and ReLU.

  Oral presentation


• Extraction of Important Sentences in Financial Documents Based on Business Confidence Information Using LSTM with Self-Attention Mechanism

  山岡周平, 小澤誠一, 小澤誠一, 廣瀬勇秀, 飯塚正昭

  人工知能学会全国大会(Web), Jun. 2019, Japanese, The Japanese Society for Artificial Intelligence,

  Investment trust and fund management companies have accumulated a large number of visit records that were summarized by their analysts after conducting hearings against companies. Such visit reports include crucial information of companies such as companies' financial conditions and future strategies, which are used to estimate investment values of individual companies. However, it is not easy even for skilled fund managers to derive suitable market outlooks and investment decisions from a huge amount of accumulated documents. In this research, to support investment decisions, we propose a new LSTM model with self-attention mechanism that can extract important sentences in analyst visit reports. Such extraction is conducted based on the sentence scoring, which is obtained as the weights in a self-attention mechanism. In our experiments for a set of 1,390 visit reports, we demonstrate that the proposed model has about 79% accuracy for extraction on average under the 5-fold cross-validation.

  Oral presentation


• Business Confidence Prediction for Analyst Report using Convolutional Neural Networks

  高山将丈, 小澤誠一, 小澤誠一, 廣瀬勇秀, 飯塚正昭

  人工知能学会全国大会(Web), Jun. 2019, Japanese, The Japanese Society for Artificial Intelligence,

  To decide valuable companies to be invested, investment trust and fund management companies, which manage funds deposited from investors, have collected information on company's budget status and plans. However, the number of visit reports are usually too large even for skilled fund managers to easily derive reliable business outlooks and investment decisions. In this research, to alleviate fund managers' and analysts' commitment for the investigation and analysis, we propose a machine learning system that can support them to make accurate predictions on business outlook from collected visit reports. We attempt to predict business confidence for specific companies and industries using CNN that is expected to have good readability and robustness for polarity perturbation. As a result, we obtain 81.4% in classification accuracy for analysts' reports provided by the Sumitomo Mitsui DS Asset Management Company, Limited. It has 5.7% better accuracy than the best baseline model using Word2Vec and SVM.

  Poster presentation


• プライバシー保護データマイニングにより拡がるビッグデータ解析

  小澤 誠一

  2019 神戸大学 数理・データサイエンスセンター シンポジウム ～AIセキュリティとフィンテック応用の最前線～, May 2019, Japanese, 神戸大学 数理・データサイエンスセンター, 大阪イノベーションハブ, Japan, 神戸大学 数理・データサイエンスセンターでは、機械学習やブロックチェーン技術の社会実装の推進とデジタルトランスフォーメーションに対応する人材の育成を目指し、2019年5月に一般社団法人デジタルトランスフォーメーション研究機構を設立します。今回はそのディセミネーションイベントとして、フィンテック分野におけるセキュリティをテーマにしたシンポジウムを開催し、デジタルトランスフォーメーションを推進するうえで必須のセキュリティ技術について参加者と広く情報共有・意見交換する場とすることを目指します。

  [Invited]

  Nominated symposium


• Automatic Soybean Growth Factor Acquisition Using RetinaNet and Object Tracking Method

  Kazuki Omura, Seiichi Ozawa, Takenao Ohkawa, Yuya Chonan, Hiroyuki Tsuji, Noriyuki Murakami

  63回システム制御情報学会研究発表講演会, May 2019, Japanese

  Oral presentation


• Advancement of Network Scanning Monitoring Using Association Rule Mining and Darknet Analysis

  Naoki Hashimoto, Tao Ban, Jumpei Simamura, Seiichi Ozawa

  63回システム制御情報学会研究発表講演会, May 2019, Japanese

  Oral presentation


• Advancement of DDoS Monitoring Using Machine Learning and Darknet Analysis

  Takuya Hatanaka, Seiichi Ozawa, Tao Ban, Jumpei Simamura

  63回システム制御情報学会研究発表講演会, May 2019, Japanese

  Oral presentation


• AI×セキュリティの現状と期待

  OZAWA SEIICHI

  第6回制御部門マルチシンポジウム, Mar. 2019, Japanese, SICE, 熊本大学, 深層学習や機械学習、自然言語処理などを使ったAI技術の進展は目覚ましく、画像認識や音声認識の能力では、すでに人間を上回っているとされます。しかし、セキュリティ分野での機械学習の活用には、まだ課題も多く、AIの強みと限界を知り、現実の問題に向き合いながらにうまく使っていくことが重要です。本講演では、3つのAI×セキュリティを取り上げます。一つ目は、AIをサイバー攻撃の検知や分類に活かす試みですが、そもそも攻撃に関するデータをどのように収集してAIのモデルを学習し、予測に役立てるかは自明ではありません。これは多くの実応用でAIを活用するときの共通の悩みになっており、講演の第1部では、まずこの点に着目した解説を試みます。次に、AIを守るためのセキュリティについて考えます。近年、クラウド上でAIを構築して、サービスを提供するMachine Learning, Domestic conference

  [Invited]

  Invited oral presentation


• Exploring Malicious URL in Dark Web Using Tor Crawler

  川口雄己, 小澤誠一

  電子情報通信学会技術研究報告, 2019, Japanese, IEICE, In recent years, various web-based attacks such as Drive-by-Download attacks are becoming serious. To protect legitimate users, it is important to collect information on malicious sites that could provide a blacklist-based detection software. In our study, we propose a system to collect URLs of malicious sites on the dark web. The proposed system automatically crawls dark web s


• セキュリティ分野におけるAI活用の現状と期待

  OZAWA SEIICHI

  第30回AIセミナー, Jan. 2019, Japanese, 産業技術総合研究所, 産総研人工知能研究センター(東京都), 深層学習や機械学習、自然言語処理などを使ったAI技術の進展は目覚ましく、画像認識や音声認識の能力では、すでに人間を上回っているとされます。しかし、セキュリティ分野での機械学習の活用には、まだ課題も多く、AIの強みと限界を知り、現実の問題に向き合いながらにうまく使っていくことが重要です。AIを使ったサイバー攻撃の検知や分類が活発に研究されていますが、近年、AI自体がサイバー攻撃の対象となることも知られており、AIをどう護るかも重要な課題になっています。一方で、セキュリティとAIを組み合わせることで、これまでにない新しいサービスへの期待も広がりつつあります。本講演では、我々の取り組みを紹介させて頂きながら、セキュリティ分野におけるAIへの期待と現状について一緒に考えたいと思います。, Domestic conference

  [Invited]

  Invited oral presentation


• Challenges and Expectations against AI in Security

  OZAWA Seiichi

  2018 Artificial Intelligence and Cloud Computing Conference, Dec. 2018, English, ACM Sigapore Chapter, Hotel Sunroute Plaza Shinjuku (Tokyo), Increasing the maliciousness and the diversity of cyber-attacks is one of the most concerned issues in recent years. There are various kinds of cyber-threads such as malware infection, DDoS attacks, probing to find security vulnerability, phishing, and spam mails to lure malicious web site, which intend to steal money/important information and to stop or disturb public services, International conference

  [Invited]

  Keynote oral presentation


• セキュリティ分野におけるAIへの期待と現状

  OZAWA SEIICHI

  AC・Net研究会,, Oct. 2018, Japanese, AC・Net, 大阪大学中之島センター(大阪市), 深層学習や機械学習、自然言語処理などを使ったAI技術が注目されていますが、これらの強みと限界を知り、現実の問題に向き合いながらに正しく使うことが重要です。サイバーセキュリティにおいて、これら「万能でないAI」をどう活かせばよいでしょうか？また、人工知能が社会実装されていく上で、AI自体がサイバー攻撃の対象となり得ることが懸念されています。これに、どう対応していけばよいのでしょうか？このような懸念と不安がくすぶりつつも、一方では、セキュリティとAIを組み合わせることで、これまでにない新しいサービスへの期待も広がりつつあります。このようなセキュリティ分野における人工知能への期待と現状について、その動向をご紹介できればと思います。, Domestic conference

  [Invited]

  Invited oral presentation


• AIのAIによるAIのためのセキュリティ：セキュリティ×AIの現状と期待

  OZAWA SEIICHI

  制御技術部会研究会講演, Oct. 2018, Japanese, SICE, 東京電機大学 東京千住キャンパス(東京都), 深層学習や機械学習、自然言語処理などを使ったAI技術が注目されていますが、これらの強みと限界を知り、現実の問題に向き合いながらに正しく使うことが重要です。サイバーセキュリティにおいて、これら「万能でないAI」をどう活かせばよいでしょうか？また、人工知能が社会実装されていく上で、AI自体がサイバー攻撃の対象となり得ることが懸念されています。これに、どう対応していけばよいのでしょうか？このような懸念と不安がくすぶりつつも、一方では、セキュリティとAIを組み合わせることで、これまでにない新しいサービスへの期待も広がりつつあります。このようなセキュリティ分野における人工知能への期待と現状について、その動向をご紹介できればと思います。, Domestic conference

  [Invited]

  Invited oral presentation


• Privacy-Preserving Naive Bayes Classifier based on Homomorphic Encryption

  KIM Sangwook, OMORI Toshiaki, OMORI Masahiro, HAYASHI Takuya, WANG Lihua, OZAWA Seiichi

  The 13th International Workshop on Security (IWSEC2018), Sep. 2018, English, IWSEC, Sakura Hall, Tohoku University (仙台市), International conference

  Poster presentation


• Detection of JavaScript-based Attacks Using Doc2Vec Feature Learning

  NIDCHU Samuel, KIM Sangwook, OZAWA Seiichi, MISU Takeshi, MAKISHIMA Kazuo

  The 13th International Workshop on Security (IWSEC2018), Sep. 2018, English, IWSEC, Sakura Hall, Tohoku University (仙台市), Websites attracts millions of visitors due to the convenience of services they offer. These include news, entertainment and educational contents among many others. However, a large number of users frequenting these sites are considered as interesting targets for cyber attackers. These websites are injected with malicious codes by exploiting vulnerabilities in servers, plugins a, International conference

  Poster presentation


• AI・機械学習における各種手法・技術と適用のポイント・事例

  OZAWA SEIICHI

  日本テクノセンターセミナー, Sep. 2018, Japanese, 日本テクノセンター, たかつガーデン(大阪市), ＡＩの基礎とその限界を理解し、データや目的に応じて、どのような手法を適用したらよいかの見当をつける能力を習得できる。, Domestic conference

  [Invited]

  Invited oral presentation


• AIの躍進の背景と最新技術動向

  OZAWA SEIICHI

  次世代AI技術セミナー, Sep. 2018, Japanese, 兵庫エレクトロニクス研究会, 兵庫県立工業技術センター, AIとは何か？AIは使い物になるのか？AIで産業や技術はどう変わっていくのか？昨今のAIの躍進とその背景を振り返りながら、この問いに答えていきたいと思います。, Domestic conference

  [Invited]

  Invited oral presentation


• A New Direction of Machine Learning: Privacy-Preserving Data Mining (PPDM)

  OZAWA Seiichi

  BESK Workshop, Aug. 2018, English, BESK, Gangneung Green City Experience Center (Gangneung, Korea), International conference

  [Invited]

  Invited oral presentation


• A Machine Learning Approach to Privacy-Preserving Data Mining Using Homomorphic Encryption

  OZAWA Seiichi

  AI Flagship Project Workshop, Aug. 2018, English, Kyungpook National University, Gangneung–Wonju National University (Gangneung, Korea), International conference

  [Invited]

  Invited oral presentation


• サイバー攻撃対策としてのAIへの期待と現状

  OZAWA SEIICHI

  SCSK講演：AIに関する基礎・将来講座, Jul. 2018, Japanese, SCSK, 豊洲フロント(東京都), 機械学習や深層学習、自然言語処理などのAI技術への期待は高まるばかりですが、一方でその限界も認識されつつあります。本講演では、AIの強みと限界を知り、サイバー攻撃にどのように向き合い、活かしていくべきなのか、我々が行っている研究事例を紹介しながら考えてみたいと思います。, Domestic conference

  [Invited]

  Invited oral presentation


• 大豆の生育情報を自動取得する画像センシング手法の開発 - Single Shot MultiBox Detectorの導入

  大村 和暉, 八幡 壮, OZAWA SEIICHI, OHKAWA TAKENAO, 村上 則幸, 辻 博之

  2018年人工知能学会全国大会, Jun. 2018, Japanese, 人工知能学会, 城山ホテル（鹿児島市）, Observing growth state automatically is a very important task in smart farming. In this paper, we propose an image sensing method to detect soy flowers and seeds by using a state-of-art deep learning architecture called Single Shot MultiBox Detector (SSD). We also suggest a new method for counting seeds from a video. We used SSD, fast to compute and has high accuracy. We colle, Domestic conference

  Oral presentation


• AI・機械学習の基礎と広がるAI応用

  OZAWA SEIICHI

  2018年AI・機械学習シンポジウム, May 2018, Japanese, NPO法人M2M・IoT研究会, 藤沢商工会館みなパーク (藤沢市), 深層学習や機械学習，自然言語処理などを使ったAI技術が注目されているが，これらの強みと限界を知り，現実の問題に向き合いながらに正しく使うことが重要である．機械学習のツールやライブラリーをブラックボックスとして使うのではなく，その中身を知ることで，正しい手法を正しい目的で使えるよう機械学習の基礎を講述する．また，AI技術を使った最新の応用事例を紹介する．, Domestic conference

  [Invited]

  Invited oral presentation


• 人工知能技術の基礎と応用

  OZAWA SEIICHI

  KansAI0.6 事業開発講座, Apr. 2018, Japanese, Scribble Osaka Lab, Scribble Osaka Lab (大阪市), Domestic conference

