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Online Credibility Assessment of Equipment Digital Twin for Discrete Manufacturing

IEEE Transactions on Automation Science and Engineering 2025年 22卷 14763-14774页

作者： Lu, Han Zhang, Lin Deen, M. Jamal Wang, Kunyu Cheng, Hongbo Yang, Laurence T. Beihang University Hangzhou International Innovation Institute Zhejiang 311115 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China AI Atlas Inc Canada Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case. © 2025 IEEE.

关键词： Fabrication

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NumScout: Unveiling Numerical Defects in Smart Contracts using LLM-Pruning Symbolic Execution

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IEEE Transactions on software Engineering 2025年第5期51卷 1538-1553页

作者： Chen, Jiachi Shao, Zhenzhe Yang, Shuo Shen, Yiming Wang, Yanlin Chen, Ting Shan, Zhenyu Zheng, Zibin Hangzhou Normal University Intelligent Transportation and Information Security Laboratory Hangzhou311121 China Sun Yat-sen University School of Software Engineering Zhuhai519082 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China Kashi Institute of Electronics and Information Industry Kashi844000 China

In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numerical defects, posing potential security risks. Existing research primarily focuses on traditional numerical defects like integer overflow, and there is currently a lack of systematic research and effective detection methods targeting new types of numerical defects. In this paper, we identify five new types of numerical defects through the analysis of 1,199 audit reports by utilizing the open card method. Each defect is defined and illustrated with a code example to highlight its features and potential consequences. We also propose NumScout, a symbolic execution-based tool designed to detect these five defects. Specifically, the tool combines information from source code and bytecode, analyzing key operations such as comparisons and transfers, to effectively locate defects and report them based on predefined detection patterns. Furthermore, NumScout uses a large language model (LLM) to prune functions which are unrelated to numerical operations. This step allows symbolic execution to quickly enter the target function and improve runtime speed by 28.4%. We run NumScout on 6,617 real-world contracts and evaluated its performance based on manually labeled results. We find that 1,774 contracts contained at least one of the five defects, and the tool achieved an overall precision of 89.7%. © 2025 IEEE.

关键词： Smart contract

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A Global Visual Information Intervention Model for Medical Visual Question Answering

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computers in Biology and Medicine 2025年第PT A期192卷 110195-110195页

作者： Peng, Peixi Fan, Wanshu Shen, Yue Yang, Xin Zhou, Dongsheng National and Local Joint Engineering Laboratory of Computer-Aided Design School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Medical Visual Question Answering (Med-VQA) aims to furnish precise responses to clinical queries related to medical imagery. While its transformative potential in healthcare is undeniable, current solutions remain nascent and are yet to see widespread clinical adoption. Med-VQA presents heightened complexities compared to standard visual question answering (VQA) tasks due to the myriad of clinical scenarios and the scarcity of labeled medical imagery. This often culminates in language biases and overfitting vulnerabilities. In light of these challenges, this study introduces Global Visual Information Intervention (GVII), an innovative Med-VQA model designed to mitigate language biases and improve model generalizability. GVII is centered on two key branches: the Global Visual Information Branch (GVIB), which extracts and filters holistic visual data to amplify the image's contribution and reduce question dominance, and the Forward Compensation Branch (FCB), which refines multimodal features to counterbalance disruptions introduced by GVIB. These branches work in tandem to enhance predictive accuracy and robustness. Furthermore, a multi-branch fusion mechanism ensures cohesive integration of features and losses across the model. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art models, achieving a 2.6% improvement in accuracy on the PathVQA dataset. In conclusion, the GVII-based Med-VQA model not only successfully mitigates prevalent language biases and overfitting issues but also significantly improves diagnostic precision, offering a considerable stride toward robust, clinically applicable VQA systems. © 2025

关键词： Question answering

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Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liang, Fang-Yi Zhan, Yu-Wei Liu, Jiale Zhang, Chong-Yu Chen, Zhen-Duo Luo, Xin Xu, Xin-Shun Shandong University School of Software Jinan250101 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Yunnan Key Laboratory of Software Engineering Kunming650504 China

