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GMS:A Novel Method for Detecting Reentrancy Vulnerabilities in Smart Contracts

computers, Materials & Continua 2025年第5期83卷 2207-2220页

作者： Dawei Xu Fan Huang Jiaxin Zhang Yunfang Liang Baokun Zheng Jian Zhao School of Computer Science Changchun UniversityChangchun130012China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China School of InformationManagement for Law China University of Political Science and LawBeijing102249China

With the rapid proliferation of Internet ofThings(IoT)devices,ensuring their communication security has become increasingly *** and smart contract technologies,with their decentralized nature,provide strong security guarantees for ***,at the same time,smart contracts themselves face numerous security challenges,among which reentrancy vulnerabilities are particularly *** detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer *** address these limitations,this paper proposes a novel detection method that combines pattern matching with deep ***,we carefully identify and define three common patterns of reentrancy vulnerabilities in smart ***,we extract key vulnerability features based on these ***,we employ a Graph Attention Neural Network to extract graph embedding features from the contract graph,capturing the complex relationships between different components of the ***,we use an attention mechanism to fuse these two sets of feature information,enhancing the weights of effective information and suppressing irrelevant information,thereby significantly improving the accuracy and robustness of vulnerability *** results demonstrate that our proposed method outperforms existing state-ofthe-art techniques,achieving a 3.88%improvement in accuracy compared to the latest vulnerability detection model AME(Attentive Multi-Encoder Network).This indicates that our method effectively reduces false positives and false negatives,significantly enhancing the security and reliability of smart contracts in the evolving IoT ecosystem.

关键词： Smart contract Internet of Things reentrancy vulnerabilities graph neural network

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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science China(Information sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Revealing the exceptional cryogenic strength-ductility synergy of a solid solution 6063 alloy by in-situ EBSD experiments

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材料科学技术（英文版） 2025年第5期208卷 313-322页

作者： Youhong Peng Li Wang Chenglu Liu Chao Xu Lin Geng Guohua Fan School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Key Laboratory for Light-weight Materials Nanjing Tech UniversityNanjing211816China

A solid solution 6063 aluminium alloy features an exceptional combination of strength and ductility at 77 ***,the deformation mechanisms responsible for superior strength-ductility synergy and excellent strain hardening capacity at a cryogenic temperature of the alloy were comparatively investigated by in-situ electron backscatter diffraction(EBSD)observations coupled with transmission electron microscopy(TEM)characterization and fracture morphologies at both 298 and 77 *** is found that kernel average misorientation(KAM)mappings and quantified KAM in degree suggest a higher proportion of geometri-cally necessary dislocations(GNDs)at 77 *** existence of orientation scatter partitions at 77 K implies the activation of multiple slip systems,which is consistent with the results of potential slip systems cal-culated by Taylor ***,dislocation tangles characterized by brief and curved dislocation cells and abundant small dimples have been observed at 77 *** temperature-mediated activation of dislo-cations facilitates the increased dislocations,thus enhancing the strain hardening capacity and ductility of the *** research enriches cryogenic deformation theory and provides valuable insights into the design of high-performance aluminium alloys that are suitable for cryogenic applications.

关键词： Aluminium alloy Cryogenic temperature Strength-ductility synergy In-situ EBSD Deformation mechanisms

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Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4619-4634页

作者： Yu, Xiaoyan Zhou, Shen Li, Huafeng Zhu, Liehuang Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

The use of a single image restoration framework to achieve multi-task image restoration has garnered significant attention from researchers. However, several practical challenges remain, including meeting the specific and simultaneous demands of different tasks, balancing relationships between tasks, and effectively utilizing task correlations in model design. To address these challenges, this paper explores a multi-expert adaptive selection mechanism. We begin by designing a feature representation method that accounts for both the pixel channel level and the global level, encompassing low-frequency and high-frequency components of the image. Based on this method, we construct a multi-expert selection and ensemble scheme. This scheme adaptively selects the most suitable expert from the expert library according to the content of the input image and the prompts of the current task. It not only meets the individualized needs of different tasks but also achieves balance and optimization across tasks. By sharing experts, our design promotes interconnections between different tasks, thereby enhancing overall performance and resource utilization. Additionally, the multi-expert mechanism effectively eliminates irrelevant experts, reducing interference from them and further improving the effectiveness and accuracy of image restoration. Experimental results demonstrate that our proposed method is both effective and superior to existing approaches, highlighting its potential for practical applications in multi-task image restoration. © 1991-2012 IEEE.

关键词： Restoration

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Computational approaches for circRNA-disease association prediction:a review

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Frontiers of computer science 2025年第4期19卷 99-113页

作者： Mengting NIU Yaojia CHEN Chunyu WANG Quan ZOU Lei XU Center for Informational Biology University of Electronic Science and Technology of ChinaChengdu 610054China School of Applied Chemistry and Biological Technology Shenzhen Polytechnic UniversityShenzhen 518055China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Faculty of Computing Harbin Institute of TechnologyHarbin 150006China

Circular RNA(circRNA)is a covalently closed RNA molecule formed by back *** role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological *** are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and *** associations prediction is one of the current research hotspots in the field of *** research on circRNAs has made great progress,the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much ***,advances in computational methods have made considerable progress in circRNA *** review comprehensively discussed the functions and databases related to circRNA,and then focused on summarizing the calculation model of related predictions,detailed the mainstream algorithm into 4 categories,and analyzed the advantages and limitations of the 4 *** not only helps researchers to have overall understanding of circRNA,but also helps researchers have a detailed understanding of the past algorithms,guide new research directions and research purposes to solve the shortcomings of previous research.