  [Invited]

  Invited oral presentation


• Collecting Cybersecurity-related Contents in Dark Web

  OZAWA SEIICHI

  The 2nd Nanyang Technological University and Kobe University Workshop on Data Science and Artificail Intelligence, Mar. 2018, English, Nanyang Technological University, Singapore, International conference

  Poster presentation


• 万能でないAIのサイバーセキュリティでの活かし方

  OZAWA SEIICHI

  AIセキュリティ最前線2018, Feb. 2018, Japanese, 東京都, Domestic conference

  [Invited]

  Public discourse


• 人工知能分野における最新の研究・技術動向

  OZAWA SEIICHI

  データサイエンスセミナー, Feb. 2018, Japanese, 大阪市, Domestic conference

  [Invited]

  Public discourse


• 機械学習によるサイバーセキュリティとプライバシー保護データマイニングへの取組み

  OZAWA SEIICHI

  NICT サイバーセキュリティシンポジウム, Feb. 2018, Japanese, NICT, 東京都, Domestic conference

  [Invited]

  Public discourse


• なぜ『セキュリティ×機械学習』？

  OZAWA SEIICHI

  第45回SICE知能システムシンポジウム, Feb. 2018, Japanese, SICE, 豊中市, Domestic conference

  [Invited]

  Public discourse


• Detection of Malicious JavaScript Contents Using Doc2vec Feature Learning

  Wangar Samuel Ndichu, OZAWA Seiichi, MISU Takeshi, OKADA Koichiro

  2018年暗号と情報セキュリティシンポジウム, Jan. 2018, English, 電子情報通信学会, 新潟市, To add more functionality and usability to web applications, JavaScript (JS) which is a dynamic and lightweight scripting language is frequently used. However, even with its advantages and usefulness in web applications, it is problematic that there exist malicious JS codes aiming for cyberattacks such as drive-by-download attacks. In general, malicious JS code are not easy to, Domestic conference

  Oral presentation


• AI・機械学習の観点からの 次世代セキュリティ

  OZAWA SEIICHI

  第4回ASF次世代セキュリティシンポジウム, Dec. 2017, Japanese, 東京都, Domestic conference

  [Invited]

  Public discourse


• Recent Challenges to Cybersecurity and Privacy-Preserving Data Mining Using Machine Learning

  OZAWA SEIICHI

  Nanyang Technological University and Kobe University Workshop on Data Science, Nov. 2017, English, Kobe University, 神戸市, Domestic conference

  Public discourse


• A Brief Introduction to Data Science Center and Research Topics on Machine Learning for Big Data

  OZAWA SEIICHI

  2nd Bilateral Workshop on Research Exchange between National Taiwan University and Kobe University, Nov. 2017, English, National Taiwan University, Taipei, Taiwan, International conference

  Public discourse


• 匿名ネットワークTorにおけるマーケット商品とセキュリティ事件との関連性に関する考察

  KAWAGUCHI Yuki, YAMADA Akira, OZAWA SEIICHI

  コンピュータセキュリティシンポジウム 2017, Oct. 2017, Japanese, 情報処理学会, 山形市, Domestic conference

  Oral presentation


• 加法準同型暗号を用いたプライバシー保護Extreme Learning Machine

  KURI Shohe, HAYASHI Takuya, OMORI Toshiaki, OZAWA Seiichi, AONO Yoshinori, Phong Le Trieu, Wang Lihua, MORIAI Shiho

  コンピュータセキュリティシンポジウム 2017, Oct. 2017, Japanese, 情報処理学会, 山形市, Domestic conference

  Oral presentation


• ダークネットトラフィックデータの頻出パターン解析

  HASHIMOTO Naoki, OZAWA SEIICHI, BAN TAO, NAKAZATO JUNJI, SHIMAMURA JUMPEI

  コンピュータセキュリティシンポジウム 2017, Oct. 2017, Japanese, 情報処理学会, 山形市, Domestic conference

  Oral presentation


• Challenge to Building Agricultural Cyber-Physical System for Smart Agriculture: Image Sensing Approach to Automatic Phenotyping for Soybean Plants

  OZAWA SEIICHI

  The 2017 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM2017), Aug. 2017, English, Surabaya, Indonesia, International conference

  [Invited]

  Public discourse


• A Challenge to Discover Rules from the Real World Using Big Data Analysis and Machine Learning

  OZAWA SEIICHI

  Seminar at Universitas Airlangga, Aug. 2017, English, Surabaya, Indonesia, International conference

  Public discourse


• A Challenge to Discover Rules from the Real World Using Big Data Analysis and Machine Learning

  OZAWA SEIICHI

  Seminar at Institut Teknologi Sepuluh Nopember (ITS), Aug. 2017, English, Surabaya, Indonesia, International conference

  Public discourse


• IoTとサイバーフィジカルシステムを知能化するAI技術の動向

  OZAWA SEIICHI

  関西部会第4回 技術研究講演会, Jun. 2017, Japanese, M2M・IoT研究会, 大阪市, Domestic conference

  [Invited]

  Public discourse


• Development of an Image Sensing Method to Automatically Obtain Soybean Growth Condition

  YAHATA SOH, OZAWA SEIICHI, YOSHIDA TAKESHI, OHKAWA TAKENAO, MURAKAMI NORIYUKI, TSUI HIROYUKI

  2017年度人工知能学会全国大会, May 2017, Japanese, 人工知能学会, 名古屋市, We propose an image sensing method to acquire the growth information (the positions and the numbers of flowers, grains and stems) automatically in the crowd of soybean plants for a smart cyber-physical system in agriculture. In our method, we combine image processing and machine learning methods to detect flowers, seeds and stems robustly and accurately. To evaluate the detecti, Domestic conference

  Oral presentation


• SNS Flaming Event Detection Based on Sentiment Polarity Prediction with Transfer Learning

  OZAWA SEIICHI

  IJCNN2017 Post-Confence Workshop: 3rd International Workshop on Advances in Learning from with Multiple Learners (ALML 2017), May 2017, English, IEEE, Anchorage, USA, International conference

  [Invited]

  Nominated symposium


• AI・機械学習における各種手法・技術と適用のポイント・事例

  OZAWA SEIICHI

  AI・機械学習における各種手法・技術と適用のポイント・事例, May 2017, Japanese, テクノセンター, 大阪市, コンピュータやネットワーク、携帯電話、監視カメラ、各種センサなど電子機器の普及に伴い、日々膨大な量の通信データやセンサデータが生成・蓄積されるようになりました。いわゆる「ビッグデータ」です。ビッグデータはメールやSNSなどのテキスト情報だけでなく、画像や動画、音声、各種センサ情報、顧客情報、医療情報など、様々な情報の集合体であり、一般に多様な種類の情報から構成される高次元ベクトルで表されます。このような大量かつ高次元のデータから、認識や予測、診断などを高性能に行うためには、適切な識別器（予測器）モデルを選択して学習するだけでなく、必要最小限の情報に縮約する特徴選択や特徴抽出などの技術も熟知している必要があります。 本講義では、ニューラルネットや機械学習、データマイニングでよく使われている技術をいくつか紹介し、実際の問題を取り上げて、どのようなケースで, Domestic conference

  [Invited]

  Public discourse


• 北海道におけるフルチアセットメチルの散布がダイズの収量に及ぼす影響

  辻 博之, 村上 則幸, 中村 卓司, OZAWA SEIICHI, OHKAWA TAKENAO

  第243回日本作物学会講演会, Mar. 2017, Japanese, Domestic conference

  Oral presentation


• Learning and Visualization of High-dimensional Big Data and Its Application to Cybersecurity

  OZAWA SEIICHI

  UAB-Kobe University Joint Workshop on Smart Cyber-Physical Systems, Feb. 2017, English, Universitat Automata de Barcelona, Barcelona, Spain, International conference

  [Invited]

  Nominated symposium


• Development of an Image Sensing Method to Detect Grainｓ of Soybeans

  YAMAGUCHI KANTA, OZAWA SEIICHI, KITAZONO JUN, YOSHIDA TAKESHI, OHKAWA TAKENAO, MURAKAMI NORIYUKI, TSUI HIROYUKI

  第10回コンピューテーショナル・インテリジェンス研究会, Dec. 2016, Japanese, SICE, 富山市, 農業にICT技術を用いる「スマートアグリ」が近年注目されている．スマートアグリでは農作物の育つ環境や生育情報をコンピュータで管理し，農作業の効率化を目指している．農作物の成長度合いを自動で収集し，解析することで更なる収量の増加が期待されている．子実は収量に直結する重要な生育情報として，群落内で農作物を任意の速度で昇降する単軸ロボットを用いて，下から上へと撮影した連続画像から，子実の検知を行う画像処理技術の開発を行った．本稿では農作物として大豆を扱い，一画像から子実の陰影情報より検知領域を挙げ、色情報から畳み込みニューラルネットワークを用いて検知を行う．実験には農場で撮影した画像を用い，実験結果として目視で確認した子実との比較を行った．トレーニング画像，テスト画像共にF値で80%以上の子実検知精度を得た．, Domestic conference

  Oral presentation


• Development of an Image Sensing Method to Detect and Count Flowers of Soybeans

  OHNISHI TETSU, OZAWA SEIICHI, KITAZONO JUN, YOSHIDA TAKESHI, OHKAWA TAKENAO, MURAKAMI NORIYUKI, TSUI HIROYUKI

  第10回コンピューテーショナル・インテリジェンス研究会, Dec. 2016, Japanese, SICE, 富山市, スマート農業は，センサーから得た農作物の生育環境をコンピュータ制御により最適に保ち，収量の増加を行っている．さらなる収量の増加のため，農作物の成長の様子を表した生育情報を自動的に得ることで，データの増加を行う．花は実のできる前段階であり重要な生育情報であるとして，群落内で農作物を任意の速度で昇降する単軸ロボットを用いて，下から上へと撮影した連続画像から，花の検知と一株当たりの花数の計測を行う画像処理技術の開発を行った．本稿では農作物として大豆を扱い，領域分割と色相情報を用いて得た花候補に対して，畳込みニューラルネットワークで花であるかの判別を行い，一画像での花の検知を行う．またそれぞれの画像において，検地された花周辺でFASTを用いて特徴点を取得しORB特徴量を求め，単軸ロボットの速度と連続画像の撮影間隔から求めた連続画像内の花の移動量に基づいて対応, Domestic conference

  Oral presentation


• Recent Research on Information and Computer Science in The Department of Electrical and Electronic Engineering

  OZAWA SEIICHI

  1st Bilateral Workshop on Research Exchange between National Taiwan University and Kobe University, Dec. 2016, English, KobeUniversity, 神戸市, Domestic conference

  [Invited]

  Nominated symposium


• ダークネットトラフィックに基づくサイバー攻撃の分類と可視化

  OZAWA SEIICHI

  第9回NICTERプロジェクトワーショップ, Nov. 2016, Japanese, NICT, 秋田市, Domestic conference

  [Invited]

  Nominated symposium


• ダークネットトラフィックの可視化とオンライン更新によるモニタリング

  HATANAKA TAKUYA, KITAZONO JUN, OZAWA SEIICHI, BAN TAO, NAKAZATO JUNJI, SHIMAMURA JUMPEI

  コンピュータセキュリティシンポジウム2016論文集, Oct. 2016, Japanese, 情報処理学会, 秋田市, 未使用のIP アドレス空間であるダークネットには， DDoS 攻撃への返信やスキャンなど，不正な通信に伴うパケットが大量に届く．それらを観測・分析することで，インターネット上で発生している悪性な活動の動向を把握することが可能になると期待されている．本論文では，ダークネットの通信パターンの分布を可視化しモニタリングする手法を提案する．提案法では，通信パターンを特徴ベクトルとして表現し，次元圧縮することで2 次元の散布図として可視化する．また，新たな観測データが得られる毎に散布図を逐次更新することで，リアルタイムに変化を捉える．これにより，攻撃の傾向の変化や新たな攻撃の発生の検知を行うことが期待される．, Domestic conference

  Oral presentation


• AI・機械学習における各種手法・技術と適用のポイント・事例

  OZAWA SEIICHI

  AI・機械学習における各種手法・技術と適用のポイント・事例, Oct. 2016, Japanese, テクノセンター, 大阪市, コンピュータやネットワーク、携帯電話、監視カメラ、各種センサなど電子機器の普及に伴い、日々膨大な量の通信データやセンサデータが生成・蓄積されるようになりました。いわゆる「ビッグデータ」です。ビッグデータはメールやSNSなどのテキスト情報だけでなく、画像や動画、音声、各種センサ情報、顧客情報、医療情報など、様々な情報の集合体であり、一般に多様な種類の情報から構成される高次元ベクトルで表されます。このような大量かつ高次元のデータから、認識や予測、診断などを高性能に行うためには、適切な識別器（予測器）モデルを選択して学習するだけでなく、必要最小限の情報に縮約する特徴選択や特徴抽出などの技術も熟知している必要があります。 本講義では、ニューラルネットや機械学習、データマイニングでよく使われている技術をいくつか紹介し、実際の問題を取り上げて、どのようなケースで, Domestic conference

  [Invited]