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from limited samples while preventing catastrophic forgetting. With the increasing distribution of learning data across different clients and privacy concerns, FSCIL faces a more realistic scenario where few learning samples are distributed across different clients, thereby necessitating a Federated Few-Shot Class-Incremental Learning (FedFSCIL) scenario. However, this integration faces challenges from non-IID problem, which affects model generalization and training efficiency. The communication overhead in federated settings also presents a significant challenge. To address these issues, we propose Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning (FedCAP). Our framework leverages pre-trained models enhanced by a class-wise prompt pool, where shared class-wise keys enable clients to utilize global class information during training. This unifies the understanding of base class features across clients and enhances model consistency. We further incorporate a class-level information fusion module to improve class representation and model generalization. Our approach requires very few parameter transmission during model aggregation, ensuring communication efficiency. To our knowledge, this is the first study to explore the scenario of FedFSCIL. Consequently, we designed comprehensive experimental setups and made the code publicly available. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Image Computation for Quantum Transition Systems

Image Computation for Quantum Transition Systems

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Design, Automation and Test in Europe Conference and Exhibition

作者： Xin Hong Dingchao Gao Sanjiang Li Shenggang Ying Mingsheng Ying Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems.

关键词： Quantum system Tensors Europe Model checking software Hardware Partitioning algorithms Computational efficiency Quantum circuit

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Improving Multi-Label Contrastive Learning by Leveraging Label Distribution

arXiv

引用

arXiv 2025年

作者： Chen, Ning Lyu, Shen-Huan Wu, Tian-Shuang Wang, Yanyan Tang, Bin Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies selected positive and negative samples based on the overlap between labels and used them for label-wise loss balancing. However, these methods suffer from a complex selection process and fail to account for the varying importance of different labels. To address these problems, we propose a novel method that improves multi-label contrastive learning through label distribution. Specifically, when selecting positive and negative samples, we only need to consider whether there is an intersection between labels. To model the relationships between labels, we introduce two methods to recover label distributions from logical labels, based on Radial Basis Function (RBF) and contrastive loss, respectively. We evaluate our method on nine widely used multi-label datasets, including image and vector datasets. The results demonstrate that our method outperforms state-of-the-art methods in six evaluation metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition

arXiv

引用

arXiv 2025年

作者： Wu, Tian-Shuang Lyu, Shen-Huan Chen, Ning Qu, Zhihao Ye, Baoliu Key Laboratory of Water Big Data Technology Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security concerns. However, when the number of available samples is extremely limited, class imbalance becomes a common and tricky problem. Achieving an equal number of samples across all classes is often costly and impractical in real-world applications, and previous studies on few-sample model compression have mostly ignored this significant issue. Our experiments comprehensively demonstrate that class imbalance negatively affects the overall performance of few-sample model compression methods. To address this problem, we propose a novel and adaptive framework named OOD-Enhanced Few-Sample Model Compression (OE-FSMC). This framework integrates easily accessible out-of-distribution (OOD) data into both the compression and fine-tuning processes, effectively rebalancing the training distribution. We also incorporate a joint distillation loss and a regularization term to reduce the risk of the model overfitting to the OOD data. Extensive experiments on multiple benchmark datasets show that our framework can be seamlessly incorporated into existing few-sample model compression methods, effectively mitigating the accuracy degradation caused by class imbalance. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Building Modeling Tools Based on Metamodeling and Product Line Technologies

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Chinese Journal of Electronics 2025年第2期23卷 219-226页

作者： Zhiyi Ma Xiao He Software Institute School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, modeling tools for larger modeling languages are usually diversity in function and complexity in implementation technology. Taking building modeling tools as a domain, this paper presents an approach to building software modeling tools based on metamodeling and product line technologies. The paper provides the concept system of the approach and a feature model from diverse functions of modeling tools in order to specify the commonality and variability of the tools by deeply making the domain analysis, discusses the design and implementation of a general tool framework that provides the conveniences for reusing components and generating code for components, and specifies the mapping between the feature model and the components for modeling tools.

