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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Hanbing Zhang Yinan Jing Wei Zhang Zhenying He Kai Zhang X. Sean Wang Zhenqiang Chen Changchun Zhang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Transwarp Technology(Shanghai) Co. Ltd Shanghai China

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Exploratory data analysis (EDA) is usually human-in-the-loop and time tedious since identifying insights that show interesting characteristics and trends in data is laborious. Insight discovery helps users gain structured knowledge when understanding the dataset by categorizing interesting characteristics and trends into commonnesses and exceptions. However, existing insight discovery methods cannot interact with users when discovering insights. More importantly, the efficiency of insight discovery for these methods is low. In this paper, we propose a framework that supports and accelerates interactive insight discovery. We first divide the dataset into multiple subsets, and precompute the pattern snippets that include the distribution characteristics of subsets. Considering efficiency, we only pre-compute for subsets that cannot be further split. Then we design a novel cube structure, called InsightCube, to store precomputed pattern snippets. In order to accelerate insight discovery, we reuse the precomputed pattern snippets stored in InsightCube to infer the pattern of subsets that cannot be split. Furthermore, during insight discovery, we propose pruning strategies to accelerate the pattern inference for subsets that can be split. Experimental results on real-world datasets show that InsightCube improves the insight discovery efficiency by up to $2.5 \times$ compared to the state-of-the-art insight discovery method.

关键词： Data analysis Big Data Market research Human in the loop Optimization

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Visual Analyze the Impact of Structural Features and Node Attributes on Collaborative Relationships 19th

Visual Analyze the Impact of Structural Features and Node...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). The first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. The second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. The third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4Vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4Vis visual analysis system through specific case studies and structured interviews with users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Bao, Yanqi Ding, Tianyu Huo, Jing Liu, Yaoli Li, Yuxin Li, Wenbin Gao, Yang Luo, Jiebo Nanjing University State Key Laboratory for Novel Software Technology 210023 China Microsoft Corporation Applied Sciences Group Redmond United States University of Rochester Department of Computer Science United States

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view images into explicit 3D Gaussian through efficient training, and achieve real-time rendering of novel views. This survey aims to analyze existing 3DGS-related works from multiple intersecting perspectives, including related tasks, technologies, challenges, and opportunities. The primary objective is to provide newcomers with a rapid understanding of the field and to assist researchers in methodically organizing existing technologies and challenges. Specifically, we delve into the optimization, application, and extension of 3DGS, categorizing them based on their focuses or motivations. Additionally, we summarize and classify nine types of technical modules and corresponding improvements identified in existing works. Based on these analyses, we further examine the common challenges and technologies across various tasks, proposing potential research opportunities. © 1991-2012 IEEE.

关键词： 3D modeling

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv

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arXiv 2025年

作者： Xu, Junhao Zhang, Yanan Cai, Zhi Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth tradeoffs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://***/Xu2729/CoSDH. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation

arXiv

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arXiv 2025年

作者： Chen, Kai Li, Xinfeng Yang, Tianpei Wang, Hewei Dong, Wei Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore School of Computer Science Carnegie Mellon University United States

Large Language Models (LLMs) have made significant progress in various fields. However, challenges remain in Multi-Disciplinary Team (MDT) medical consultations. Current research enhances reasoning through role assignment, task decomposition, and accumulation of medical experience. Multi-role collaboration in MDT consultations often results in excessively long dialogue histories. This increases the model’s cognitive burden and degrades both efficiency and accuracy. Some methods only store treatment histories. They do not extract effective experience or reflect on errors. This limits knowledge generalization and system evolution. We propose a multi-agent MDT medical consultation framework based on LLMs to address these issues. Our framework uses consensus aggregation and a residual discussion structure for multi-round consultations. It also employs a Correct Answer Knowledge Base (CorrectKB) and a Chain-of-Thought Knowledge Base (ChainKB) to accumulate consultation experience. These mechanisms enable the framework to evolve and continually improve diagnosis rationality and accuracy. Experimental results on the MedQA and PubMedQA datasets demonstrate that our framework achieves accuracies of 90.1% and 83.9%, respectively, and that the constructed knowledge bases generalize effectively across test sets from both datasets. Our code is publicly available at: https://***/MDTeamGPT. © 2025, CC BY.

