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Alternating refinement relations for fuzzy concurrent game structures

Theoretical computer Science 2025年 1044卷

作者： Zhang, Shasha Zhou, Jie Ma, Yanfang Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Mathematics and Science College Shanghai Normal University Shanghai200234 China School of Computer Science and Information Engineering Changzhou Institute of Technology Jiangsu Changzhou213032 China

Fuzzy concurrent game structures (FCGSs), an extension of concurrent game structures in the fuzzy setting, combine both fuzzy and nondeterministic choices. They are therefore a powerful semantic foundation for modeling fuzzy systems that interact with their environments. In recent years, refinement relations in the field of formal verification, including trace containment and simulation, have been successfully introduced to special classes or related ones of FCGSs, such as fuzzy transition systems and fuzzy automata, and have been developed. However, these refinement relations are not directly applicable to FCGSs as they constrain the possible behaviors of the environment. To further satisfy the need for analysis and verification of fuzzy systems, this paper introduces alternating refinement relations for FCGSs. First, we present two general notions of alternating trace containment for FCGSs. Although these notions coincide with the corresponding classical ones, they do not coincide for FCGSs. Then, we provide algorithms to solve the problems of deciding two types of alternating trace containments in finite FCGSs under memoryless strategies. On the other hand, we propose two types of alternating simulations for FCGSs via weight functions and closed sets, and discover a novel result that they have different discriminating power, with one alternating simulation implying the other. Finally, we investigate the relationships among these notions mentioned above. © 2025 Elsevier B.V.

关键词： Alternating simulation Bisimulation Fuzzy automata Fuzzy language

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WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining

arXiv

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arXiv 2025年

作者： Wang, Haoran Huai, Lian Li, Wenbin Qi, Lei Jiang, Xingqun Shi, Yinghuan National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing China National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing Drum Tower Hospital Jiangsu Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China Lab School of Computer Science and Engineering Southeast University Jiangsu Nanjing China BOE Technology Group Co. Ltd. Beijing China

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervised manner. In this paper, to reduce the labeling cost, we investigate a novel weakly-supervised SAM-based segmentation model, namely WeakMedSAM. Specifically, our proposed WeakMedSAM contains two modules: 1) to mitigate severe co-occurrence in medical images, a sub-class exploration module is introduced to learn accurate feature representations. 2) to improve the quality of the class activation maps, our prompt affinity mining module utilizes the prompt capability of SAM to obtain an affinity map for random-walk refinement. Our method can be applied to any SAM-like backbone, and we conduct experiments with SAMUS and EfficientSAM. The experimental results on three popularly-used benchmark datasets, i.e., BraTS 2019, AbdomenCT-1K, and MSD Cardiac dataset, show the promising results of our proposed WeakMedSAM. Our code is available at https://***/wanghr64/WeakMedSAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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Embedded System Security Protocols: Latest Trends and Issues

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International Journal of High Speed Electronics and Systems 2025年第3期34卷

作者： Han, Huimin Gulzar, Yonis Nizamani, Ahsan Ahmed Nizamani, Abdul Haseeb Ali, Sijjad Aslam Bhatti, Mughair Mechanical and Electrical Engineering College Hainan Vocational University of Science and Technology Haikou571126 China Department of Management Information Systems King Faisal University P.O. Box 380 Al-Ahsa31982 Saudi Arabia School of Computer Science and Engineering Central South University Changsha410083 China College of Computer Science and Software Engineering Shenzhen University Guandong Shenzhen China School of Geography Nanjing Normal University Jiangsu Nanjing China

Given the increasingly serious security threats faced by embedded systems, an analysis has been conducted on the characteristics and vulnerabilities of embedded systems. Detailed discussions are provided on security threats faced by embedded systems from two major aspects: hardware-based attacks such as side-channel attacks, hardware Trojans, fault injection, microprobes, and hardware reverse engineering, and software attacks aimed at damaging, tampering, and stealing. The analysis includes existing security measures both domestically and internationally, highlighting their shortcomings. Based on the concept of trusted computing, the paper offers future research suggestions from several perspectives, including theoretical research models, trusted measurement, trusted reporting, trusted sharing, and isolation of resources. Finally, an ideal comprehensive joint security enhancement scheme is proposed, and future research directions are discussed. © 2025 World Scientific Publishing Company.

关键词： Embedded software

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Quality Scores Compression of Genomic Sequencing Data: A Comprehensive Review and Performance Evaluation

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 817-831页

作者： Yuansheng Liu Tao Tang Zexuan Zhu Xiangxiang Zeng Quan Zou Keqin Li College of Information Science and Engineering Hunan University Changsha Hunan China School of Mordern Posts Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China National Supercomputing Center Changsha Hunan China Department of Computer Science State University of NY New Paltz NY USA

Advanced sequencing technologies have profoundly revolutionized biology and produced vast amounts of raw sequencing data during the past decades. The enormous amount of sequencing data proposed significant challenges of data storage and transmission. Compressing a big file into a small file is an encouraging method to tackle these challenges. Howver, it has been found that traditional text data compression algorithms are not well-suited for handing the vast sequencing datasets. Therefore, several algorithms are designed specifically for the efficient compression of sequencing data. Recently, considerable research has been devoted to compressing quality scores stored in the FASTQ format file, resulting in substantial advances in compression performance. Despite these advances, there has been no systematic review and evaluation of these algorithms or software. In this review, we aim to conduct a broad review of the existing quality score compression algorithms. We mainly discuss those algorithms from two categories, i.e., lossless and lossy compression. Additionally, we benchmark the compression performance of 12 tools using 14 real datasets. We anticipate that our review will provide practical guidance for others seeking to design an appropriate algorithm for compressing quality scores.

