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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Dey, Prianka Khan, Abhinandan Saha, Goutam Pal, Rajat Kumar Narula Institute of Technology Department of Computer Science and Engineering 81 Nilgunj Road Kolkata700109 India University of Calcutta Department of Computer Science and Engineering Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector-III Saltlake Kolkata700106 India Product Development and Diversification Arp Engineering 147 Nilgunj Road Kolkata700056 India North Eastern Hill University Department of Information Technology Meghalaya Shillong793022 India

ISBN: (纸本)9798350308365

Genes act as the blueprint for regulating all activities of a living system. Genes produce proteins, which in turn, sit on the promoter regions of other genes to regulate their activity. Thus, a gene regulatory network is formed. This network is critical in disclosing the various mysteries in the operations of living systems. Often it is very difficult to find these networks in the Wet Lab. As a result, various computational approaches have been used to reconstruct these networks from gene ex-pression data. The techniques primarily used for this purpose include Bayesian networks, Boolean networks, recurrent neural networks, S-systems, and mutual information based methods. The contemporary literature indicates that these techniques often fail to reliably reconstruct real-life networks. In this paper, we have proposed a new technique based on a modified recurrent neural network strategy that is augmented by mutual information. The proposed methodology has been implemented on an 8-gene network of Escherichia coli and a lO-gene network, which have been extensively used by other researchers. The experimental results indicate that the proposed technique achieves satisfactory results when compared to other such techniques developed by contemporary researchers. © 2024 IEEE.

关键词： Recurrent neural networks

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THE POWER OF DIGITAL PLATFORMS: FACILITATING THE COLLABORATION OF UNDERGRADUATE computer science PROJECT TEAMS 20

THE POWER OF DIGITAL PLATFORMS: FACILITATING THE COLLABORATI...

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20th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2023

作者： Scaturro Heil, Margaret R. North Carolina State University Department of Computer Science Engineering Building 2-1202A 890 Oval Drive RaleighNC27695 United States

A required undergraduate computer science 15-week project course at North Carolina State University, department of computer science, USA, has grown rapidly in 29 years from serving 3 student teams to up to 39 teams. To maintain the integrity of the award-winning program and to facilitate the collaboration of hundreds of students in an academic year, the use of digital platforms to augment the face-to-face experience has become necessary. The use of commercial tools (Slack, Zoom, Google Workspace, and Panopto) are described. Customized digital tools to facilitate a feedback loop for student team assignments and to monitor student participation are also outlined. Balancing face-to-face advising with the creative and consistent use of appropriate digital platforms is a necessity for teaching and monitoring effective student collaboration. © CELDA *** rights reserved.

关键词： Students

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Traffic Sign Recognition System

Traffic Sign Recognition System

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ahmed, Maqsood Ali, Raja Hashim Ali, Nisar Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 321-330页

作者： Namakshenas, Danyal Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam University of Guelph Cyber Science Lab Canada Cyber Foundry GuelphONN1G2W1 Canada Kennesaw State University Decentralized Science Lab College of Computing and Software Engineering MariettaGA30060 United States Brandon University Department of Math and Computer Science ManitobaMBR7A6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Lebanese American University Department of Computer Science and Mathematics Beirut1102 Lebanon

The expansion of Industrial Cyber-Physical Systems (ICPS) has introduced new challenges in security and privacy, highlighting a research gap in effective anomaly detection while preserving data confidentiality. In the ICPS landscape, where vast amounts of sensitive industrial data are exchanged, ensuring privacy is not just a regulatory compliance issue but a critical shield against industrial espionage and cyber threats. Existing solutions often compromise data privacy for enhanced security, leaving a significant void in protecting sensitive information within ICPS networks. Addressing this, our research presents the IP2FL model, an Interpretation-based Privacy-Preserving Federated Learning approach tailored for ICPS. This model combines Additive Homomorphic Encryption (AHE) for privacy with advanced feature selection methods and Shapley Values (SV) for enhanced explainability. The proposed solution mitigates privacy concerns in federated learning, where traditional methods fall short due to computational constraints and lack of interpretability. By integrating AHE, the IP2FL model minimizes computational overhead and ensures data privacy. Our dual feature selection approach optimizes system performance while incorporating SV to provide critical insights into model decisions, advancing the field towards more transparent and understandable AI systems in ICPS. The validation of our model using ICPS-specific datasets demonstrates its effectiveness and potential for practical applications. © 2023 IEEE.

关键词： Embedded systems

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Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network

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computers, Materials & Continua 2023年第12期77卷 3867-3894页

作者： Yuting Zhu Yanfang Fu Yang Ce Pan Deng Jianpeng Zhu Huankun Su China Electronics Technology Group Corporation 20th Research Institute Xi’an710068China School of Computer Science and Engineering Xi’an Technological UniversityXi’an710021China Department of Statistics and Actuarial Science University of WaterlooWaterlooON N2L 3G1Canada

