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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Zhu, Haojia Ma, Ran Jin, Jiahui Lu, Hongru School of Computer Science and Engineering Southeast University China School of Software Engineering Southeast University China School of Computer Science Nanjing Audit University Nanjing China

ISBN: (纸本)9798331531904

Modeling urban mobility behaviours with micro-scopic traffic flow simulation is now crucial for studying intel-ligent urban decision-making algorithms, such as traffic light control and road congestion charging. However, in urban-scale traffic environments, simulation is computationally intensive. Most existing studies use distributed traffic simulations based on the Bulk Synchronous Parallel (BSP) model, leading to significant time and data transfer costs due to the need for coordination and data synchronization. To address these issues, we propose an enhanced Stale Synchronous Parallel (ESSP) model. Our model reduces the waiting time of the simulation compute nodes, and we design it to control and measure errors during simulation. We also develop a distributed traffic simulator to validate our model, and the results show a performance improvement of 30% to 50% compared to conventional synchronization methods. © 2024 IEEE.

关键词： Traffic congestion

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Traffic sign detection algorithm based on improved YOLOv5 4

Traffic sign detection algorithm based on improved YOLOv5

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4th International Conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Ma, Chi Li, Qinrong Hu, Hui Li, Jingyan Guo, Qiang School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510682528

This paper introduces an enhanced YOLOv5 algorithm tailored for real-world traffic sign detection applications. Through the incorporation of Coordinate Attention after the SPPF module of the YOLOv5 backbone, the YOLOv5 neck pays more attention to key areas in the image and preserves accurate positional information. In response to the fact that traffic signs are mostly small targets, the PAN and FPN in the original algorithm's neck network are upgraded by substituting BiFPN for the previous feature fusion method, improving the algorithm's ability to detect traffic signs of different sizes, specifically targeting improved detection accuracy for small-scale targets. To validate the effectiveness of these modifications, we compared our improved model with other object detection model on the TT100K dataset and conducted ablation experiments. The experiments result revealed that the enhanced algorithm achieved an mAP of 94.2%, surpassing the original YOLOv5 model by 6.9%. The detection speed was 49.2 FPS, meeting the real-time requirements. © 2024 SPIE.

关键词： Traffic signals

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Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

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Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

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computer Modeling in engineering & sciences 2023年第8期136卷 2093-2109页

作者： Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application *** the introduction of end-to-end direct regression methods,the field has entered a new stage of ***,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal *** this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external ***,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding *** call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关键词： Multi-view multi-person pose estimation attention mechanism computer vision

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Video Editing 24

High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Vid...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Luo, Yan Zuo, Zhichao Zhang, Zhao Zhao, Zhongqiu Zhang, Haijun Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798331506681

Text-guided image generative diffusion models achieve fast development on the generation and editing of high-quality images. To extend such success to video editing, some efforts combining image generation with video editing have been made, which however only achieve inferior performance. We attribute it to two challenges: 1) different from the static image generation, it is tricky for dynamic video information to ensure the temporal fidelity of motion consistency across different frames;2) the randomness of the frame generation process makes it hard to continuously retain the similar spatial fidelity for the original detailed features. In this paper, we propose a new high-fidelity diffusion model-based zero-shot text-guided video editing network, called HiFiVEditor, which aims to conduct effective video editing with high fidelity of the original video's detailed and dynamic information. Specifically, we propose a Spatial-Temporal Fidelity Block (STFB) that enables the model to restore the spatial features by enlarging the spatial perceptual field to avoid loss of important information, and capture more dynamic information between different frames by using all frames for preserving temporal consistency to achieve better temporal fidelity. In addition, we introduce Null-Text Embedding to create a soft text embedding to optimize the noise learning process, so that the latent noise can be aligned with the prompt. Furthermore, to tune the video style and render it more realistic, we employ a Prior-Guided Perceptual Loss to constrain the prediction results to avoid deviating from the original video style. Extensive experiments demonstrate the superior video editing capability compared to existing works. © 2024 IEEE.