关键词： Sequential analysis Genomics Bioinformatics Compression algorithms Data compression Image coding Electronic mail Data mining Computational biology Training

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

arXiv

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arXiv 2025年

作者： Zhang, Quanjun Fang, Chunrong Zheng, Yi Zhang, Yaxin Zhao, Yuan Huang, Rubing Zhou, Jianyi Yang, Yun Zheng, Tao Chen, Zhenyu Department of Computing Technologies Swinburne University of Technology Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Macau University of Science and Technology Jiangsu Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Shenzhen Research Institute of Nanjing University Guangdong Shenzhen China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training corpus. In this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66

关键词： Semantics

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MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Wu, Mengjia Xiao, Bo Yang, Zhiyun Sun, Jiawei Liu, Qi Zhang, Yonghong Liu, Xiaodong Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology School of Chemistry and Materials Science Nanjing210044 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China Nanjing University of Information Science and Technology School of Automation Nanjing210044 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom

With the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study proposes a lightweight deep learning model, MMST-LSTM, which integrates Multiscale Context Feature Fusion Mechanism (MCFFM) and Mixed-Domain Attention Fusion Mechanism (MAFUM). While maintaining high prediction accuracy, MMST-LSTM significantly improves forecast speed. The MMST-LSTM model is particularly suitable for deployment in Mobile Edge Computing (MEC) environments, enabling fast localized forecasting. Experimental results demonstrate MMST-LSTM’s excellent predictive performance on two radar echo datasets, particularly in rapid response and handling localized data. Moreover, leveraging Smart Data-Driven Modeling (SDDM) technology with consumer-generated data enhances its application potential in smart consumer electronics products, providing an efficient tool for disaster weather alerts. This study introduces an innovative meteorological forecasting method and provides robust technical support for accurate weather warning systems, offering consumers timely and reliable weather information. This enables them to make more informed decisions, effectively reducing the potential risks and economic losses caused by extreme climate events. © 1975-2011 IEEE.

关键词： Deep learning

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Hybrid CNN-transformer network with frequency-aware fusion for efficient single image super-resolution

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Expert Systems with Applications 2025年 288卷

作者： Ying Zhou Zhichao Zheng Quansen Sun Rui Chen Li Wang Linghui Ge School of Computer and Software Nanjing Vocational University of Industry Technology Nanjing 210023 Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 Jiangsu China School of Computer and Electronic Information/ School of Artificial Intelligence Nanjing Normal University Nanjing 210023 Jiangsu China

Convolutional Neural Networks (CNN) are highly effective at capturing local details, whereas Transformers is effective for modeling long-range dependencies and global patterns in Single Image Super-Resolution (SISR) task. However, the optimal integration of these two components to fully exploit their complementary strengths for improving SISR performance remains an unresolved challenge. To address this issue, we introduce a flexible basic module, the Frequency-Aware Fusion Block (FAFB), which allows for the variable splitting of features along the channel dimension and processes them in parallel branches. One branch leverages CNNs to capture high-frequency details, while the other uses a Transformer to capture low-frequency information. These outputs are then aggregated to produce a comprehensive feature representation. Building on FAFB, we propose the Hybrid-former, a U-Net style network designed to efficiently harness the complementary strengths of CNNs and Transformers. The key innovation lies in the dynamic adjustment of the split ratio within the FAFB, which is determined by the location in the hierarchical architecture. This design allows CNN and Transformer to each focus on their respective strengths, overcoming the CNN’s limitations in modeling global dependencies and the Transformer’s high computational complexity. Extensive experiments across five benchmark datasets demonstrate that this design can significantly enhance efficiency while maintaining comparable performance.

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Worst-Input Mutation Approach to Web Services Vulnerability Testing Based on SOAP Messages

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Tsinghua Science and Technology 2014年第5期19卷 429-441页

作者： Jinfu Chen Huanhuan Wang Dave Towey Chengying Mao Rubing Huang Yongzhao Zhan School of Computer Science and Communication Engineering Jiangsu University School of Computer Science The University of Nottingham Ningbo China School of Software and Communication Engineering Jiangxi University of Financeand Economics

The growing popularity and application of Web services have led to increased attention regarding the vulnerability of software based on these services. Vulnerability testing examines the trustworthiness and reduces the security risks of software systems. This paper proposes a worst-input mutation approach for testing Web service vulnerability based on Simple Object Access Protocol （SOAP） messages. Based on characteristics of SOAP messages, the proposed approach uses the farthest neighbor concept to guide generation of the test suite. The corresponding automatic test case generation algorithm, namely, the Test Case generation based on the Farthest Neighbor （TCFN）, is also presented. The method involves partitioning the input domain into sub-domains according to the number and type of SOAP message parameters in the TCFN, selecting the candidate test case whose distance is the farthest from all executed test cases, and applying it to test the Web service. We also implement and describe a prototype Web service vulnerability testing tool. The tool was applied to the testing of Web services on the Internet. The experimental results show that the proposed approach can find more vulnerability faults than other related approaches.

关键词： security testing Web service vulnerability SOAP message test case generation mutation operator

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