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Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems

IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

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Topological Modes Observed in Si Photonics SSH Integrated Circuit

Topological Modes Observed in Si Photonics SSH Integrated Ci...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： R. Nakamura T. Nakama A. Balčytis T. Ozawa Y. Ota S. Iwamoto H. Ito T. Baba Dept of Electrical &Computer Eng. Yokohama National Univ. Hodogava-ku Yokohama Japan Integrated Photonics and Applications Centre (InPAC) School of Engineering RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku Univ. Sendai Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. Kohoku-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e...

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Clustering big data for novel health care system

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar S. P. Maniraj Anindita Das Barshan Santanu Das Hasibul Hasan Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamilnadu Chennai India Dept. of Medicine Dhaka Medical College Hospital Dhaka Bangladesh Dept. of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Dept. of Applied Nutrition and Food Technology Islamic University Bangladesh Faculty of Computer Science and Informatics Amman Arab University Jordan

In research, the relevance of data science in health care is being explored. It has been proposed that the health-care sector benefit from a Nutrition Prescription System. In the proposed model, the Map Reduce, Enhanced K-Mean clustering technique was used. Actual datasets are represented by rectangular rectangles, which contain the health care centre dataset and frequency dataset generated by the map reduction method. Operations in original data flow are represented by the elliptical boxes. Patients will be able to use this technique to access specific and specialized health care institution. As a consequence, consumers will get better services that are tailored to their specific requirements. Patients would have secure access to planned system because of backup facilities. Because diagnosis has no side effects, the concept of nutrition prescription system is better than that of medicine prescription system. In suggested dietary prescription system, patient selects symptoms. The patient is provided information on his nutritional deficits, requirements and food supply. In addition, the patient is provided additional nutritional data stored in an unstructured dataset. The outcome was derived from the data set. In milliseconds, the report also shows the time spent on traditional and contemporary duties.

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-dept. analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Cognitive Inspired Sound-Based Automobile Problem Detection: A Step Toward Xai

SSRN

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SSRN 2024年

作者： Hamad, Abdulsattar Abdullah Nasim, Muhammad Fawad Jaffar, Arfan Khalaf, Osamah Ibrahim Ouahada, Khmaies Hamam, Habib Akram, Sheeraz Siddique, Abubakar Faculty of Computer Science and Information Technology The Superior University Lahore54000 Pakistan Lahore55150 Pakistan University of Samarra College of Education Department of physics Iraq Lahore 54000 Lahore55150 Pakistan Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Baghdad Jadriya Iraq School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Faculty of Engineering Uni de Moncton NBE1A3E9 Canada Hodmas University College Somalia Bridges for Academic Excellence Taleh Area Mogadishu Centre-Ville Tunis Tunisia School of Electrical Engineering University of Johannesburg South Africa Riyadh12571 Saudi Arabia Wellington Institute of Technology Te Pukenga Wellington6022 New Zealand

The automobile has become an essential part of modern human life, and one of the critical factors that determine a vehicle’s value is its reliability. Engine breakdowns are not only hazardous but also financially burdensome. Early detection of engine failure can help save costs and improve the average lifespan of a car. Traditionally, manual inspection of engine sounds by expert mechanics has been used as an effective approach for the early detection of engine problems. There have been recent attempts to develop sound detection-based methods for the automatic diagnosis of engine problems. However, the majority of these techniques are neither robust nor explainable. These systems act like black boxes, making their decision-making process difficult to *** Learning Classifier System, also known as LCS, is a machine learning technique that operates on a set of rules. It has shown promise in producing robust, explainable, and generalizable solutions for many problem domains. The objective of this work is to develop a novel LCS-based system for the automatic detection of engine problems. The developed system’s decision-making process is interpretable, which is a step toward eXplainable artificial intelligence. Using time domain, frequency domain, and time-frequency domain features, the innovative system’s generalizability is assessed. While the developed system’s robustness is evaluated using noisy sound data collected under various normal and abnormal conditions, The experimental results indicate that the novel approach outperformed conventional state-ofthe-art techniques by 2.6% − 6.0% and achieved the highest performance accuracy of 98.6%. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Digital Innovation in Faculty Development: Crafting academic Portfolios for the Next Era

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Procedia computer Science 2024年 246卷 4075-4083页

