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STPGTN-AMulti-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement data

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Computer Modeling in Engineering & sciences 2023年第9期136卷 2635-2654页

作者： Shuai Zhang Liguo Weng Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and TechnologyNanjing210044China

Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission *** existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious ***,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this *** Graph Attention network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission ***-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches *** show that themethod has good accuracy and *** to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions.

关键词： Transmission lines parameter identification graph modeling method deep learning

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LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection

LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Zhao Li, Yiming Zhang, Jin Xu, Hui CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Stance detection aims at inferring an author’s attitude towards a specific target in a text. Prior methods mainly consider target-related background information for a better understanding of targets while neglecting the accompanying input texts. In this study, we propose to prompt Large Language Models (LLMs) to explicitly extract the relationship between paired text and target as contextual knowledge. We then inject such LLM-driven knowledge into a generation model BART to exploit the rich contexts and semantics. Moreover, to further enhance the decoding capability of BART, a novel prototypical contrastive scheme is designed to align input contents with stance labels. Our experimental results demonstrate the state-of-the-art performance across several publicly available datasets, showcasing effectiveness in both zero-shot and cross-target stance detection scenarios. We publicly release our code to facilitate future research. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Classifier Guidance Enhances Diffusion-Based Adversarial Purification by Preserving Predictive Information 27

Classifier Guidance Enhances Diffusion-Based Adversarial Pur...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhang, Mingkun Li, Jianing Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781643685489

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods. © 2024 The Authors.

关键词： Generative adversarial networks

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Black holes as the source of dark energy:A stringent test with high-redshift JWST AGNs

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science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 181-188页

作者： Lei Lei Lei Zu Guan-Wen Yuan Zhao-Qiang Shen Yi-Ying Wang Yuan-Zhu Wang Zhen-Bo Su Wen-Ke Ren Shao-Peng Tang Hao Zhou Chi Zhang Zhi-Ping Jin Lei Feng Yi-Zhong Fan Da-Ming Wei Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China School of Astronomy and Space Science University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory for Research in Galaxies and Cosmology Department of AstronomyUniversity of Science and Technology of ChinaHefei 230026China

Studies have proposed that there is evidence for cosmological coupling of black holes(BHs)with an index of k≈3;hence,BHs serve as the astrophysical source of dark ***,the data sample is limited for the redshifts of≤*** recent years,the James Webb Space Telescope(JWST)has detected many high-redshift active galactic nuclei(AGNs)and *** the JWST NIRSpec-/NIRCam-resolved AGNs,three are determined to be in early-type host galaxies with a redshift of z~***,their M_(*)and MBH are in tension with the predicted cosmological coupling of black holes with k=3 at a confidence level of~2σ,which challenges the hypothesis that BHs serve as the origin of dark *** work on high-redshift AGNs using the JWST will further assess such a hypothesis by identifying more early-type host galaxies in the higher mass range.

关键词： dark energy supermassive black hole active galactic nuclei James Webb Space Telescope

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Recent Advances in Generative Information Retrieval 24

Recent Advances in Generative Information Retrieval

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Tang, Yubao Zhang, Ruqing Sun, Weiwei Guo, Jiafeng de Rijke, Maarten CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China Shandong University Qingdao China University of Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400701726

Generative retrieval (GR) has witnessed significant growth recently in the area of information retrieval. Compared to the traditional "index-retrieve-then-rank" pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and applications of GR. We end by outlining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Information retrieval

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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MDPO: Customized Direct Preference Optimization with a Metric-based Sampler for Question and Answer Generation 31

MDPO: Customized Direct Preference Optimization with a Metri...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yihang Tian, Bowen Su, Yueyang Fan, Yixing Guo, Jiafeng Beijing University of Posts and Telecommunications Beijing China Guangzhou China CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

With the extensive use of large language models, automatically generating QA datasets for domain-specific fine-tuning has become crucial. However, considering the multifaceted demands for readability, diversity, and comprehensiveness of QA data, current methodologies fall short in producing high-quality QA datasets. Moreover, the dependence of existing evaluation metrics on ground truth labels further exacerbates the challenges associated with the selection of QA data. In this paper, we introduce a novel method for QA data generation, denoted as MDPO. We proposes a set of unsupervised evaluation metrics for QA data, enabling multidimensional assessment based on the relationships among context,question and answer. Furthermore, leveraging these metrics, we implement a customized direct preference optimization process that guides large language models to produce high-quality and domain-specific QA pairs. Empirical results on public datasets indicate that MDPO's performance substantially surpasses that of state-of-the-art methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Sensing–Communication–Computation Integration for Federated Edge Learning With Controllable Model Dropout

