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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks

Effective node vaccination and containing strategies to halt...

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IEEE International Conference on research, Innovation and Vision for the Future

作者： Ngoc-Kim-Khanh Nguyen Thanh-Trung Nguyen Tuan-Anh Nguyen Fabio Sartori Massimiliano Turchetto Francesco Scotognella Roberto Alfieri Davide Cassi Quang Nguyen Michele Bellingeri Faculty of Fundamental Sciences Van Lang University Ho Chi Minh Vietnam Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Ho Chi Minh Vietnam Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Parma Italy Dipartimento di Fisica Politecnico di Milano Piazza Leonardo da Vinci 32 Milano Italy Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh Vietnam

ISBN: (纸本)9781665461672

We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the ‘facemask use’ and ‘vaccination policies’ to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability $\beta$ . We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability $\beta=0$ , which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected.

关键词： COVID-19 Epidemics Monte Carlo methods Social networking (online) Sociology Communications technology Vaccines

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Brain-computer interface with corrupted EEG data: A tensor completion approach

arXiv

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arXiv 2018年

作者： Solé-Casals, J. Caiafa, C.F. Zhao, Q. Cichocki, A. University of Vic Central University of Catalonia Data and Signal Processing Research Group Vic Catalonia Spain - CCT-La Plata CONICET CICPBA Villa Elisa Buenos Aires Argentina Department of Psychological and Brain Sciences Indiana University BloomingtonIN United States Tensor Learning Unit RIKEN Center for Advanced Intelligence Tokyo Japan School of Automation Guangdong University of Technology China Skolkovo Institute of Science and Technology Moscow Russia Department of Informatics Nicolaus Copernicus University Torun87-100 Poland College of Computer Science Hangzhou Dianzi University Hangzhou310018 China

Background / Introduction: One of the current issues in Brain-Computer Interface (BCI) is how to deal with noisy Electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). Methods: We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al, 2011), 3DPB-TC (Caiafa et al, 2013), BCPF (Zhao et al, 2015) and HaLRT (Liu et al. (2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. Results: We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by

关键词： Electroencephalography

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Improving Chinese Character Representation with Formation Tree

SSRN

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SSRN 2024年

作者： Hong, Yang Li, Yinfei Qiao, Xiaojun Li, Rui Zhang, Junsong National Engineering Research Center for E-learning Central China Normal University No.152 Luoyu Road Hubei Wuhan430079 China Mind Art and Computation Group Department of Artificial Intelligence Xiamen University No. 4221 South Xiang’an Road Xiang’an District Fujian Xiamen361104 China National Engineering Research Center of Educational Big Data Central China Normal University No.152 Luoyu Road Hubei Wuhan430079 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism Fujian Xiamen China

Learning effective representations for Chinese characters presents unique challenges, primarily due to the vast number of characters and their continuous growth, which requires models to handle an expanding category space. Additionally, the inherent sparsity of character usage complicates the generalization of learned representations. Prior research has explored radical-based sequences to overcome these issues, achieving progress in recognizing unseen characters. However, these approaches fail to fully exploit the inherent tree structure of such sequences. To address these limitations and leverage established data properties, we propose Formation Tree-CLIP (FT-CLIP). This model utilizes formation trees to represent characters and incorporates a dedicated tree encoder, significantly improving performance in both seen and unseen character recognition tasks. We further introduce masking for to both character images and tree nodes, enabling efficient and effective training. This approach accelerates training significantly (by a factor of 2 or more) while enhancing accuracy. Extensive experiments show that processing characters through formation trees aligns better with their inherent properties than direct sequential methods, significantly enhancing the generality and usability of the representations. © 2024, The Authors. All rights reserved.

关键词： Character recognition

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A Fabric Defect Detection system Based Improved YOLOv5 Detector

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Journal of Physics: Conference Series 2021年第1期2010卷

作者： Ying Wang Zhengyang Hao Fang Zuo Shanshan Pan Institute of Intelligence Networks System Henan University Kaifeng Henan 475001 China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng Henan 475001 China Intelligent Data Processing Engineering Research Center of Henan Province Kaifeng Henan University Kaifeng Henan 475001 China

Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet's bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.

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g-circulant matrix and its matrix-vector multiplication algorithm on transformer neural network

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AIP Conference Proceedings 2024年第1期3201卷

作者： Euis Asriani Intan Muchtadi-Alamsyah Ayu Purwarianti Doctoral Program of Mathematics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Algebra Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing and Big Data Analytics (U-CoE AI-VLB) Institut Teknologi Bandung Indonesia Informatics Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

A g-circulant matrix is an n-dimensional matrix Cn,g that can be expressed as Cn,g=[c(gr−s)modn]r,s=0n−1 . This study will investigate the use of g-circulant matrices as weight matrices on transformer neural networks for machine translation. On transformer model tested, the g-circulant weight matrix undergoes multiplication with any vector and involves the DCT-DST algorithm. This algorithm is proposed as a substitute for the commonly used FFT algorithm. For this reason, an orthogonal matrix Un,g was defined by multiplying the Un matrix and appropriate permutation matrix, where Un matrix is a matrix that is spanned by the combination of the real and imaginary parts of eigenvectors of the real 1-circulant matrix. Then we form a Schur decomposition of the g-circulant matrix, composes the Qn,g matrix, and using the DCT-DST to formulate the multiplication of Qn,gUn,g. We discovered that 2-circulant matrices are outperform compared to the other g-circulant matrices and the original model of transformer in terms of accuracy and loss training.

