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arXiv 2024年

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024, CC BY.

关键词： Federated learning

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Response

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Gastrointestinal endoscopy 2024年第3期99卷 476-477页

作者： Xiang Zhang Dehua Tang Jin-Dong Zhou Wu-Jun Li Lei Wang Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China. National Institute of Healthcare Data Science at Nanjing University Nanjing China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China. National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Center for Medical Big Data Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China.

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state/Action-based Fairness Verification for Non-determinism

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Chinese Journal of Electronics 2023年第4期20卷 603-606页

作者： XU Guangquan PANG Shanchen GAO Honghao XIAO Yingyuan LI Xiaohong FENG Zhiyong School of Computer Science and Technology Tianjin University Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China School of Computer Engineering and Science Shanghai University Shanghai China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Most of the existing work about fairness verification was carried out by checking the fairness based on state, action, or process respectively. It is hard to resolve the fairness issue for complex software systems, especially in life-critical systems and safety-critical systems. In this paper, we verify the fairness based on both state and action, which is realized mainly by introducing an extended Kripke structure and four kinds of fairness (weak global/local fairness and strong global/local fairness). The experimental results show the superiority of our method by the savings in memory (2.08%) and time consumption (26.45%).

关键词： Memory management software systems

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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RAT: A Refactoring-Aware Tool for Tracking Code History

RAT: A Refactoring-Aware Tool for Tracking Code History

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Feifei Niu Junqian Shao Chaofan Xu Christoph Mayr-Dorn Wesley K. G. Assunção Liguo Huang Chuanyi Li Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina USA Department of Computer Science and Engineering Southern Methodist University Dallas Texash USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT_Demo Demo Video: https://***/VI_xwUaIPp4

关键词： Location awareness software maintenance Codes Source coding computer bugs Maintenance engineering History

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MoMa: A Modular Deep Learning Framework for Material Property Prediction

arXiv

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arXiv 2025年

作者： Wang, Botian Ouyang, Yawen Li, Yaohui Wang, Yiqun Cui, Haorui Zhang, Jianbing Wang, Xiaonan Ma, Wei-Ying Zhou, Hao Tsinghua University China Department of Computer Science and Technology Tsinghua University China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Department of Chemical Engineering Tsinghua University China

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa’s potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Transferring fashion to surveillance with weak labels

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Neural Computing and Applications 2023年第18期35卷 13021-13035页

作者： Zheng, Qi He, Zheng Liang, Chao Chen, Jun Lin, Chia-Wen Tao, Dapeng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan School of Information Science and Engineering Yunnan University Yunnan Kunming650504 China

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as "jeans" and "T-shirt." Although clothing parsing has achieved great success in the fashion domain, it is quite challenging to parse target under practical surveillance conditions due to the presence of complex environmental interference, such as that from low resolution, viewpoint variations and lighting changes. Our method is developed to capture target information from the fashion domain and apply this information to a surveillance domain by weakly supervised transfer learning. Most target tags convey strong location information (e.g., "T-shirt" is always shown in the upper region), which can be used as weak labels for our transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets demonstrate the effectiveness of the proposed surveillance data enhancing method. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Monitoring

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Infrared: A Meta Bug Detector

arXiv

引用

arXiv 2022年

作者： Zhang, Chi Wang, Yu Wang, Linzhang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210023 China

The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection, which offers three crucial advantages over existing learning-based bug detectors: bug-type generic (i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable (i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient (i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector (MBD) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs;in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method. © 2022, CC BY.

关键词： Static analysis

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Modular Mix-and-Match Complementation of Büchi Automata (Technical Report)

arXiv

引用

arXiv 2023年

作者： Havlena, Vojtěch Lengál, Ondřej Li, Yong Šmahlíková, Barbora Turrini, Andrea Faculty of Information Technology Brno University of Technology Brno Czech Republic Department of Computer Science University of Liverpool United Kingdom State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Institute of Intelligent Software Guangzhou China

Complementation of nondeterministic Büchi automata (BAs) is an important problem in automata theory with numerous applications in formal verification, such as termination analysis of programs, model checking, or in decision procedures of some logics. We build on ideas from a recent work on BA determinization by Li et al. and propose a new modular algorithm for BA complementation. Our algorithm allows to combine several BA complementation procedures together, with one procedure for a subset of the BA’s strongly connected components (SCCs). In this way, one can exploit the structure of particular SCCs (such as when they are inherently weak or deterministic) and use more efficient specialized algorithms, regardless of the structure of the whole BA. We give a general framework into which partial complementation procedures can be plugged in, and its instantiation with several algorithms. The framework can, in general, produce a complement with an Emerson-Lei acceptance condition, which can often be more compact. Using the algorithm, we were able to establish an exponentially better new upper bound of O(4n) for complementation of the recently introduced class of elevator automata. We implemented the algorithm in a prototype and performed a comprehensive set of experiments on a large set of benchmarks, showing that our framework complements well the state of the art and that it can serve as a basis for future efficient BA complementation and inclusion checking algorithms. © 2023, CC BY.

关键词： Automata theory

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