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Anomaly Detection of Industrial Control systems Based on Transfer Learning

Tsinghua science and Technology 2021年第6期26卷 821-832页

作者： Weiping Wang Zhaorong Wang Zhanfan Zhou Haixia Deng Weiliang Zhao Chunyang Wang Yongzhen Guo School of Computer and Communication Engineering the Beijing Key Laboratory of Knowledge Engineering for Materials Scienceand the Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingGuangzhou 528399China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China Donlinks School of Economics and Management University of Science and Technology Beijing Beijing 100083China School of Automation Beijing Institute of TechnologyBeijing 100081 China Software Testing Center Beijing 100048China.

Industrial Control systems(ICSs)are the lifeline of a ***,the anomaly detection of ICS traffic is an important *** paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee *** developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to *** utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown ***-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of *** show that the proposed method achieves a high score and solves the time problem associated with deep learning model *** model can give reliable predictions for unknown or differently distributed abnormal data through short-term ***,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.

关键词： anomaly detection transfer learning deep learning Industrial Control System(ICS)

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RSMA-assisted SHAPTINs: secrecy performance under imperfect hardware and channel estimation errors

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Neural Computing and Applications 2024年 1-18页

作者： Feng, Zhou Kefeng, Guo Jian, Cheng Sunder Ali, Khowaja Kapal, Dev Reddy, Gadekallu Thippa Hussam Al, Hamadi School of Information Technology Yancheng Institute of Technology Yancheng224051 China School of Space Information Space Engineering University Beijing101407 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Faculty of Computing Digital and Data Technological University Dublin DublinD07EWV4 Ireland ADAPT Centre and Department of Computer Science Munster Technological University Cork BishopstownT12 P928 Ireland Department of institute of intelligent systems University of Johannesburg Auckland Park Johannesburg2006 South Africa Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Division of Research and Development Lovely Professional University Phagwara144001 India College of Engineering and IT College of Engineering and IT University of Dubai PO Box 14143 Dubai United Arab Emirates

Satellite high aerial platform terrestrial integrated networks have become the hot topic these years, which have been regarded as the major part of the intelligence of things for future networks. During this work, we investigate the secrecy performance for rate-splitting multiple access-assisted satellite high aerial platform terrestrial integrated networks. Besides, imperfect hardware and channel estimation errors are considered in the secrecy networks. Moreover, to enhance the energy utilization efficiency, rate-splitting multiple access scheme is utilized into the considered network, which is prior to that of the non-orthogonal multiple access scheme. What's more, to enhance the satellite transmission, multiple high aerial platforms are used to forward the transmission along with multiple eavesdroppers. In addition, the direct transmission link is not considered in the secrecy networks due to the heavy fading and obstacles. Relied on the former considerations, the exact and asymptotic analysis for the secrecy performance is further obtained to confirm the rightness of the analysis. Finally, some representative Monte Carlo simulations are carried out to validate the obtained results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Asymptotic analysis

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M-MNFT: A Novel Modified (m, n)-Fault Tolerance Approach for Service Migration in Vehicular Edge Computing

M-MNFT: A Novel Modified (m, n)-Fault Tolerance Approach for...

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software Services engineering (SSE) IEEE International Conference on

作者： YaWen Li Xiaoning Sun Yunni Xia Peng Chen Yin Li Qinglan Peng College of Computer Science Chongqing University Chongqing China College of Computer Science Chongqing Normal University Chongqing China School of Computer and Software Engineering Xihua University Chengdu China Guangzhou Institute of Software Application Technology Guangzhou China School of Artificial Intelligence Henan University Zhengzhou China

Vehicle Edge Computing (VEC) is the deployment of applications close to edge servers to provide low latency and highly responsive services to users. However, due to the complexity and dynamics of the VEC environment, it is prone to errors and failures, and the reliability of edge service migration may be compromised if no measures are taken to cope with different levels of failures. To address this issue, this paper proposes an modified (m, n)-fault tolerance strategy (M-MNFT). Unlike the traditional one, which only considers ES failures, M-MNFT additionally selects redundant edge base stations to ensure task reliability during task migration, and takes into account the fact that the relative distance between the request and the base station is as small as possible when the request is sent, so as to avoid the impact of the edge base station failure on the Quality of Service (QoS) during task migration. In addition, we have performed extensive simulations to show that M-MNFT outperforms existing methods in terms of the number of delayed requests, on-time finish rate, and average waiting time.

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AI for social science and social science of AI: A Survey

arXiv

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arXiv 2024年

作者： Xu, Ruoxi Sun, Yingfei Ren, Mengjie Guo, Shiguang Pan, Ruotong Lin, Hongyu Sun, Le Han, Xianpei School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent. © 2024, CC BY-NC-ND.

