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[null,"1.","Record Nr.",null,"UNISA996587859503316"]
[null,null,"Autore",null,"Tari Zahir"]
[null,null,"Titolo",null,"Algorithms and Architectures for Parallel Processing : 23rd International Conference, ICA3PP 2023, Tianjin, China, October 20-22, 2023, Proceedings, Part V"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"981-9708-08-7"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (525 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14491"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part V -- EQFF: An Efficient Query Method Using Feature Fingerprints -- 1 Introduction -- 2 Related Work -- 2.1 Privacy of Bloom Filters -- 2.2 Fuzzy Query -- 3 System Overview -- 4 Construction of Data Feature Fingerprints -- 4.1 Construction and Chunking of Data Feature Fingerprints -- 4.2 Fingerprint Compression Storage Algorithm -- 5 Precise Query Library and Rapid Query -- 5.1 Acceleration Algorithm Based on Inverted Index Library -- 5.2 Precise Query Algorithm Based on Bloom Filter -- 6 Fuzzy Query and Similarity Measurement -- 6.1 Similarity Measurement Based on Counting Bloom Filters -- 6.2 Search Algorithm Acceleration Using Parallel Computing -- 7 Evaluation -- 7.1 Experimental Settings and Dataset -- 7.2 Storage Overhead and Parameter Selection for Feature Fingerprints -- 7.3 Performance for Precise Query -- 7.4 Performance for Fuzzy Query -- 8 Conclusion -- References -- Joint Controller Placement and Flow Assignment in Software-Defined Edge Networks -- 1 Introduction -- 2 Related Work -- 2.1 Assignment Schemes -- 2.2 Placement and Assignment Schemes -- 2.3 Placement and Assignment Schemes in SDN-Enabled Edge Network -- 3 System Model and Problem Formulation -- 3.1 System Model -- 3.2 Flow-Setup Delay Model -- 3.3 Problem Formulation -- 4 Controller Placement and Flow Assignment -- 4.1 Controller Placement -- 4.2 Flow Assignment -- 5 Experiment and Performance Evaluation -- 5.1 Controller Response Time -- 5.2 High Queuing Delay Flows -- 5.3 Folw-Setup Delay Constraint Violated Flows -- 5.4 Algorithm Execution Time -- 6 Conclusion -- References -- Distributed Latency-Efficient "]
[{"$nulls":4},"Beaconing for Multi-channel Asynchronous Duty-Cycled IoT Networks -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Distributed Latency-Efficient Scheduling Algorithm."]
[{"$nulls":4},"4.1 Special Structures for Collision Avoidance -- 4.2 Computing Candidate Node Transmission Schedule -- 4.3 The Distributed Beaconing Schedule Algorithm -- 5 Performance Analysis -- 6 Simulation Results -- 6.1 Performance Comparison in Single-channel Duty-Cycled Scenarios -- 6.2 Performance Analysis in Multi-channel Duty-Cycled Scenarios -- 7 Conclusions -- References -- Improved Task Allocation in Mobile Crowd Sensing Based on Mobility Prediction and Multi-objective Optimization -- 1 Introduction -- 2 Related Work -- 3 System Overview and Problem Formulation -- 3.1 System Overview -- 3.2 Problem Formulation -- 3.3 Hardness Analysis of M3P Problem -- 4 Fuzzy Control System -- 4.1 Fuzzy Set and Membership Function -- 4.2 Fuzzy Inference Engine -- 4.3 COG Defuzzification -- 5 Task Allocation Algorithm -- 5.1 Most Possible First Algorithm -- 5.2 Improved NSGA-II Algorithm -- 6 Evaluation -- 6.1 Dataset -- 6.2 Baselines -- 6.3 Performance of Mobility Prediction -- 6.4 Performance of Task Allocation -- 7 Conclusion -- References -- SMCoEdge: Simultaneous Multi-server Offloading for Collaborative Mobile Edge Computing -- 1 Introduction -- 2 Related Work -- 2.1 Edge-Cloud Collaboration -- 2.2 Edge-Edge Collaboration -- 3 System Model and Problem Formulation -- 3.1 System Model -- 3.2 Problem Formulation -- 4 Proposed Approach -- 4.1 SMCoEdge Framework -- 4.2 Improved DQN for Multi-ES Selection -- 4.3 Closed-Form Solution for Task Allocation -- 4.4 DRL-SMO Algorithm -- 5 Performance Evaluation -- 5.1 Setting -- 5.2 Baselines -- 5.3 Results -- 6 Conclusions -- References -- Image Inpainting Forensics Algorithm Based on Dual-Domain Encoder-Decoder Network -- 1 Introduction -- 2 Related Works -- 2.1 Image Inpainting Forensics -- 2.2 Attention Mechanism -- 3 Proposed Method -- 3.1 S-Encoder -- 3.2 F-Encoder -- 3.3 CMAF -- 3.4 Attention-Gated Decoder Network."]