关键词： Diagnosis

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Robust Industrial Anomaly Detection via Style Shift Estimation and Cascade Distillation

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Yuan, Shaojiang Song, Mengke Song, Jia Liu, Xinyu Chen, Chenglizhao China University of Petroleum (East China) Qingdao Institute of Software College of Computer Science and Technology Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China College of Sichuan Province Key Laboratory of Forensic Science and Technology China

Knowledge distillation (KD) has demonstrated remarkable performance in industrial anomaly detection (IAD) field, but its immense potential is constrained by the trade-off between generalization of normal sample and robustness of abnormal sample. High generalization often weakens the robustness against anomalies, leading to overlooked defects, while enhancing robustness can cause normality forgetting, where normal samples are misclassified as anomalies. This dilemma stems from the existing methods commonly assuming style invariance across normal and abnormal areas, focusing solely on the abnormal content information while style information is usually neglected and put on the shelf. In this paper, our main novelty lies in leveraging anomaly style shift estimation for enlarging abnormal robustness while maintaining high normal generalization. This is the first attempt to introduce the style information into the knowledge distillation-based industrial anomaly detection methods. By employing the bidirectional fusion of style and content information, our approach achieves more comprehensive and practical anomaly detection and localization results. Thus, our method can provide a more balanced and robust solution for the future industrial anomaly detection task. Experimental results on the MVTec2D, MVTec3D, BTAD, and KolektorSDD2 datasets demonstrate that our method outperforms major state-of-the-art (SOTA) methods in both accuracy and speed, with image-level and pixel-level AUROC scores of 99.6% and 98.6% on MVTec2D, respectively. Furthermore, the efficiency of our method is validated in a real-world plastic crate defect detection system on a logistics production line. © 1963-2012 IEEE.

关键词： anomaly localization defect detection Industrial anomaly detection salient object detection

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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A Blockchain-Based Traceable and Verifiable Digital Circuit Trade Method with Dual-Signature Strategy 1st

A Blockchain-Based Traceable and Verifiable Digital Circuit...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Wang, Wei Xie, Zhenping Liu, Yuan School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation Jiangnan University Wuxi214122 China

ISBN: (纸本)9789819794119

In the integrated circuit (IC) industry, due to the inadequate protection mechanisms for IP cores, the design achievements of engineers are difficult to effectively safeguard, leading to a large number of excellent IP cores unable to legally circulate among designers. To alleviate these troubles, we propose a blockchain-based trade method. First, we design unique chain structures for storing and verifying IP core ownership and transaction contracts to reduce the misuse of intellectual property circuits. Then, we also design smart contract algorithms to automates the generation and verification process of digital identities and contract subscriptions. Additionally, we propose a dual-signature strategy to ensure the non-repudiation of contract signatures. Finally, we implement a prototype system and conduct security analysis. Experimental results demonstrate that the model can effectively resist several attacks and has good performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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SSAST-Adapter: A Parameter-efficient Incremental Learning Algorithm for Underwater Acoustic Target Recognition

SSAST-Adapter: A Parameter-efficient Incremental Learning Al...

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

作者： Qian Zhu Qisheng Xu Boqing Zhu Zijian Gao Lingbin Zeng Kele Xu College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater acoustic target recognition involves identifying and classifying targets in underwater environments using acoustic signals. In recent years, deep learning has made significant progress in this field. However, the models require the entire dataset to be available upfront, and classification categories must be predefined. In practical scenarios, new objects or species may appear in underwater environments over time, making it impractical to retrain a model from scratch each time new data is introduced. At the same time, training on new data inevitably leads to catastrophic forgetting of past data. To address these challenges, we propose a large-scale pre-training strategy combined with adapters to enable incremental learning without the need for complete retraining. To minimize the number of parameters during model fine-tuning, we employ a adapter structure at various network layers, reducing the number of trainable parameters to less than 2%. Experimental results demonstrate that our proposed method effectively reduces the need for full retraining by allowing the model to update in a resource-efficient manner using only new data, and the model has achieved high recognition accuracy in underwater acoustic target recognition tasks.

关键词： Training Adaptation models Incremental learning Target recognition Signal processing algorithms Speech recognition Transformers Data models Underwater acoustics Speech processing

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