  Public discourse


• 知識獲得支援を目的とした時系列栽培データに基づく最適パターン発見

  梅島 昂平, 有満 史人, OZAWA SEIICHI, 村上 則幸, 辻 博之, OHKAWA TAKENAO

  平成28年 電気学会 電子・情報・システム部門大会, Aug. 2016, Japanese, Domestic conference

  Oral presentation


• 時系列栽培データから抽出された最適パターンの意思決定支援への適用

  難波 みどり, OZAWA SEIICHI, 村上 則幸, 辻 博之, OHKAWA TAKENAO

  平成28年 電気学会 電子・情報・システム部門大会, Aug. 2016, Japanese, Domestic conference

  Oral presentation


• Online Learning of Unstructured Data in Cybersecurity

  OZAWA SEIICHI, TAO BAN

  2016 World Congress on Computational Intelligence, Jun. 2016, English, IEEE, Vancouver, Canada, Increasing the maliciousness and the diversity of cyber-attacks is one of the most concerned issues in recent years. There are various kinds of cyber-threads such as malware infection, DDoS attacks, probing to find security vulnerability, phishing, and spam mails to lure malicious web site, which intend to steal money/important information and to stop disturb public services, e, International conference

  [Invited]

  Public discourse


• Challenges to Autonomous Learning from Big Stream Data,

  OZAWA SEIICHI

  Seminar at LIPN, Paris 13 University, Jun. 2016, English, Villetaneuse, France, International conference

  Public discourse


• Development of Multidimensional Unfolding Based on Stochastic Neighbor Relationship

  MURATA NAOKI, KITAONO JUN, OZAWA SEIICHI

  60回システム制御情報学会研究発表講演会, May 2016, Japanese, システム制御情報学会, 京都市, The multidimensional unfolding is a data visualization technique that arranges data of different attributes in a single low-dimensional space considering the relationship between data as the distance. Emphasizing unimportant relationships, existing multidimensional unfolding method cannot preserve important relationships. In this paper, to solve this problem, we propose a metho, Domestic conference

  Oral presentation


• Stochastic Collapsed Variational Inference Algorithm for Biterm Topic Model

  AWAYA NARUTAKA, KITAZONO JUN, OMORI TOSHIAKI, OZAWA SEIICHI

  60回システム制御情報学会研究発表講演会, May 2016, Japanese, システム制御情報学会, 京都市, Recently, as a topic model speci cally for short texts, Biterm Topic Model was proposed. However, the original implementation uses collapsed Gibbs sampling, which is not applicable to large datasets. For faster inference, we develop stochastic collapsed variational Bayesian inference for BTM (SCVB0-BTM) based on SCVB0 for LDA. Experimental results showed that our algorithm foun, Domestic conference

  Oral presentation


• 北海道ダイズの収量および収量構成要素に及ぼす除草剤薬害の影響

  辻 博之, 村上 則幸, 中村 卓司, OZAWA SEIICHI, OHKAWA TAKENAO

  日本作物学会第241回講演会, Mar. 2016, Japanese, Domestic conference

  Oral presentation


• Malicious-Spam-Mail Detection Systemwith Autonomous Learning Ability

  OSAKA Shogo, KITAZONO Jun, OZAWA Seiichi, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei

  情報通信システムセキュリティ研究会, Mar. 2016, Japanese, The Institute of Electronics, Information and Communication Engineers, Kyoto University, Recently, damages caused by spam mails that guide receivers to malicious web pages become more and more serious. In this study, we propose an autonomous learning system to detect such malicious spam mails. In the proposed system, the main body of a mail is transformed into a feature vector based on the tf-idf weight, and the feature vector is classi ed by machine learning class, Domestic conference

  Public symposium


• ダークネットトラフィック解析による学習型DDoSバックスキャッタ検出システム

  UKAWA Yuki, KITAZONO Jun, OZAWA Seiichi, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei

  情報通信システムセキュリティ研究会, Mar. 2016, Japanese, 電子情報通信学会, Kyoto University, 本研究では，ダークネットで観測されたUDP通信トラフィックからDDoS攻撃によるバックスキャッタか否かを判定するオンライン学習型の判定システムを提案する．DDoSバックスキャッタを識別するため，17の特徴量からなる特徴ベクトルを作成し，L2-SVM識別器により分類を行う．また，新たなDDoS攻撃パターンに対応するため，1クラスSVMによる外れ値検出を導入し，L2-SVM識別器の継続的な更新を行う．評価実験では，NICTのダークネットセンサで観測された半年間のパケットデータを用いて評価を行う．提案手法により，平均のF値が 0.90という高い性能でバックスキャッタ判定を行えることを示す．, Domestic conference

  Public symposium


• Learning from unstructured data stream in cybersecurity

  OZAWA Seiichi

  Seminar, Mar. 2016, English, Lancaster University, Lancaster, UK, Increasing the maliciousness and the diversity of cyber-attacks is one of the most concerned issues in recent years. There are various kinds of cyber-threads such as malware infection, DDoS attacks, probing to find security vulnerability, phishing, and spam mails to lure malicious web site, which intend to steal money and important information and to stop and disturb public ser, International conference

  Public discourse


• 次元圧縮によるダークネットトラフィックデータの可視化

  KITAZONO Jun, FURUTANI Nobuaki, UKAWA Yuhki, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei, OZAWA Seiichi

  2016 Symposium on Cryptography and Information Security, Jan. 2016, Japanese, 電子情報通信学会 情報セキュリティ研究専門委員会, ANAクラウンプラザホテル熊本ニュースカイ, 特定のホストが割り当てられていないIP アドレス空間はダークネットと呼ばれる．このダークネットには，本来，パケットが到達することはないが，実際には大量のパケットが届く．それらの多くは，送信元を偽装したDDoS 攻撃に対する返信や，マルウェアによるスキャンなど，不正な活動に伴うものである．ダークネットに届くパケットを観測・分析することによって，インターネット上で発生している不正な活動の傾向を把握することが可能となる．本研究では，ダークネット観測網で得られたデータに対して，t 分布型確率的近傍埋め込み法と呼ばれる次元圧縮手法を適用し，不正活動パターンの分布を可視化した結果について報告する．この可視化により，不正活動のトレンドの把握や，新たな不正活動パターンの発生の検知などを行うことが可能になると期待される．, Domestic conference

  Oral presentation


• 携帯型簡易葉色・植生測定器による大豆生育診断と低収要因の解明

  OZAWA Seiichi, OHKAWA Takenao

  平成27年度農林水産省委託プロジェクト「多収阻害要因の診断法及び対策技術の開発委託事業」推進会議, Jan. 2016, Japanese, 農林水産省, 農林水産技術会議事務局筑波産学連携支援センター, Domestic conference

  Others


• Plant Stem Detection and Estimation of Plant Height Using Image Sensing

  SHUHEI Arakawa, OZAWA Seiichi, KITAZONO Jun, YOSHIDA Takeshi, OHKAWA Takenao, MURAKAMI Noriyuki, TSUJI Hiroyuki

  計測自動制御学会 システム・情報部門学術講演会 2015, Nov. 2015, Japanese, The Society of Instrument and Control Engineers, 函館アリーナ, 本研究では，群落画像を用いた画像センシングにより，農作物の茎の位置，草丈 を推定する手法を提案する．単軸ロボットとデジタルカメラを用いて農作物正面 から撮影した連続画像を使用し，色情報による株元検出の後，近傍領域の探索を 繰り返し茎全体の検出を行なう．また，SIFTを用いて農作物の先端の位置を検出 し，撮影位置との関係から草丈を推定する．実験では，株元が中央付近に存在する大豆の画像から株元，節，先端を検出し，検出率の評価を行なった．, Domestic conference

  Oral presentation


• 炎上検知のためのTwitterユーザーの分類

  YOKOTA Ryoichi, AWAYA Narutaka, KITAZONO Jun, OZAWA Seiichi

  計測自動制御学会 システム・情報部門学術講演会 2015, Nov. 2015, Japanese, The Society of Instrument and Control Engineers, 函館アリーナ, Twitter has more than 20 million users in Japan nowadays, and it is widely used as a useful communication tool, not only for personal but also for business use. On the other hand, `flaming’, which implies a soaring of negative sentiment on SNS through the diffusion of negative retweets, has been a serious problem. Main triggers of flaming incidents would be thoughtless comments, Domestic conference

  Oral presentation


• ダークネットトラフィックに基づくサイバー攻撃の分類と可視化

  OZAWA Seiichi

  NICTER Workshop, Nov. 2015, Japanese, NICT, 鳴門教育大学, Domestic conference

  [Invited]

  Nominated symposium


• Bot Decision for Twitter Accounts

  YOKOTA Ryoichi, OZAWA Seiichi, KITAZONO Jun, HAGA Tatsuya, SUGAWARA Takahiro

  計測自動制御学会 システム・情報部門学術講演会 2015, Nov. 2015, Japanese, The Society of Instrument and Control Engineers, 函館アリーナ, 近年，TwitterなどのSNSが急速に普及しているが，それに伴いBotアカウントの存 在が問題となっている．これは，自動的に発言する機能等が備わっており，スパムや悪意のある内容を拡散させる原因となることがあるからである．本研究では，Botを主として使用しているアカウントとそうでないアカウント （人間ユーザー）を分類する試みについて報告する．Twitterで収集した902ユーザーのツィートに対し，F値で88%の精度が得られた．, Domestic conference

  Oral presentation


• Online Learning of Unstructured Data in Cybersecurity

  OZAWA Seiichi

  2015 International Data Mining and Cybersecurity Workshop, Nov. 2015, English, APNNA, Istanbul, Turkey, International conference

  [Invited]

  Invited oral presentation


• Development of Adaptive Event-Monitoring System for DDoS Attacks

  FURUTANI Nobuaki, KITAZONO Jun, OZAWA Seiichi, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei

  コンピュータセキュリティシンポジウム 2015, Oct. 2015, Japanese, 情報処理学会 コンピュータセキュリティ研究会, 長崎ブリックホール, TCP as discriminate DDoS backscatter and incremental learning. Its adaptability is further illustrated by visualization of the host activities during the time expanse using dimension reduction techniques., Domestic conference

  Oral presentation


• Image Sensing Method for Smart Agriculture

  OZAWA Seiichi

  Kobe University Brussels European Centre Symposium, Oct. 2015, English, Kobe University, Brussels, Belgium, International conference

  [Invited]

  Invited oral presentation


• ダークネットトラフィックに基づいたDDoSバックスキャッタ判定

  FURUTANI Nobuaki, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei, KITAZONO Jun, OZAWA Seiichi

  第59会システム制御情報学会研究発表講演会, May 2015, Japanese, The Institute of Systems, Control and Information Engineers, 京都テルサ, In this work, we propose a method to quickly discriminate DDoS backscatter packets from those of other traffic observed by darknet sensors (i.e., backscatter or non-backscatter). For packets sent by a host during a short time, we define 17 features on packet traffic, which are then provided to an SVM classifier for classification. We apply incrimental learning to discriminate b, Domestic conference

  Oral presentation


• Fast Learning of t-Distributed Stochastic Neighbor Embedding Using Minimum Probability Flow

  KITAZONO Jun, OMORI Toshiaki, OZAWA Seiichi

  第59会システム制御情報学会研究発表講演会, May 2015, Japanese, The Institute of Systems, Control and Information Engineers, 京都テルサ, 本研究では，次元圧縮手法の一つであるt分布型確率的近傍埋め込み法（t-Distributed Stochastic Neighbor Embedding，t-SNE）について，Minimum Probability Flowと呼ばれるアルゴリズムに基づく学習法を提案する．通常の学習法では，計算量がサンプル数に対して二次のオーダーであるのに対し，提案法では線形オーダーに削減されることを示す．実験により，学習が高速化され大規模データに対してもt-SNEを適用可能となることを示す．, Domestic conference

  Oral presentation


• A Study on the Estimation of Plant Height Using Image Sensing

  ARAKAWA Shuhei, YOSHIDA Takeshi, KITAZONO Jun, OZAWA Seiichi, FUKAO Takanori, OHKAWA Takenao

  SICE, Dec. 2014, Japanese, Osaka, In this work, we propose an image sensing method to estimate the height of agricultural plants. In the proposed method, several images are taken by moving a digital camera attached to a single-axis robot. Then, the two consecutive images with a plant tip are automatically selected and the plant height is estimated from the two images based on the triangulation. Here, plant tips, Domestic conference

  Oral presentation


• ダークネットトラフィック観測によるDDoSバックスキャッタ判定

  FURUTANI Nobuaki, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei, KITAZONO Jun, OZAWA Seiichi

  第28回情報通信システムセキュリティ研究会, Nov. 2014, Japanese, 一般社団法人電子情報通信学会, 宮城県仙台市, Domestic conference

  Oral presentation


• A Proposal of Negative Tweets Classifier Based on Sentence Structure and Empirical Knowledge

  NISHIKAZE Hironori, OZAWA Seiichi, YAZAWA Takashi, HAGA Tatsuya, SUGAWARA Takahiro

  SCI'14, May 2014, Japanese, Kyoto, Twitter は有益な情報交換ツールとして普及してきたが，その一方で個人や企業のイメージを故意に損なうことを目的とした行為も増加する傾向にある．本稿では，過度な中傷による企業イメージの毀損を防ぐため，係り受けなどの文構造だけでなく，企業名や製品名などを含んだツイートに対する経験則を取り入れたナイーブベイズ識別器を提案する．特定企業や製品名を含むツイートに対して，ネガティブツイートを78%(F-Measure)，ポジティブツイートを90%の精度で識別できることを示した．, Domestic conference

  Oral presentation


• A Sequential Multi-task Learning Model with Multi-label Pattern Recognition Function

  HIGUCHI Daisuke, OZAWA Seiichi

  電気学会 平成26年電気学会 電子・情報・システム部門大会, May 2014, Japanese, Matsue, In this paper, we propose an incremental neural network model for a general class of sequential multi-task classification problems where a training data may have multiple class labels. To handle this type of classification problems, the proposed model consists of a three-layer feedforward neural network with long-term/short-term memories, and it has the following functions: one, Domestic conference

