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Extraction Method of Judicial Language Entities Based On Regular Expression

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Extraction Method of Judicial Language Entities Based On Reg...

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： JIAO Kainan LI Xin School of Information Network Security People’s Public Security University of China Beijing China China Security Prevention Technology and Risk Assessment Key Laboratory Ministry of Public Security Beijing China

With the coming of the era of rule of law and intelligence, natural language processing technology plays a pivotal role. At present, a large number of unstructured judicial texts rely on manual processing and archiving. In order to make better use of them and achieve professional application, this paper proposes the goal of analyzing the structure of judgments, extracting the judicial language entities, and describing cases in the form of entity circulation map. As the text carrier of unstructured public events, the judicial document is of better standard format, finely crafted and easy processing, and becomes the research object of this paper. Through the survey of the development of named entity recognition technology, testing and contrasting the use of extraction tool, GATE, as well as considering the cost and effectiveness in the judicial field, this paper put forward a rule-based regular expression method for entity recognition. The scrapy crawler framework is used to obtain judgments classified from China Judgments Online website, so as to realize the task of analyzing the structure of judgments and extracting the judicial language entities.

关键词： Crawlers Manuals Tools Signal processing Logic gates Natural language processing Task analysis

Robust Resource Allocation for STAR-RIS Assisted SWIPT Systems

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arXiv 2024年

作者： Zhu, Guangyu Mu, Xidong Guo, Li Huang, Ao Xu, Shibiao The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Engineering Research Center Ministry of Education for Chain Network Convergence Technology China The National Engineering Research Center for Mobile Internet Security Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), where two practical STAR-RIS operating protocols, namely energy splitting (ES) and time switching (TS), are employed. Under the imperfect channel state information (CSI) condition, a multi-objective optimization problem (MOOP) framework, that simultaneously maximizes the minimum data rate and minimum harvested power, is employed to investigate the fundamental rate-energy trade-off between IUs and EUs. To obtain the optimal robust resource allocation strategy, the MOOP is first transformed into a single-objective optimization problem (SOOP) via the ǫ-constraint method, which is then reformulated by approximating semi-infinite inequality constraints with the S-procedure. For ES, an alternating optimization (AO)-based algorithm is proposed to jointly design AP active beamforming and STAR-RIS passive beamforming, where a penalty method is leveraged in STAR-RIS beamforming design. Furthermore, the developed algorithm is extended to optimize the time allocation policy and beamforming vectors in a two-layer iterative manner for TS. Numerical results reveal that: 1) deploying STAR-RISs achieves a significant performance gain over conventional RISs, especially in terms of harvested power for EUs;2) the ES protocol obtains a better user fairness performance when focusing only on IUs or EUs, while the TS protocol yields a better balance between IUs and EUs;3) the imperfect CSI affects IUs more significantly than EUs, whereas TS can confer a more robust design to attenuate these effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

A new dance generation task from start to end 2

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A new dance generation task from start to end

2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Xu, Xiao Duan, Lijuan Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China Natl. Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China

The dance generation task is an interesting task. The general task is to predict the next dance sequence through a given initial frame, or to convert from music to dance through audio information. However, these works cannot control the direction of dance generation and lack suitable ground truth to evaluate the generated results. In our work, we give the start frame and the end frame, which can effectively solve the above problems, and can also be understood as a standardized new task. Our experimental results show that through a two-stream CNN network, we can well complete the new task that we proposed. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Audio acoustics

Generalization Boosted Adapter for Open-Vocabulary Segmentation

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arXiv 2024年

作者： Xu, Wenhao Wang, Changwei Feng, Xuxiang Xu, Rongtao Huang, Longzhao Zhang, Zherui Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China The Aerospace Information Research Institute Chinese Academy of Sciences China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging due to their lack of pixel-level granularity and the limited data available for fine-tuning, leading to overfitting and poor generalization. To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation. GBA comprises two core components: (1) a Style Diversification Adapter (SDA) that decouples features into amplitude and phase components, operating solely on the amplitude to enrich the feature space representation while preserving semantic consistency;and (2) a Correlation Constraint Adapter (CCA) that employs cross-attention to establish tighter semantic associations between text categories and target regions, suppressing irrelevant low-frequency "noise" information and avoiding erroneous associations. Through the synergistic effect of the shallow SDA and the deep CCA, GBA effectively alleviates overfitting issues and enhances the semantic relevance of feature representations. As a simple, efficient, and plug-and-play component, GBA can be flexibly integrated into various CLIP-based methods, demonstrating broad applicability and achieving state-of-the-art performance on multiple open-vocabulary segmentation benchmarks. © 2024, CC BY.

关键词： Semantics

Covert Communication in Satellite-Terrestrial Systems with a Full-Duplex Receiver

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Covert Communication in Satellite-Terrestrial Systems with a...