[{"$nulls":4},"3.5 Loss Function -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Comparing with Previous Methods -- 4.3 Abation Study -- 4.4 Robustness Evaluation -- 5 Conclusions -- References -- Robust Medical Data Sharing System Based on Blockchain and Threshold Rroxy Re-encryption -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Blockchain -- 3.2 Threshold Proxy Re-encryption -- 4 BMDSS Model -- 4.1 System Model -- 4.2 Application -- 5 System Evaluation -- 5.1 Environment -- 5.2 Data Storage Performance -- 5.3 Data Sharing Performance -- 5.4 Security Analysis -- 6 Conclusion -- References -- FRAIM: A Feature Importance-Aware Incentive Mechanism for Vertical Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Federated Learning -- 2.2 Incentive Mechanism of Federated Learning -- 3 System Model and Problem Definition -- 3.1 Vertical Federated Learning -- 3.2 System Model -- 3.3 Problem Definition -- 4 Incentive Mechanism Design of Vertical Federated Learning -- 4.1 Data Synthesis and Feature Importance Estimation -- 4.2 Design of FRAIM -- 4.3 Theoretical Analysis -- 5 Experimental Evaluation -- 5.1 Experiment Setup -- 5.2 Performance on Three Datasets -- 5.3 Performance with Different Budgets -- 6 Conclusion -- References -- A Multi-objective Method for Energy-Efficient UAV Data Collection Communications -- 1 Introduction -- 2 Models and Preliminaries -- 2.1 System Model -- 2.2 Network Model -- 2.3 Energy Consumption Model of the UAV -- 3 Problem Formulation and Analysis -- 3.1 Problem Statement -- 3.2 Optimization Objectives -- 4 Algorithm -- 4.1 Conventional MOGOA -- 4.2 EMOGOA -- 5 Simulation Results and Analysis -- 5.1 Simulation Setups -- 5.2 Simulation Results "]
[{"$nulls":4},"-- 6 Conclusion -- References -- Black-Box Graph Backdoor Defense -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Network Backdoor -- 2.2 Black Box Backdoor Defenses."]
[{"$nulls":4},"3 Methodology -- 3.1 Defense Assumptions and Goals -- 3.2 Key Intuition -- 3.3 BloGBaD -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Evaluation Metrics -- 4.3 GTA Defense -- 4.4 GBA Defense -- 5 Conclusion -- References -- DRL-Based Optimization Algorithm for Wireless Powered IoT Network -- 1 Introduction -- 2 Related Work -- 2.1 Binary Offloading -- 2.2 Partial Offloading -- 3 System Model and Problem Formulation -- 3.1 System Model -- 3.2 Local Computing Mode -- 3.3 Edge Computing Mode -- 3.4 Problem Formulation -- 4 Deep-Reinforcement-Learning-Based Offloading Algorithm -- 4.1 Deep-Reinforcement-Learning-Based Method for Solving Top-Problem -- 4.2 Mixed Optimization Method for Solving Sub-problem -- 5 Numerical Results -- 5.1 Convergence Performance -- 5.2 Influence of Different Parameters on Result -- 5.3 Numerical Evaluation -- 6 Conclusion -- References -- Efficiently Running SpMV on Multi-core DSPs for Banded Matrix -- 1 Introduction -- 2 Background -- 2.1 FT-M7032 Heterogeneous Processor -- 2.2 Sparse Matrix Storage Format -- 2.3 Banded Matrix -- 3 Our Approach -- 3.1 Architecture Analysis -- 3.2 SpMV_Band Design and Implementation -- 4 Performance Results -- 4.1 Experimental Setup -- 4.2 Performance Evaluation -- 5 Related Work -- 6 Conclusion -- References -- SR-KGELS: Social Recommendation Based on Knowledge Graph Embedding Method and Long-Short-Term Representation -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 The Proposed Model -- 4.1 Embedding Layer and Input Data Pre-processing -- 4.2 User Modeling -- 4.3 Item Modeling -- 4.4 Relative Location Difference Modeling -- 4.5 Model Training -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Comparative Results -- 5.3 Ablation Study -- 5.4 Analysis of Parameters -- 6 Conclusion -- References."]
[{"$nulls":4},"CMMR: A Composite Multidimensional Models Robustness Evaluation Framework for Deep Learning -- 1 Introduction -- 2 Attacks, Defense, and Metrics -- 2.1 Attack Methods -- 2.2 Defense Methods -- 2.3 Comparison Metrics -- 3 Composite Multidimensional Model Robust Evaluation Method -- 3.1 Motivation -- 3.2 CMMR Framework -- 4 Experimental Analysis for CMMR -- 4.1 3-Dimension Models Robustness Evaluation. -- 4.2 Evaluating the Robustness of Two Sets of Models by CMMR -- 5 Conclusion -- References -- Efficient Black-Box Adversarial Attacks with Training Surrogate Models Towards Speaker Recognition Systems -- 1 Introduction -- 2 Related Work -- 2.1 White-Box and Gray-Box Attacks -- 2.2 Black-Box Attacks -- 3 Methodology -- 3.1 Problem Description -- 3.2 The Proposed Framework -- 3.3 Surrogate Models -- 3.4 The Training of Surrogate Model -- 3.5 Adversarial Examples Generation -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Transferability Analysis -- 4.3 Frequency Analysis -- 4.4 Performance Comparison -- 5 Discussion of Potential Defenses -- 6 Conclusions and Future Work -- References -- SW-LeNet: Implementation and Optimization of LeNet-1 Algorithm on Sunway Bluelight II Supercomputer -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network -- 2.2 The Works Based on the Sunway Supercomputers -- 3 The Sunway Bluelight II Supercomputer and LeNet-1 -- 3.1 The Architecture of the Sunway Bluelight II Supercomputer -- 3.2 The Architecture of LeNet-1 -- 3.3 Serial Execution Mode of LeNet-1 -- 4 SW-LeNet - Parallel Optimization of LeNet-1 on the Sunway Bluelight II Supercomputer -- 4.1 Thread-Level Optimization -- 4.2 Process-Level Optimization -- 5 Evaluation -- 5.1 The Performance of Thread-Level Optimization -- 5.2 The Performance "]
[{"$nulls":4},"of Process-Level Optimization -- 5.3 The Performance of SW-LeNet -- 5.4 Scaling -- 6 Conclusion -- References."]
[{"$nulls":4},"Multi-label Detection Method for Smart Contract Vulnerabilities Based on Expert Knowledge and Pre-training Technology."]