In light of the escalating demand and intricacy of services in contemporary terrestrial,maritime,and aerial combat operations,there is a compelling need for enhanced service quality and efficiency in airborne cluster communication ***-Defined Networking(SDN)proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts,due to its intrinsic ability to flexibly allocate and centrally administer network *** study pivots around the optimization of SDN controller deployment within airborne data link clusters.A collaborative multi-controller architecture predicated on airborne data link clusters is thus *** this architectural framework,the controller deployment issue is reframed as a two-fold problem:subdomain partition-ing and central interaction node *** advocate a subdomain segmentation approach grounded in node value ranking(NDVR)and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm(AFSA).The advanced NDVR-AFSA(Node value ranking-Improved artificial fish swarm algorithm)algorithm makes use of a chaos algorithm for population initialization,boosting population diversity and circumventing premature algorithm *** the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations,the algorithm’s search range adaptability is enhanced,thereby increasing the possibility of obtaining globally optimal solutions,while concurrently augmenting cluster *** simulation results verify the advantages of the NDVR-IAFSA algorithm,achieve a better load balancing effect,improve the reliability of aviation data link cluster,and significantly reduce the average propagation delay and disconnection rate,respectively,by 12.8%and 11.7%.This shows that the optimization scheme has important significance in pra

关键词： Aviation cluster software defined network controller deployment Airborne network data link

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An Overview of Artificial Intelligence Ethics

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 799-819页

作者： Huang, Changwu Zhang, Zeqi Mao, Bifei Yao, Xin Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen518055 China Huawei Technologies Company Ltd. Trustworthiness Theory Research Center Shenzhen518055 China University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom

Artificial intelligence (AI) has profoundly changed and will continue to change our lives. AI is being applied in more and more fields and scenarios such as autonomous driving, medical care, media, finance, industrial robots, and internet services. The widespread application of AI and its deep integration with the economy and society have improved efficiency and produced benefits. At the same time, it will inevitably impact the existing social order and raise ethical concerns. Ethical issues, such as privacy leakage, discrimination, unemployment, and security risks, brought about by AI systems have caused great trouble to people. Therefore, AI ethics, which is a field related to the study of ethical issues in AI, has become not only an important research topic in academia, but also an important topic of common concern for individuals, organizations, countries, and society. This article will give a comprehensive overview of this field by summarizing and analyzing the ethical risks and issues raised by AI, ethical guidelines and principles issued by different organizations, approaches for addressing ethical issues in AI, and methods for evaluating the ethics of AI. Additionally, challenges in implementing ethics in AI and some future perspectives are pointed out. We hope our work will provide a systematic and comprehensive overview of AI ethics for researchers and practitioners in this field, especially the beginners of this research discipline. © 2020 IEEE.

关键词： Industrial robots

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Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

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npj Computational Materials 2024年第1期10卷 2171-2181页

作者： Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WayInnovis#08-03Singapore 138634Republic of Singapore Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore School of Mechanical Sciences Indian Institute of Technology GoaGoa 403401India Institute of Sustainability for Chemicals Energy and Environment(ISCE2)Agency for ScienceTechnology and Research(A*STAR)1 Pesek RoadSingapore 627833Republic of Singapore Department of Mathematics National University of SingaporeSingapore 119077Singapore Institute for Functional Intelligent Materials National University of SingaporeSingapore 117544Singapore School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision *** reach target properties efficiently,these platforms are increasingly paired with intelligent experimental ***,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex ***,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.

关键词： optimization driving constrained

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A Deep Learning Driven Feature Based Steganalysis Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 2213-2225页

作者： Yuchen Li Baohong Ling Donghui Hu Shuli Zheng Guoan Zhang College of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China College of Information Engineering Anhui Broadcasting Movie and Television CollegeHefei230011China Department of Informatics Faculty of Natural&Mathematical SciencesKing’s College LondonLondonWC2R2LSUK

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic *** traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic *** phenomenon is called as steganographic algorithm mismatch in *** resolve this pro-blem,we propose a deep learning driven feature-based *** advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each *** a multi-classiﬁer implemented as lightgbm is used to predict the matching *** results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.

关键词： Image steganalysis algorithm mismatch convolutional neural network JPEG images

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Cybertwin-Assisted Mode Selection in Ultra-Dense LEO Integrated Satellite-Terrestrial Network

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Journal of Communications and Information Networks 2022年第4期7卷 360-374页

作者： Xin Zhang Bo Qian Xiaohan Qin Ting Ma Jiachen Chen Haibo Zhou Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Laboratory Shenzhen 518000China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

Ultra-dense low earth orbit(LEO)integrated satellite-terrestrial network(ULISTN)has become an emerging paradigm to support massive access of Internet of things(IoT)in beyond fifth generation mobile networks(B5G).In ULISTN,there are two communication modes:cellular mode and satellite mode,where IoT users assessing terrestrial small base stations(TSBSs)and terrestrial-satellite terminals(TSTs)***,how to optimize the network performance and guarantee self-interests of the operator and IoT users in ULISTN is a challenging *** this paper,we propose a cybertwin-assisted joint mode selection and dynamic pricing(JMSDP)scheme for effective network management in ULISTN,where cybertwin serves as the intelligent *** JMSDP,the operator determines optimal access prices of TSBSs and TSTs,while each user selects the access mode according to access ***,the operator conducts the Stackelberg game aiming at maximizing average throughput depending on the mode selection results of IoT ***,IoT users as followers adopt the evolutionary game to choose an access mode based on the access prices provided by the *** results show that the proposed JMSDP can improve the average throughput and reduce the delay effectively,comparing with random access(RA)and maximum rate access.

关键词： ULISTN massive IoT cybertwin mode selection dynamic pricing game theory

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