检索结果-内蒙古大学图书馆

31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Shiyu Zhou, Gang Lu, Jicang Chen, Jing Huang, Ningbo State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9798891761964

Nowadays, research of Text Classification (TC) based on graph neural networks (GNNs) is on the rise. Both inductive methods and transductive methods have made significant progress. For transductive methods, the semantic interaction between texts plays a crucial role in the learning of effective text representations. However, it is difficult to perform inductive learning while modeling interactions between texts on the graph. To give a universal solution, we propose the graph neural network based on pre-trained semantic interaction called PaSIG. Firstly, we construct a text-word heterogeneity graph and design an asymmetric structure to ensure one-way message passing from words to the test texts. Meanwhile, we use the context representation capability of the pre-trained language model to construct node features that contain classification semantic information. Afterward, we explore the adaptative aggregation methods with a gated fusion mechanism. Extensive experiments on five datasets have shown the effectiveness of PaSIG, with the accuracy exceeding the baseline by 2.7% on average. While achieving state-of-the-art performance, we have also taken measures of subgraph sampling and intermediate state preservation to achieve fast inference. © 2025 Association for Computational Linguistics.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Intrusion Tolerance System Based on Behavioral Baseline and Defense-in-Depth Architecture

Research on Intrusion Tolerance System Based on Behavioral B...

引用

2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Meng, Qi Wang, Xuemeng Yang, Kai Meng, Long Wang, Jiancheng Fei, Jinlong State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Tolerance technology is the third-generation network security technology commonly used in the world. It is derived from the category of information survival and endogenous security technology. A scholar from Carnegie Mellon University gave this survival technology a definition: the so-called "invasion survival technology".It is the ability of the system to perform its own tasks within a limited time when external attacks, failures and accidents have occurred. It assumes that we cannot detect intrusions to the system completely and correctly. When external intrusions or failures occur suddenly, we can use tolerance technology to solve the problem of survival of the system to ensure the confidentiality, integrity, and integrity of the information system. reliability and non-repudiation. In the current Internet security incidents, countless experiences and lessons tell us that it is not enough to rely on blocking and defensing for network *** on the existing network defense-in-depth architecture and the common behavior and parameter baselines of current emergency response systems, this paper designs an intrusion-tolerant system based on the behavioral baseline and network-in-depth architecture, which realizes intrusion tolerance and malicious behavior blocking under high confrontation intensity. To adapt to the maximum recovery and necessary tolerance under the attack technology of international APT organizations, and to protect the integrity and availability of the system to the greatest extent. © 2025 SPIE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Social Media User Geolocation Based on Large Language Models 2nd

Social Media User Geolocation Based on Large Language Model...

引用

2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Zhang, Meng Luo, Xiangyang Huang, Ningbo Key Laboratory of Cyberspace Situation Awareness of Henan Province Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China

ISBN: (纸本)9789819785391

In the digital age, social media platforms have amassed a wealth of user-generated content, which contains valuable geographic information. However, the irregularities and noise in user-generated text, have led to suboptimal performance in traditional text-based user geolocation methods. We propose a unsupervised framework for user geolocation based on Large Language Models (LLMs), which utilizes the LLMs’ powerful text processing abilities to geolocate users based on user-generated text unsupervisedly. Firstly, preprocess the text using regularization rules and LLM to denoise and normalize user-generated text, thus enhancing data quality. Subsequently, appropriate prompts are designed to guide the knowledgeable LLM in understanding the user text’s geolocating mechanism, thereby profiling users. To refine user geolocation accuracy, five independent positioning iterations are conducted, with the most frequent occurrence identified as the final user location. Through a series of experiments, we have demonstrated the potential of utilizing large language models for processing noisy text and the effectiveness of geolocating users in an unsupervised setting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large language model Social media Unsupervised user geolocation User-generated text

来源：评论

学校读者我要写书评

暂无评论

Byzantine-robust distributed support vector machine

引用

Science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

来源：评论

学校读者我要写书评

暂无评论

Fairness-Aware Budgeted Edge Server Placement for Connected Autonomous Vehicles

引用

IEEE Transactions on Mobile computing 2025年第06期24卷 4762-4776页

作者： Wu, Jintao Xu, Xiaolong Cui, Guangming Zhang, Yiwen Qi, Lianyong Dou, Wanchun Cai, Zhipeng Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software Nanjing210044 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China Anhui University School of Computer Science and Technology Anhui230031 China College of Computer Science and Technology Qingdao266580 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Georgia State University Department of Computer Science United States

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency. © 2002-2012 IEEE.

关键词： Integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Mitigating Language Confusion through Inference-time Intervention 31

Mitigating Language Confusion through Inference-time Interve...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Xie, Yunfan Zou, Lixin Luo, Dan Tang, Min Li, Chenliang Dong, Liming Luo, Xiangyang Wuhan University Wuhan China Lehigh University Bethlehem United States Monash University Melbourne Australia National Defense University Beijing China State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798891761964

Although large language models (LLMs) trained on extensive multilingual corpora exhibit impressive language transfer, they often fail to respond in the user's desired language due to corpus imbalances, an embarrassingly simple problem known as the language confusion. However, existing solutions like in-context learning and supervised fine-tuning (SFT) have drawbacks: in-context learning consumes context window space, diminishing attention as text lengthens, while SFT requires extensive, labor-intensive data collection. To overcome these limitations, we propose the language-sensitive intervention (LSI), a novel, lightweight, and label-free approach. Specifically, we analyze language confusion from a causal perspective, revealing that the training corpus's language distribution acts as a confounder, disadvantaging languages that are underrepresented in the dataset. Then, we identify a language-sensitive dimension in the LLM's residual stream, i.e., the language vector, which allows us to estimate the average causal effect of prompts on this dimension. During inference, we directly intervene on the language vector to generate responses in the desired *** further advance research on this issue, we introduce a new benchmark that detects language confusion and assesses content quality. Experimental results demonstrate that our method effectively mitigates language confusion without additional complex mechanisms. Our code is available at https://***/SoseloX/LSI. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiankun Pi Jicang Lu Yepeng Sun Qinlong Fan Xukun Zhou Shouxin Shang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model’s understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model’s adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model’s stance discriminative ability and generalization performance.

关键词： Adaptation models Weak supervision Social networking (online) Annotations Semantics Blogs Transfer learning Signal processing Data models Speech processing

来源：评论

学校读者我要写书评

暂无评论

Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

来源：评论

学校读者我要写书评

暂无评论

On the Cycle Structure of Some Nonlinear Feedback Shift Registers

引用

Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

来源：评论

学校读者我要写书评

暂无评论

Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

引用

Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Materials Science and Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Network Information Center Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Low-Co Li-rich layered oxides Li/Ni disordering TM-O covalency Cationic and anionic redox activity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：