关键词： Video recording

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Formalization of Android Activity-Fragment Multitasking Mechanism and Static Analysis of Mobile Apps

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Formal Aspects of Computing 2025年第2期37卷 1-86页

作者： He, Jinlong Wu, Zhilin Chen, Taolue Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computing and Mathematical Sciences Birkbeck University of London Northern Ireland London United Kingdom

The multitasking mechanism between activities and fragments plays a fundamental role in the Android operating system, which involves a wide range of features, including launch modes, intent flags, task affinities, and structured activities containing fragments. All of them are being widely used in Android apps, both open source and commercial ones. In this article, we present a formal semantics of the Android multitasking mechanism between activities and fragments, which accommodates all the important features and gives insofar the most comprehensive and accurate formalization. In particular, our semantics is formulated based on multi-stack systems, and fully captures the behavior of task stacks and activity stacks regarding fragments. Based on the semantics, we provide new static analysis algorithms, which are both multi-stack aware and fragment sensitive, thus achieve more precise static analysis for Android apps. We validate our approach by extensive experiments on both open source and commercial Android apps. The results highlight the benefits of the considering the semantics of the multitasking mechanism between activities and fragments in static analysis, and confirm the efficacy of our approach. © 2025 Copyright held by the owner/author(s).

关键词： Android multitasking mechanism activities and fragments formal semantics stack unboundedness static analysis

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Location Enhancement and Multi-template Fusion for Fast Long-Term Tracking 5

Location Enhancement and Multi-template Fusion for Fast Long...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Chen, Shixin Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou510980 China

ISBN: (纸本)9798350366174

Owing to the challenge of target occlusion leading to tracking failure during the target tracking process, achieving efficient and robust tracking of targets under occlusion scenarios has become a focal point of research. In this paper, we addresses the issue of tracking failure caused by the reduction or disappearance of target appearance information in occlusion scenarios. It proposes a study on target tracking under occlusion scenes based on deep learning methodologies. A foundation tracker is constructed that leverages location enhancement and multi-template fusion. Built upon this, graph attention mechanisms and target loss detection mechanisms are introduced. In cases where the target is lost due to occlusion, a re-detection strategy is employed to resume tracking once the target reappears. Experiments conducted on datasets demonstrate that the proposed algorithm can effectively determine whether a target is undergoing occlusion and significantly enhances tracking performance under such conditions. © 2024 IEEE.

关键词： Target tracking

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Theoretical Analysis of an Adaptive Closeness Centrality-Based Algorithm for Dynamic Optimization of Transportation Networks

Theoretical Analysis of an Adaptive Closeness Centrality-Bas...

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2024 International Conference on engineering Management of Communication and Technology, EMCTECH 2024

作者： Mann, Michael Afeka - the Academic College of Engineering School of Software Engineering and Computer Science Tel Aviv Israel

ISBN: (数字)9798331507909

ISBN: (纸本)9798331507909

Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closeness Centrality (ACC) metric and the DynaTrans algorithm, providing formal proofs of correctness, convergence, and efficiency. The analysis employs graph theory, algorithmic complexity theory, and competitive analysis techniques. Findings: We prove that DynaTrans converges to a local optimal state in O(|V|/ϵ) iterations, with each iteration has limited computational and memory requirements. The algorithm achieves solutions within a factor of O(log|V|) from the global optimum, outperforming simple greedy approaches. Practical implications: DynaTrans offers a theoretically sound foundation for real-time traffic management systems, potentially improving urban mobility and reducing congestion. Originality/value: This work introduces a new paradigm for dynamic transportation network optimization, combining adaptive centrality measures with efficient graph algorithms. The rigorous theoretical analysis provides a solid basis for practical implementation and future research in urban traffic management. © 2024 IEEE.

关键词： Traffic congestion

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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