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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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Graph Convolutional Network-Based Repository Recommendation System

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computer Modeling in engineering & sciences 2023年第10期137卷 175-196页

作者： Zhifang Liao Shuyuan Cao Bin Li Shengzong Liu Yan Zhang Song Yu School of Computer Science and Engineering Central South UniversityChangshaChina School of Information Technology and Management Hunan University of Finance and EconomicsChangshaChina School of Engineering and Built Environment Glasgow Caledonian UniversityGlasgowUK

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

关键词： Repository recommendation graph convolutional network open-source software GitHub

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Hybrid Query of Boolean Filter and Vector Similarity Search: Benchmark, Comparison and Direction 5

Hybrid Query of Boolean Filter and Vector Similarity Search:...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Zhu, Zijian Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528409

The growing use of vectors for unstructured data has made efficient hybrid queries-combining boolean filters with vector similarity searches-essential. However, publicly available datasets for evaluating DBMS performance on such queries are limited. To address this, we introduce two datasets: the singletable fungi dataset (STFD) with 295,938 base entities and the multi-table movie dataset (MTMD) with 284,713 base vectors, available at this link. These datasets provide a framework for evaluating DBMS performance on hybrid queries. Our experiments with Milvus, pgvector, ClickHouse, and Elasticsearch show that current DBMSs struggle with hybrid queries, each system exhibiting strengths and weaknesses. QPS varies up to 50 ×, and recall differences reach 55 %. These results underscore the importance of query optimization and index utilization. We outline future research directions, including specialized data structures, algorithms for hybrid queries, techniques for estimating query difficulty and predicate selectivity, and enhanced query optimization strategies, to support the development of more efficient DBMS solutions. © 2025 IEEE.

关键词： Structured Query Language

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Age estimation from facial images based on Gabor feature fusion and the CIASO-SA algorithm

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CAAI Transactions on Intelligence technology 2023年第2期8卷 518-531页

作者： Di Lu Dapeng Wang Kaiyu Zhang Xiangyuan Zeng School of Measurement and Communication Engineering Harbin University of Science and TechnologyHarbinChina Cheriton School of Computer Science University of WaterlooWaterlooOntarioCanada

Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic search algorithm for feature selection is ***,texture features of five scales and eight directions in the face region are extracted by Gabor wavelet *** statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor ***,a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing(CIASO-SA)is presented,which is based on an improved atomic search algorithm and the simulated annealing ***,the CIASO-SA algorithm introduces a chaos mechanism during atomic initialisation,significantly improving the convergence speed and accuracy of the ***,a support vector machine(SVM)is used to get classification results of the age *** verify the performance of the proposed algorithm,face images with three resolutions in the Adience dataset are *** the Gabor real part fusion feature at 48�48 resolution,the average accuracy and 1-off accuracy of age classification exhibit a maximum of 60.4%and 85.9%,*** results prove the superiority of the proposed algorithm over the state-of-the-art methods,which is of great referential value for application to the mobile terminals.

关键词： age estimation atom search algorithm feature selection Gabor feature simulated annealing

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Res-SRNN: Residual Spiking Recurrent Neural Network for Efficient Video Recognition

Res-SRNN: Residual Spiking Recurrent Neural Network for Effi...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Zhang, Ya Luo, Xiaoling Sun, Qian Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

ISBN: (纸本)9798331504755

Action recognition in videos is a critical task in computer vision, with a wide range of applications including security surveillance, behavior analysis, and cooperative control. Despite significant advancements in action recognition accuracy achieved by existing video recognition methods, they commonly suffer from high computational complexity and substantial consumption of memory and computational resources. To address the urgent demand for concise, energy-efficient, and high-performance video recognition methods, we propose a residual spiking recurrent neural network (Res-SRNN). Firstly, we design the residual convolution module based on 2D convolution operations to effectively extract spatial information from input video frames. Moreover, drawing inspiration from the recurrent connections in the visual cortex, we incorporate the microscale spiking mechanism of the brain and put forward the spiking recurrent neural network (SRNN). Our SRNN not only excels at capturing complex temporal dependencies but also conserves computational energy costs. Experimental results demonstrate that our proposed Res-SRNN method significantly outperforms most comparative methods on benchmark datasets, achieving the best balance between parameter efficiency and performance. © 2024 IEEE.