  Oral presentation


• A Study on Judgment of Backscatter by DDoS Attacks for Darknet Packet

  FURUTANI Nobuaki, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei, OZAWA Seiichi

  SCI'14, May 2014, Japanese, Kyoto, In this work，we propose a method to discriminate backscatters caused by DDos attacks from normal traffic．Since the DDos attacks are imminent threats which could give serious economic damages to private companies，it is quite important to detect DDos backscatters as early as possible. To do this，11 features of port\/IP information are defined for network packets which are sent wi, Domestic conference

  Oral presentation


• A Study on Internet Subnets Categorization with Darknet Traffic Data Analysis

  NISHIKAZE Hironori, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei, OZAWA Seiichi

  SCI'14, May 2014, Japanese, Kyoto, In this research, we propose a method to analyze darknet traffic data in order to find subnetworks with security vulnerability. In general, packets reaching the darknet are considered to be generated by malwares. Thus, we can infer the vulnerability of a subnetwork by analyzing the port\/IP information of the darknet packets. To classify darknet traffic data into typical malici, Domestic conference

  Oral presentation


• Fast Online Feature Extraction Using Chunk Incremental Kernel Principal Component Analysis

  JOSEPH Anak Annie, OZAWA Seiichi

  SCI'14, May 2014, English, Kyoto, Kernel Principal Component Analysis (KPCA) is a widely-used feature extraction as it has been proven that KPCA is powerful in many pattern recognition problems. Considering that the conventional KPCA should decompose a kernel matrix of all training data, this would be an unrealistic assumption for data streams in real-world applications. Takeuchi et al. proposed an Incremental-, Domestic conference

  Oral presentation


• A Neural Network Model for Incremental Learning of Large-Scale Stream Data

  Ali Siti, Hajar Aminah, FUKASE Kiminori, OZAWA Seiichi

  SCI'14, May 2014, English, Kyoto, Resource Allocating Network with Long Term Memory (RAN-LTM) can avoid the interference in incremental learning and reduce memory capacity during the learning. However, when a large set of training data are given at a time, the incremental learning in RAN-LTM can be seriously slow. A remedy for this is to select only useful data online depending on the importance of data. In thi, Domestic conference

  Oral presentation


• Development of Online Malicious Decision System for Spam Mails

  TADA Shunsuke, NAKAZATO Junji, BAN Tao, OZAWA Seiichi

  The 31st Symposium on Cryptography and Information Security, Jan. 2014, Japanese, The Institute of Electronics, Information and Communication Engineers, Technical Committee on Information Security, 城山観光ホテル, 本稿ではスパムメールに対するオンライン悪性度判定システムを提案する． スパムメールの悪性度をオンラインで判定することで，ユーザへの悪性度別警告を発したり，悪性度の高いスパムメール送信者の特定に役立てたりすることが期待される．しかしながら，スパムメール中にURLが含まれている場合，その危険性を判断するのは一般に困難であり，さらにスパムメールの文面などは不定期に変化する．そのためブラックリスト方式で対応するのは難しい．そこで提案システムでは，教師あり学習および追加学習を用いてスパムメールの悪性度を逐次的に判定する．学習用ラベルには，クローリングシステムを利用し，特徴選択，学習，更新の3つの機能を実装する．性能評価実験では，2013年3月から8月に収集されたダブルバウンスメールを用いて，提案システムが高精度でスパムメールを逐次的に悪性度判定可能であることを, Domestic conference

  Oral presentation


• A Radial Basis Function Network with Locality Sensitive Hashing for Large-Scale Stream Data

  Aminah Ali Siti Hajar, OZAWA Seiichi

  The Society of Instrument and Control Engineers, Symposium of System and Information Division, Nov. 2013, Japanese, The Society of Instrument and Control Engineers, System and Information Division, ピアザ淡海, The developments of the high-speed machines and advanced technology services have provided the facilities for the researchers to obtain and store a large-scale stream data that are continuously generated. The biggest challenges to process the large-scale stream data are to mine useful information not only from huge amount of data but also from high dimensions, besides to provid, Domestic conference

  Oral presentation


• Acceleration of Incremental Kernel Principal Component Analysis for Stream Data

  Annie anak Joseph, OZAWA Seiichi

  The Society of Instrument and Control Engineers, Symposium of System and Information Division, Nov. 2013, Japanese, The Society of Instrument and Control Engineers, System and Information Division, ピアザ淡海, Online feature extraction has becomes an important topic in pattern recognition. On the other hand, Kernel Principal Component Analysis (KPCA) has been known as a powerful nonlinear feature extraction. Takeuchi et al. have proposed an Incremental type of KPCA (IKPCA) where the learning is sequentially conducted for stream data. However, since the eigenvalue decomposition should, Domestic conference

  Oral presentation


• A Study on Vulnerability Inspection of Internet Subnets by Darknet Traffic Data Analysis

  NISHIKAZE Munenori, BAN Tao, OZAWA Seiichi

  Computer Security Symposium 2013, Oct. 2013, Japanese, Information Processing Society of Japan Computer Security Group, Sunport Takamatsu, In the research, we develop a dynamical analysis method of malware activities using NICTER darknet Dataset 2013, which includes unsolicited packet data generated by malware scan activities, infection activities, backscatter by DDos attacks, human setup errors, etc. We analyze packets sent out from specific IP regions (subnetworks) at all the destination ports, and identify the, Domestic conference

  Oral presentation


• Extension of Sequential Multi-Task Learning Model to Multi-label Pattern Recognition

  HIGUCHI Daisukei, OZAWA Seiichi

  57th Annual Confernece of the Institute of Systems, Control and Information Engineers, May 2013, Japanese, the Institute of Systems, Control and Information Engineers, 兵庫県民会館, In this work, we propose a new sequential multitask learning model that can learn training samples with multiple class labels. Since each training sample is assumed to have no task label, the proposed model must allocate each class to an appropriate task. In the proposed model, this is conducted based on the prediction errors. In addition, we introduce a new consolidation mecha, Domestic conference

  Oral presentation


• A Study on Fast Learning for Large-Scale Stream Data

  FUKASE Kiminori, Aminah Ali Siti Hajar, OZAWA Seiichi

  57th Annual Confernece of the Institute of Systems, Control and Information Engineers, May 2013, Japanese, the Institute of Systems, Control and Information Engineers, 兵庫県民会館, Recently, mining knowledge from stream data such as access logs of computer, commodity distribution data and sales data, and human life-log has been attracting attention. As one of the techniques suitable for such an environment, active learning has been studied for long time. In this work, we propose a fast learning technique for neural networks by introducing Locality Sensiti, Domestic conference

  Oral presentation


• 樹状突起における電気特性の不均一性による情報伝播の向上

  島本 貴文, OMORI TOSHIAKI, 青西 亨, OZAWA SEIICHI

  第57回システム制御情報学会研究発表講演会, May 2013, Japanese, システム制御情報学会, 兵庫県民会館, Domestic conference

  Oral presentation


• A Study on Tweet Classification Using Automatic Indexing

  YOSHIDA Shun, OZAWA Seiichi

  57th Annual Confernece of the Institute of Systems, Control and Information Engineers, May 2013, Japanese, the Institute of Systems, Control and Information Engineers, 兵庫県民会館, In predicting personal emotion from tweets, it is not easy to infer the implication of personal emotion from tweets without human's intervention in the emotional analysis. This causes various difficulties in the tweet analysis in terms not only of heavy human workload, but also of keeping the consistency in the emotional evaluation by humans.To alleviate this problem, we propos, Domestic conference

  Oral presentation


• A Study on Behavior Recognition by Traffic Monitoring

  NISHIKAZE Munenori, TADA Shunsuke, OZAWA Seiichi

  57th Annual Confernece of the Institute of Systems, Control and Information Engineers, May 2013, Japanese, the Institute of Systems, Control and Information Engineers, 兵庫県民会館, This study presents a method of behavior recognition of PC users using packet information. Network packets contain information on e-mails, applications, websites, etc. In this work, packet information is obtained using Wireshark (packet analyzer) and converted into a feature vector. Then, feature selections are applied to each feature vector to select important information, and, Domestic conference

  Oral presentation


• Handling Concept Drift Using Incremental Linear Discriminant Analysis with Knowledge Transfer in Non-stationary Data Streams

  Annie anak Joseph, OZAWA Seiichi

  57th Annual Confernece of the Institute of Systems, Control and Information Engineers, May 2013, Japanese, the Institute of Systems, Control and Information Engineers, 兵庫県民会館, In real life, data are not always generated under stationary environments. However, traditional learning systems normally assumed that the property of data streams is stationary over time and this sometimes leads to the degradation in the system performance when there are some hidden context changes in data streams. Such context changes are called concept drift, and several app, Domestic conference

  Oral presentation


• Extension of Incremental Linear Discriminant Analysis for Online Feature Extraction under Unstationary Environments

  Annie anak Joseph, OZAWA SEIICHI

  Electronics, Information and Systems Conference 2012, IEE of Japan, Sep. 2012, English, Hirosaki University, One of the recent topics in online learning is the adaptation to “concept drift”, in which an increasing loss of the relevance between the current data to the previous concept representations leads to imposing model changes. On the other hand, online feature extraction for high-dimensional data streams is very important to ensure high-performance and real-time adaptation to dyn, Domestic conference

  Oral presentation


• A Fast Incremental Principal Component Analysis for Real-time Computation

  Aoki Daijiro, OZAWA SEIICHI

  The 56th Annual Conference of ISCIE, May 2012, Japanese, Kyoto Terrsa, When the conventional incremental principal component analysis (IPCA) is used for extracting features of a data stream, IPCA has to be carried out for each of the high-dimensional input featuress; thus, the eigenvalue problem should be solved for every high-dimensional data. This may lead to the deterioration in the online performance of the feature extraction for a data stream, Domestic conference

  Oral presentation


• Incremental Learning of Stream Data

  OZAWA SEIICHI

  The 56th Annual Conference of ISCIE, May 2012, Japanese, Kyoto Terrsa, In real-world applications, concepts to be learned can sometimes be changed over time. Such a problem in machine learning is called concept drift. To adapt to the dynamic environments with concept drift, a system should have the mechanisms (1) to learn the change in the environment, (2) to detect the drift and/or its magnitude, and (3) to forget what is no longer relevant. In t, Domestic conference

  Oral presentation


• ストリームデータに対するカーネル主成分分析アルゴリズム

  TOKUMOTO Takaomi, OZAWA Seiichi

  第39回知能システムシンポジウム, Mar. 2012, Japanese, 計測自動制御学会, 千葉, Domestic conference

  Oral presentation


• Fast Incremental Principal Component Analysis and Its Application to Face Image Recognition

  青木大二郎, 小澤誠一

  電子情報通信学会技術研究報告, 2012, Japanese, The Institute of Electronics, Information and Communication Engineers, In the conventional Incremental Principal Component Analysis (IPCA), an eigenvalue problem has to be solved whenever one or a small number of training data are given in sequence. Since the eigenvalue decomposition requires high computational costs in general, solving the eigenvalue problem repeatedly results in the deterioration in the real-time learning property of IPCA. Hence, in this work, we propose an improved version of IPCA whose real-time learning property is enhanced without sacrificing the recognition performance by reducing the number of times to solve eigenvalue problems. We show that the improved IPCA can learn principal components real time from a stream of face images.


• ラジアル基底関数ネットの自律追加学習アルゴリズムの改良

  NAKASAKA Sho, OZAWA Seiichi

  第24回自律分散システムシンポジウム, Jan. 2012, Japanese, 計測自動制御学会, 神戸, Domestic conference

  Oral presentation


• 非定常環境下でのストリームデータの追加学習方式

  OZAWA Seiichi

  SICEシステム・情報部門学術講演会, Nov. 2011, Japanese, 計測自動制御学会, 神戸, Domestic conference

  Oral presentation


• 移転メトリック学習に基づいたマルチタスク学習モデルの開発

  Gaku Shimon, OZAWA Seiichi

  第21回インテリジェントシステムシンポジウム, Sep. 2011, Japanese, 計測自動制御学会, 神戸, Domestic conference

  Oral presentation


• ラジアル基底関数ネットにおける追加型自律学習アルゴリズム

  NAKASAKA Sho, OZAWA Seiichi

  第21回インテリジェントシステムシンポジウム, Sep. 2011, Japanese, 計測自動制御学会, 神戸, Domestic conference

  Oral presentation


• マルチタスクパターン認識における複数ラベルの学習

  TAKATA Tomoyasu, OZAWA Seiichi

  第21回インテリジェントシステムシンポジウム, Sep. 2011, Japanese, 計測自動制御学会, 神戸, Domestic conference

  Oral presentation


• チャンクデータに対する追加学習型カーネル主成分分析アルゴリズム

  TOKUMOTO Takaomi, OZAWA Seiichi

  電子情報通信学会ニューロコンピューティング研究会, Jul. 2011, Japanese, 電子情報通信学会, 神戸, Domestic conference

  Oral presentation


• メトリックに基づいた知識移転を行うマルチタスク学習モデルの開発

  Gaku Shimon, OZAWA Seiichi

  55回システム制御情報学会研究発表講演会講演, May 2011, Japanese, システム制御情報学会, 大阪, Domestic conference

  Oral presentation


• マルチモーダル・マルチタスクパターン認識の追加学習方式に関する研究

  Kou Shugen, OZAWA Seiichi, Youngmin Jang, Minho Lee

  55回システム制御情報学会研究発表講演会講演, May 2011, Japanese, システム制御情報学会, 大阪, Domestic conference

  Oral presentation


• 1C1-1 Improvement of Incremental Autonomous Learning Algorithm for Radial Basis Function Networks

  NAKASAKA Sho, OZAWA Seiichi

  インテリジェントシステム・シンポジウム講演論文集, 2011, Japanese, 日本機械学会, In this paper, we propose to implement an pruning algorithm to an autonomous incremental learning model called Automated Learning algorithm for Resource Allocating Network (AL-RAN). The proposed pruning algorithm determines whether Radial Basis Functions (RBF) are useful or not by activation level of them and remove unuseful RBFs. By implementing this algorithm to AL-RAN, we reduce the number of basis and improve the learning time. From the experimental results, we confirm that the above functions work well and the efficiency in terms of learning time is improved without sacrificing the recognition accuracy as compared with the previous version of AL-RAN.