关键词： Behavioral research

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Weighted cross-integrated fusion network based on knowledge distillation for multi-modal personality recognition

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Applied Intelligence 2025年第10期55卷 1-18页

作者： Bao, Yongtang Liu, Xiang Li, Xiao Wang, Zhihui Qi, Yue School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Management for Law China University of Political Science and Law Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China

Personality recognition is crucial for deeply understanding social relationships. Although significant advancements have been made in personality recognition research in recent years, challenges still need to be addressed, particularly the heterogeneity in cross-modal information sharing. To address this, we propose a framework based on the Weighted Cross-Integrated Fusion Network (WCIF-Net). This framework comprises five modules and integrates three modalities (visual, audio, and text) to fuse multi-modal features for accurate personality recognition. Our proposed Weighted Frame Allocation Module optimizes the quality of input video frames by strategically allocating weight calculations. We also incorporate knowledge distillation and contrastive learning into the network, effectively resolving the heterogeneity problem in cross-modal information sharing. We evaluate our method on the ChaLearn First Impressions V2 and ELEA datasets, comparing it with several state-of-the-art methods using different architectures. The experimental results confirm the functionality of the individual modules and their combinations as designed. Based on two key evaluation metrics (ACC and PCC), our performance surpasses the state-of-the-art networks based on the three modalities. Furthermore, our work demonstrates the significant role that Transformers can play in understanding mental phenomena, indicating that our method has broad applicability in multi-modal affective computing.

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Switching Controller Synthesis for Hybrid systems Against STL Formulas

arXiv

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arXiv 2024年

作者： Su, Han Feng, Shenghua Zhan, Sinong Zhan, Naijun Institute of Software CAS University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Electrical and Computer Engineering Northwestern University United States School of Computer Science Peking University Beijing China

Switching controllers play a pivotal role in directing hybrid systems (HSs) towards the desired objective, embodying a "correct-by-construction" approach to HS design. Identifying these objectives is thus crucial for the synthesis of effective switching controllers. While most of existing works focus on safety and liveness, few of them consider timing constraints. In this paper, we delves into the synthesis of switching controllers for HSs that meet system objectives given by a fragment of STL, which essentially corresponds to a reach-avoid problem with timing constraints. Our approach involves iteratively computing the state sets that can be driven to satisfy the reach-avoid specification with timing constraints. This technique supports to create switching controllers for both constant and non-constant HSs. We validate our method’s soundness, and confirm its relative completeness for a certain subclass of HSs. Experiment results affirms the efficacy of our approach. © 2024, CC BY.

关键词： Hybrid systems

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Semi-Bandit Learning for Monotone Stochastic Optimization*

Semi-Bandit Learning for Monotone Stochastic Optimization*

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Annual IEEE Symposium on Foundations of computer science

作者： Arpit Agarwal Rohan Ghuge Viswanath Nagarajan Computer Science & Engineering Indian Institute of Technology Bombay Mumbai India H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta USA Industrial and Operations Engineering University of Michigan Ann Arbor USA

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation) algorithms if these distributions are unknown, and the algorithm needs to learn them through repeated interactions? In this paper, we resolve this question for a large class of “monotone” stochastic problems, by providing a generic online learning algorithm with $\sqrt{T\log T}$ regret relative to the best approximation algorithm (under known distributions). Importantly, our online algorithm works in a semi-bandit setting, where in each period, the algorithm only observes samples from the random variables that were actually probed. Our frame-work applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora's box, stochastic knapsack, stochastic matchings and stochastic submodular optimization.

关键词： computer science Uncertainty Stochastic processes Approximation algorithms Probability distribution Random variables Optimization

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Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

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computer Modeling in engineering & sciences 2023年第7期136卷 323-345页

作者： Smita Khade Shilpa Gite Sudeep D.Thepade Biswajeet Pradhan Abdullah Alamri Symbiosis International(Deemed University) Symbiosis Institute of TechnologyPune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Computer Engineering Pimpri Chinchwad College of EngineeringPune411044India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School for Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNew SouthWales2007Australia Earth Observation Center Institute of Climate ChangeUniversity Kebangsaan MalaysiaUKMBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from *** systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print *** work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris *** assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and *** predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and *** standard datasets were used in the *** main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature *** fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%***,conduct an extensive experiment on cross datasets for detailed *** results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

关键词： Iris images liveness identification Haar transform machine learning biometric feature formation ensemble model

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Deep Learning-based software engineering: Progress, Challenges, and Opportunities

arXiv

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arXiv 2024年

作者： Chen, Xiangping Hu, Xing Huang, Yuan Jiang, He Ji, Weixing Jiang, Yanjie Jiang, Yanyan Liu, Bo Liu, Hui Li, Xiaochen Lian, Xiaoli Meng, Guozhu Peng, Xin Sun, Hailong Shi, Lin Wang, Bo Wang, Chong Wang, Jiayi Wang, Tiantian Xuan, Jifeng Xia, Xin Yang, Yibiao Yang, Yixin Zhang, Li Zhou, Yuming Zhang, Lu Ministry of Education School of Computer Science Peking University Beijing100871 China School of Journalism and Communication Sun Yat-sen University Guangzhou510275 China School of Software Technology Zhejiang University Hangzhou310058 China School of Software Engineering Sun Yat-sen University Guangzhou510275 China School of Software Dalian University of Technology Dalian116024 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100864 China School of Computer Science Fudan University Shanghai200433 China School of Software Beihang University Beijing100191 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China School of Computer Science Wuhan University Wuhan430072 China Huawei Technologies Hangzhou China

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas, we highlight the major advances achieved by applying deep learning techniques with pointers to the available data

关键词： Life cycle

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort.

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