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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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A Review of State-of-Art Methods for Passive Driver Fatigue Detection: Progress and Prospect

A Review of State-of-Art Methods for Passive Driver Fatigue ...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), International Conference on

作者： Dong Wang Pianhui Wu Big Data and Computing Institute Guangdong Baiyun University Guangzhou China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In recent years, there has been a noticeable rise in traffic accidents attributed to driver fatigue, sparking increased interest among researchers. Current research on fatigue driving is broadly categorized into two approaches: active detection and passive detection. Passive detection involves the use of technological tools to unobtrusively monitor physical signs of fatigue. This approach is favored over active detection for several reasons, including its higher precision, enhanced dependability, and independence from the driver's personal judgment. Passive detection has become the predominant focus in the study of driver fatigue monitoring. This paper presents a comprehensive review of the latest advancements in passive driver fatigue detection technologies over the last few years. It highlights key passive detection techniques, thoughtfully assesses the merits and drawbacks of various fatigue monitoring strategies, and concludes by suggesting potential areas for future research in fatigue detection.

关键词： Reviews Fatigue Feature extraction Monitoring Autonomous robots Accidents

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Calligraphy Alphabet Perception Using Artificial Intelligence

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Venkatesh S D. Jeevitha J. Anitha Gnanaselvi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Jeppiaar Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal.

关键词： Training Productivity Accuracy Rectifiers User interfaces Mathematical models Classification algorithms

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SeiAttentionNet: An Epilepsy Detection Model Using Attention-based Graph Convolutional Network

SeiAttentionNet: An Epilepsy Detection Model Using Attention...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yanhao Chen Peng Zheng Shamini Raja Kumaran School of Computing & Data Science Xiamen University Malaysia Sepang Selangor Malaysia School of Electrical Engineering & Artificial Intelligence Xiamen University Malaysia Sepang Selangor Malaysia

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Accurate and timely prediction of epileptic seizures is crucial in preventing unintended harm to patients. To enhance the precision of seizure prediction, a self-supervised attention-based graph convolution network model called SeiAttentionNet has been proposed. This model pre-processes EEG data and transform it into temporal and spatial features, which are inputted into a deep learning model. Electrode-generated connection graphs are used to reveal the underlying brain structure and network. An improved graph convolutional neural network model is then designed, wherein the graph convolutional layers further excavate the temporal and spatial information hidden in the brain area. Moreover, through an embedding's attention mechanism combined with residual network and dropout layers, important node information is screened, enhancing model comprehension and feature optimization while avoiding redundancy and improving learning ability and robustness. Multiple validation and ablation experiments were conducted on the TUH EEG Seizure Corpus (TUSZ). Results showed that the proposed model's accuracy in the seizure multi-class classification task was 12.64% higher than the original graph convolutional model and 7.1% higher than the optimal performance of existing convolutional network models.

关键词： Accuracy Convolution Computational modeling Transforms Brain modeling Electroencephalography data models

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Online Multi-Object Tracking Based On Global Feature Enhancement Network And Historical Feature Complement Iteration

Online Multi-Object Tracking Based On Global Feature Enhance...

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Cognitive computing and Complex data (ICCD), International Conference on the

作者： Zunwang Ke Puping An Gang Wang Yugui Zhang School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Multi-object tracking is an important part of computer vision and plays a crucial role in many real-world application scenarios. The existing integrated tracking framework loses the key features of the target when fusing features and iteratively updating the trajectory apparent features, resulting in tracking instability. In order to solve the above problems, this paper proposes an online multi-object tracking method based on global feature enhancement network and historical feature complement iteration. In this method, a dual global feature fusion unit is used to replace the global feature enhancement network of summation or splicing operation, which is used to extract more discriminative key features and enhance the recognition ability of objects in multiple scenes. In addition, the historical features are used to supplement the iterative mechanism, which introduces random features and average features from the historical appearance features stored in the trajectory as supplementary features to supplement the lack of object features in the current frame. Comprehensive experiments are carried out on MOT15, MOT16, MOT17, and MOT20 datasets, in which the MOTA score of 73.2% is achieved on MOT16 dataset, indicating that the proposed method has good tracking performance. Source code: http://***/Anbping/gfen-hfci

关键词： Computer vision Target tracking Splicing Source coding Computational modeling Feature extraction Trajectory Iterative methods Object tracking Cognitive systems

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Load Prediction in Edge computing Using Deep Auto-Regressive Recurrent Networks

Load Prediction in Edge Computing Using Deep Auto-Regressive...

