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arXiv 2023年

作者： Si, Jiasheng Zhu, Yingjie Shi, Xingyu Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of informatics King’s College London United Kingdom Alan Turing Insitute United Kingdom

Given a controversial target such as "nuclear energy", argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics;(ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Through-Wall Multihuman Activity Recognition Based on MIMO Radar

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computers, Materials & Continua 2025年第6期83卷 4537-4550页

作者： Changlong Wang Jiawei Jiang Chong Han Hengyi Ren Lijuan Sun Jian Guo College of Computer Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and TelecommunicationsNanjing210023China College of Information Science and Technology Nanjing Forestry UniversityNanjing210000China

Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human ***,static individuals,lacking prominent motion features,do not generate Doppler ***,radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls,limiting recognition *** address these challenges,this study proposes a novel through-wall human activity recognition method based on MIMO *** a MIMO radar operating at 1–2 GHz,we capture activity data of individuals through walls and process it into range-angle maps to represent activity *** tackle the issue of minimal variation in reflection areas caused by static individuals,a multi-scale activity feature extraction module is designed,capable of extracting effective features from radar signals across multiple ***,a temporal attention mechanism is employed to extract keyframe information from sequential signals,focusing on critical moments of ***,this study introduces an activity recognition network based on a Deformable Transformer,which efficiently extracts both global and local features from radar signals,delivering precise human posture and activity *** experimental scenarios involving 24 cm-thick brick walls,the proposed method achieves an impressive 97.1%accuracy in activity recognition classification.

关键词： MIMO radar human activity Transformer through-wall

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EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

arXiv

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arXiv 2023年

作者： Zhu, Yingjie Si, Jiasheng Zhao, Yibo Zhu, Haiyang Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain data. One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfactual data augmentation techniques fail to handle multi-hop fact verification due to their incapability to preserve the complex logical relationships within multiple correlated texts. In this paper, we overcome this limitation by developing a rationale-sensitive method to generate linguistically diverse and label-flipping counterfactuals while preserving logical relationships. In specific, the diverse and fluent counterfactuals are generated via an Explain-Edit-Generate architecture. Moreover, the checking and filtering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships1 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Leveled Group key Management with Efficient Revocations for Wireless Sensor networks

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Chinese Journal of Electronics 2023年第3期18卷 494-499页

作者： Fenghua Li Junzhi Yan Jianfeng Ma Wei Wang Key Laboratory of Computer Networks and Information Security (Ministry of Education) Xidian University Xi'an China Graduate School Beijing Electronic Science and Technology Institute Beijing China No. 36 Research Institute of CETC Jiaxing China

Wireless sensor networks are often deployed in hostile environments. Protecting the communication against node capture is one of the most important design objectives. Focused on the revocations of resource constrained nodes, an efficient leveled group key management scheme is proposed. This scheme is based on a novel application of an $a$ -ary logical key tree. Since sensor nodes may leave or join the WSN frequently, a sub-level session key is introduced to protect the sensitive information transmitted in the network. In a leave event, the remaining legal sensors in the key tree are partitioned into disjointed subtrees. The group key can be updated securely by encrypting the new group key with the common keys shared by the sensors in the same subtree. When a sensor requests to join the communication group, it is assigned a sublevel session key which is used to communicate with GCKS and other legal sensors. The current session key is sent to the new sensor only after it gets the trust of GCKS. This scheme is scalable, stateless and simple to employ.

关键词： Wireless communication Wireless sensor networks Law information security Cryptography Communication system security Research and development

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Intelligent Contract Vulnerability Detection Method Based on Bic-RL

Intelligent Contract Vulnerability Detection Method Based on...