关键词： Recurrent neural networks

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Gannet Optimization Algorithm Enhanced by Quasi-Affine Transformation Algorithm and Its Application in 3D Coverage of Wireless Sensor Network

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 69-87页

作者： Peng, Yun-Feng Chu, Shu-Chuan Wang, Ru-Yu Zhao, Jia Pan, Jeng-Shyang School of Computer Science Northeast Electric Power University Jilin132012 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

The gannet optimization algorithm (GOA) is an effective group intelligence algorithm inspired by the foraging behavior of gannets. Despite its merits, considerable potential exists for enhancing its exploration and convergence capabilities. A gannet optimization algorithm improved by the quasi-affine transformation evolutionary algorithm, restart strategy, and elite selection strategy (QRE-GOA) is proposed in this paper. The algorithm employs the quasi-affine transformation evolutionary algorithm (QUATRE) to matrix the gannet optimization algorithm to search for optimal solutions more accurately. The use of a restart strategy and elite selection can be more effective in preventing the algorithm from falling into local optimal solutions. Comparative experiments were conducted using the CEC2017 benchmark series. A detailed analysis was conducted, we per-formed a detailed analysis to check the accuracy and convergence speed of the algorithm search in different dimensions. Subsequently, a comprehensive analysis was undertaken to assess the accuracy and convergence speed of the algorithm’s search process across various dimensions. The experimental statistical results show that the new algorithm has stronger exploration ability and better convergence ability than the original algorithm. In addition, this paper focuses on process justification so that the experimental results are reliable, trustworthy, and interpretable. Finally, the newly proposed QRE-GOA algorithm is deployed to address the 3D Coverage challenge in Wireless Sensor Networks. Experimental findings unequivocally demonstrate the superior performance of QRE-GOA. These results, underscore its potential for addressing existing limitations in optimization methods. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Evolutionary algorithms

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Multi-task learning andjoint refinement between camera localization and object detection

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Computational Visual Media 2024年第5期10卷 993-1011页

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518052China Qingdao Research Institute of Beihang University Qingdao 266104China School of Computer Science and Technology Shandong UniversityQingdaoChina

Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.

关键词： visual localization object detection joint optimization multi-task learning

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Resource-oriented architecture toward efficient device management for the Internet of Things

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 15929-15941页

作者： Agyemang, Brighter Xu, Yang Sulemana, Nantogma Hu, Haixiao School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

The Internet-of-Things concept has evolved from providing network connectivity for devices in our physical world to composing complex tasks with the representations of these things in service mashups. Since most of these devices (e.g. wireless sensors, tagged objects) are resource-constrained, it is easy to realize that such objects provide basic services individually and thus, motivates the need to aggregate such services for complex tasks. However, the existence of fragmentation in protocols and standards for device management and service enablement has been a challenge for the domain. In this work, we propose a resource-oriented device management and service enablement architecture that addresses this problem. We also present a smart home application scenario to demonstrate the performance and feasibility of our approach. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

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Frontiers of computer science 2023年第3期17卷 91-102页

作者： Mingdi HU Long BAI Jiulun FAN Sirui ZHAO Enhong CHEN School of Communications and Information Engineering&School of Artificial Intelligence Xi’an University of Posts&TelecommunicationsXi’an 710121China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation ***,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual ***,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in *** address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance ***,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth *** former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with ***,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR *** extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail *** Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

关键词： vehicle color recognition benchmark dataset multi-scale feature fusion long-tail distribution improved smooth l1 loss

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A Reverse Auction-Based Incentive Mechanism for Cost-Effective Data Collection in Mobile Crowdsensing 29

A Reverse Auction-Based Incentive Mechanism for Cost-Effecti...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, MingHe Wang, BoTao Liu, Gang Huang, Jing Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Shanghai University School of Computer Engineering and Science Shanghai China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350330717

Incentive mechanisms play a crucial role in mobile crowdsensing between mobile users, third-party platforms, and clients. However, existing mechanisms often do not sufficiently consider users' future potential and past contributions to data sharing when recruiting or retaining them. Moreover, few people consider that the client in this mechanism requires high-quality data collection for training artificial intelligence models. To solve these problems, this paper proposes an innovative mobile crowdsensing incentive mechanism based on reverse auctions. This mechanism not only attracts users who provide high-quality data but also helps the client collect an appropriate amount of cost-effective data for model training. This is achieved by setting prerequisites for the release of tasks by the platform, that is, only after the client's model training effect reaches a certain level of improvement, the platform can release new tasks for data collection. Additionally, when recruiting users to participate in auctions, the platform will selectively recruit based on their potential value to ensure high-quality data collection. In cases where users might consider leaving the platform, measures for retention will be implemented. This will involve offering rewards adjusted proportionally according to their potential and contribution value to encourage them to stay. The comprehensive simulation results demonstrate the effectiveness of this new mechanism, highlighting its superior performance over existing mechanisms. © 2023 IEEE.

关键词： data collection incentive mechanism Mobile crowdsensing reverse auction

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