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Record Nr. |
UNINA9910841857303321 |
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Autore |
Tari Zahir |
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Titolo |
Algorithms and Architectures for Parallel Processing : 23rd International Conference, ICA3PP 2023, Tianjin, China, October 20-22, 2023, Proceedings, Part IV |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (508 pages) |
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Collana |
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Lecture Notes in Computer Science Series ; ; v.14490 |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part IV -- CSDSE: Apply Cooperative Search to Solve the Exploration-Exploitation Dilemma of Design Space Exploration -- 1 Introduction -- 2 Background and Motivation -- 2.1 Design Space of Deep Neural Network Accelerator -- 2.2 Design Space Exploration Methods -- 2.3 Challenge of Exploration-Exploitation Dilemma -- 3 CSDSE Method -- 3.1 Profile of CSDSE -- 3.2 Definition of RL Framework -- 3.3 Cooperative Search and Heterogeneous Agents -- 3.4 Weighted Compact Buffer -- 4 Experiments and Analyses -- 4.1 Experiment Setup -- 4.2 Experiment Result and Analysis -- 5 Related Works -- 6 Conclusion -- References -- Differential Privacy in Federated Dynamic Gradient Clipping Based on Gradient Norm -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Threat Model -- 3.2 Gradient Cropping -- 4 Differential Privacy in Federated Dynamic Gradient Clipping Based on Gradient Norm -- 4.1 Overall Framework -- 4.2 Relationship Between Gradient Variation and Clipped Threshold -- 4.3 Gradient Attenuation Clipped Threshold Based on Gradient Norm -- 4.4 Convergence Analysis -- 4.5 Algorithm -- 5 Performance Evaluation -- 5.1 Experimental Setup -- 5.2 Performance of Privacy Protection -- 5.3 Global Model Availability -- 5.4 Universality of Clipped Thresholds -- 6 Conclusions -- References -- SolGPT: A GPT-Based Static Vulnerability Detection Model for |
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Enhancing Smart Contract Security -- 1 Introduction -- 2 Problem Formulation -- 3 Related Work -- 3.1 Deep Learning-Based Approaches for Smart Contract Vulnerability Detection -- 3.2 GPT Models -- 4 SolGPT: Solidity Adapative GPT -- 4.1 SolTokenizer -- 4.2 Solidity Adaptive Pre-training -- 4.3 Vulnerability Detection Fine-Tunning -- 5 Experiment and Analysis -- 5.1 Comparisons with Traditional Approaches -- 5.2 Comparisons with Deep Learning Approaches. |
5.3 Ablation Studies -- 6 Conclusion -- References -- Neuron Pruning-Based Federated Learning for Communication-Efficient Distributed Training -- 1 Introduction -- 2 Related Work -- 2.1 FL with Edge Computing -- 2.2 Communication Overhead Reduction in FL -- 3 Algorithm Description -- 3.1 Basic Model Selection -- 3.2 Model Pruning -- 4 Simulation Results and Analysis -- 4.1 Experimental Setting -- 4.2 Performance Analysis -- 4.3 Network Traffic Classification -- 5 Conclusion and Future Work -- References -- A Novel Network Topology Sensing Method for Network Security Situation Awareness -- 1 Introduction -- 2 Preliminaries and Related Works -- 2.1 Network Security Situation Awareness (NSSA) -- 2.2 Group Decision Making (GDM) -- 3 Network Topology Sensing Method for NSSA -- 3.1 Overview -- 3.2 Agent Model -- 3.3 Group Decision Making Based on Trusted Agents -- 4 Experimental Studies and Analysis -- 4.1 Prerequisites and Problem Descriptions -- 4.2 Experimental Process and Numerical Example -- 4.3 Comparisons and Discussions -- 5 Conclusions -- References -- Distributed Task Offloading Method Based on Federated Reinforcement Learning in Vehicular Networks with Incomplete Information -- 1 Introduction -- 2 Related Work -- 3 System Model -- 3.1 Local Computing -- 3.2 Edge Computing -- 4 Problem Formulation -- 5 Algorithm Design -- 5.1 Deep Q-Network -- 5.2 State Space -- 5.3 Action Space -- 5.4 Reward Function -- 5.5 Communication Mask -- 5.6 FDQN Algorithm -- 6 Performance Evaluation -- 6.1 Simulation Setup -- 6.2 Evaluation Results -- 7 Conclusion -- References -- Carbon Trading Based on Consortium Chain: Building, Modeling, and Analysis -- 1 Introduction -- 2 DC-Chain for Carbon Trading -- 2.1 Role Assignment -- 2.2 DC-Chain Construction and Smart Contract Design -- 3 Modeling Blockchain Performance -- 4 Analytical and Experimental Results. |