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IEEE International Conference on Communications (ICC)

作者： Zhanghui Liu Lixian Chen Zheyi Chen Yifan Huang Jie Liang Zhengxin Yu Wang Miao College of Computer and Data Science Fuzhou University China School of Computing and Communications University of Lancaster Lancaster UK School of Engineering Computing and Mathematics University of Plymouth Plymouth UK

Load prediction is an essential technique to improve edge system performance by proactively configuring and allocating system resources. Traditional load prediction methods obtain high prediction when handling loads exhibiting cyclical trend behavior, but they are unable to capturing highly-variable loads in edge computing environments. Existing studies fit prediction models via independent time series and output single-point real-value predictions. However, in practical edge scenarios, it is more valuable to obtain application value by utilizing the probability distribution of future loads rather than directly predicting specific values. To solve these problems, we propose an Edge Load Prediction method empowered by Deep Auto-regressive Recurrent networks (ELP-DAR). The ELP-DAR uses the time-series data of edge loads to train deep auto-regressive recurrent networks, which integrate Long Short-Term Memory (LSTM) into the S2S framework to calculate the parameters of the probability distribution at the next time-point. Therefore, the ELP-DAR can efficiently extract the essential representations of edge loads and learn their complex patterns, and the probability distribution for highly-variable edge loads can be accurately predicted. Extensive simulation experiments are conducted to validate the effectiveness of the proposed ELP-DAR method based on real-world edge load datasets. The results show that the ELP-DAR achieves higher prediction accuracy than other benchmark methods with different prediction lengths.

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

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arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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Optimizing AoI in RIS-Aided Wireless Powered Communication Networks

Optimizing AoI in RIS-Aided Wireless Powered Communication N...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Huimin Hu Qiuyu Yu Haizhou Tang Ruichen Zhang YuanQing Li School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

This study investigates in depth the expected sum age of information (AoI) in a wireless powered communication network (WPCN) incorporating reconfigurable intelligent surfaces (RIS). In this network, a sensor harvests energy from RF signals emitted from the base station (BS) and transmits time-sensitive information to the data center. An optimization problem is formulated to minimize the expected sum AoI by jointly optimizing the phase shift of RIS and scheduling decisions. To address this problem, we develop a solution framework. Specifically, the original problem is decoupled into two sub-problems, i.e., the transmission decision problem and the phase-shift optimization problem. The transmission decision problem is analytically given a feasible solution, and the phase shift optimization problem is solved by the semi-definite relaxation (SDR) method. Simulation results demonstrate that the system's expected sum AoI is significantly enhanced by employing RIS and provide valuable insights into RIS's importance for realizing minimization AoI in WPCNs. Besides, with the increment of the generation probability, the expected sum AoI decreases.

关键词： Wireless communication Wireless sensor networks data centers Simulation RF signals Reconfigurable intelligent surfaces Minimization Information age Communication networks Optimization

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Understanding Graph databases: A Comprehensive Tutorial and Survey

arXiv

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arXiv 2024年

作者： Anuyah, Sydney Bolade, Emmanuel Agbaakin, Oluwatosin Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States Data Science Department Edyah Consulting Anthony Lagos Nigeria

This tutorial is curated as a one-stop shop for understanding graph databases, as it emphasizes the foundations of graph theory and explores practical applications across multiple fields. The paper begins with foundational concepts, and further explains the structure of graphs through nodes and edges, including various types such as undirected, directed, weighted, and unweighted graphs. The tutorial outlines essential graph properties, terminologies, and key algorithms for network analysis, including Dijkstra’s shortest path algorithm and techniques for calculating node centrality and graph connectivity. Graph databases, as discussed in the tutorial, offer advantages over traditional relational databases by enabling efficient management of complex, interconnected data. Overall, this paper discusses prominent graph database systems like Neo4j, Amazon Neptune, and ArangoDB, each with unique features for handling large datasets. There are practical instructions on implementing graph operations using NetworkX and Neo4j cover node and edge creation, attribute assignment, and advanced queries with Cypher. Furthermore, the paper includes common graph visualization techniques using tools such as Plotly and Neo4j Bloom which enhance the usability of graph data, while also focusing on community detection algorithms, such as the Louvain method, which support clustering in large networks. We conclude by providing future directions to researchers who are looking to enter into the world of graphs. © 2024, CC BY.

关键词： Graph databases

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Md Nasiruzzaman Maaruf Ali Iftekhar Salam Mahdi H. Miraz School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science and IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David (UWTSD) Wales UK Mahdi H. Miraz is with the School of Computing and Data Science Xiamen University Malaysia Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University UK Faculty of Computing Engineering and Science University of South Wales UK

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats.

关键词： Multi-factor authentication Education Machine learning Market research Threat assessment Real-time systems Zero Trust Complexity theory Information technology Identity management systems

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