Satellite Internet (SAT-NET), International Conference on

作者： Zewei Guo Ranran Sun Ji He Yulong Shen Xiaohong Jiang School of Systems Information Science Future University Hakodate Hakodate Japan Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi’an China Academy of Military Sciences Institute of Systems General Academy of Systems Engineering Beijing China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China

ISBN: (数字)9798350375176

ISBN: (纸本)9798350387469

This paper investigates the covert communication in a satellite-terrestrial system, including a satellite transmitter Alice, a terrestrial full-duplex receiver Bob and a terrestrial warden Willie. To help the covert transmission from Alice, the full-duplex Bob transmits the jamming signal to confuse Willie and also receives the signal from Alice simultaneously. To explore the fundamental covertness in such a system, we first provide theoretical modelings for satellite-terrestrial downlink, and the detection error probability of Willie. Based on these models, we then study the transmit power optimization for covert rate maximization. Finally, we present extensive numerical results to validate our theoretical analysis and to illustrate the impact of various system parameters on the maximum covert rate.

关键词： Satellites Error probability Transmitters Full-duplex system Receivers Space-air-ground integrated networks Downlink Numerical models Jamming Optimization

Cross-Layer security for Semantic Communications: Metrics and Optimization

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arXiv 2025年

作者： Wang, Lingyi Wu, Wei Zhou, Fuhui Qin, Zhijin Wu, Qihui College of Science Nanjing University of Posts and Telecommunications Nanjing210003 China College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing China The State Key Laboratory of Space Network and Communications Beijing China

Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to fill this gap, a unified semantic security metric, the cross-layer semantic secure rate (CLSSR), is defined to estimate cross-layer security requirements at both the physical layer and the application layer. Then, we formulate the maximization problem of the CLSSR with the mixed integer nonlinear programming (MINLP). We propose a hierarchical AI-native semantic secure communication network with a reinforcement learning (RL)based semantic resource allocation scheme, aiming to ensure the cross-layer semantic security (CL-SS). Finally, we prove the convergence of our proposed intelligent resource allocation, and the simulation results demonstrate that our proposed CLSS method outperforms the traditional physical layer semantic security (PL-SS) method in terms of both task reliability and CLSSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

Deep Learning Algorithms Design and Implementation Based on Differential Privacy 3rd

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Deep Learning Algorithms Design and Implementation Based on ...

3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Xu, Xuefeng Yao, Yanqing Cheng, Lei Key Laboratory of Aerospace Network Security Ministry of industry and information technology School of Cyberspace Science and Technology Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing China Shenzhen Research Institute of Big Data Shenzhen518000 China

ISBN: (纸本)9783030622220

Deep learning models bear the risks of privacy leakage. Attackers can obtain sensitive information contained in training data with some techniques. However, existing differentially private methods such as Differential Privacy-Stochastic Gradient Descent (DP-SGD) and Differential Privacy-Generative Adversarial network (DP-GAN) are not very efficient as they require to perform sampling multiple times. More importantly, DP-GAN algorithm need public data to set gradient clipping threshold. In this paper, we introduce our refined algorithms to tackle these problems. First, we employ random shuffling instead of random sampling to improve training efficiency. We also test Gaussian and Laplace Mechanisms for clipping gradients and injecting noise. Second, we employ zero Concentrated Differential Privacy (zCDP) to compute overall privacy budget. Finally, we adopt dynamical gradient clipping in DP-GAN algorithm. During each iteration, we random sample training examples and set the average gradients norm as the new threshold. This not only makes the algorithm more robust but also doesn’t increase the overall privacy budget. We experiment with our algorithms on MNIST data sets and demonstrate the accuracies. In our refined DP-SGD algorithm, we achieve test accuracy of 96.58%. In our refined DP-GAN algorithm, we adopt the synthetic data to train models and reach test accuracy of 91.64%. The results show that our approach ensures model usability and provides the capability of privacy protection. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

Multi-dimensional Intelligent network Asset Identification

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Multi-dimensional Intelligent Network Asset Identification

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yi Xiong Xiaoyan Zhang Ling Pang Zhechao Lin Topsec Network Technology Inc. Beiijng China National Key Laboratory of Science and Technology on Information System Security BeijingChina

ISBN: (纸本)9781665418164

In the field of cyberspace detection and identification, how to improve the accuracy of asset identification is always a great challenge for people. This paper proposes a multi-input fusion model based on TextRCNN, which uses multi-dimensional features to identify asset types, so as to improve the accuracy of the identification. Firstly, we analyze many kinds of assets and select the protocols with symbolic characteristics. The data set is obtained based on the detection results of the selected protocols. Then, the deep learning multi-input fusion model is constructed for training. Finally, on the basis of active detection and identification technology, this model is used to improve the accuracy and credibility of asset type identification. The characteristics of this method are as follows: the fusion network can process multi-protocol input data at the same time, so as to build a multi-dimensional identification model; the identification of key attributes of assets is based on multi-dimensional characteristics, and no longer depends on the identification result of a single port. The experimental results show that the accuracy of the multi-dimensional intelligent recognition method can reach 89.48%, which can effectively identify the asset types.

关键词： Training Deep learning Intelligent networks Protocols Conferences Cyberspace Data science

On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software