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IEEE Internet of Things Journal 2025年第12期12卷 19767-19781页

作者： Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Shenzhen Research Institute of Big Data Shenzhen International Center For Industrial and Applied Mathematics Shenzhen518172 China University of Science and Technology of China CAS Key Laboratory of Wireless Optical Communication Hefei230052 China ShanghaiTech University Network Intelligence Center School of Information Science and Technology Shanghai201210 China

Federated edge learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: 1) sensing;2) computation;and 3) communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into integrated sensing-computation–communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters, such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a submodel through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables, such as batch size, sensing power, dropout rate, and communication power. This nonconvex problem is decomposed into two subproblems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes. © 2014 IEEE.

关键词： Federated learning

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A Novel Construction of Updatable Identity-Based Hash Proof System and Its Applications

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Chinese Journal of Electronics 2023年第3期32卷 564-576页

作者： QIAO Zirui ZHOU Yanwei YANG Bo ZHANG Wenzheng ZHANG Mingwu School of Computer Science Shaanxi Normal University State Key Laboratory of Cryptology Science and Technology on Communication Security Laboratory Henan Key Laboratory of Network Cryptography Technology School of Computere Hubei University of Technology

In the previous works, to further provide the continuous leakage resilience for the identity-based encryption scheme, a new cryptography primitive, called updatable identity-based hash proof system(U-IB-HPS),was proposed. However, most of the existing constructions have some deficiencies, they either do not have perfect key update function or the corresponding security with tight reduction relies on a non-static complexity assumption. To address the above problems, a new construction of U-IB-HPS is created, and the corresponding security of our system is proved based on the static complexity assumption. Also, the corresponding comparisons and analysis of performances show that our proposal not only achieves the perfect key update function and the anonymity, but also has the tight security reduction. In additional, our proposal achieves the same computational efficiency as other previous systems. To further illustrate the practical function of U-IB-HPS, a generic method of non-interactive data authorization protocol with continuous leakage resilience is designed by employing U-IB-HPS as an underlying tool, which can provide continuous leakage-resilient data authorization function for the cloud computing. Hence, the application field of U-IB-HPS is further extended through our study.

关键词： Authorization Cloud computing Protocols Identity-based encryption Complexity theory Computational efficiency Proposals

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Semantic-Enhanced Static Vulnerability Detection in Baseband Firmware 24

Semantic-Enhanced Static Vulnerability Detection in Baseband...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Liu, Yiming Zhang, Cen Li, Feng Li, Yeting Zhou, Jianhua Wang, Jian Zhan, Lanlan Liu, Yang Huo, Wei Institute of Information Engineering Cas School of Cyber Security Ucas Beijing China Key Laboratory of Network Assessment Technology Cas China Beijing Key Laboratory of Network Security and Protection Technology China Nanyang Technological University Singapore

ISBN: (纸本)9798400702174

Cellular network is the infrastructure of mobile communication. Baseband firmware, which carries the implementation of cellular network, has critical security impact on its vulnerabilities. To handle the inherent complexity in cellular communication, cellular protocols are usually implemented as message-centric systems, containing the common message processing phase and message specific handling phase. Though the latter takes most of the code (99.67%) and exposed vulnerabilities (74%), it is rather under-studied: existing detectors either cannot sufficiently analyze it or focused on analyzing the former phase. To fill this gap, we proposed a novel semantic-enhanced static vulnerability detector named BVFinder focusing on message specific phase vulnerability detection. Generally, it identifies a vulnerability by locating whether a predefined sensitive memory operation is tainted by any attacker-controllable input. Specifically, to reach high automation and preciseness, it made two key improvements: a semantic-based taint source identification and an enhanced taint propagation. The former employs semantic search techniques to identify registers and memory offsets that carry attacker-controllable inputs. This is achieved by matching the inputs to their corresponding message and data types using textual features and addressing patterns within the assemblies. On the other hand, the latter technology guarantees effective taint propagation by employing additional indirect call resolution algorithms. The evaluation shows that BVFinder outperforms the state-of-the-art detectors by detecting three to four times of amount of vulnerabilities in the dataset. Till now, BVFinder has found four zero-day vulnerabilities, with four CVEs and 12,410 USD bounty assigned. These vulnerabilities can potentially cause remote code execution to phones using Samsung shannon baseband, affecting hundreds of millions of end devices. © 2024 ACM.

关键词： Semantics

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