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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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PLC-MDT: A Framework for Detecting Anomalies with Digital Twins of Industrial Control Systems

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IEEE Sensors Journal 2025年第10期25卷 17739-17749页

作者： Xu, Zidong Zhang, Zhenyong He, Tuzeqiu Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

The attack events (such as the Stuxnet and BlackEnergy) that targeted the Industrial Control System (ICS) have validated its vulnerability to cyber intrusions. The prevention of ICS from cyberattacks is undoubtedly important and practical as it operates dangerous equipment and produces products that people need. Traditional anomaly detection methods, which rely heavily on SCADA data, are often unreliable due to the limitation of such data in accurately reflecting real-time system states under attacks. Therefore, we propose PLC-MDT, a novel anomaly detection method based on digital twins (DT), exploiting the modern communication technology and computing resources of ICS. PLC-MDT does not depend solely on SCADA data, instead, it leverages the interaction between DT and real-world ICS, utilizing both DT and field-site data to perform anomaly detection. Specifically, for PLC-MDT, the prediction model in DT is trained based on historical data, and the predicted values are used as input to the mechanism model in DT to generate simulated data. Networked structure of ICS guarantees real-time data collected from field sites for DT. Therefore, PLC-MDT detects attacks by monitoring the difference between the predicted and real-time data, revealing control channel corruption when the difference exceeds a threshold. The effectiveness of PLC-MDT is demonstrated through experiments on real-world systems, showing its potential for real-time detection of anomalies in ICS. © 2001-2012 IEEE.

关键词： SCADA systems

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Einocchio: Efficiently Outsourcing Polynomial Computation with Verifiable Computation and Optimized Newton Interpolation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1981-1992页

作者： Pei, Xintao Chen, Yuling Long, Yangyang Sang, Haiwei Luo, Yun Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: A verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client's input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio's quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations. © 2005-2012 IEEE.

关键词： Cloud platforms

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Knowledge-Centered Dual-Process Reasoning for Math Word Problems With Large Language Models

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3457-3471页

作者： Liu, Jiayu Huang, Zhenya Liu, Qi Ma, Zhiyuan Zhai, Chengxiang Chen, Enhong University of Science and Technology of China School of Data Science State Key Laboratory of Cognitive Intelligence Hefei230000 China University of Science and Technology of China School of Computer Science and Technology State Key Laboratory of Cognitive Intelligence Hefei230000 China University of Illinois at Urbana-Champaign Department of Computer Science ChampaignIL61820 United States

Math word problem (MWP) serves as a critical milestone for assessing the text mining ability and knowledge mastery level of models. Recent advancements have witnessed large language models (LLMs) showcasing remarkable performance on MWP. However, current LLMs still frequently exhibit logical errors, which highlights their inability to fully grasp the knowledge required for genuine step-by-step mathematical reasoning. To this end, in this paper, we propose a novel Knowledge-guided Solver (KNOS) framework that empowers LLMs to simulate human mathematical reasoning, whose core idea is to Invoke-Verify-Inject necessary knowledge to solve MWP. We draw inspiration from the dual-process theory to construct two cooperative systems: a Knowledge System and an Inference System. Specifically, the Knowledge System employs LLMs as the knowledge base and develops a novel knowledge invoker that can elicit their relevant knowledge to support the strict step-level mathematical reasoning. In the Inference System, we propose a knowledge verifier and a knowledge injector to evaluate the knowledge rationality and further guide the step-wise symbolic deduction in an interpretable manner based on human cognitive mechanism, respectively. Moreover, to tackle the potential scarcity issue of mathematics-specific knowledge in LLMs, we consider an open-book exam scenario and propose an improved version of KNOS called EKNOS. In EKNOS, we meticulously design knowledge selectors to extract the most relevant commonsense and math formulas from external knowledge sources for each reasoning step. This knowledge is utilized to assist the knowledge invoker in better stimulating LLMs' reasoning abilities. Both KNOS and EKNOS are flexible to empower different LLMs. Our experiments with GPT3, ChatGPT, and GPT4 not only demonstrate their reasoning accuracy improvement but also show how they bring the strict step-wise interpretability of mathematical thinking. © 2025 IEEE.

关键词： Inference engines

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Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based on Internet of Things

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IEEE Internet of Things Journal 2025年第10期12卷 13746-13756页

作者： Lyu, Zhihan Wan, Zhibo Bian, Zengxu Liu, Yuqi Zhao, Wei Xidian University State Key Laboratory of Integrated Services Networks School of Computer Science and Technology Shaanxi Xi'an China Qingdao University College of Computer Science and Technology China

Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors such as weather conditions, load changes, holidays, seasons and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, Particle Swarm Optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into an digital twins system including transformer condition monitoring, oil temperature prediction and intelligent maintenance based on internet of things(IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This paper presents a hybrid framework that achieves a accurate transformer oil temperature prediction. © 2014 IEEE.

关键词： Oil filled transformers

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FDCPNet:feature discrimination and context propagation network for 3D shape representation

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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Four-wave mixing Bragg scattering for small frequency shift from silicon coupled microrings

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Chinese Physics B 2025年第1期34卷 351-355页

作者： Chang Zhao Chao Wu Pingyu Zhu Yuxing Du Yan Wang Miaomiao Yu Kaikai Zhang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength *** frequency conversion between two light beams with a small frequency interval is a central *** this work,we design a pair of coupled silicon microrings wherein coupled-induced modesplitting exists to achieve a small frequency shift by the process of four-wave mixing Bragg *** an example,the signal can be up or down converted to the idler which is 15.5 GHz spaced when two pumps align with another pair of split *** results unveil the potential of coupled microring resonators for small interval frequency conversion in a high-fidelity,all-optical,and signal processing quantum frequency interface.

关键词： four-wave mixing Bragg scattering silicon coupled microrings

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Privacy-Preserving Supply Chain Payment Solution Based on Blockchain

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International Journal of Network Security 2025年第1期27卷 25-34页

作者： Wang, Zhenzhen Liu, Momeng Shao, Mingqiang Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi'an Polytechnic University State Key Laboratory of Integrated Service Networks Xidian University Xi'an China

The development of information technology has gradually automated supply chain management. Although it has greatly improved efficiency, there still exists some deficiencies. Firstly, there may be fund defaults in the financial flow of the supply chain. In addition, complex transactions or settlement operations among participants and manual intervention in processing may lead to low efficiency and high costs. Moreover, the data generated during the transactions contains sensitive information of all parties. If information leakage occurs, it may cause serious damage to enterprises or individuals. Since blockchain has the advantages of tamper-proof, anonymity, and distributed storage, it can be used as a platform for recording transactions. In this paper, we propose a blockchain-based supply chain payment solution. In our scheme, the payment channel is used to provide the atomicity of payment and data privacy, combined with the time-lock to avoid fund defaults. At the same time, we adopt a ring signature so as to ensure the privacy of identity. Through the security analysis and experiments, it shows that our scheme can meet the requirements of value privacy and identity privacy, and the high efficiency in practice. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Differential privacy

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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