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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness

arXiv

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arXiv 2023年

作者： Kang, Jiawen Wen, Jinbo Ye, Dongdong Lai, Bingkun Wu, Tianhao Xiong, Zehui Nie, Jiangtian Niyato, Dusit Zhang, Yang Xie, Shengli The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a cross-chain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical cross-chain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a user-centric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Comparative Analysis of Two Implementation Methods of the Equivalents Generator in the Code Converters 8

Comparative Analysis of Two Implementation Methods of the Eq...

引用

8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Makarenko, Anna Rakhlis, Dariia Kavun, Sergii Simon Kuznets Kharkiv National University of Economics Department of Computer Systems And Technologies Kharkiv Ukraine National University of Radioelectronics Kharkiv Design Automation Department Kharkiv Kharkiv Ukraine V. N. Karazin Kharkiv National University Kyiv Computer Information Systems And Technologies Department Kharkiv Ukraine

ISBN: (纸本)9781665406826

The algorithm of functioning tables' formation of the basic non-standard node (equivalents generator) by the method of equivalents accumulation was been considered in the code converters. Formulas for lines amount in the functioning table of the equivalent's generator were been obtained. In comparison with the serial strategy, the proposed author's method allows to reduce the amount of the conversion cycles of the two-steps parallel code converters from 7 to 5 steps (increase the performance at 28,57%), and for the three-step parallel code converters-from 4 to 3 steps (increase the performance at 25%). The conducted researches allow accelerating a stage of a system design of the code converters with the parallel strategy of the step usage, increasing code converters performance and reducing hardware expenses of the construction of the main non-standard node of the equivalents generator and code converters in generally. An advantage by the method of equivalents accumulation of code converters in comparison with other methods is a high calculation performance and the possibility of changing in the wide boundaries of the ratio between speed and hardware expenses. © 2021 IEEE.

关键词： Algorithm Code converters Comparative analysis Equivalents generator Implementation method

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SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

arXiv

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arXiv 2024年

作者： Xu, Rongtao Wang, Changwei Zhang, Jiguang Xu, Shibiao Meng, Weiliang Zhang, Xiaopeng The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CASIA Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer’s global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future. Our code is available at https://***/RongtaoXu/SkinFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Inverse PageRank-Based Fault Propagation Analysis of Production Lines Under Multiple Uncertain Performance Indicators

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IFAC-PapersOnLine 2024年第4期58卷 300-305页

作者： Shoujin Huang Binda Huang Bin Jiang Ningyun Lu Yajie Ma Ronghua Li Wei Du Jie Cao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China AVIC Jincheng Nanjing Engineering Institute of Aircraft Systems Nanjing 211100 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China

In production lines, quality defects and safety incidents often originate from the propagation of faults resulting from the coupling of multiple factors. After fault propagation, multiple performance indicators on coupling devices are influenced, such as reduction of workpiece quantity, and decrease of processing time. However, the variation of indicators cannot definitively confirm the occurrence of fault propagation, it only provides uncertain information. This credibly uncertain information from various indicators can be conflict, leading to difficulties in analyzing fault propagation issues. To this end, an inverse PageRank-based method is developed for fault propagation analysis under multiple uncertain performance indicators. To fuse multiple uncertain indicators, the Dempster-Shafer evidence theory is adopted. Note that a piece of equipment is harmful if the failure generated from it can spread to other important equipment, which is in line with the idea of inverse PageRank. To assess the fault propagation risks, the inverse PageRank algorithm is used resorting to the fusion information. Subsequently, the fault propagation path can be identified by means of the obtained propagation risks. Finally, the method is validated on a real impeller production line.

关键词： Fault propagation production line performance indicators inverse PageRank algorithm

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Lightweight Wireless Sensing Through RIS and Inverse Semantic Communications

Lightweight Wireless Sensing Through RIS and Inverse Semanti...

引用

IEEE Conference on Wireless Communications and Networking

作者： Hongyang Du Jiacheng Wang Dusit Niyato Jiawen Kang Zehui Xiong Junshan Zhang Xuemin Sherman Shen The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of California Davis USA The Department of Electrical and Computer Engineering University of Waterloo Canada

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.

关键词： Wireless communication Wireless sensor networks Semantics Self-supervised learning Encoding Sensors Decoding

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IRS-Assisted Covert Communication via Joint Prior Probability and Noise Power Design

IRS-Assisted Covert Communication via Joint Prior Probabilit...

引用

IEEE Conference on Wireless Communications and Networking

作者： Chao Wang Zehui Xiong Meng Zheng Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian P. R. China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Shenyang Institute of Automation Chinese Academy of Sciences Shenyang P. R. China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Wireless communications are susceptible to eaves-dropping, and intelligent reflecting surface (IRS) as a relay capable of reconfiguring the propagation environment to extend the range of covert communication. In this paper, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Furthermore, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. We aim to maximize expected error detection probability of warden subject to the covertness constraint. To this end, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

关键词： Wireless communication Transmitters Noise Receivers Full-duplex system Probability Power system reliability

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Predicting Software Functional Size Using Natural Language Processing: An Exploratory Case Study

Predicting Software Functional Size Using Natural Language P...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Hüseyin Ünlü Samet Tenekeci Can Çiftçi İbrahim Baran Oral Tunahan Atalay Tuna Hacaloğlu Burcu Musaoğlu Onur DemirǶrs Department of Computer Engineering Izmir Institute of Technology Izmir Turkiye Bilgi Grubu Izmir Turkiye Department of Information Systems Engineering Atilim University Ankara Turkiye Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Siskon Software & Automation Izmir Turkiye

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

Software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

关键词： Training Schedules Accuracy Estimation Project management Companies Predictive models Size measurement Software Natural language processing

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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