• 1C1-3 Development of Multitask Learning Model Based on Transfer Metric learning

  Yue Simeng, OZAWA Seiichi

  インテリジェントシステム・シンポジウム講演論文集, 2011, Japanese, 日本機械学会, Multi-task learning is a learning paradigm which allows a system to learn related multiple tasks in parallel or sequentially and to improve the generalization performance for a learning task by transferring the knowledge to other related tasks. In order to transfer knowledge effectively, it is necessary to estimate the relatedness between tasks properly. In this paper, we propose a multi-task learning algorithm for pattern recognition where the task relatedness is estimated based on the metric learning. Experimental results demonstrate the effectiveness of our method.


• 1B2-3 Multi-Label Learning for Multi-Task Pattern Recognition

  TAKATA Tomoyasu, OZAWA Seiichi

  インテリジェントシステム・シンポジウム講演論文集, 2011, Japanese, 日本機械学会, In this paper, we extend the multi-task learning (MTL) model proposed by Nishikawa et al. such that a classifier can learn training data with multiple class labels which belong to different classification tasks. In order to identify the a suitable task associated with the class of a given data, the proposed MTL model checks the prediction errors in all output sections. Then, the corresponding task is predicted from the output section with a minimum error. If all the prediction errors are large, the MTL model suspends to learn training data until the output section with a small error is found. In the experiments, we evaluate the proposed MTL model using the COIL-100 and ORL Face datasets which is extended to multi-task learning. The experimental results show that each class label given concurrently can be classified into a suitable task by the proposed system and multi-label data are efficiently learned by classifier.


• 省メモリな追加学習型カーネル主成分分析アルゴリズム

  徳本隆臣, 小澤誠一

  知能システムシンポジウム資料, 2011


• マルチタスク顔画像認識のための追加型二方向二次元線形判別分析

  LIU Chunyu, 小澤誠一

  知能システムシンポジウム資料, 2011


• Development of Autonomous Learning Algorithm for Incremental Radial Basis Function Networks

  Nakasaka Sho, Ozawa Seiichi

  SICEシステム・情報部門学術講演会, Nov. 2010, Japanese, 計測自動制御学会, 京都市, Domestic conference

  Oral presentation


• Development of Semi-Supervised Multitask Learning Model for Pattern Recognition

  Takata Tomoyasu, Ozawa Seiichi

  SICEシステム・情報部門学術講演会, Nov. 2010, Japanese, 計測自動制御学会, 京都市, Domestic conference

  Oral presentation


• Online Feature Extraction by Incremental Recursive Fisher Linear Discriminant

  Ozawa Seiichi, Ohta Ryohei

  20回インテリジェントシステムシンポジウム, Sep. 2010, Japanese, 計測自動制御学会, 八王子市, Domestic conference

  Oral presentation


• A Fast Incremental Learning Algorithm of Kernel Principal Component Analysis

  Tokumoto Takaomi, Ozawa Seiichi

  平成22年電気学会電子・情報・システム部門大会, Sep. 2010, Japanese, 電気学会, 熊本市, Domestic conference

  Oral presentation


• Enhanced Incremental Learning Algorithm for Kernel Principal Component Analysis

  Tokumoto Takaomi, Ozawa Seiichi

  54回システム制御情報学会研究発表講演会, May 2010, Japanese, システム制御情報学会, 京都市, Domestic conference

  Oral presentation


• An Incremental Learning Model for Multitask Pattern Recognition

  Ozawa Seiichi

  54回システム制御情報学会研究発表講演会, May 2010, Japanese, システム制御情報学会, 京都市, Domestic conference

  Oral presentation


• パターン認識における半教師有りマルチタスク学習モデルの開発

  高田丈靖, 小澤誠一

  知能システムシンポジウム資料, 2010


• 追加学習型ラジアル基底関数ネットの自律学習アルゴリズムの開発

  中坂翔, 小澤誠一

  知能システムシンポジウム資料, 2010


• Development of Incremental Recursive Fisher Linear Discriminamt

  太田良平, 小澤誠一

  システム制御情報学会研究発表講演会講演論文集(CD-ROM), 2009, The Institute of Systems, Control and Information Engineers, 従来の線形判別分析（LDA）による特徴抽出では，得られる特徴ベクトルの次元は訓練データのクラス数未満で制限される．これに対し，再帰的フィッシャー判別分析（RFLD）では，LDA特徴空間の補空間に対して再帰的にLDAを行うことで，クラス数の制限を越えて任意の次元の特徴ベクトルが得られる．一方，LDAを追加学習環境に拡張した追加型線形判別分析（ILDA）がPangらによって提案されている．本研究では，ILDAの導出法に基づきRFLDを追加学習可能なように拡張し，クラス分離度に基づいて最適な特徴数を決定する方法を導入した追加型再帰的フィッシャー判別分析を提案する．この学習アルゴリズムでは，過去の訓練データを保持することなく特徴空間の更新を行うことが可能であり，クラス数以上の特徴数を得ることができる．ベンチマークデータを用いた計算機実験を通して，ILDAとの比較を行い，IRFLDはいくつかのデータにおいてILDAを超える性能を有することを示す．


• 特徴抽出と識別器を追加学習するマルチタスク・パターン認識モデルの提案

  戚暁偉, 小澤誠一

  電気関係学会関西支部連合大会講演論文集(CD-ROM), 2009


• 追加型再帰フィッシャー判別による認識性能のオンライン改善

  太田良平, 小澤誠一

  電気学会電子・情報・システム部門大会講演論文集(CD-ROM), 2009


• A Reinforcement Learning Model with Function of Generating Macro-Actions in Grid-World Maze Problems and a Study on its Learning Property

  恩田宏, 小澤誠一

  電気学会論文誌 C, 2009, Japanese, The Institute of Electrical Engineers of Japan, A macro-action is a typical series of useful actions that brings high expected rewards to an agent. Murata et al. have proposed an Actor-Critic model which can generate macro-actions automatically based on the information on state values and visiting frequency of states. However, their model has not assumed that generated macro-actions are utilized for leaning different tasks. In this paper, we extend the Murata's model such that generated macro-actions can help an agent learn an optimal policy quickly in multi-task Grid-World (MTGW) maze problems. The proposed model is applied to two MTGW problems, each of which consists of six different maze tasks. From the experimental results, it is concluded that the proposed model could speed up learning if macro-actions are generated in the so-called correlated regions.


• Dynamic Selection of Accumulation Ratio for Incremental Principal Component Analysis

  Yuki Kawashima, Seiichi Ozawa

  自律分散システム・シンポジウム, Jan. 2009, Japanese, 計測自動制御学会, Tottori City, Domestic conference

  Oral presentation


• An Automated Incremental Learning Algorithm for RBF networks

  Toshihisa Tabuchi, Seiichi Ozawa

  自律分散システム・シンポジウム, Jan. 2009, Japanese, 計測自動制御学会, Tottori City, Domestic conference

  Oral presentation


• An Incremental Principal Component Analysis Based on Dynamic Accumulation Ratio

  Seiichi Ozawa, Kazuya Matsumoto, Shaoning Pang, Nikola Kasabov

  SICE Annual Conf. 2008, Aug. 2008, English, 計測自動制御学会, Fuchu City, Tokyo, Domestic conference

  Oral presentation


• Improvement of Incremental Principal Component Analysis

  Kazuya Matsumoto, Seiichi Ozawa

  システム制御情報学会研究発表講演会, May 2008, Japanese, Kyoto City, Domestic conference

  Oral presentation


• Speed Up of Reinforcement Learning by Introducing Macro-actions

  Yuki Kawashima, Ryuhei Ohta, Seiichi Ozawa, Hiroshi Onda

  システム制御情報学会研究発表講演会, May 2008, Japanese, システム制御情報学会, Kyoto City, Domestic conference

  Oral presentation


• 特徴選択による追加学習型カーネル主成分分析の高速化とその性能評価

  Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

  平成19年電気関係学会関西支部連合大会, Nov. 2007, Japanese, Kobe University, Domestic conference

  Poster presentation


• マクロアクション生成機能を有する強化学習アルゴリズム

  Hiroshi Onda, Seiichi Ozawa

  平成19年電気関係学会関西支部連合大会, Nov. 2007, Japanese, Kobe University, Domestic conference

  Oral presentation


• Development of Incremental Kernel Principal Component Analysis and Its Performance Evaluation

  Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

  平成19年電気学会電子・情報・システム部門大会, Sep. 2007, Japanese, 電気学会, 大阪府立大学, Domestic conference

  Oral presentation


• Knowledge Transfer Algorithm Using Task Relatedness for Sequential Multi-task Pattern Recognition

  Hitoshi Nishikawa, Seiichi Ozawa

  平成19年電気学会電子・情報・システム部門大会, Sep. 2007, Japanese, 電気学会, 大阪府立大学, Domestic conference

  Oral presentation


• Reinforcement Learning Agent Model with Function of Generating Macro-Actions

  Hiroshi Onda, Seiichi Ozawa

  平成19年電気学会電子・情報・システム部門大会講演論文集, Sep. 2007, Japanese, 電気学会, 大阪府立大学, Domestic conference

  Oral presentation


• An Online Face Recognition System with Incremental Learning Ability

  Seiichi Ozawa, Michiro Hirai, Shigeo Abe

  SICE Annual Conference, Sep. 2007, English, 計測自動制御学会, Kagawa University, Domestic conference

  Oral presentation


• Development of Incremental Kernel Principal Component Analysis

  Yohei Takeuchi, Seiichi Ozawa, Shigeo Abe

  51回システム制御情報学会研究発表講演会, May 2007, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• Basic Research on Selective Knowledge Transfer for Sequential Multi-task Learning

  Hitoshi Nishikawa, Seiichi Ozawa

  51回システム制御情報学会研究発表講演会, May 2007, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• Online Feature Selection and Incremental Learning of Neural Network

  Seiichi Ozawa

  51回システム制御情報学会研究発表講演会, May 2007, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 追加学習型ブースティング識別器の開発

  KIDERA Takuya, OZAWA Seiichi, ABE Shigeo

  平成18年電気関係学会関西支部連合大会, Nov. 2006, Japanese, 電気学会, 大阪工業大学, Domestic conference

  Oral presentation


• 追加学習型カーネル主成分分析の評価

  KIMURA Shosuke, TAKEUCHI Yohei, OZAWA Seiichi, ABE Shigeo

  平成18年電気関係学会関西支部連合大会, Nov. 2006, Japanese, 電気学会, 大阪工業大学, Domestic conference

  Oral presentation


• Speed-up of Reinforcement Learning by Generating Macro-actions

  ONDA Hiroshi, MURATA Makoto, OZAWA Seiichi

  第49回自動制御連合講演会, Nov. 2006, Japanese, システム制御情報学会, 機械学会, 計測自動制御学会, 神戸大学, Domestic conference

  Oral presentation


• A Multi-task Learning Algorithm for Pattern Recognition

  ZHANG Keyu, OZAWA Seiichi

  第49回自動制御連合講演会, Nov. 2006, English, システム制御情報学会, 機械学会, 計測自動制御学会, 神戸大学, Domestic conference

  Oral presentation


• A study on Incremental Learning for Boosting Classifier

  KIDERA TAKUYA, OZAWA Seiichi, ABE Shigeo

  50回システム制御情報学会研究発表講演会, May 2006, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• Character Recognition for Malaysian License Plate

  竹内洋平, 福見稔, 赤松則夫, KARUNGARU Stephen, 小澤誠一

  システム制御情報学会研究発表講演会講演論文集, 2006, The Institute of Systems, Control and Information Engineers, 近年，自動文字認識システムはあらゆる環境で有用であり，車両認知・郵便番号認識・手書き文字の文書化など広く活用・研究されている．本論文では，マレーシアのライセンスプレート文字認識を提案する．まず，車両進入時の動画像から車両領域を抽出し，プレート位置を特定．そして，ニューラルネットワークをベースにした文字認識手法を使い，車両情報を取得する．今回はシミュレーション上での本システムの結果を報告する．


• Incremental Learning Algorithm of Committee Machine

  KIDERA Takuya, OZAWA Seiichi, ABE Shigeo

  49回システム制御情報学会研究発表講演会, May 2005, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• カーネルパラメータの選択メカニズムをもつブースティングカーネル判別分析法

  喜多 伸次, 前川 聡, 小澤 誠一, 阿部 重夫

  第32回知能システムシンポジウム, Mar. 2005, Japanese, 計測自動制御学会 システム・情報部門, 京都工芸繊維大学, Domestic conference

  Oral presentation


• マクロアクション生成機能をもつメモリベース強化学習アルゴリズム

  村田 真, 小澤 誠一

  第17回自立分散システム・シンポジウム, 2005, Japanese, 計測自動制御学会 システム・情報部門, 未記入, Domestic conference

  Oral presentation


• 特徴空間のオンライン学習に関する研究

  小田 梓, 小澤 誠一, 阿部 重夫

  48回システム制御情報学会研究発表講演会, May 2004, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 追加学習可能な顔認識システムとその性能改善