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Data Security and Privacy Protection (DSPP), International Conference on

作者： Shuhui Zhang Changdong Hu Tian Lan LianHai Wang Shujiang Xu Wei Shao 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Blockchain technology is praised for its decentralization, anonymity, and tamper-proof nature, but its complex architecture has led to numerous attacks, particularly against smart contracts. These attacks have caused significant economic losses and hindered the development of blockchain technology. Therefore, research on smart contract vulnerability detection methods has significant practical and application value. Existing detection technologies (such as symbolic execution, formal verification, and so on) have limitations, including heavy reliance on contract source code and incomplete feature extraction. This paper proposes a fully automated smart contract vulnerability detection method based on Bic-RL(Smart contracts Detection Based on Bicubic interpolation and Deep Residual Neural networks - Long short-term memory) to address these issues. This method not only simplifies the data processing process but also realizes the complete feature extraction and ensures the high efficiency of detection performance. Performance evaluation shows this method has higher detection accuracy and generalization ability, with an accuracy rate of 84.5%.

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A Higher-Order Semantic Dependency Parser

arXiv

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arXiv 2022年

作者： Li, Bin Fan, Yunlong Sataer, Yikemaiti Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Higher-order features bring significant accuracy gains in semantic dependency parsing. However, modeling higher-order features with exact inference is NP-hard. Graph neural networks (GNNs) have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks. Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser by applying GNNs. Instead of explicitly extracting higher-order features from intermediate parsing graphs, GNNs aggregate higher-order information concisely by stacking multiple GNN layers. Experimental results show that our model outperforms the previous state-of-the-art parser on the SemEval 2015 Task 18 English datasets. © 2022, CC BY-NC-SA.

关键词： Graph neural networks

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Yue Liu Li Wang Yanyan Chen Qi Liu Jingshan Pan 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor’s storage structure; combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency; optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication; employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation.

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Qi Liu Yanlong Li Wei Du Zenghui Ren Yue Liu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%.

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Zhenguo Wei Yanlong Li Wei Du Lei Xiao Chaoshuai Xu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%.

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Unknown-Aware Bilateral Dependency Optimization for Defending Against Model Inversion Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Peng, Xiong Liu, Feng Wang, Nannan Lan, Long Liu, Tongliang Cheung, Yiu-Ming Han, Bo Hong Kong Baptist University Department of Computer Science Hong Kong The University of Melbourne School of Computing and Information Systems Australia Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China National University of Defense Technology State Key Laboratory of High Performance Computing School of Computer Hunan Changsha410000 China The University of Sydney Sydney AI Centre School of Computer Science Faculty of Engineering Australia

By abusing access to a well-trained classifier, model inversion (MI) attacks pose a significant threat as they can recover the original training data, leading to privacy leakage. Previous studies mitigated MI attacks by imposing regularization to reduce the dependency between input features and outputs during classifier training, a strategy known as unilateral dependency optimization. However, this strategy contradicts the objective of minimizing the supervised classification loss, which inherently seeks to maximize the dependency between input features and outputs. Consequently, there is a trade-off between improving the model's robustness against MI attacks and maintaining its classification performance. To address this issue, we propose the bilateral dependency optimization strategy (BiDO), a dual-objective approach that minimizes the dependency between input features and latent representations, while simultaneously maximizing the dependency between latent representations and labels. BiDO is remarkable for its privacy-preserving capabilities. However, models trained with BiDO exhibit diminished capabilities in out-of-distribution (OOD) detection compared to models trained with standard classification supervision. Given the open-world nature of deep learning systems, this limitation could lead to significant security risks, as encountering OOD inputs—whose label spaces do not overlap with the in-distribution (ID) data used during training-is inevitable. To address this, we leverage readily available auxiliary OOD data to enhance the OOD detection performance of models trained with BiDO. This leads to the introduction of an upgraded framework, unknown-aware BiDO (BiDO+), which mitigates both privacy and security concerns. As a highlight, with comparable model utility, BiDO-HSIC+ reduces the FPR95 by 55.02% and enhances the AUCROC by 9.52% compared to BiDO-HSIC, while also providing superior MI robustness. © 1979-2012 IEEE.

关键词： Differential privacy

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