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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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Method and Practice of Trusted Embedded computing and data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement data leakage Android

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Identity-Preserving Adversarial Training for Robust network Embedding

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Journal of Computer science & technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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A fully connected polarization-entangled network via integrated spontaneous four-wave mixing engineering

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 76-83页

作者： Pingyu Zhu Yan Wang Yuxing Du Miaomiao Yu Kaikai Zhang Kun Wang Ping Xu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense Technology Hefei National Laboratory

Quantum communication is rapidly developing and is gradually being commercialized due to its technological maturity. Establishing dense communication links among multiple users in a scalable and efficient way is of great significance for realizing a large-scale quantum communication network. Here, we propose a novel scheme to construct a fully connected polarizationentangled network, utilizing the engineering of spontaneous four-wave mixings(SFWMs) and a path-polarization converter. It does not require active optical switches which limit the communication speed, or trusted nodes which lead to potential security risks. The required frequency channels in the network grow linearly with the number of users. We experimentally demonstrate a six-user fully connected network with on-chip SFWM processes motivated by four pumps. Each user in the network receives a frequency channel, and all fifteen connections between the users are implemented simultaneously. Our work opens up a promising scheme to efficiently construct fully connected large-scale networks.

关键词： quantum network quantum entanglement nonlinear optics integrated optics

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A data representation method using distance correlation

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Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Inductive Link Prediction in N-ary Knowledge Graphs 31

Inductive Link Prediction in N-ary Knowledge Graphs

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

N-ary Knowledge Graphs (NKGs), where a fact can involve more than two entities, have gained increasing attention. Link Prediction in NKGs (LPN) aims to predict missing elements in facts to facilitate the completion of NKGs. Current LPN methods implicitly operate under a closed-world assumption, meaning that the sets of entities and roles are fixed. These methods focus on predicting missing elements within facts composed of entities and roles seen during training. However, in reality, new facts involving unseen entities and roles frequently emerge, requiring completing these facts. Thus, this paper proposes a new task, Inductive Link Prediction in NKGs (ILPN), which aims to predict missing elements in facts involving unseen entities and roles in emerging NKGs. To address this task, we propose a Meta-learning-based Nary knowledge Inductive Reasoner (MetaNIR), which employs a graph neural network with meta-learning mechanisms to embed unseen entities and roles adaptively. The obtained embeddings are used to predict missing elements in facts involving unseen elements. Since no existing dataset supports this task, three datasets are constructed to evaluate the effectiveness of MetaNIR. Extensive experimental results demonstrate that MetaNIR consistently outperforms representative models across all datasets. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Large Language Model-Based Event Relation Extraction with Rationales 31

Large Language Model-Based Event Relation Extraction with Ra...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (纸本)9798891761964

Event Relation Extraction (ERE) aims to extract various types of relations between different events within texts. Although Large Language Models (LLMs) have demonstrated impressive capabilities in many natural language processing tasks, existing ERE methods based on LLMs still face three key challenges: (1) Time Inefficiency: The existing pairwise method of combining events and determining their relations is time-consuming for LLMs. (2) Low Coverage: When dealing with numerous events in a document, the limited generation length of fine-tuned LLMs restricts the coverage of their extraction results. (3) Lack of Rationale: Essential rationales concerning the results that could enhance the reasoning ability of the model are overlooked. To address these challenges, we propose LLMERE, an LLM-based approach with rationales for the ERE task. LLMERE transforms ERE into a question-and-answer task that may have multiple answers. By extracting all events related to a specified event at once, LLMERE reduces time complexity from O(n2) to O(n), compared to the pairwise method. Subsequently, LLMERE enhances the coverage of extraction results by employing a partitioning strategy that highlights only a portion of the events in the document at a time. In addition to the extracted results, LLMERE is also required to generate corresponding rationales/reasons behind them, in terms of event coreference information or transitive chains of event relations. Experimental results on three widely used datasets show that LLMERE achieves significant improvements over baseline methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Machine learning empowered UAV-based beamforming design in ISAC systems

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science China(Information sciences) 2025年第5期68卷 285-286页

作者： Xiangyang DUAN Xiaoqi ZHANG Shuqiang XIA Zhongbin WANG Yihua MA Weijie YUAN State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless and Computing Research InstituteZTE Corporation School of Electrical and Data Engineering University of Technology Sydney School of Automation and Intelligent Manufacturing Southern University of Science and Technology

Integrated sensing and communication (ISAC) has been proposed as an enabling technology for the realization of the next-generation wireless system,which focuses on performing wireless communication and sensing *** the various potential ISAC-based applications,unmanned aerial vehicle (UAV)-based ISAC plays a significant part in unlocking the potential of future next-generation wireless communication,facilitating low-latency data transmission in high-mobility *** by recent advancements,a variety of effective techniques have been investigated to optimize beamforming design in ISAC *** instance,the authors in [1] introduced an extended Kalman filtering (EKF)-based method tailored for millimeter wave (mmWave) ISAC ***,Ref.[2]proposed an extended interacting multiple model (IMM)-EKF framework designed for vehicular networks with intricate roadway *** these advancements,the aforementioned methods typically employ a separate scheme for channel prediction and beam alignment,which introduces additional signaling overhead in real-world ***,there is a demand for an end-to-end beamforming design approach specifically for UAV-based ISAC systems.

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Subspace Clustering via Block-Diagonal Decomposition

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Chinese Journal of Electronics 2024年第6期33卷 1373-1382页

作者： Zhiqiang FU Yao ZHAO Dongxia CHANG Yiming WANG Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology China Construction Bank

The subspace clustering has been addressed by learning the block-diagonal self-expressive matrix. This block-diagonal structure heavily affects the accuracy of clustering but is rather challenging to obtain. A novel and effective subspace clustering model, i.e., subspace clustering via block-diagonal decomposition(SCBD), is proposed,which can simultaneously capture the block-diagonal structure and gain the clustering result. In our model, a strict block-diagonal decomposition is introduced to directly pursue the k block-diagonal structure corresponding to k clusters. In this novel decomposition, the self-expressive matrix is decomposed into the block indicator matrix to demonstrate the cluster each sample belongs to. Based on the strict block-diagonal decomposition, the block-diagonal shift is proposed to capture the local intra-cluster structure, which shifts the samples in the same cluster to get smaller distances and results in more discriminative features for clustering. Extensive experimental results on synthetic and real databases demonstrate the superiority of SCBD over other state-of-the-art methods.

关键词： Accuracy databases data models Matrix decomposition

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