  木村 星介, 小澤 誠一, 阿部 重夫

  48回システム制御情報学会研究発表講演会, May 2004, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• RBFネットワークを用いたメモリベース強化学習アルゴリズム

  松岡 幹泰, 小澤 誠一, 阿部 重夫

  48回システム制御情報学会研究発表講演会, May 2004, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 動的環境下で追加学習可能なニューラルネットモデル

  小澤 誠一, 津守 研二

  第16回自律分散システムシンポジウム, 2004, Japanese, 計測自動制御学会 システム・情報部門, 京都テルサ, Domestic conference

  Oral presentation


• ラジアル基底関数ネットのメモリベース追加学習

  小澤 誠一, 岡本 圭介

  第16回自律分散システムシンポジウム, 2004, Japanese, 計測自動制御学会 システム・情報部門, 京都テルサ, Domestic conference

  Oral presentation


• ニューラルネットの追加学習とその応用

  小澤 誠一

  平成15年度電気関係学会関西支部連合大会, 2004, Japanese, 電気関係学会関西支部, 未記入, Domestic conference

  Oral presentation


• 長期記憶をもつニューラルネットワークによる動的環境下での教師あり学習

  小澤 誠一, 津守 研二

  第30回知能システムシンポジウム, Mar. 2003, Japanese, 計測自動制御学会 システム・情報部門, 学術総合センター, Domestic conference

  Others


• Pattern Recognition Method Using Independent Components for Each Class

  Kinukawa Shuhei, Kotani Manabu, Ozawa Seiichi

  Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, 2003, The Institute of Systems, Control and Information Engineers, Applications of independent component analysis (ICA) to feature extraction have been a topic of research interest. Here, we propose a novel recognition method using features extracted by ICA. The proposed method consists of some modules for each category and a synthesizer. A module has a feature extraction and a classification. Features are independent components extracted by ICA algorithm using the training data for each class and classification are made by these features. These output of the module are combined and categories are decided by a majority rule. We evaluate the performance of the proposed method for several recognition tasks. From these results, we confirm the effectiveness of the recognition method using independent components for each class.


• Reinforcement Learning Using Neural Networks with Memory Mechanism

  Ozawa Seiichi, Shiraga Naoto, Abe Shigeo

  Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, 2003, The Institute of Systems, Control and Information Engineers, In reinforcement learning problems, agents should learn from only rewards that are provided by the environment; hence, learning by trial and error is inevitable. In order to acquire right policies of actions, action-value functions are often estimated. In many cases, the action-value functions are approximated by parametric linear/nonlinear functions such as RBF networks. However, when the RBF networks are trained in incremental fashion, we often suffer from a serious problem called interference that results in the forgetting of input-output relations acquired in the past. In this work, we propose a new approach to learning action-value functions using an RBF network with memory mechanism. In the simulations, we verify that our proposed model can acquire the proper policies even in difficult situations.


• Fast Incremental Learning Algorithm for Neural Networks based on Linear Method

  Okamoto Keisuke, Ozawa Seiichi, Abe Shigeo

  Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers, 2003, The Institute of Systems, Control and Information Engineers, As an incremental learning model, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only a new training sample but also some memory items stored in Long-Term Memory are trained based on a gradient descent method. The gradient descent method is generally slow and tends to be fallen into local minima. To improve these problems, we propose a fast incremental learning of RAN-LTM based on the linear method. In this algorithm, centers of basis functions are not trained but selected based on the output errors. A distinctive feature of the proposed model is that this model dose not need so much memory capacity. To evaluate the performance of our proposed model, we apply it to some function approximation problems. From the experimental results, it is verified that the proposed model can learn fast and accurately unless incremental learning is conducted over a long period of time.


• 追加学習機能を有するRBFネットワークの高速学習法

  小澤 誠一, 白神 那央人, 阿部 重夫

  第47回システム制御情報学会研究発表講演会, 613-614, 2003, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 追加学習機能を有するRBFネットワークの高速学習法

  岡本 圭介, 小澤 誠一, 阿部 重夫

  第47回システム制御情報学会研究発表講演会,531-532, 2003, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 長期記憶を有するニューラルネットワークによる動的環境への適応

  津守 研二, 小澤 誠一, 阿部 重夫

  第47回システム制御情報学会研究発表講演会, 471-472, 2003, Japanese, システム制御情報学会, 京都テルサ, Domestic conference

  Oral presentation


• 顔画像認識に基づく追加学習型個人認証システムに関する研究

  卓 順利, 小澤 誠一

  第46回自動制御連合講演会, 2003, Japanese, システム制御情報学会, 岡山大学, Domestic conference

  Oral presentation


• A Reinforcement Learning Algorithm for a Class of Dynamical Environments Using Neural Networks.

  MURATA Makoto, OZAWA Seiichi

  SICE Annual Conf. 2003, 2003, English, 未記入, 未記入, Domestic conference

  Oral presentation


• A Supervised Independent Component Analysis Maximizing Distances between Features of Different Classes

  OZAWA Seiichi, SAKAGUCHI Yoshinori, KOTANI Manabu

  IEICE technical report. Circuits and systems, Nov. 2002, Japanese, The Institute of Electronics, Information and Communication Engineers, Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of pattern features extracted by conventional ICA algorithms greatly depends on pattern sets; that is, how patterns are distributed in the feature space. As one of the reasons, we have pointed out that conventional ICA features are obtained by increasing only their independence even if class information is available. In this context, we can expect that more high-performance features can be obtained by introducing class information into conventional ICA algorithms. In this paper, we propose a supervised ICA (SICA) algorithm that maximizes Mahalanobis distances between features of different classes as well as maximize their independence. In the simulation, the performance of the proposed SICA algorithm is evaluated using three data sets of UCI Machine Learning Repository. We demonstrate that the better recognition accuracy for these data sets is obtained using our proposed SICA. Furthermore, we show that pattern features extracted by SICA are better than those extracted by only maximizing the Mahalanobis distances.


• Approximation of Action-Value Functions Using Neural Networks with Incremental Learning Ability

  OZAWA Seiichi, SHIRAGA Naoto, ABE Shigeo

  知能システムシンポジウム資料, Mar. 2002, Japanese


• A Neural Network with Incremental Learning Ability and Its Reinforcement Learning Algorithm

  Ozawa Seiichi, Shiraga Naoto, Abe Shigeo

  SICE Division Conference Program and Abstracts, 2002, The Society of Instrument and Control Engineers, In reinforcement learning, forgetting by incremental learning can be serious when neural networks are utilized for approximating action-value functions of agents. To avoid the forgetting, we present a feedforward neural network model with long-term memories and its reinforcement learning algorithm. In the simulations, we apply our model to an extended mountain-car task, and the number of steps to the goal is evaluated as compared with a neural network without long-term memories. As a result, we demonstrate that the forgetting is effectively suppressed in the proposed model.


• Feature Extraction Using Independent Component Analysis With Category Information

  Takabatake Hiroki, Kotani Manabu, Ozawa Seiichi

  SICE Division Conference Program and Abstracts, 2002, The Society of Instrument and Control Engineers, There are some studies about applications of independent component analysis (ICA) to feature extractions in pattern recognition. Since the ICA is an unsupervised learning, independent components are not always useful features for recognition. Therefore, we propose an ICA using a category information. The proposed ICA is realized by three-layered neural networks of which learning algorithm is combination of the error back-propagated algorithm and the fast ICA algorithm. Simulations are performed for three databases from the UCI database to evaluate the effectiveness of the proposed algorithm. We obtain higher recognition accuracy than the original ICA.


• Pattern recognition method based on subspace methods using ensemble learning for independent components

  Katsura Masanori, Kotani Manabu, Ozawa Seiichi

  SICE Division Conference Program and Abstracts, 2002, The Society of Instrument and Control Engineers, We propose a new recognition method using independent components based on a subspace method. Independent components are calculated by the ensemble learning that can reduce redundant basis functions. The proposed method has some modules for each class and a combiner. A module consists of feature extraction and classification. Features are extracted by the ensemble learning and classification is made on k nearest neighbors. Outputs of modules are combined in the combiner and a category is decided by a majority rule. Simulations are performed for hand-written characters to evaluate the proposed method. Results show the effectiveness of the proposed method.


• 210 Detetion of Leakage sound Under Dynamic Environment

  KOTANI Manabu, KATSURA Masanori, OZAWA Seiichi

  Dynamics and Design Conference : 機械力学・計測制御講演論文集 : D & D, Aug. 2001, Japanese, The Japan Society of Mechanical Engineers, It is important to detect the leakage of the gas to be flammable or poisonous from the cracks in pipes of chemical plants. We examined the application of modular neural networks to the detection of the leakage sound. The modular neural network has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions for a long term. The discrimination accuracy with the proposed network was about 93%, which was better than 83% with the simple network. From the results, we confirmed the effectiveness for the application of the modular neural network to the detection of the leakage sound for the practical use.


• Detection of Leakage Sound with Independent Component Analysis

  KOTANI Manabu, ARIMOTO Takahiko, OZAWA Seiichi, AKAZAWA Kenzo

  Dynamics and Design Conference : 機械力学・計測制御講演論文集 : D & D, Sep. 2000, Japanese, The Japan Society of Mechanical Engineers, We describe what characteristics an independent component analysis can extract from leakage sounds. The learning algorithm of a network was an information-maximization approach proposed by Bell and Sejnowski. We applied the independent component analysis to the leakage sound. After the learning, most of the basis functions that are columns of a mixing matrix were localized in frequency. Furthermore, there were some basis functions to extract the feature of leakage sound. From these results, we confirmed that the application of independent component analysis to the signal processing of the acoustic signal was effective.


• Incremental Learning for Neural Networks with Long-Term Memory

  OZAWA Seiichi, TAMAOKI Toshiya, BABA Norio

  知能システムシンポジウム資料, Mar. 2000, Japanese


• Application of Independent Component Analysis to Signal Processing of Speech and Acoustic Signal

  KOTANI Manabu, OZAWA Seiichi, MAEKAWA Satoshi, AKAZAWA Kenzo

  知能システムシンポジウム資料, Mar. 2000, Japanese


• Feature Extraction of Character Patterns Utilizing Independent Component Analysis

  OZAWA Seiichi, KOTANI Manabu, BABA Norio

  インテリジェント・システム・シンポジウム講演論文集 = FAN Symposium : fuzzy, artificial intelligence, neural networks and computational intelligence, Oct. 1999, Japanese


• Autoassociative memory derived from cross-coupled Hopfield nets and its association properties

  OZAWA Seiichi, TSUTSUMI Kazuyoshi, BABA Norio

  インテリジェント・システム・シンポジウム講演論文集 = FAN Symposium : fuzzy, artificial intelligence, neural networks and computational intelligence, Nov. 1997, Japanese


• An Improvement of Pseudoinverse-Type Autoassociative Neural Memory and Its Dynamical Characteristics

  OZAWA Seiichi, TSUTSUMI Kazuyoshi, BABA Norio

  日本神経回路学会全国大会講演論文集 = Annual conference of Japanese Neural Network Society, Nov. 1997, Japanese


• The Design of Modular Neural Network Architecture Using Genetic Algorithms

  OZAWA Seiichi, BABA Norio, TSUTSUMI Kazuyoshi

  インテリジェント・システム・シンポジウム講演論文集 = FAN Symposium : fuzzy, artificial intelligence, neural networks and computational intelligence, Oct. 1996, Japanese

• Information Processing Society of Japan

  Jun. 2020 - Present


• ACM

  Jan. 2018 - Present


• Asia Pacific Neural Network Society (APNNS)

  Jan. 2016 - Present


• International Neural Network Society (INNS)

  Mar. 2015 - Present


• IEEE

  Jan. 2001 - Present


• The Japanese Society for Artificial Intelligence


• Japanese Neural Network Society


• システム制御情報学会


• 電子情報通信学会


• 計測自動制御学会

• Privacy Control Technologies for Smartglasses AI

  塚本 昌彦, 寺田 努, 小澤 誠一, 森井 昌克, 塚原 東吾, 喜多 伸一, 新川 拓哉

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A), Grant-in-Aid for Scientific Research (A), Kobe University, 01 Apr. 2022 - 31 Mar. 2027

  本年度は、工学、社会科学の研究者の連携により、スマートグラスAI普及における将来のプライバシ要求を明らかにした。いくつかの具体的な応用を考え、プライバシ問題を検討すると同時にいくつかのプロトタイプシステムを実現し要求事項を抽出した。研究統括とメタAI(担当：塚本)については、システム全体の統括エンジンを作るために、問題分析及びシステム設計を行った。状況認識機構(担当：寺田)については、実世界での周辺・自己状況を認識するための要件を抽出しいくつかの認識機構を実装した。制御可能AIシステム(担当：小澤)については、プライバシに関わるAIの機能を制御し説明できるAIを作るために機構の設計を行った。プライバシ機構(担当：森井)については、プライバシを守るためのメカニズム構築のために、アプリケーションイメージ及びシステム要件を明確にした。科学技術社会的観点からの分析(担当：塚原)については、上記のアプリケーションイメージの具体化の中で社会の中での技術の使い方やあり方を考えた。心理的観点からの分析(担当：喜多)については、上記アプリケーションイメージの中で使う側、使われる側の心理を考えた。哲学・倫理的観点からの分析(担当：新川)については、上記のアプリケーションイメージの中でプライバシがどうあるべきかを考えた。 さらに年度後半のChatGPTやGPT-4などの大規模言語モデル(LLM)の出現によりAI技術が急速 に進歩したことで前提条件が根底から変化することから、全般的な計画の見直しを行った。 同時に、社会問題、倫理問題を体系化し、ガイドライン策定に向けた組織作りと運営方法を検討した。また、メンバー以外の人を交えたワークショップを複数回開催した。さらに講演会等で積極的にプロジェクトの紹介を行うとともに、プロジェクトのホームページとYouTubeチャンネルを立ち上げた。


