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arXiv 2025年

作者： Tang, Rui Yong, Ziyun Jiang, Shuyu Chen, Xingshu Liu, Yaofang Zhang, Yi-Cheng Sun, Gui-Quan Wang, Wei School of Cyber Science and Engineering Sichuan University Chengdu610065 China Cyber Science Research Institute Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China Department of Reproductive Technology The Affiliated Hospital of Southwest Medical University Luzhou646000 China Physics Department University of Fribourg Chemin du Musée 3 Fribourg1700 Switzerland Sino-Europe Complex Science Center School of Mathematics North University of China Shanxi Taiyuan030051 China Complex Systems Research Center Shanxi University Shanxi Taiyuan030006 China School of Public Health Chongqing Medical University Chongqing400016 China

Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advance- © 2025, CC BY-NC-ND.

关键词： Large scale systems

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A Multi-Stream Structure-Enhanced Network for Mesh Denoising

A Multi-Stream Structure-Enhanced Network for Mesh Denoising

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yutian Yang Lingyu Liang Jie Yan Yong Xu South China University of Technology Guangzhou China Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management Henan China Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Triangular meshes provide an efficient representation of 3D shapes. Various applications such as 3D simulation suffer from degradation in geometric quality. This paper proposes a novel Multi-stream Structure-Enhanced Network (MSE-Net) based on graph convolutional networks. The network uses multi-scale features besides vertex position to guide face normal filtering, which can better preserve the geometric feature during the denoising process. In contrast to former methods that focus on filtering vertex coordinate and face normal apart, MSE-Net innovatively fuses more structure features like face area, inner product between face normal and vertex normals, and the interior angles of face to guide the face normal and vertex position updating, utilizing the inherent structural characteristic of Mesh. Our method achieves state-of-the-art performance on several publicly available datasets, demonstrating its effectiveness.

关键词： Degradation Three-dimensional displays Filtering Shape Graph convolutional networks Fuses Noise reduction Faces Cybernetics

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SE-Chain:A Scalable Storage and Efficient Retrieval Model for Blockchain

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Journal of Computer Science & Technology 2021年第3期36卷 693-706页

作者： Da-Yu Jia Jun-Chang Xin Zhi-Qiong Wang Han Lei Guo-Ren Wang School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Key Laboratory of Big Data Management and Analytics of Liaoning Province Shenyang 110819China College of Medicine and Biological Information Engineering Northeastern UniversityShenyang 110169China Neusoft Institute of Intelligent Healthcare Technology Co.Ltd. Shenyang 110179China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China

Massive data is written to blockchain systems for the destination of keeping safe. However, existing blockchain protocols still demand that each full node has to contain the entire chain. Most nodes quit because they are unable to grow their storage space with the size of data. As the number of nodes decreases, the security of blockchains would significantly reduce. We present SE-Chain, a novel scale-out blockchain model that improves storage scalability under the premise of ensuring safety and achieves efficient retrieval. The SE-Chain consists of three parts:the data layer, the processing layer and the storage layer. In the data layer, each transaction is stored in the AB-M tree (Adaptive Balanced Merkle tree), which adaptively combines the advantages of balanced binary tree (quick retrieval) and Merkle tree (quick verification). In the processing layer, the full nodes store the part of the complete chain selected by the duplicate ratio regulation algorithm. Meanwhile, the node reliability verification method is used for increasing the stability of full nodes and reducing the risk of imperfect data recovering caused by the reduction of duplicate number in the storage layer. The experimental results on real datasets show that the query time of SE-Chain based on the AB-M tree is reduced by 17% when 16 nodes exist. Overall, SE-Chain improves the storage scalability extremely and implements efficient querying of transactions.

关键词： SE-Chain AB-M(adaptive balanced Merkle)tree efficient retrieval scale-out blockchain

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Learning Representation for Clustering via Dual Correlation 6

Learning Representation for Clustering via Dual Correlation

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Zhang, Tao Hu, Mingming Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Shenyang China Center for Clinical Studies Data Management Department Beijing China

ISBN: (纸本)9781665460033

Contrastive learning is a self-supervised learning method used to learn the general features of a dataset without labels by letting the model learn which data points are similar or different. It constructs labeled samples through data augmentation. Therefore, the core and difficulty of self-supervised contrastive learning are to construct high-quality contrastive samples, especially negative sample pairs. In this paper, a cluster-based contrast sample selection method is proposed, which selects the positive contrast samples from the same cluster and the negative contrast samples from different clusters. By minimizing the forward contrast sample loss and maximizing the reverse sample loss, the deep representation clustering result of the image is obtained. The experimental results show the excellence and adaptability of the algorithm. © 2023 IEEE.

