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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

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

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

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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

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Social Media User Geolocation Based on Large Language Models 2nd

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

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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

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Adaptive cyclic gradient methods with interpolation

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Computational Optimization and Applications 2025年 1-25页

作者： Xie, Yixin Sun, Cong Yuan, Ya-Xiang Beijing100876 China Ministry of Education Beijing100876 China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

Gradient method is an important method for solving large scale problems. In this paper, a new gradient method framework for unconstrained optimization problem is proposed, where the stepsize is updated in a cyclic way. The Cauchy step is approximated by the quadratic interpolation. And the cycle for stepsize update is adjusted adaptively. Combining with the adaptive nonmonotone line search technique, we prove the global convergence of the proposed method. Furthermore, its sublinear convergence rate for convex problems and R-linear convergence rate for problems with quadratic functional growth property are analyzed. Numerical results show that our proposed algorithm enjoys good performances in terms of both computational cost and obtained function values. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Interpolation

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On greedy randomized coordinate updating iteration methods for solving symmetric eigenvalue problems

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Applied Numerical Mathematics 2025年 216卷 76-97页

作者： Bai, Zhong-Zhi State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences P.O. Box 2719 Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

In order to compute the smallest eigenvalue and its corresponding eigenvector of a large-scale, real, and symmetric matrix, we propose a class of greedy randomized coordinate updating iteration methods based on the principle that the indices of larger entries in absolute value of the current residual are selected with a higher probability and, with respect to this index set, the next iterate is updated from the current iterate along with the selected coordinate such that the corresponding entry of the residual is annihilated, resulting in fast convergence rates of the proposed iteration methods. Under appropriate conditions, we prove the convergence of both sequences of the Rayleigh-quotients and the acute angles between the iterates and the eigenvector in terms of the expectation. By numerical experiments, we show that this class of greedy randomized coordinate updating iteration methods are advantageous over the parameterized power method and the coordinate descent method in both iteration counts and computing times. Moreover, with theoretical analysis and computational performance, we confirm that the convergence property of this class of iteration methods can be improved significantly by suitably choosing the arbitrary nonnegative parameter involved in the greedy probability criterion. © 2025 IMACS

关键词： Eigenvalues and eigenfunctions

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DidTrust: Privacy-Preserving Trust Management for Decentralized Identity

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 3105-3120页

作者： Yin, Jie Xiao, Yang Feng, Jie Yang, Mengmeng Pei, Qingqi Yi, Xun Xidian University State Key Laboratory of Integrated Services Networks and Shaanxi KeyLaboratory of Blockchain and Secure Computing Xi’an710071 China Xidian University State Key Laboratory of Integrated ServicesNetworks School of Cyber Engineering Engineering Research Center of Trusted Digital Economy Universities of Shaanxi Province Xi’an710071 China Commonwealth Scientific and Industrial Research Organization CanberraACT2600 Australia RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Decentralized identity (DID) is rapidly emerging as a promising alternative to centralized identity infrastructure, offering numerous real-world applications. However, existing DID systems are confronted with trust concerns, as any distributed node can act as a credential issuer and be considered trusted, which is impractical. Effective trust management (TM) protocols are critical for system trustworthiness but face two primary challenges: preserving user feedback privacy to meet regulation requirements and building resilience against trust attacks to prevent manipulation. While privacy-preserving TM protocols effectively safeguard sensitive data, they often obscure feedback, hindering anomaly detection and complicating efforts to counter trust attacks. To address these issues, we propose DidTrust, a novel decentralized identity trust management protocol that bridges data privacy and resilience to trust attacks. DidTrust features a feedback data privacy preservation protocol that conceals feedback data while maintaining authorizability and verifiability. It also implements countermeasures against cooperative and individual trust attacks, improving detection accuracy without compromising privacy. To improve efficiency, we introduce a feedback compression module for large-scale sparse matrices. Rigorous analysis proves DidTrust to be universally composable (UC) secure under a malicious model, and experiments demonstrate its improved computational and storage efficiency while achieving higher trust attack detection rates compared to BC-Trust. © 2004-2012 IEEE.

关键词： Differential privacy

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

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

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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

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Attribute-Based Access Control Encryption

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2227-2242页

作者： Wang, Xiuhua Yu, Mengyang Wang, Yaohui Pi, Yinjia Xu, Peng Wang, Shuai Jin, Hai Han, Min Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan430074 China State Key Laboratory of Cryptology Beijing100878 China Huazhong University of Science and Technology 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 School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The burgeoning complexity of communication necessitates a high demand for security. Access control encryption is a promising primitive to meet the security demand but the bulk of its constructions rely on formulating the access control policy with identities. Attribute-based access control policy in attribute-based encryption (ABE) is known to be more expressive without relying on enumerating identities. We propose a generic framework to build attribute-based access control encryption from ciphertext-policy ABE. Our instantiations prioritize different emphases on expressiveness and efficiency. The first instantiation supports multi-valued AND-gate access control structures, while the second supports the linear-secret-sharing access structure. Both are prototyped with efficiency validated empirically. © 2024 IEEE.

关键词： Ciphertext

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FASTEN: Video Event Localization Based on Audio-Visual Feature Alignment and Multi-Scale Temporal Enhancement

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IEEE Signal Processing Letters 2025年 32卷 2010-2014页

作者： Liu, Yin Wu, Qin Zeng, Mingyong Liu, Yahan Pan, Yuying Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214083 China Jiangnan University School of Internet of Things Engineering Wuxi214122 China

The audio-visual event localization task investigates how audio and visual modalities can mutually enhance video event localization. Current methods often rely on single-modality features or lack effective initial alignment between modalities. Although feature pyramids with multiple resolutions are commonly used, the effective and efficient mining of cross-modal and cross-level semantics remains a challenge. In this letter, we propose FASTEN, which stands for audio-visual Feature Alignment and multi-Scale Temporal ENhancement. Novel features derived from an initially aligned multimodal model are introduced and have been shown to be remarkably beneficial. We design an efficient encoder by sharing weights across all pyramid levels and propose a weighted cross-modal attention module to facilitate interaction between modalities. Additionally, global and local pyramids are fused to jointly capture multi-scale temporal dependencies. Evaluated on UnAV-100 and AVE, our method significantly outperforms competing solutions while reducing parameters and computational FLOPs greatly. © 1994-2012 IEEE.

关键词： Image compression

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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