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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Dynamic hidden-variable network models

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Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks Network structure Block models Evolving network models Fitness models

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Increasing Fairness via Combination with Learning Guarantees

arXiv

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arXiv 2023年

作者： Bian, Yijun Zhang, Kun Qiu, Anqi Chen, Nanguang The Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore119276 Singapore School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Department of Biomedical Engineering National University of Singapore Singapore Research Institute National University of Singapore Suzhou China The Department of Biomedical Engineering The Johns Hopkins University BaltimoreMD United States The Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong

The concern about underlying discrimination hidden in machine learning (ML) models is increasing, as ML systems have been widely applied in more and more real-world scenarios and any discrimination hidden in them will directly affect human life. Many techniques have been developed to enhance fairness including commonly-used group fairness measures and several fairness-aware methods combining ensemble learning. However, existing fairness measures can only focus on one aspect-either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even if one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named discriminative risk to reflect both individual and group fairness aspects. Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. The analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Dynamic Gate Drive for SiC Power MOSFETs with Sub-nanosecond Timings

Dynamic Gate Drive for SiC Power MOSFETs with Sub-nanosecond...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Rophina Li Zhaozheng Hou Tiantian Liu Mohamed Elshazly Sut Leung Xingqiang Peng Wai Tung Ng The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada Huawei Digital Energy Technology Co Shenzhen China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China

A dynamic gate driving strategy is proposed to control the turn-off $\boldsymbol{dV}_{\boldsymbol{DS}}/\boldsymbol{dt}$ transients for SiC power MOSFETs. Compared with a conventional gate driver with fixed gate resistance, dynamic gate drive can effectively suppress the VDS overshoot without a significant reduction in switching speed or increase in turn-off loss. In double pulse testing, using a 40 A, 1200 V SiC power MOSFET, the dynamic gate driver exhibits a 78% reduction in turn-off energy loss $(\boldsymbol{E}_{off})$ when compared to a conventional gate driver for a 35% overshoot. Moreover, it is also found that SiC power MOSFETs require much more stringent timing resolution (< 1 ns) for the dynamic gate driving pattern when compared to Si IGBTs with the similar current and voltage ratings to fine tune $\boldsymbol{dV}_{\boldsymbol{DS}}/\boldsymbol{dt}$ . This paper demonstrates and analyzes the advantages of dynamic gate driving for SiC power MOSFETs with precision timing.

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GainNet: Coordinates the Odd Couple of Generative AI and 6G Networks

arXiv

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arXiv 2024年

作者： Chen, Ning Yang, Jie Cheng, Zhipeng Fan, Xuwei Liu, Zhang Huang, Bangzhen Zhao, Yifeng Huang, Lianfen Du, Xiaojiang Guizani, Mohsen The School of Informatics Xiamen University Xiamen361005 China The National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China The School of Future Science and Engineering Soochow University Suzhou215006 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

The rapid expansion of AI-generated content (AIGC) reflects the iteration from assistive AI towards generative AI (GAI) with creativity. Meanwhile, the 6G networks will also evolve from the Internet-of-everything to the Internet-of-intelligence with hybrid heterogeneous network architectures. In the future, the interplay between GAI and the 6G will lead to new opportunities, where GAI can learn the knowledge of personalized data from the massive connected 6G end devices, while GAI’s powerful generation ability can provide advanced network solutions for 6G network and provide 6G end devices with various AIGC services. However, they seem to be an odd couple, due to the contradiction of data and resources. To achieve a better-coordinated interplay between GAI and 6G, the GAI-native networks (GainNet), a GAI-oriented collaborative cloud-edge-end intelligence framework, is proposed in this paper. By deeply integrating GAI with 6G network design, GainNet realizes the positive closed-loop knowledge flow and sustainable-evolution GAI model optimization. On this basis, the GAI-oriented generic resource orchestration mechanism with integrated sensing, communication, and computing (GaiRom-ISCC) is proposed to guarantee the efficient operation of GainNet. Two simple case studies demonstrate the effectiveness and robustness of the proposed schemes. Finally, we envision the key challenges and future directions concerning the interplay between GAI models and 6G networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Queueing networks

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Leveraging Big Data for Effective Healthcare Management with Artificial Intelligence

