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14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： Ma, Yafei Jiang, Dong Liu, Zicheng Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China

ISBN: (纸本)9798350328233

In this paper, a common-mode voltage (CMV) elimination and neutral point potential balance control algorithm is proposed for the dual three-phase motor (DTPM) driven by neutral point clamped (NPC) parallel three-level inverters. In this paper, the CMV is eliminated by pulse phase shift alignment, and the neutral-point potential (NPP) balance is controlled by zero sequence voltage. At the same time, the PWM signal of the system can be realized by sawtooth carrier, and the algorithm can adapt to DTPM load with arbitrary angle displacement (AD). Finally, simulation results verify the effectiveness and superiority of the proposed CMV elimination algorithm. © 2023 IEEE.

关键词： Electric inverters

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Advancing Evasion: distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th international Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. This paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Exploring Large Language Models for Verilog hardware design generation

Exploring Large Language Models for Verilog hardware design ...

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： D'Hollander, Erik H. Danneels, Ewout Decorte, Karel-Brecht Loobuyck, Senne Vanheule, Ame Van Kets, Ian Stroobandt, Dirk Univ Ghent Dept Elect & Informat Syst Ghent Belgium Univ Ghent Sch Comp Sci & Engn Ghent Belgium

ISBN: (纸本)9798350364613;9798350364606

Deep Learning Large Language Models (LLMs) have the potential to automate and simplify code writing tasks. One of the emerging applications of LLMs is hardware design, where natural language interaction can be used to generate, annotate, and correct code in a Hardware Description Language (HDL), such as Verilog. This work provides an overview of the current state of using LLMs to generate Verilog code, highlighting their capabilities, accuracy, and techniques to improve the design quality. It also reviews the existing benchmarks to evaluate the correctness and quality of generated HDL code, enabling a fair comparison of different models and strategies.

关键词： large language models Verilog AI fine-tuning prompt engineering pass@k metric

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C-Fuzz: Fuzzing with Forced Program Control Flow Jumps 22

C-Fuzz: Fuzzing with Forced Program Control Flow Jumps

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Pan, Zhe Chen, Xu Chen, Liwei Shi, Gang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331509712

Fuzzing is a widely recognized and effective technique for software vulnerability detection. In recent years, significant progress has been made in bypassing complex checks, such as magic values and checksums, and improving code coverage. Increased code coverage improves the chances of discovering vulnerabilities. However, in reality, the code containing vulnerabilities constitutes only a small fraction of the entire complex program space. This means that fuzzing tools still require substantial time and computational resources to traverse the code paths that do not contain any vulnerabilities. To address this issue, we propose C-Fuzz, a novel fuzzing technique. It forcibly controls the jumps of the program's control flow during fuzzing, quickly bypassing complex checks and extensively exploring the program space. During this process, numerous crashes are discovered, and we mark these crash sites as high-risk areas. Subsequently, we use AFLGO to perform directed fuzzing on these previously stored high-risk areas to uncover more software vulnerabilities. We implemented C-Fuzz based on the traditional fuzzing tool AFL and tested it on 10 real-world programs. Experimental results show that C-Fuzz outperforms T-Fuzz and AFL in identifying unique crashes and can quickly identify vulnerability crash sites in real-world programs within 12 hours. © 2024 IEEE.

关键词： Complex networks

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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30th IEEE international Conference on parallel and distributed systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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An Efficient Flow Rule Conflict Comprehensive Detection Scheme for SDN Networks 22

An Efficient Flow Rule Conflict Comprehensive Detection Sche...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Han, Bo Liu, Yuan Zhou, Yongbin Gao, Yiwen Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9798331509712