4.1 Transaction Confirmation Time -- 4.2 Block Size -- 5 Conclusion -- References -- CAST: An Intricate-Scene Aware Adaptive Bitrate Approach for Video Streaming via Parallel Training -- 1 Introduction -- 2 Background and Motivation -- 2.1 ABR Algorithms -- 2.2 Scene Complexity -- 3 Design -- 3.1 CAST Overview -- 3.2 Neural Network Structure -- 3.3 Implementation -- 4 Evaluation -- 4.1 Methodology -- 4.2 Comparison with Existing ABR Algorithms -- 4.3 CAST vs. Existing ABR Approaches -- 4.4 Performance Under Different Network Environments -- 4.5 Performance Under Different Player Settings -- 4.6 Subjective Tests from Real Users -- 5 Conclusions -- References -- A Hybrid Active and Passive Cache Method Based on Deep Learning in Edge Computing -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 Proposed Methodology -- 4.1 Feature Extraction from Four Modalities -- 4.2 Multimodal Prediction Model -- 5 Experiment Setup -- 5.1 Dataset Setup -- 5.2 Evaluating Indicator -- 5.3 Baselines -- 6 Cached Updates -- 7 Evaluation Results -- 7.1 Simulation Setup -- 7.2 Evaluation Results -- 8 Conclusion -- References -- We Will Find You: An Edge-Based Multi-UAV Multi-Recipient Identification Method in Smart Delivery Services -- 1 Introduction -- 2 Related Work -- 3 Framework and Algorithms -- 3.1 Framework -- 3.2 Algorithms -- 4 Evaluation -- 4.1 Case Study -- 4.2 Experiments -- 5 Conclusions and |
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Future Work -- References -- Multi-objective Optimization for Joint Handover Decision and Computation Offloading in Integrated Communications and Computing 6G Networks -- 1 Introduction -- 2 Related Work -- 3 System Model and Problem Formulations -- 3.1 System Model -- 3.2 Communication Model -- 3.3 Computation Model -- 3.4 Problem Formulation -- 4 Joint Handover Decision and Computation Offloading Method Based on MOO -- 4.1 The Flowchart of Mobility Management in CCN. |
4.2 Trigger Mechanisms of Handover and Offloading -- 4.3 Joint Decision Method Based on MOO -- 4.4 The Convergence Analysis of JD-MOO Method -- 5 Performance Evaluation -- 5.1 Experiments Setting -- 5.2 Performance of Handover Decision -- 5.3 Performance of Computation Offloading -- 5.4 Analysis of Services Satisfaction Degree -- 5.5 Analysis of Running Time and Convergence -- 6 Conclusion -- References -- A Joint Optimization Scheme in Heterogeneous UAV-Assisted MEC -- 1 Introduction -- 2 Related Work -- 2.1 MEC -- 2.2 UAV Scheduling -- 2.3 Task Allocation -- 3 System Model -- 3.1 Problem Statement -- 3.2 System Overview -- 3.3 UAV Scheduling Model -- 3.4 Task Execution Model -- 3.5 UAV Hovering Model -- 3.6 Problem Formulation -- 4 Proposed Approach -- 4.1 UAV Scheduling Algorithm -- 4.2 Task Allocation Algorithm -- 5 Performance Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Conclusion -- References -- Multi-UAV Collaborative Face Recognition for Goods Receiver in Edge-Based Smart Delivery Services -- 1 Introduction -- 2 Framework Design and Workflow -- 2.1 Framework Overview -- 2.2 Framework Execution Process -- 3 Related Technologies and Algorithms -- 3.1 Face Detection -- 3.2 Face Fusion -- 3.3 Face Recognition -- 4 Experiments and Evaluation -- 4.1 Case Studies -- 4.2 Evaluation -- 5 Conclusions -- References -- Distributed Generative Adversarial Networks for Fuzzy Portfolio Optimization -- 1 Introduction -- 2 Generative Adversarial Networks Architecture -- 2.1 Theoretical Background -- 2.2 Architecture -- 2.3 Generator -- 2.4 Discriminator -- 2.5 Adversarial Training -- 3 Generative Adversarial Networks Architecture -- 4 Experimental Results -- 4.1 Experimental Setting -- 4.2 Experimental Results -- 4.3 Experimental Evaluation -- 5 Parallel Fuzzy Portfolio Optimization -- 5.1 Fuzzy Mean-CVaR Portfolio Model. |