Leveraging Big Data for Effective Healthcare Management with...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Krati Dubey Chaitali B. Kasar Guravaiah Thulluri Anuradha Yesugad Mehaboob Karishma Dipanjali Padhi Department of Information technology Manipal University Jaipur Jaipur India Department of Electronics and Telecommunication Engineering Dr. D. Y. Patil Institute of Technology Pune Embedded Senior Software Developer SAK informatics Hyderabad Institute of Management & Entrepreneurship Development Bharati Vidyapeeth (Deemed to be University) Pune Department of Computer Application Sir C R R College Eluru Department of Electronics & Telecommunication Engineering Dhole Patil college of Engineering Pune

To improve clinical judgment, resource allocation, and patient outcomes, this study investigates the integration of big data with artificial intelligence (AI) for medical management. Utilizing a descriptive design with an emphasis on secondary data collecting, the study adopts an interpretive perspective and a deductive approach. Data collection, AI algorithm implementation, integration into the system, and infrastructure issues are all included in the technical setup. Accuracy, sensitivity, quickness of processing, and scalability are examples of performance measures, and the influence on patients and healthcare providers is highlighted by user viewpoints and experiences. Informed permission, data security, and legal compliance are all included in the ethical considerations. The results highlight the potential advantages of data-driven medical services, but they also highlight how crucial ethical precautions and user acceptance are. Upcoming studies ought to concentrate on improving algorithms, protecting privacy, and evaluating long-term effects

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Nitin Naik Paul Jenkins Paul Grace Dishita Naik Jingping Song Shaligram Prajapat Durgesh Mishra Longzhi Yang Tossapon Boongoen Natthakan Iam-On School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Department of Computer and Information Sciences Northumbria University United Kingdom Department of Computer Science Aberystwyth University United Kingdom

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks.

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Mask Wearing Classification using CNN 7

Mask Wearing Classification using CNN

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7th International Conference on Advanced informatics: Concepts, Theory and Applications, ICAICTA 2020

作者： Pramita, Maharani Devira Kurniawan, Budi Utama, Nugraha Priya School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia Prosa.ai Computer Vision Department Bandung Indonesia

ISBN: (纸本)9781728180380

Indonesian official team for handling COVID-19 stated that from total 358,659 tested people, 42,762 of them are confirmed to be positive COVID-19, approximately 11.7% from total tested people and continues to increase every day. Therefore, wearing a mask becomes a new habit to adapt new normal life, in order to prevent COVID-19 virus spread in Indonesia. It is important for everyone to wear a mask especially in public places with high intense of human interaction. Public area managers need to monitor and ensure everyone is wearing mask properly. To do a better public places monitoring, this work proposes a deep-learning based image classification model to detect whether a person is wearing a mask or not wearing a mask in real-time condition. Proposed architecture is using Convolutional Neural Network (CNN) to learn from annotated private dataset to match the Indonesian characteristics. Result gives 97% accuracy for classifying face. © 2020 IEEE.

关键词： Convolutional neural networks

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Mitigation Methods of Reducing DDOS Attack Frequency on Blockchain

Mitigation Methods of Reducing DDOS Attack Frequency on Bloc...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： R. Sathees Kumar Phanikanth Chintamaneni Madarapu Naresh Kumar Ankita Joshi Bajila Swathi Joshuva Arockia Dhanraj Department of CSE Narasaraopeta Institute of Technology JNTUK University Narasaraopet Andhra Pradesh Department Of CSE Koneru Lakshmaiah Education Foundation Scientist B National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi National Informatics Centre Ministry of Electronics and Information Technology (MEITY) New Delhi Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai India

A serious risk to genuine organization demands is a distributed denial of service (DDoS) attack. The main DDoS attack was archived in 1996, however, from that point forward, their intricacy and refinement have consistently expanded. A 2 TBps attack on a crucial foundation, including the monetary area, was accounted for in August 2020 during the Coronavirus pandemic. In the accompanying two years, it is anticipated that the number of attacks would twofold, stretching around 15 million. Since its beginning, many relief plans have been made, however, the intricacy is rising, requiring modern arrangements in view of new innovation. A potential and common-sense method for DDoS relief is blockchain. The point of the review is to give an exhaustive examination of the fundamental and intrinsic properties of blockchain, including their decentralization, inside and outer trust lessness, unchanging nature, honesty, obscurity, and evidence, which have demonstrated to be a fantastic contender for battling this destructive cyber threat.

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