关键词： Deep learning

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Reconfiguration of the brain during aesthetic experience on Chinese calligraphy—Using brain complex networks

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Visual Informatics 2022年第1期6卷 35-46页

作者： Rui Li Xiaofei Jia Changle Zhou Junsong Zhang National Engineering Laboratory for Educational Big Data Central China Normal UniversityWuhanHubeiChina Mind Art&Computation GroupDepartment of Artificial IntelligenceSchool of InformaticsXiamen UniversityChina Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and TourismChina Center for the Study of Brain and Language Qufu Normal UniversityRizhaoChina Faculty of Education The Edmond J.Safra Brain Research Center for the Study of Learning DisabilitiesUniversity of HaifaHaifaIsrael

Chinese calligraphy,as a well-known performing art form,occupies an important role in the intangible cultural heritage of *** studies focused on the psychophysiological benefits of Chinese *** attention has been paid to its aesthetic attributes and effectiveness on the cognitive *** complement our understanding of Chinese calligraphy,this study investigated the aesthetic experience of Chinese cursive-style calligraphy using brain functional network *** stayed on the coach and rested for several ***,they were requested to appreciate artwork of cursive-style *** showed that(1)changes in functional connectivity between frontooccipital,fronto-parietal,bilateral parietal,and central–occipital areas are prominent for calligraphy condition,(2)brain functional network showed an increased normalized cluster coefficient for calligraphy condition in alpha2 and gamma *** results demonstrate that the brain functional network undergoes a dynamic reconfiguration during the aesthetic experience of Chinese *** evidence that the aesthetic experience of Chinese calligraphy has several similarities with western art while retaining its unique characters as an eastern traditional art form.

关键词： Chinese calligraphy Aesthetic experience Electroencephalogram Brain functional connectivity Complex network

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Target detection method based on deep learning applied in integrity evaluation of explosion proof electrical equipment

Target detection method based on deep learning applied in in...

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2022 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2022

作者： Li, Bohan Liu, Weijing Lin, Yuchen Lin, Tao School of Artificial Intelligence and Data Sciences Hebei University of Technology Tianjin300130 China Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin300130 China School of Economics and Management Hebei University of Technology Tianjin300130 China

ISBN: (纸本)9781510657304

There are many types of explosion-proof equipment, so the problems in use are not the same. In view of these situations, this project takes the explosion-proof electrical equipment as the research object, and develops a set of integrity evaluation system of explosion-proof electrical equipment. This paper presents a defect detection method applied in the integrity evaluation system of explosion-proof electrical equipment. The proposed method first designs a lightweight network capable of achieving high-definition image detection under the YOLOV4 detection framework. Then, according to the specific data set, a priori box corresponding to the data distribution is generated. Finally, the distance-based K-means clustering method is used to extract the anchor block. The STN-OCR detects and identifies the defects from the natural images, thus applying the defect detection method in the explosion-proof electrical integrity evaluation system. This method improves the traditional evaluation process of traditional explosion-proof electrical equipment, which is cumbersome and inefficient, and has a certain impact on the application in some other fields in the future. © 2022 SPIE.

关键词： K-means clustering

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ReZG: Retrieval-Augmented Zero-Shot Counter Narrative Generation for Hate Speech

arXiv

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arXiv 2023年

作者： Jiang, Shuyu Tang, Wenyi Chen, Xingshu Tang, Rui Wang, Haizhou Wang, Wenxian School of Cyber Science and Engineering Sichuan University Chengdu China Cyber Science Research Institute Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education Chengdu China

The proliferation of hate speech (HS) on social media poses a serious threat to societal security. Automatic counter narrative (CN) generation, as an active strategy for HS intervention, has garnered increasing attention in recent years. Existing methods for automatically generating CNs mainly rely on re-training or fine-tuning pre-trained language models (PLMs) on human-curated CN corpora. Unfortunately, the annotation speed of CN corpora cannot keep up with the growth of HS targets, while generating specific and effective CNs for unseen targets remains a significant challenge for the model. To tackle this issue, we propose Retrieval-Augmented Zero-shot Generation (ReZG) to generate CNs with high-specificity for unseen targets. Specifically, we propose a multi-dimensional hierarchical retrieval method that integrates stance, semantics, and fitness, extending the retrieval metric from single dimension to multiple dimensions suitable for the knowledge that refutes HS. Then, we implement an energy-based constrained decoding mechanism that enables PLMs to use differentiable knowledge preservation, countering, and fluency constraint functions instead of in-target CNs as control signals for generation, thereby achieving zero-shot CN generation. With the above techniques, ReZG can integrate external knowledge flexibly and improve the specificity of CNs. Experimental results show that ReZG exhibits stronger generalization capabilities and outperforms strong baselines with significant improvements of 2.0%+ in the relevance and 4.5%+ in the countering success rate metrics. © 2023, CC BY-NC-ND.