• Refinement of Cyberattack Generation Process Model by Using Machine Learning and Domain Knowledge

  Seiichi Ozawa, Sangwook Kim, Katsunari Yoshioka, Yoshiaki Shiraishi, Tao Ban

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Fund for the Promotion of Joint International Research (Fostering Joint International Research (B)), Fund for the Promotion of Joint International Research (Fostering Joint International Research (B)), Kobe University, Oct. 2021 - Mar. 2027

  本研究では、防御側の観測・検知を回避・無効化する攻撃の仕組みなど、ドメイン知識を有する専門家と国際連携体制を築き、観測のみに頼るリアクティブな対策だけでなく、新たな攻撃への予測と迅速な対応を行うプロアクティブなセキュリティ対策の構築を目指している。本年度は海外渡航が不可能であったため、国内チームの吉岡（横国大）、班（NICT）、金、小澤（神戸大）と以下の研究を実施した。
  1)小澤、班、金は、URL文字列から悪性が疑われるWebサイトのHTMLコンテンツを取得し、それぞれのHTMLタグ階層構造（DOM）をグラフ表現し、それをgraph2vecで埋め込みベクトルに変換して、正規サイトを装うフィッシングサイトを見つける方法を提案した。PhishTankとOpenPhishのフィッシングサイト151件を用いた実験では、80%のフィッシングサイトがクラスタを形成し、これらクラスタが共通してもつ外部リンクから正規サイトを特定したところ、AmazonとFacebookを騙るフィッシングサイト群を見つけた。また、VirusTotalで取得可能なドメインのWhois情報、レビュー情報、DNSレコード、SSL証明書情報などを特徴量として機械学習で悪性判定する方法を提案した。その結果、1550サイトに対し、フィッシングサイトは88%、マルウェアホストサイトは91%の精度で検知できた。
  2)吉岡は、WarpDrive実証実験に参加している508ユーザが受信したSMS、合計23,133件（良性 22,800件、悪性333件）を調べたところ、SMSを多く受信するユーザが悪性SMSを受信しやすいわけではなく、現時点では、攻撃者はランダムに悪性SMSを送付していることが推測された。また、悪性SMSは深夜と早朝には送られず、午後2時から5時の期間に集中していることがわかった。


• 細胞外小胞を用いたリキッドバイオプシーによる癌に対する術前化学療法の効果予測

  竹内 俊文, 三好 康雄, 小澤 誠一

  日本学術振興会, 科学研究費助成事業 基盤研究(A), 基盤研究(A), 神戸大学, 01 Apr. 2022 - 31 Mar. 2026


• Research and development on the advancement of privacy preserving federated learning - Advancing privacy protection coalition learning for fraud transaction monitoring that contributes to continuous actual operation -

  National Institute of Information and Communications Technology, Advanced communications/broadcasting research and development contract research, Kobe University, Feb. 2023 - Mar. 2025, Principal investigator


• Secure computation-based implementation of privacy-preserving inter-organizational data collaboration meeting social demands

  Goichiro Hanaoka, Seiichi Ozawa, Takahiro Sugawara, Shiho Moriai

  Japan Science and Technology Agency, AIP Acceleration Research, Apr. 2022 - Mar. 2025, Coinvestigator


• Modelling Attack Generation Process by Introducing Machine Learning and Domain Knowledge and Its Verification for Real Attack Data

  Seiichi Ozawa, Sangwook Kim, Katsunari Yoshioka, Yoshiaki Shiraishi, Tao Ban

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, Apr. 2021 - Mar. 2025


• R&D of Machine Learning Mechanism for Privacy Preserving Data Mining over Different Industries

  王 立華, 小澤 誠一

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), National Institute of Information and Communications Technology, 01 Apr. 2020 - 31 Mar. 2024

  課題1.「セキュアなクラウド・エッジコンピューティングに関する研究」の子課題「準同型計算と大小比較の融合」に取り組み、プライバシー保護データマイニングに活用されるセキュアな大小比較アプローチについて研究し、効率性、安全性、及び柔軟性を向上させるために、従来研究の最も効率がよいセキュアな大小比較アプローチSK17を改良した3つの方式を提案した。その中で、Efficiency-enhanced提案方式は既存方式SK17より50%程度で効率化を実現した；Security-enhanced提案方式はデータ所有者とクラウドサーバの間Oneランド通信（非対話型）で暗号化したまま大小比較の結果計算でき、サーバからデータを完全に守るより高い安全性を実現した。成果は国際会議The 23rd International Conference on Network-Based Information Systems（NBiS2020）発表した。 課題2.「プライバシー保護しつつ直・並列学習メカニズムの設計」の子課題「同・異業種データを柔軟に処理可能な直・並列学習メカニズムの提案」に取り組み、プライバシー保護決定木推測の効率化アプローチを提案した。提案手法は同業種か異業種かにも関わらず、適用可能であるので、汎用性がある。また、決定木の各ノードで分岐する時、クラウドサーバ経由で特徴値と閾値の大小比較を計算しなければいけないので、上記のEfficiency-enhancedセキュアな大小比較提案方式を利用した。成果は論文誌投稿中。


• Social Implementation of Privacy-Preserving Data Analysis

  Seiichi Ozawa

  Japan Science and Technology Agency, Strategic Basic Research Programs, Kobe University, Apr. 2019 - Mar. 2022, Coinvestigator

  Competitive research funding


• WarpDrive: Web-based Attack Response with Practical and Deployable Research Initiative

  Seiichi Ozawa

  National Institute of Information and Communications Technology, NICT委託研究, Kobe University, Oct. 2016 - Mar. 2021, Coinvestigator


• Online Learning Algorithms for Real-time Detection, Classification, and Visualization of Cyberattacks

  Ozawa Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, Apr. 2016 - Mar. 2020, Principal investigator

  In this project, we have proposed several online learning algorithms to continuously perform the detection, classification, and visualization of cyberattacks by analyzing communication packets observed by a large-scale darknet (i.e., unused IP address range) sensor, while following the ever-evolving cyberattacks. In addition, we have developed three types of adaptive attack-monitoring systems. The first is a DDoS backscatter monitoring system, which applies communication traffic features in combination with support vector machines and deep neural networks to achieve detection accuracy of 97% or more and high-speed learning characteristics. Moreover, we have developed a new type of cyberattack monitoring systems that can detect unknown cyber-threats and monitor changing behaviors of malware by association rule mining and the representation learning of port-number embedding.

  Competitive research funding


• Development of Diagnostic Method and Countermeasure Technology for High Yield Impeding Factor

  Seiichi Ozawa

  Agriculture,Forestry and Fisheries Research Council, 戦略的プロジェクト研究推進事業, Kobe University, Apr. 2015 - Mar. 2020, Coinvestigator


• 複数組織データ利活用を促進するプライバシー保護データマイニング

  Seiichi Ozawa

  Japan Science and Technology Agency, Strategic Basic Research Programs (CREST), Kobe University, Oct. 2016 - Mar. 2019, Coinvestigator

  Competitive research funding


• Development of Malware Detection/Classification System Introducing Incremental Learning and Active Learning

  OZAWA Seiichi, ANDO Ruo, KITAZONO Jun, BAN Tao, NAKAZATO Junji, SHIMAMURA Jumpei

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, Apr. 2012 - Mar. 2015, Principal investigator

  In order to protect network uses from malicious spam mail attacks that can lead to malware infections and to conduct a large-scale monitoring of malicious activities by malwares, we developed three types of learning systems introducing machine learning techniques. First, we developed a malicious spam mail detection system with the following three sophisticated functions: an automatic mechanism to collect suspected malicious spam mails, an automatic labelling (malicious or benign) function for collected spam mails by a crawler-type of web security analyzer, and online learning function for automatically collected training data. Second, we developed a large-scale monitoring system which can observe transitions of subnet infection states by allocating the most similar typical patters obtained by performing the hierarchical clustering for darknet traffic features. Finally, we developed a large-scale monitoring system which can detect DDoS backscatter from observed darknet traffic features.

  Competitive research funding


• 二国間交流「マルチモーダル・マルチタスク個人認証システムの開発」

  小澤 誠一

  二国間交流事業（韓国との共同研究）, 2012, Principal investigator

  Competitive research funding


• 二国間交流「マルチモーダル・マルチタスク個人認証システムの開発」

  小澤 誠一

  二国間交流事業（韓国との共同研究）, 2011, Principal investigator

  Competitive research funding


• Development of Multitask Pattern Recognition Model with Knowledge Transfer of Feature Space and Application to Person Identification

  OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, 2008 - 2010, Principal investigator

  In the environments where multiple pattern recognition tasks with some relatedness are learned sequentially, it is known that the learning is conducted efficiently even with a small number of training data by using "knowledge transfer" from one task to another. In the research project, we developed a multitask learning algorithm with an efficient knowledge transfer mechanism where a useful feature space is learned incrementally in an efficient way by transferring a part of previous learned knowledge to an unknown task. The proposed multitask learning algorithm is implemented as a person identification system using face images and the effectiveness of this system is verified.

  Competitive research funding


• 二国間交流「マルチモーダル・マルチタスク個人認証システムの開発」

  小澤 誠一

  二国間交流事業, 2010, Principal investigator

  Competitive research funding


• Optimizing and Visualizing Kernel Classifiers

  ABE Shigeo, OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, 2007 - 2008

  a) KDA (Kernel Discriminant Analysis)を特徴選択の基準として特徴選択する方式を開発した. b) 特徴空間上のKDA に基づいてパターン認識する方式を開発した.またファジィ識別器の可視化のプリミティブな方式を開発した. c) カーネルファジィ識別器のメンバーシップ関数をSVM のマージン最大化の概念によりチューニングする方式を開発した. d) 相関のある複数のパターン認識問題が逐次的に与えられるマルチタスク学習問題に対し,少ない訓練データで高い汎化能力が得られるマルチプルクラシファイアシステムを開発した.

  Competitive research funding


• Research of Sequential Multitask Learning and ItsApplication to Patlern Recognition

  OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, 2006 - 2007, Principal investigator

  This research project developed a new learning algorithm for the multi-task pattern recognition problem. This project considers learning multiple classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is “online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. It is assumed that the classification tasks are related to each other and that their training examples appear in random sequences during “online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and fast and utilize knowledge of previous tasks to learn new tasks. Overall, automated task recognition falls in the category of unsupervised learning since no information about task categories of training examples is provided to the algorithm. The performance of the algorithm is evaluated using several artificially generated and three UCI datasets. The experiments in this project verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks.

  Competitive research funding


• Research of Knowledge Acquisition and System Development by Data Mining

  ABE Shigeo, OZAWA Seiichi, YOSHIMURA Motohide

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, 2004 - 2005

  We conducted research on knowledge acquisition and system development and got the following results : 1. Knowledge acquisition and system development under steady state environments (1) We have developed a clustering method using support vector machines (SVMs) by dividing the image data in segments and feature selection method using SVM ensembles. (2) We have developed an incremental training method that keeps only support vector candidates and demonstrated that the generalization ability was maintained while deleting training data. 2. Knowledge acquisition by data mining and system development under dynamic environments (1) Dynamic feature space learning : we have developed incremental learning algorithms of Principal Component Analysis (PCA) and Kernel PCA (KPCA), and demonstrated that the feature selection was successfully carried out by adapting to the variation of data distributions. (2) System development : We proposed a method to integrate the developed dynamic feature selection algorithm into a classifier model, in which the k-nearest neighbor method was combined with a dynamic clustering algorithm, and a neural network model. We demonstrated that the proposed method enabled the classifier to conduct stable incremental learning, and that the developed system had excellent performance for not only bench-mark datasets but also facial recognition datasets. Although we tried to develop an incremental SVM system, it has not been completed yet. This is reserved as our future work. 3. Development of image segmentation system by data mining (1)Development of clustering method for image segmentation : we have developed a basic system to detect and classify the uncertain object imagery and demonstrated that the detection and classification of crystal imagery was properly carried out using image database of protein crystallizations. (2) Development of feature extraction method for image segmentation : we have developed a feature extraction method based on the multiresolution spectral histograms by wavelet transformation and demonstrated that the proposed feature is available for the image retrieval.


• Proposal of a Pattern Recognition System Capable of Incremental Learning and Its Application to Facial Image Recognition

  小澤 誠一

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, 2004 - 2005

  追加学習可能なパターン認識システムの開発に必要不可欠な学習アルゴリズムを考案した.成果の概要を以下にまとめる. (1)特徴空間の追加学習として,従来のIncremental Principal Component Analysis (IPCA)の改良を行った.具体的には,特徴空間の次元増加の判定基準として,寄与率による方法を提案し,その更新式を求めた. (2)従来のIPCAは,1つデータが与えられるたびに固有値問題を解く必要があった.これに対し,複数のデータをまとめて1回の更新で新しい固有基底を求める学習アルゴリズム(Chunk IPCA)を提案した. (3)改良IPCAアルゴリズムおよびCIPCAアルゴリズムを顔画像認識に適用し,追加学習が進むにつれて,認識精度が高まることとFalse Positive Rateが小さくなることを確認した.また,CIPCAを導入することによって,学習時間が大幅に短縮されることを確認した. (4)特徴空間の更新に伴い,識別機(ニューラルネット)の更新も同時に行う必要があり,結合荷重の更新だけでなく,入力変数の個数の変動にも追従できなければいけない.この問題に対し,長期記憶を導入したニューラルネットの記憶アイテムを特徴空間に合わせて更新し,それらを訓練データと一緒に学習するアルゴリズムを開発した. (5)従来の独立成分分析(Independent Component Analysis ; ICA)を教師あり学習に拡張する方式を提案し,独立性とクラス分離性を同時に高める学習アルゴリズムを導出した.また,いくつかのベンチマークデータで性能評価を行い,従来のICAやPCAで求めた特徴量に比べて,性能がよいデータもあることを確認した.


