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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xinlei Wei Yujuan Tan Duo Liu Daitao Wu Yu Wu Xianzhang Chen Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Node failure is one of the most typical issues in distributed storage systems. The classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. The traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%.

关键词： data centers Fault tolerant systems Redundancy Distributed databases Memory Bandwidth Maintenance engineering

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SSF-EDZL Scheduling Algorithm on Heterogeneous Multiprocessors

SSF-EDZL Scheduling Algorithm on Heterogeneous Multiprocesso...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Peng Wu Chengzhuo Han Tao Yan Lu Chen Tianhao Guo Zhi Li Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China College of Physics and Electronic Engineering Shanxi University Taiyuan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the increasing demand for high performance and low power consumption in embedded real-time systems, performance asymmetric multiprocessor architecture is beginning to be applied to embedded real-time systems, because this heterogeneous architecture allows the system to allocate computational resources as needed to satisfy the demands of each application and dynamic workloads. Most of the previous studies on EDZL scheduling algorithms have focused on task prioritization, and they simply assign the highest priority tasks to the fastest processors. In this paper, we choose to assign the highest priority tasks on the slowest processor and propose SSFEDZL (slowest speed fit for earliest deadline first until zero-laxity) scheduling algorithm. We compare SSF-EDZL scheduling algorithm with other scheduling algorithms and the experimental results show that SSF-EDZL is a better scheduling algorithm which has higher processor utilization and better scheduling performance.

关键词： Privacy Program processors Power demand Scheduling algorithms computer architecture Dynamic scheduling Real-time systems

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An Anti-disguise Authentication System Using the First Impression of Avatar in Metaverse

arXiv

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arXiv 2024年

作者： Zhang, Zhenyong Yang, Kedi Tian, Youliang Ma, Jianfeng State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Shandong Jinan250353 China Institute of Cryptography & Data Security GuiZhou University Guiyang550025 China School of Cyber Engineering Xidian University Xi'An710126 China

Metaverse is a vast virtual world parallel to the physical world, where the user acts as an avatar to enjoy various services that break through the temporal and spatial limitations of the physical world. Metaverse allows users to create arbitrary digital appearances as their own avatars by which an adversary may disguise his/her avatar to fraud others. In this paper, we propose an anti-disguise authentication method that draws on the idea of the first impression from the physical world to recognize an old friend. Specifically, the first meeting scenario in the metaverse is stored and recalled to help the authentication between avatars. To prevent the adversary from replacing and forging the first impression, we construct a chameleon-based signcryption mechanism and design a ciphertext authentication protocol to ensure the public verifiability of encrypted identities. The security analysis shows that the proposed signcryption mechanism meets not only the security requirement but also the public verifiability. Besides, the ciphertext authentication protocol has the capability of defending against the replacing and forging attacks on the first impression. Extensive experiments show that the proposed avatar authentication system is able to achieve anti-disguise authentication at a low storage consumption on the blockchain. © 2024, CC BY-NC-ND.

关键词： Authentication Protocol

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujun Zhang Yanqu Chen Jiakai Wang Jin Hu Renshuai Tao Xianglong Liu State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression.

关键词： Accuracy Target recognition Perturbation methods Noise Robustness Acoustics Speech processing Digital audio players Automatic speech recognition Resilience

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Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning 39

Improving Complex Reasoning over Knowledge Graph with Logic-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Tianle Ding, Liang Wan, Guojia Zhan, Yibing Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia JD Explore Academy College of Computing and Data Science Nanyang Technological University 639798 Singapore

ISBN: (纸本)157735897X

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging task. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements (brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Curricula

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Comprehensive evaluation method of driving behavior based on neural network 4

Comprehensive evaluation method of driving behavior based on...

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2021 4th International Conference on computer Information science and Application Technology, CISAT 2021

作者： Li, Jiayao Li, Li Jiao, Ge Zhao, Xianjing He, Yuhong College of Computer Science and Technology Hengyang Normal University Hunan Hengyang China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing University of Education Chongqing China Artificial Intelligence and Big Data College Chongqing College of Electronic Engineering Chongqing China

In recent years, the number of traffic accidents in the world has increased sharply. Reasonable mining of the OBD data generated in the process of vehicle driving will help to improve traffic safety. However, the existing driving behavior scoring models have the following shortcomings: the indexes are not comprehensive enough, and the selection of weights is not objective. In order to solve these shortcomings, this paper proposes 16 driving behavior indexes and related definitions;the neural network is used to construct the model to distribute the weight reasonably;a new comprehensive evaluation model of driving behavior is constructed. The feasibility and efficiency of the model are verified by experiments on real vehicle OBD data sets. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Steel Surface Defect Detection Based on Improved YOLOv8

Steel Surface Defect Detection Based on Improved YOLOv8

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jichen Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling; the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets; the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model.

关键词： Convolution Computational modeling Object detection Feature extraction Knowledge discovery Steel data mining Background noise Defect detection Fuzzy systems

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Research Progress on Optimization-Based Disassembly Sequence Planning

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computer engineering and Applications 2025年第11期61卷 51-66页

作者： Guo, Hongfei Fu, Wenjie Li, Leixiao Lin, Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data Hohhot010080 China College of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Disassembly sequence planning, as the first step to achieve product recycling, plays a decisive role in the quality and efficiency of recycling waste products. This paper addresses the three core issues of modelling methods, solution algorithms and uncertainty handling methods in demolition sequence planning. Firstly, the current mainstream modelling approaches for disassembly sequences and the disassembly scenarios tackled by various modelling approaches are collated and analyzed. Secondly, from the perspective of decision solving, the characteristics of three different solving methods, namely mathematical planning methods, intelligent optimization algorithms and reinforcement learning algorithms, as well as the challenges faced when making optimal decisions, are discussed. And the solving methods of single-objective optimization problems and multi-objective optimization problems in the field of disassembly are systematically analyzed. Thirdly, the strategies for dealing with uncertain disassembly problems in the disassembly process are summarized. The advantages and disadvantages of the different strategies are analyzed. Finally, the current state of research on disassembly sequence planning is summarized, and future research directions are discussed. © 2025 Journal of computer engineering and Applications Beijing Co., Ltd.;science Press. All rights reserved.

关键词： Multiobjective optimization

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Research on Enhanced Perception and Attention-Driven Person Re-Identification

Research on Enhanced Perception and Attention-Driven Person ...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Dong Li Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness.

关键词： Pedestrians Accuracy Attention mechanisms Computational modeling Noise Knowledge discovery Robustness Noise measurement Identification of persons Fuzzy systems

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