software-Defined Networking (SDN) has introduced flexibility and efficiency to network management but also faces challenges from flow rule conflicts, including static, dynamic, and dependency conflicts. Existing detection algorithms often focus on a single conflict type, resulting in inefficiencies and high false positive rates. To address these issues, we propose a comprehensive flow rule conflict detection scheme that improves real-time detection of explicit conflicts (both static and dynamic) and reduces false positives in implicit (dependency) conflict detection. Specifically, we present a real-time explicit conflict detection algorithm based on the Protocol-Divided Trie (PDT), which categorizes flow rules by protocol type and uses a prefix tree for rapid matching. Experimental results show that this approach significantly reduces detection times by at least 39.2% and achieves 100% detection accuracy. Additionally, we propose a two-stage detection (TSD) algorithm that combines the precision of path-based detection (PBD) with the efficiency of alias set-based detection (ASD). Our experiments reveal a 51% reduction in false positives compared to ASD and a 48% reduction in detection time compared to PBD, while maintaining equivalent false positive rates. This approach provides a robust solution for conflict detection in SDN, improving network security and resource utilization efficiency. © 2024 IEEE.

关键词： Conflict Detection explicit conflicts implicit conflict software-Defined Networks

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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2023 6th international Conference on software engineering and Computer Science, CSECS 2023

2023 6th International Conference on Software Engineering an...

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6th international Conference on software engineering and Computer Science, CSECS 2023

ISBN: (纸本)9798350306378

The proceedings contain 71 papers. The topics discussed include: multi-class failure prediction for distributed systems based on KPI data and feature selection;accelerating high-precision vulnerability detection in c programs with parallel graph summarization;PointStack: a point cloud processing network with enhanced global feature aggregation;improving visual speech recognition for small-scale datasets via speaker embedding;GNN and encoder integrated model for distributed solar and wind power forecasting;automatic detection method for software requirements text with language processing model;MAFF: a novel MobileNetV3 attention feature fusion network for automatic vehicle classification;and interval-valued Z-number aggregation operators and its application in airbus type selection problem.

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FPGA Design and Implementation of Approximate Radix-8 Booth Multiplier 3

FPGA Design and Implementation of Approximate Radix-8 Booth ...

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3rd IEEE international Conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Orugu, Rangarao Padamata, Subhash Kollati, Yuvaraju Nakka, Lakshmeepathi Nunna, Yashwanth Mamidi, Rathnakar Sai Midhilesh Vishnu Institute Of Technology Electronics And Communication Engineering Bhimavaram India

ISBN: (纸本)9798350318609

In digital systems, multiplication operations are crucial, particularly in applications like image processing. This paper proposes an approximate radix-8 Booth multiplier designed to enhance performance and reduce complexity compared to traditional counterparts. Specifically tailored for signed-unsigned numbers, the proposed multiplier undergoes evaluation via simulation using Xilinx Vivado software with Artix-7 FPGA Board. Leveraging techniques such as modified Booth encoding and parallel partial product generation, it aims to achieve reduced delay and power consumption. The application of this multiplier is demonstrated through integration into Sobel edge detection. Quantitative metrics, including speedup factor and area reduction, are provided to assess performance improvements over existing systems. Furthermore, the proposed design is highly accurate, detecting 98.87% of edges, demonstrating its practicality and usefulness in digital image processing applications. © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Mining Relational Similarity in Social Networks for Enhanced Recommendations 22

Mining Relational Similarity in Social Networks for Enhanced...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Guo, Pengcheng Wang, Qingyang Jiang, Jielin Wu, Siyu Xiang, Haolong Ji, Xinyue Xue, Shengjun Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China Silicon Lake College Suzhou China

ISBN: (纸本)9798331509712

Social perception recommendation systems can effectively alleviate the user cold start problem by leveraging the side information of social networks. However, most social perception recommendation systems treat user relations as independently existing entities for learning, thereby overlooking potential connections between relations. Additionally, as the number of relations increases, it inevitably imposes a significant computational burden on the servers. To address these issues, we propose the Social perception recommendation based on relational clustering(SPRRC). SPRRC conducts relational clustering of social networks and projects knowledge graphs, effectively mining information about the similarity between relations. First, we cluster relationships in item knowledge graphs and social networks through unsupervised learning. After that, we use local weighted smoothing to aggregate the map information of the clustered items and social network virtual subgraphs respectively, and use the attention networks to learn the representation of users and items in the interactive bipartite graph. Finally, a large number of experiments have verified the high accuracy of our method, compared with the latest methods. © 2024 IEEE.

关键词： Knowledge graph

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