关键词： Semantics

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GPTCN: Gated Parallel Transformer Convolutional Networks for Downstream-Task User Representation Learning on App Usage

GPTCN: Gated Parallel Transformer Convolutional Networks for...

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

作者： Yingjie Sun Fanrui Zeng Jiamin Xiao Yuxiao Deng Yifan Ding Yizhou Li School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Shenzhen Hexun Huagu Information Technology Co. Ltd Shanghai China

With the development of mobile applications into a part of modern life, the user usage behavior data of mobile applications can well reflect the attribute characteristics of users. For many downstream applications, including advertising, recommendations provide effective support. To provide users with customized services and optimize user experience, industry and scholars have been exploring feasible solutions. However, automatic user modeling based on mobile app usage faces unique challenges, including (1) poor generalization performance of modeling with a single downstream task, (2) uneven distribution of user behavior over time, and severe sparsity in many long-tail apps. In this paper, we propose a custom model (GPTCN) by which we overcome these challenges and optimize on top of it, achieving the goal of reducing manual effort and improving performance. Experimental results show that GPTCN outperforms the state-of-the-art general vector generation models in ACC, providing a more comprehensive, robust and general user representation model.

关键词：

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Prompt Packer: Deceiving LLMs through Compositional Instruction with Hidden Attacks

arXiv

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arXiv 2023年

作者： Jiang, Shuyu Chen, Xingshu Tang, Rui School of Cyber Science and Engineering Sichuan University Chengdu China Cyber Science Research Institute Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu China

Recently, Large language models (LLMs) with powerful general capabilities have been increasingly integrated into various Web applications, while undergoing alignment training to ensure that the generated content aligns with user intent and ethics. Unfortunately, they remain the risk of generating harmful content like hate speech and criminal activities in practical applications. Current approaches primarily rely on detecting, collecting, and training against harmful prompts to prevent such risks. However, they typically focused on the "superficial" harmful prompts with a solitary intent, ignoring composite attack instructions with multiple intentions that can easily elicit harmful content in real-world scenarios. In this paper, we introduce an innovative technique for obfuscating harmful instructions: Compositional Instruction Attacks (CIA), which refers to attacking by combination and encapsulation of multiple instructions. CIA hides harmful prompts within instructions of harmless intentions, making it impossible for the model to identify underlying malicious intentions. Furthermore, we implement two transformation methods, known as T-CIA and W-CIA, to automatically disguise harmful instructions as talking or writing tasks, making them appear harmless to LLMs. We evaluated CIA on GPT-4, ChatGPT, and ChatGLM2 with two safety assessment datasets and two harmful prompt datasets. It achieves an attack success rate of 95%+ on safety assessment datasets, and 83%+ for GPT-4, 91%+ for ChatGPT (gpt-3.5-turbo backed) and ChatGLM2-6B on harmful prompt datasets. Our approach reveals the vulnerability of LLMs to such compositional instruction attacks that harbor underlying harmful intentions, contributing significantly to LLM security development. Warning: this paper may contain offensive or upsetting content!. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Analysis of conditional connectivity based on two lanes for VANETs 12th

Analysis of conditional connectivity based on two lanes for ...

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12th International Conference on Communications and Networking in China, CHINACOM 2017

作者： Pan, Bin Wu, Hao State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Intelligent Traffic Data Safety and Privacy Protection Technology Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9783319781389

Conditional connectivity is important for the design of the upper layer communication protocol and the network deployment in different scenarios. This paper analyzes the performance of conditional connectivity with the network topology change by establishing the model of inter-vehicle communication. The paper aims at the two-lane highway scenario for vehicular ad hoc networks (VANETs), considering the factors of communication range, vehicle flow characteristics and moving speed. The main conditional connectivity performance index is the conditional connectivity probability. Based on the simulation, the correctness of the theoretical analysis is verified. We also make an explanation of the simulation results. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.

关键词： Vehicular ad hoc networks

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