• Research about Motor Unit Visualization with Surface EMG Signals

  MAEKAWA Satoshi, OZAWA Seiichi, KOTANI Manabu

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), 2003 - 2004

  This research was executed for the motor unit decomposition and making the activity visible from the surface electromyogram as follows. A)Improvement of motor unit decomposition technique We proposed a new motor unit decomposition technique with overcomplete bases to introduce the statistical model. It was confirmed that this technique was applied to the real measurement surface EMG signals, and it had the decomposition performance equal with blind deconvolution. Moreover, it was confirmed that each motor unit activity might be separable even if the number of observation channels were less than the number of active motor units. B)Examination of effectiveness of recognition technique using statistical model We proposed the recognition system using both KDA and boosting mechanism and confirmed that it's recognition performance was just like an existing technique such as SVM. In general, to maximize generalization performance, parameter tuning process such as cross validation, whose computation cost is very expensive, is needed. To solve this problem, we proposed new index value for parameter selection, and with this index, appropriate parameters can be selected without so expensive computation cost. C)The three dimensional position estimation of each decomposed motor unit The 3D position of the depolarization of individual motor unit was estimated by using the 3D finite element method from the potential distribution on the skin surface which was estimated with motor unit decomposition technique from surface EMG signals. As a result, it was confirmed that 3D position and dynamics of dopolarization of individual motor unit might be estimated. Moreover, the size of innervation zone and temporal dynamics of current intensity of depolarization could be estimated. In addition, this study had been started in 2003 fiscal year under Dr.Kotani head researcher, Assistant Professor of Kobe University. However he died in May, 2004, so the head was changed suddenly. I write down that contribution of Dr.Kotani covers the whole of the above results.


• Development of Multiclass Support Vector Machines and Their Application to Diagnosis and Image Processing

  ABE Shigeo, YOSHIMURA Motohide, KOTANI Manabu, OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, 2002 - 2003

  We have developed multiclass support vector machines and applied them to diagnosis problems and image processing. The major results of the project are as follows : 1. Development of multiclass support vector machines ・We have developed fuzzy support vector machines that resolve unclassifiable regions in multiclass problems. ・We have developed optimal ordering of decision-tree and pairwise support vector machines to improve the generalization ability. 2. Development of fast training methods ・We have developed steepest ascent methods for pattern classification and function approximation, in which more than two data are processed at a time. 3. Evaluation for medium to large sized data sets ・We confirmed that our methods improve the generalization ability and speed up training for large sized data sets. 4. Application to diagnosis ・We have examined the feature extraction based on independent component analysis (ICA) to enhance the discrimination performance of support vector machines. We confirmed that ICA could extract the effective features from the gas leakage sound in pipes, digit patterns, and the various benchmark datasets. ・We have developed the evolutionary feature extraction using margin maximization method. 5. Application to diagnosis ・We have developed the multi-resolution feature extraction method by using 2-dimensional wavelet decomposition for the inputs of support vector machines. ・We have developed the feature extraction method as a preparation for classifying the state of protein crystals by using support vector machines.


• 長期記憶を導入したニューラルネットモデルの提案と動的環境におけるロバスト性の検証

  小澤 誠一

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), Grant-in-Aid for Young Scientists (B), Kobe University, 2002 - 2003

  ダイナミックに変化する環境の下では,過去に得た知識が常に有効であるとは限らず,環境に適応するため絶えず修正を要求される.しかし,同じ環境が将来において再び現れるようなケースでは,過去に獲得したすべての知識を修正するのは必ずしも効率的とはいえない.つまり,ある時点で通用しなくなった知識であっても,長期的な記憶として保存し,その知識が有用となる環境が再び現れたときに想起・利用できるようなメカニズムをもつことが望ましい.また,学習期間に終わりのないlife-long学習としての性質をもつには,知識を効率よくメモリに蓄積できなければならない. 本研究では,上記のような機能を有するニューラルネットモデルを提案した.このモデルは,(1)入出力関係を学習するニューラルネット部,(2)ニューラルネット部で抽出された知識を蓄える連想バッファ,(3)連想バッファにある知識のうち必要なものを長期的に保持するための長期記憶,(4)環境の変動を検知する検知部の4つのモジュールで構成される. 平成14年度において提案したモデルでは,ロバストな環境変動の検知を行うためのメカニズムと高速な環境への適用を実現するための連想メカニズムを開発し,この機能を実装した.複数の異なる一次元関数が順次移り変わっていく単純な動的環境の下で提案モデルの適応能力をシミュレーション実験で調べた.その結果,提案したモデルは環境変動を正確に検知し,過去に経験した環境の知識を活かして,高速に環境に追従できることを確認した.また,動的環境の下であっても追加学習を安定に行えることを示した. 平成15年度では,移り変わっていく個々の環境に特有の知識と不変な知識を区別して,共有知識を抽出・利用する知識移転のメカニズムを付加した.シミュレーション実験を通して,この知識転移の機能が正しく機能し,さらに高速な環境適応が可能となることを確認した.


• Development of Intelligent Acoustic Diagnosis System Under Dynamic Environment

  KOTANI Manabu, OZAWA Seiichi, OGAWA Kazuhiko

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), Kobe University, 2001 - 2003

  We have developed the acoustic diagnosis system which is capable of adapting to the dynamic environment. The major results of the project as follows : 1. Development diagnostic networks We proposed a novel model of modular neural networks which has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions to imitate the change of environment for a long term. The discrimination accuracy with the proposed network was observed to be about 93%. Result shows that the proposed model is effective for detection of the leakage sound for the practical use. 2. Independent component analysis and evolutionary computation as feature extraction We have examined the feature extraction for acoustic signal using independent component analysis and genetic algorithms to obtain the stable diagnosis. We proposed a novel recognition method using features extracted by ICA. The proposed method consists of some modules for each category and a synthesizer. We evaluate the performance of the proposed method for several recognition tasks including acoustic diagnosis. From these results, we confirmed the effectiveness of the recognition method using independent components for each class. The effectiveness of the proposed method were also confirmed for biological instruments.


• Study on Signal Processing Using Independent Component Analysis

  KOTANI Manabu, OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, 2001 - 2002

  We examine an application of independent component analysis (ICA) to feature extraction of signal processing such as digit patterns and acoustic signals. In order to evaluate the effectiveness of independent components as features, we compare discrimination accuracy using independent components with those using principal components. Furthermore, we apply the ICA to biological signal processing. We obtain the following results : 1. Acoustic diagnosis In order to detect the leakage from pipesaccurately, we should select a feature extraction method for sounds properly. The purpose of this research is to examine whether independent component analysis (ICA) is useful as a feature extraction method for acoustic signals. We confirm that the feature extraction using the ICA algorithm is very useful for detecting gas leakage sounds. 2. Digit recognition We propose a novel recognition method using features extracted by ICA. The proposed method consists of some modules for each category and a synthesizer. We evaluate the performance of the proposed method for several recognition tasks. From these results, we confirm the effectiveness of the recognition method using independent components for each class. 3. Deconvolution for EMG We apply a multichannel blind deconvolution method based on ICA to surface EMG signals. We obtained a few components of which firing patterns is similar to motor units.


• Development of Handy Acoustic Diagnosis System

  KOTANI Manabu, OZAWA Seiichi, OGAWA Kazuhiko, AKAZAWA Kenzo

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Grant-in-Aid for Scientific Research (C), Kobe University, 1998 - 1999

  The detection of gas leakage sound from pipes is important in petroleum refining plants and chemical plants, as often the gas used in these plants are flammable or poisonous. In order to establish the acoustic diagnosis technique for the leakage sound, we examined the application of modular neural networks to the stable detection. The modular neural network has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions to imitate the change of environment for a long term. We applied Fast Fourier Transform(FFT) as the pre-processing method and examine features of power spectrum for the gas leakage sound. The feature is that the power spectrum for the gas leakage sound are more than those for the normal sound within the range from about 5kHz to 20kHz. The discrimination accuracy with the proposed network was observed to be about 93%. From the results, we confirmed the effectiveness for the application of the modular neural network to the detection of the leakage sound for the practical use. Furthermore, we have developed the handy system based on the diagnostic technique.


• モジュール構造ニューラルネットの機能形成モデルとヘルスモニタリングへの応用

  小澤 誠一

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Encouragement of Young Scientists (A), Grant-in-Aid for Encouragement of Young Scientists (A), Osaka Kyoiku University, 1998 - 1999

  構造物の健全度を非破壊で調べるヘルスモニタリングの基本技術として,ウェーブレット解析と階層型ニューラルネットのハイブリッド化手法を提案した。ウェーブレット解析は観測信号(構造物の加速度や速度情報)から正常/異常の判断に用いる特徴量の抽出に利用され,ニューラルネットは構造物の劣化を推定するのに用いられる。構造物を3自由度ダイナミカルシステムで近似し,観測信号から解析的に解が求まらない設定のもとで,バネ定数および減衰定数の劣化の程度を推定する問題に提案したハイブリッド手法を適用した。計算機シミュレーションによって推定精度を調べた結果,バネ定数はある程度の精度で推定可能であったが,減衰定数については十分な推定精度が得られなかった.ここで用いたニューラルネットはモジュール構造をもたないものであるが,この代わりに創発的に機能獲得が可能なモジュール構造ニューラルネットを適用することで,前述の問題を改善できる可能性がある.そこで,まずニューラルネットの基本特性を調べる際によく取り上げられる連想記憶の問題を取り上げ,モジュール構造ニューラルネットの有効性を検討した.モジュール構造を決定するパラメータの探索に遺伝的アルゴリズムを用い,所望の特性に適合したモジュール構造(各モジュールは異なる機能をもつ)が自動的に決定されるニューラルシステムを開発した。シミュレーション実験の結果,試行錯誤では発見の難い高性能なモジュール構造の探索が,本システムで可能になることを確認した.最終的には,これをヘルスモニタリングに適用して性能が改善されることを確認する必要がある.しかし,本研究期間内では残念ながらこれを達成できなかった.今後の研究課題としたい.また,特徴抽出手法として,ウェーブレット解析の代わりに最近ブラインド信号分離問題で盛んに研究されている独立成分分析の適用も検討したことを付記しておく.


• モジュール型神経回路網モデルの動的性質に関する定量的評価

  小澤 誠一

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Encouragement of Young Scientists (A), Grant-in-Aid for Encouragement of Young Scientists (A), Osaka Kyoiku University, 1993 - 1993

  モジュール型神経回路網モデルとして、対称結合をもつ相互結合型神経回路網を複数個結合したものを取り上げた。このようなモデルにおける各モジュールの活性ダイナミクスとモジュール間の結合ダイナミクスは、ネットワーク全体のエネルギー関数を定義することで与えらる。 本年度の研究は、このようなモデルの連想記憶能力をシミュレーション実験で定量的に評価することを目的とした。記銘するパターンとしては、各ビットが2値(±1)であるランダムベクトルを用い、総ニューロン数に対するパターン数(記憶率:r)で評価した。その結果、モジュール構造をもたない従来のホップフィールドネットに比べ、モジュール数が多すぎなければ、飛躍的に連想能力が向上することがわかった。例えば、総ニューロン数が500、モジュール数が2のとき、r=0.5で限界方向余弦d_C≒0.5、r=1.0でd_C≒0.7となった(ホップフィールドネットの場合、r≒0.2でd_C≒1.0となる)。さらに、与えられた問題に対して最適なモジュール数が存在し、それを越えると急速に連想能力が劣化する現象が見られた。この原因についての詳しい考察は、今後の課題とする。 また、ここで用いたモジュール型神経回路網には、モジュールの状態間に多対多の関係がある場合でもうまく動作するような機能を付加している。これは、2つのモジュール間の相互作用を決定する階層型ネットワーク(インターネット)に両モジュールの状態を入力することで実現している。本研究では、この機能がうまく動作することも、多対多関係をもつ文字パターン対を使った実験により確認している。


• Development of Automatic High Performance Seal Imprint Verification system with Imprint Quality Identification Function

  UEDA Katsuhiko, OZAWA Seiichi

  Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Developmental Scientific Research (B), Grant-in-Aid for Developmental Scientific Research (B), Nara National College of Technology, 1990 - 1991

  This research project has been carried out over two years (1990-1991). A major aim of this research project is to develop an automatic seal imprint verification system which is adoptable to seal imprints with various qualities. We were able to achieve our aim of this research project. The summary of results obtained by the research project is as follows : 1. We developed the quality identification method of seal imprint on the basis of characteristics of its gray level histogram. Through the quality identification experiment using actual seal imprints with various qualities, it is confirmed that the quality identification result by this method coincides with the result by document examiners. 2. We developed the new method for automatic seal imprint verification by adding the imprint quality identification process to our previous verification method. Each partial region of an examined seal imprint is identified first, and then only good quality partial regions of an examined imprint are verified with registered one. A major advantage of this method is that this method can verify seal imprints with various flaws. 3. On the basis of above result, automatic seal imprint verification system for practical applications was realized by using a small hardware system which consists of a personal computer and an image processing unit. Experimental results show that this system might be feasible for practical applications.