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Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

Journal of Social Computing 2022年第4期3卷 287-302页

作者： Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen the Shanghai Key Lab of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200438China the Faculty of Social Sciences Tampere UniversityTampere 33100Finland School of Information Science and Technology Fudan UniversityShanghai 200438China Shanghai Artificial Intelligence Laboratory Shanghai 200232China. School of Social Development and Public Policy Fudan UniversityShanghai 200433China. Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China.

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social *** this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography *** have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research *** findings will be helpful for researchers and practitioners working in relevant fields.

关键词： social computing Digital Bibliography and Library Project(DBLP) bibliometric evolution visualization

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature Information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

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International Conference on computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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Joint Task Scheduling and Container Image Caching in Edge Computing

Joint Task Scheduling and Container Image Caching in Edge Co...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Fangyi Mou Zhiqing Tang Jiong Lou Jianxiong Guo Wenhua Wang Tian Wang Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Institute of Artificial Intelligence and Future Networks Beijing Normal University China Yunnan Key Laboratory of Software Engineering Kunming Yunnan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Processor scheduling Computational modeling Markov decision processes Image edge detection Containers Dynamic scheduling Delays

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Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative networks

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IEEE Transactions on Communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Semantic Communication-Aware End-to-End Routing in Large-Scale LEO Satellite networks

Semantic Communication-Aware End-to-End Routing in Large-Sca...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Binquan Guo Zehui Xiong Bo Wang Tony Q. S. Quek Zhu Han State Key Laboratory of Integrated Service Networks Xidian University Xi’an P. R. China Tianjin Artificial Intelligence Innovation Center (TAIIC) Tianjin P. R. China Pillar of ISTD Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Enhanced by inter-satellite links, large-scale satellite networks (SNs) hold promise to deliver low-latency services globally. However, given the scarcity of available spectrum and the capacity limitations of Shannon’s classic information theory, supporting the ever-growing multimedia communication services poses a challenge for SNs. A promising direction is to employ semantic communication (SC), which utilizes artificial intelligence (AI) to extract and transmit the "meaning" of raw data, thereby reducing bandwidth consumption. We identify that SC requires the raw data to be transmitted to a satellite equipped with the AI encoder supporting the same knowledge base for successful decoding at the destination. This advanced functionality renders traditional routing schemes such as contact graph routing (CGR) not seamlessly suitable. Hence, we develop the SC-aware routing (SCR) strategy aimed at optimizing SC’s delay and bandwidth consumption. Firstly, we define the problem using integer linear programming (ILP) and observe its intractability in practice. Then, we use the temporal graph to represent diverse knowledge bases, semantic encoders and transmission resources of SNs, and design SCR based on it. Simulations conducted on the Starlink constellation show that SCR runs much faster than the ILP solver. Additionally, we verify that SC can bring significant performance gains in terms of higher data throughput and lower average end-to-end delay when compared to CGR.

关键词： Satellites Spectral efficiency Knowledge based systems Bandwidth Performance gain Routing Throughput Polynomials Delays Multimedia communication

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Enabling Real-time Computing and Transmission Services in Large-Scale LEO Satellite networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Binquan Zhang, Zhou Atapattu, Saman Pan, Miao Yan, Ye Xiong, Zehui Li, Hongyan Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Tianjin300457 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Hunan University College of Computer Science and Electronic Engineering Changsha410012 China University School of Engineering MelbourneVIC3000 Australia University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Tianjin Artificial Intelligence Innovation Center Tianjin300387 China

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|L⫟| . (4 |V⫟| + 4∣ L⫟|)2|L∣) in the worst case, which grows rapidly with the growth of network sizes. Here, |V⫟| and |L⫟| denote the number of nodes and links, respectively, within the time window {\tau }. Resorting to the designed k-shortest path-based method yields a worst-case complexity of O(|V⫟|! . |V⫟|3). While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|L⫟| + (2\log |V⫟|+1) |V⫟|). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy. © 1967-2012 IEEE.

关键词： Satellite links

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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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Identification of Weather Phenomena Based on Lightweight Convolutional Neural networks

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computers, Materials & Continua 2020年第9期64卷 2043-2055页

作者： Congcong Wang Pengyu Liu Kebin Jia Xiaowei Jia Yaoyao Li Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing100124China Department of Computer Science and Engineering University of MinnesotaTwin CitiesUSA

Weather phenomenon recognition plays an important role in the field of ***,weather radars and weathers sensor have been widely used for weather ***,given the high cost in deploying and maintaining the devices,it is difficult to apply them to intensive weather phenomenon ***,advanced machine learning models such as Convolutional Neural networks(CNNs)have shown a lot of promise in meteorology,but these models also require intensive computation and large memory,which make it difficult to use them in *** practice,lightweight models are often used to solve such ***,lightweight models often result in significant performance *** this end,after taking a deep dive into a large number of lightweight models and summarizing their shortcomings,we propose a novel lightweight CNNs model which is constructed based on new building *** experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory *** memory reduction is even better than that of existing lightweight models.

关键词： Deep learning convolution neural networks lightweight models weather identification

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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

作者： Peng Xue Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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