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Computational science and technology 1

丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Rayner Alfred Yuto Lim Haviluddin Haviluddin Chin Kim On

ISBN: (数字)9789811500589

ISBN: (纸本)9789811500572;9789811500602

This book gathers the proceedings of the Sixth International Conference on Computational science and technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.

关键词： Communications Engineering, Networks Artificial Intelligence Big Data

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare Systems

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Kunyu Xie Lin Zhang Yuan Yang Xiaohe Li Ridha Khedri Zhen Chen M. Jamal Deen Simulation Technology Innovation Center Hangzhou International Innovation Institute of Beihang University Hangzhou 311115 Zhejiang China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenzhen Third People’s Hospital & Second Hospital Affiliated to Southern University of Science and Technology Shenzhen 518112 Guangdong China Dept. of Computing and Software McMaster University Hamilton Ontario Canada L8S 4K1 Department of Electrical and Computer Engineering McMaster University Hamilton ON L8S 4L8 Canada

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based Systems Engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

关键词： Digital Twin Model-based system engineering X Language Medical Cyber-Physical Systems Digital Twin Healthcare Design

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Towards On-The-Fly Code Performance Profiling

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Xing Hu Weixin Lin Zhuang Liu Xin Xia Michael Ling Yuan Wang David Lo School of Software Technology Zhejiang University China College of Computer Science and Technology Zhejiang University China Zhejiang University China Huawei Technologies Canada Canada Huawei Sweden Research Center Sweden School of Computing and Information Systems Singapore Management University Singapore

Improving the performance of software applications is one of the most important tasks in software evolution and maintenance. In the Intel Microarchitecture, CPUs employ pipelining to utilize resources as effectively as possible. Some types of software patterns or algorithms can have implications on the underlying CPU pipelines and result in inefficiencies. Therefore, analyzing how well the CPU’s pipeline(s) are being utilized while running an application is important in software performance analysis. Existing techniques, such as Intel VTune Profiler, usually detect software performance issues from CPU pipeline metrics after the software enters production and during the running time. These techniques require developers to manually analyze monitoring data and perform additional test runs to obtain relevant information about performance problems. It costs a lot of time and human effort for developers to build, deploy, test, execute, and monitor the *** alleviate these problems, we propose a novel approach named PGProf to predict the CPU pipeline before execution and provide the profiling feedback during the development process. PGProf exploits the graph neural networks to learn semantic and structural representations for C functions and then predict the fraction of pipeline slots in each category for them during the development process. Given a code snippet, we fuse different types of code structures, e.g., Abstract Syntax Tree (AST), Data Flow Graph (DFG), and Control Flow Graph (CFG) into one program graph. During offline learning, we first leverage the gated graph neural network to capture representations of C functions. PGProf then automatically estimates the final pipeline values according to the learned semantic and structural features. For online prediction, we predict pipeline metrics with four category values by leveraging the offline trained model. We build our dataset from C projects in GitHub and use Intel VTune profiler to get profiling information

关键词： Program Graphs Performance Profiling CPU utilization Graph Neural Networks

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Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaotong Wu Yuwen Liu Xiaoxiao Chi Rong Jiang Xiaokang Zhou Wajid Rafique Maqbool Khan Alibaba Business School Hangzhou Normal University China College of Computer Science and Technology China University of Petroleum (East China) China Department of Computing Macquarie University Australia Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan Department of Electrical and Software Engineering University of Calgary Canada Software Competence Center Hagenberg Austria

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

关键词： collaborative learning optimal adaption vehicle application data security self-adaptive system

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Cyber Malware 1

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丛书名： Security Informatics and Law Enforcement

1000年

作者： Iman Almomani Leandros A. Maglaras Mohamed Amine Ferrag Nick Ayres

ISBN: (数字)9783031349690

ISBN: (纸本)9783031349683;9783031349713

This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers.

关键词： Communications Engineering, Networks Cybercrime Systems and Data Security Security science and technology

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Benchmarking, Measuring, and Optimizing 1

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丛书名： Lecture Notes in computer science

1000年

作者： Wanling Gao Jianfeng Zhan Geoffrey Fox Xiaoyi Lu Dan Stanzione

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AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic computing Resources

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ACM Transactions on Sensor Networks 1000年

作者： Xin Niu Xuejiao Cao Chen Yu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

关键词： Edge intelligence changing available computing resources adaptive model selection and partition smart city sustainable intelligent services

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End-to-End Explainable Fake News Detection Via Evidence-Claim Variational Causal Inference

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ACM Transactions on Information Systems 1000年

作者： Jinguang Wang Shengsheng Qian Jun Hu Wenxiang Dong Xudong Huang Richang Hong School of Public Security and Emergency Management Anhui University of Science and Technology China and State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science and Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computing National University of Singapore Singapore Institute of Dataspace Hefei Comprehensive National Science Center China School of Computer Science and Information Engineering Hefei University of Technology China

Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection Network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.

关键词： Social media Explainable fake news detection Causal inference

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Innovative Concepts for Autonomic and Agent-Based Systems 1

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丛书名： Lecture Notes in computer science

1000年

作者： Michael G. Hinchey Patricia Rago James L. Rash Christopher A. Rouff Roy Sterritt Walt Truszkowski

ISBN: (数字)9783540692669

ISBN: (纸本)9783540692652

The second WRAC, NASA/IEEE Workshop on Radical Agent Concepts, was held at NASA Goddard Space Flight center, Greenbelt, MD, September 20–22, 2005. The workshop was sponsored by the Information Systems Division of NASA Goddard and IEEE computer Society Technical Committee on Complexity in computing and IEEE Task Force on Autonomous and Autonomic Systems. The workshop also received generous financial support from IBM, without which the workshop would not have been possible. Agent technology, along with autonomous and autonomic computing, has emerged as a major field in computing, and will greatly influence the future development of complex computer-based systems. The area of research is strongly influenced by the autonomic computing initiative as well as by developments in biologically inspired computing, and involves interdisciplinary interaction from those involved in research in social intelligence, psychology, arts, biology, computer science, computer communications and philosophy. This volume includes revised versions of papers presented at the workshop. The workshop was structured so as to allow adequate time for discussion and interaction, to exchange ideas and reflect on the motivations, scientific grounds and practical consequences of the concepts presented. Many of the ideas are truly “radical”, and so authors were given time to revise their papers to reflect further thoughts on the ideas presented and to reflect feedback received at the workshop. We are grateful to Jeff Kephart for a very interesting keynote speech describing IBM’s current and future work in this field, which fit very well with the aims and scope of the workshop.

关键词： Artificial Intelligence computer Communication Networks Software Engineering User Interfaces and Human computer Interaction computer Appl. in Social and Behavioral sciences

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