关键词： Analytical models Codes Buildings Metamodeling software Complexity theory Research and development

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A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud-Edge-End Networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1363-1394页

作者： Shi, Wanxin Li, Qing Yu, Qian Wang, Fulin Shen, Gengbiao Jiang, Yong Xu, Yang Ma, Lianbo Muntean, Gabriel-Miro Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Fudan University School of Computer Science Shanghai200433 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518066 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China Tecorigin Software Department Shanghai200120 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Northeastern University College of Software Shenyang110819 China Dublin City University Performance Engineering Laboratory School of Electronic Engineering DublinD09 DD7R Ireland

The digital age has brought a significant increase in video traffic. This traffic growth, driven by rapid Internet advancements and a surge in multimedia applications, presents both challenges and opportunities to video transmissions. Users seek high-quality video content, prompting service providers to offer high-definition options to improve user experience and increase profits. However, traditional end-to-end best-effort networks struggle to meet the demands of extensive video streaming and ensure good user Quality of Experience (QoE), especially in high user mobility scenarios or fluctuating network conditions. Addressing some of these challenges, content delivery networks (CDN) are instrumental in delivering video content, but they are under increased pressure to support high quality and reduce their deployment and maintenance costs. Currently, cloud-edge-end fusion technologies have become one of the optimization directions for network services due to their flexibility and scalability. At the same time, in the context of the recent advancements in computing-focused network paradigms, intelligent enhancement techniques (e.g., super-resolution), commonly utilized in image optimization, have been adopted as a pivotal solution for increasing video delivery quality. To illustrate the essence and employment of the intelligent enhancement solutions for video streaming, this paper first outlines the video streaming process, discusses relevant evaluation metrics, and examines aspects related to the intelligent solutions. Then the paper presents the intelligent enhancement process of video streaming, analyzes various typical intelligent models for content enhancement and highlights their distinct characteristics. This exploration delves deeper into various intelligent quality-improved solutions, scrutinizing their applicability across different transmission scenarios like Video on Demand (VoD) and live streaming, and shedding light on their strengths and weaknesses from

关键词： Video streaming

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Visual Acuity Consistent Foveated Rendering towards Retinal Resolution

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Zhang, Zhi Gai, Meng Li, Sheng Peking University School of Software and Microelectronics China Peking University School of Computer Science China National Key Laboratory of Intelligent Parallel Technology China

Prior foveated rendering methods often suffer from a limitation where the shading load escalates with increasing display resolution, leading to decreased efficiency, particularly when dealing with retinal-level resolutions. To tackle this challenge, we begin with the essence of the human visual system (HVS) perception and present visual acuity-consistent foveated rendering (VaFR), aiming to achieve exceptional rendering performance at retinal-level resolutions. Specifically, we propose a method with a novel log-polar mapping function derived from the human visual acuity model, which accommodates the natural bandwidth of the visual system. This mapping function and its associated shading rate guarantee a consistent output of rendering information, regardless of variations in the display resolution of the VR HMD. Consequently, our VaFR outperforms alternative methods, improving rendering speed while preserving perceptual visual quality, particularly when operating at retinal resolutions. We validate our approach using both the rasterization and ray-casting rendering pipelines. We also validate our approach using different binocular rendering strategies for HMD devices. In diverse testing scenarios, our approach delivers better perceptual visual quality than prior foveated rendering while achieving an impressive speedup of 6.5×-9.29× for deferred rendering of 3D scenarios and an even more powerful speedup of 10.4×-16.4× for ray-casting at retinal resolution. Additionally, our approach significantly enhances the rendering performance of binocular 8K path tracing, achieving smooth frame rates. © 1995-2012 IEEE.

关键词： Binoculars

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