5.2 Optimization Algorithms -- 5.3 Parallelization and Results -- 6 Conclusion -- References -- A Novel Transaction Processing Model for Sharded Blockchain -- 1 Introduction -- 2 Related Work -- 2.1 Blockchain Sharding Protocols -- 2.2 Transaction Processing -- 3 Proposed Model -- 3.1 Overview of Model -- 3.2 Threat Model -- 4 Transaction Admission Control Algorithm -- 4.1 Problem Formulation -- 4.2 Algorithm Design -- 5 Transaction Verification Design -- 5.1 Verification Based on TSS -- 5.2 Verification Peers Selection -- 5.3 Transaction Dependency Handling -- 6 Experiment and Evaluation -- 6.1 Implementation -- 6.2 Performance Evaluation -- 7 Conclusion -- References -- Computation Offloading Based on Deep Reinforcement Learning for UAV-MEC Network -- 1 Introduction -- 2 System Model and Problem Description -- 2.1 System Model -- 2.2 Communication Model -- 2.3 Task Calculation Model -- 2.4 Problem Formulation -- 3 Proposed Scheme -- 3.1 SDQN Algorithm -- 3.2 Reinforcement Learning Modeling -- 3.3 Training and Testing -- 4 Simulation Results -- 4.1 Simulation Setting -- 4.2 Simulation Results and Discussions -- 5 Conclusion -- References -- Privacy-Aware Scheduling Heuristic Based on Priority in Edge Environment -- 1 Introduction -- 2 Related Work -- 3 Problem Modeling -- 3.1 Application Model -- 3.2 Resource and Processing Time Model -- 4 Method -- 4.1 Task Selection Phase -- |
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4.2 Processor Selection Phase -- 5 Evaluation -- 5.1 Settings -- 5.2 The Performance Comparison Experiment for Privacy Application -- 5.3 The Performance Comparison Experiment for General Application -- 6 Conclusion -- References -- A Novel Multi-objective Evolutionary Algorithm Hybrid Simulated Annealing Concept for Recommendation Systems -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 3.1 Multi-objective Evolutionary Algorithm -- 4 The Proposed Model. |
4.1 Objective Functions. |
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Record Nr. |
UNINA9910411937003321 |
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Titolo |
Advances in Human Factors in Cybersecurity : AHFE 2020 Virtual Conference on Human Factors in Cybersecurity, July 16–20, 2020, USA / / edited by Isabella Corradini, Enrico Nardelli, Tareq Ahram |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (X, 142 p. 22 illus., 19 illus. in color.) |
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Collana |
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Advances in Intelligent Systems and Computing, , 2194-5357 ; ; 1219 |
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Disciplina |
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Soggetti |
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System safety |
Psychology |
Personnel management |
User interfaces (Computer systems) |
Security Science and Technology |
Psychology, general |
Human Resource Management |
User Interfaces and Human Computer Interaction |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book reports on the latest research and developments in the field of human factors in cybersecurity. It analyzes how the human vulnerabilities can be exploited by cybercriminals and proposes |
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methods and tools to increase cybersecurity awareness. The chapters cover the social, economic and behavioral aspects of the cyberspace, providing a comprehensive perspective to manage cybersecurity risks. By gathering the proceedings of the AHFE Virtual Conference on Human Factors Cybersecurity, held on July 16–20, 2020, this book offers a timely perspective of key psychological and organizational factors influencing cybersecurity, reporting on technical tools, training methods and personnel management strategies that should enable achieving a holistic cyber protection for both individuals and organizations. By combining concepts and methods of engineering, education, computer science and psychology, it offers an inspiring guide for researchers and professionals, as well as decision-makers, working at the interfaces of those fields. . |
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