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Short‐term load forecast by plausibility checking of announced demand: An expert‐system approach

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European Transactions on Electrical Power 1993年第5期3卷 353-358页

作者： Markovié, M.L. Fraissler, W.F. Milorad L. Marković (1960) received the Dipl.-Ing. and M.Sc. degrees in electrical engineering from the University of Beognd in 1984 and 1990 respectively. From 1985 to 1991 he was at the Institute “Mihajlo Pupin” Beo-grad up to 1987 in the Operation Research group afterwards in the Power System group. In 1990/91 he spent a year at the Austrian Electricity Board (Verbundgesellschaft VG) in Vienna/Austria on a pilot project for expert-system application for operation scheduling. Since September 1991 he is a staff member of the central dispatching department of the VG. His research interests are application of the operations research methods and expert systems in power system control and operation. (Öster-reichische Elektrizitätswirtschafts-AG (Verbundgesellschaft) Hauptlastverteiler Am Hof 6a A-1010 Wien/Austria T +431/531 13 - 23 68 Fax +431/531 13 - 23 16) Walter F. Fraissler (1961) received the Dip1.-Ing. degree in 1984 and the Doctoral degree in 1988 both in Applied Mathematics from the University of Technology of Vienna/Austria. He started his professional experience 1984 as a research assistant at the Department of Economics of the University of Vienna. 1986/87 he attended the Institute for Advanced Studies in Vienna and in 1987 he joined the Austrian Electricity Board (Verbundgesellschaft) in its Electronic Data Processing department. His main interests are applications of econometric and Operations Research methods including expert system technology. (Österreichische Elektrizitätswirtschafts-AG (Verbundgesellschaft) Daten verarbeitung Am Hof 6a A-1010 Wien/Austria T +431/531 13 - 2948 Fax +431/531 13- 4191)

Short‐Term Load Forecast at Verbundgesellschaft (VG ‐ the Austrian Electricity Board) is presented. As the VG has only regional utilities and large industries as purchasers, classical forecast methods are not appropriate. Much more suitable is a plausibility check of a set of announced purchasers demands, for which an expert system has been developed for one to three days ahead forecasts. It has been realized with the help of a rule‐oriented expert system shell based on Prolog, and contains about 250 rules. Normal days as well as special days are considered. The prototype has been tested at the central dispatcher of the VG since autumn 1990. Results and acceptance by the users are promising. Copyright © 1993 John Wiley & Sons, Ltd.

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ASYNCHRONOUS TRANSFER MODE (ATM) OPERATION VIA SATELLITE - ISSUES, CHALLENGES AND RESOLUTIONS

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第3期12卷 211-222页

作者： CHITRE, DM GOKHALE, DS HENDERSON, T LUNSFORD, JL MATHEWS, N COMSAT Laboratories 22300 Comsat Drive Clarksburg MD 20871 USA. Received his B.Sc. from the University of Bombay India an M.A. in mathematics from the University of Cambridge U.K. and a Ph.D. in physics from the University of Maryland. He is currently an Associate Executive Director of the Network Technology Division at COMSAT Laboratories. He has been involved in research and development activities in ISDN VSAT networks data communications and network systems and architectures. Prior to his current positions Dr. Chitre was a Principal Scientist in the Network Technology Division at COMSAT Laboratories. Dr. Chitre joined COMSAT Laboratories in 1980. He has made major contributions to the analysis and architecture of data communication ISDN and BISDN via satellite. Dr. Chitre directs and participates in the international and national standards activities in ISDN BISDN and data communication as they apply to satellite communication. He was Chairman of the Working Group on Protocols and Network Timing Function of the CCIR/CCITT Joint Ad Hoc Group on ISDN-Satellite Matters during 1990–1992. Currently he is the Chairman of the Working Group on New Technologies in the ITU Intersector Coordinating Group (ICG) on Satellite Matters. Dr. Chitre was a programme manager during 1990 and 1991 on a contract from INTELSAT on systems studies on satellite communications systems architectures for ISDN and broadband ISDN systems. Currently he is the technical manager of the DoD Contract on ATM via satellite demonstration and the programme manager for the INTELSAT contract on analysis and top-level specification of INTELSAT ISDN subnetworks and SDH compatible transport network. Department manager for ISDN/Data Communications in the Network Technology Division of COMSAT Laboratories. His responsibilities include managing the implementation of next generation telecommunication systems for fixed and mobile satellite systems. Current projects include the development of a high data rate gateway switch that provides for the

Key issues regarding the operation of the broadband integrated services digital network (BISDN) via satellite are presented herein. The specific issues, challenges, and their resolutions are detailed. In particular, the impact of error characteristics and propagation delay on the operation of BISDN via satellite is discussed. Solutions are presented for removing adverse effects and providing high-quality service to users of BISDN via satellite.

关键词： ATM BISDN

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An Early Stage researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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