作者： Hideyuki Kanematsu Akiko Ogawa Takeshi Kogo Risa Kawai Dana M. Barry Nobuyuki Ogawa Shin-nosuke Suzuki Goichi Hagiwara Kayoko Kurita National Institute of Technology (KOSEN) Suzuka College Shiroko-cho Suzuka Mie 510-0294 Dept. Materials & Manufacturing Science Graduate School of Engineering Osaka University 2-1 Yamada-Oka Suita Osaka 565-0871 Japan Faculty of Science Nara Women’s University Uoya Nishimachi Nara 630-8506 Nara Pref. Japan Department of Electrical & Computer Engineering Clarkson University Potsdam/SUNY Canton Canton NY USA National Institute of Technology (KOSEN) Gifu College Kami-makuwa Motosu Gifu 510-0495 Japan National Institute of Technology (KOSEN) 771 Nakakuki Oyama Tochigi 323-0806 Japan Faculty of Human Sciences Kyushu Sangyo University Matsukadai 2-chome Higashi-ku Fukuoka-shi Fukuoka 813-8503 Japan Center for Research and Development in Higher Education the University of Tokyo 7-3-1 Hongo Bunkyo-ku 113-0033m Tokyo Japan

This paper presents one project on integrating digital innovation in faculty development by creating academic portfolios. academic portfolios document the multifaceted achievements of faculty members and help them in their professional progress and self-evaluation. This initiative adopted a systematic approach following the TU guidelines, incorporating lectures, mentoring, and collaborative sessions, and guiding educators to create portfolios that integrate teaching, research, and service activities. The project’s use of digital tools enabled time efficiency, accessibility, and iterative improvement and improved the transparency and scope of faculty performance. The digital platform also facilitated real-time feedback and integration of the portfolio with a wider digital repository, which aligns with environmentally sustainable practices. Participant feedback highlighted increased motivation and a deeper understanding of the interaction between teaching and research. The project demonstrated that the digital portfolio powerfully aligned teachers’ activities with their professional ethos and promoted continuous development and personal growth goals. This approach suggests the potential for transforming faculty development in the academic community.

关键词： academic Portfolio faculty development SAP sheet

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Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

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SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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Impact of artificial intelligence on employees performance in information technology sector

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AIP Conference Proceedings 2023年第1期2587卷

作者： P. Lalitha Kumari Jay Kumar Pandey Karunendra Verma G. Taviti Naidu Ankur Gupta Malik Bader Alazzam Dept. of Computer Science and Engineering Visakha Institute of Engineering & Technology 57th Division Narava Visakhapatnam Andhra Pradesh India Dept. of Electronics and Communication Engineering Shri Ramswaroop Memorial University Dewa Road Barabanki Uttar Pradesh India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Management Dr. L. Bullayya College Visakhapatnam Andhra Pradesh India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

The focus of research is to propose smart approach to evaluate the performance of employees in IT sector. The research made use of artificial intelligence in order to fulfill the objectives. The different attributes that are influencing performance of employees are technical skill, salary, training, working environment, supervision, time schedule. A survey has been conducted where the ranking has been made considering votes of different employees in IT Company. Their person views are obtained considering influencing factors. The data set has been filtered using optimization mechanism and machine learning mechanism has been applied to train the network in order to predict the employee performance considering influencing factors. However there have been several existing researches but those research works have considered limited influencing factor. Moreover there is lack of machine learning approach that could provide suitable solution to build a mechanism that would be capable to design smart model considering influencing factors.

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Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

arXiv

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arXiv 2021年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Nodehi, Issa Mashmool, Amir Zadegan, Zeynab Kiani Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Tamadon, Tahereh Khosravi, Samiyeh Kohnehshari, Mitra Akbari Hassannatajjeloudari, Edris Sharifrazi, Danial Mosavi, Amir Loh, Hui Wen Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Engineering Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering University of Qom Qom Iran Italy Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Engineering Faculty Bu-Ali Sina University Hamedan Iran Department of Nursing School of Nursing and Allied Medical Sciences Maragheh Faculty of Medical Sciences Maragheh Iran Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Faculty of Informatics Obuda University Budapest Hungary School of Science and Technology Singapore University of Social Sciences Singapore Department of Cardiology National Heart Centre Singapore Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals. © 2021, CC BY-SA.

关键词： Deep neural networks

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