关键词： circular RNA disease association prediction machine learning data mining deep learning

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Cryptography with optimal deep learning-based authentication scheme for preserving anonymity in telecare medical information system

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Multimedia Tools and Applications 2025年第10期84卷 8311-8330页

作者： Sheik, Syed Amma Durai, Saleem School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers patients and practitioners to manage medical data efficiently, ensuring timely care provision and streamlined record maintenance. Recognizing security challenges inherent in this interconnected healthcare landscape, researchers have devised a two-factor authentication solution, increasingly leveraging password-based authentication via smart cards. This article presents the development of a tailored remote authentication system, the Deep Learning-Based Online Threat Detection with Key Agreement (DLOTDKA-MAP), designed specifically for TMIS. Comprising two critical phases, the DLOTDKA-MAP framework employs an Arithmetic Optimization Algorithm (AOA) and Deep Belief Network (DBN) model in real-time online threat detection, ensuring swift identification and mitigation of potential security threats. The subsequent phase employs Elliptic Curve Cryptography (ECC) with Schnorr's signature to establish secure data transmission among collaborating entities, preemptively addressing security breaches and ensuring mutual authentication. A comprehensive experimental study validates the presented DLOTDKA-MAP technique, showcasing its superiority over existing approaches and underscoring its role in advancing TMIS security infrastructure. Achieving remarkable performance values, includes an accuracy, precision, recall, and F1 score of 98.57%, this research significantly contributes to safeguarding the confidentiality and integrity of medical data within TMIS, particularly in a federated cloud environment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

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IEEE Transactions on Software Engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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Efficient breast cancer detection using neural networks and explainable artificial intelligence

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Neural Computing and Applications 2025年第5期37卷 3759-3776页

作者： Murugan, Tamilarasi Kathirvel Karthikeyan, Pritikaa Sekar, Pavithra School of Computer Science Engineering Vellore Institute of Technology Tamilnadu Chennai India

The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates the efficacy of various explainable artificial intelligence (XAI) techniques in comprehending deep learning models utilized for breast cancer classification from down sampled histopathology images. A comparative assessment of multiple convolutional neural network (CNN) architectures, encompassing standard CNNs, ResNet, VGG-16, and VGG-19, on down sampled images was conducted. The primary goal is to pinpoint the model exhibiting the highest accuracy and subsequently employ three prominent XAI methods—LIME, SHAP, and Saliency Maps—to get insights into the top-performing model. This study identifies VGG-19 as the best-performing model with an accuracy of 92.59% and demonstrates that among various XAI techniques, LIME provides the most accurate and clinically relevant explanations for breast cancer classification from down sampled histopathology images. These findings, validated by medical professionals, enhance the interpretability and reliability of deep learning models in clinical settings, promoting their responsible integration into healthcare practices. This validation was further corroborated through consultation with medical professionals, including doctors specializing in breast cancer diagnosis. This research endeavors to deepen the understanding of the model’s rationale and instill confidence in its outputs. The outcomes of this study hold significant promise in elevating the interpretability and reliability of deep learning models tailored for breast cancer diagnosis, thus facilitating their responsible integration into clinical settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Implementation for Lightweight Deep Learning for Anomaly Detection and Denoising on Gravitational Waves

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IEEE Access 2025年 13卷 72442-72455页

作者： Mohith Niranjen, R.K. Yogesh, C. Vinodh, Anirudh Sureshkumar, Tharun Vatchala, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

As gravitational wave astronomy has advanced, the need for effective and quick signal processing has never been more critical. New detectors such as Laser Interferometer Gravitational-Wave Observatory (LIGO) produces huge volumes of data, which poses a significant challenge to identify genuine astrophysical events amidst transient noise. With this in view, this study proposes a new, efficient deep-learning architecture designed to process gravitational wave signals. Motivated by the WaveNet model, our method uses dilated convolutions to precisely model long-term dependencies in the data ensuring that subtle characteristics are captured. In addition, higher-order recurrent layers like Long Short-Term Memory(LSTM) networks are also used to precisely model temporal characteristics so that accuracy is preserved with enhanced anomaly detection and noise removal. Our experimental validation shows that this method has achieved a Root Mean Square Error (RMSE) of 0.00228, a Residual Signal Ratio (RSR) of 0.08798, a Peak Signal-to-Noise Ratio (PSNR) of 33.34 dB, and an average volume error of 0.03965. These performance metrics show the framework’s ability to operate in real time with minimal computational overhead is a key requirement given the massive datasets involved in gravitational wave science. Also, by an explicit consideration of initial and boundary conditions, our scheme exhibits not only stability for the current missions but also scalability for future detection missions, like Laser Interferometer Space Antenna (LISA), allowing these missions to enhance their sensitivity and reliability. Our study demonstrates the development of state-of-the-art deep learning methods to surmount the specific obstacles concerning gravitational wave detection, paving the way for real-time processing of astrophysical data and an improved understanding of the Universe. © 2013 IEEE.

关键词： Mean square error

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