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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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A Community Detection Approach for Academic Social Network: The Case of the Cross-Strait Academic Conference Network 3

A Community Detection Approach for Academic Social Network: ...

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3rd International Conference on Electronics, Communications and information technology, CECIT 2022

作者： Zhang, Xinxin Xu, Li Gao, Min Fan, Jianxi Sun, Hung-Min Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China Fudan University The School of Computer Science Shanghai China Soochow University The School of Computer Science and Technology Suzhou China National Tsing Hua University The Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350331974

With the fast development of information networks, academic social network analysis has attracted significant attention, we regard these researchers sharing common interests as a research community. The community has been widely used as the underlying structure of social networks. To utilize network information as much as possible, we capture the structural characteristics and attribute features to detect communities in the cross-strait academic conference network. In this paper, we first consider 2-hop neighbors with the Pareto principle to filtrate the central seed nodes in networks. Then we propose a new seed shrinkage strategy to get the initial communities and define the similarity according to the structure and attributes of social networks to merge the communities. Finally, we define a new indicator named inner average distance (IAD) to measure the aggregation density for each community. A case study of the cross-strait academic social network was developed to test the applicability of the proposed model, the experiment analysis suggests that our method can be more accurate to identify communities in most situations, which contributes to controlling the information spreading in networks. © 2022 IEEE.

关键词： Pareto principle

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Theme-Based Literature Analysis in the Field of Transportation 16th

Theme-Based Literature Analysis in the Field of Transportati...

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16th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2020

作者： Zhang, Sifan Lu, Hao Zhang, Fuquan Zhu, Yifan Wang, Rongrong Computer School Beijing Institute of Technology Beijing10089 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Min-Jiang University Fuzhou350121 China

ISBN: (纸本)9789813367562

Transportation is an important research field of engineering science, and its related research covers multiple fields and links in policies, methods, technologies, and applications. In order to sort out the research context and development status of the transportation field and provide scientific and technological information support for scientific researchers in the field, this article analyzes the literature published in SCI journals in the transportation field from 2008 to 2018 through the topic analysis method. The analysis results show that the current main research results in the field of transportation have formed different clusters around keywords such as cognitive radio, electric vehicles, optimization and simulation, and intelligent transportation systems, and the research development process of different countries is different. For example, the research results of China in recent years have gone from energy efficiency, automobile emissions to intelligent transportation, while the United States has been focusing on the research of intelligent transportation systems. Over time, China has gradually focused on the cutting-edge technology in the field of transportation, and its research results have greatly promoted the development of research in the field of transportation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Energy efficiency

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang 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 Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Attention Residual Network for White Blood Cell Classification with WGAN Data Augmentation 11

Attention Residual Network for White Blood Cell Classificati...

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11th International Conference on information technology in Medicine and Education, ITME 2021

作者： Zhao, Meng Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781665406796

In medicine, white blood cell (WBC) classification plays an important role in clinical diagnosis and treatment. Due to the similarity between classes and lack of training data, the precise classification of WBC is still challenging. To alleviate this problem, we propose an attention residual network for WBC image classification on the basis of data augmentation. Specifically, the attention residual network is composed of multiple attention residual blocks, an adaptive average pooling layer, and a full connection layer. The channel attention mechanism is introduced in each residual block to use the feature maps of WBC learned by a high layer to generate the attention map for a low layer. Each attention residual block also introduces depth separable convolution to extract the feature of WBC and decrease the training costs. The Wasserstein Generative adversarial network (WGAN) is used to create synthetic instances to enhance the size of training data. Experiments on two image datasets show the superiority of the proposed method over several state-of-the-art methods. © 2021 IEEE.

关键词： Generative adversarial networks

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Fine Classification Method of Product Image Based on Multi-Level Convolutional Neural Networks 2

Fine Classification Method of Product Image Based on Multi-L...

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2nd Asia Symposium on Signal processing, ASSP 2021

作者： Jin, Xuesong Du, Xin Han, Xiaowei Sun, Huadong Li, Jing School of Computer And Information Engineering Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce And Information Processing Harbin University of Commerce Harbin China

ISBN: (纸本)9781728198835

To improve the classification accuracy of e-commerce product images, a classification method for multi-category product images is proposed. The classification method is based on Convolutional Neural Networks, imitating human shopping habits and combining the features of product images, summarizing the images into multiple levels of parent and child categories, and adopting multiple classifiers to convolve the neural network from bottom to top The attention of the network is put on the global features of the parent categories and the local features of the child categories. The classification is trained layer by layer, and the weighted calculation is performed to obtain the final classification result. © 2021 IEEE.

关键词： Convolutional neural networks

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Parallel Pattern Matching over Brotli Compressed Network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

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ALCencryption:A Secure and Efficient Algorithm for Medical Image Encryption

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computer Modeling in Engineering & Sciences 2020年第12期125卷 1083-1100页

作者： Jiao Ge College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage and transmission,so it needs to be *** order to solve the security problem of medical images on mobile devices,a safe and efficient medical image encryption algorithm called ALCencryption is *** algorithm first analyzes the medical image and distinguishes the color image from the gray *** gray images,the improved Arnold map is used to scramble them according to the optimal number of iterations,and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion *** color image is encrypted by cross-diffusion algorithm of double chaotic *** and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels,large key space,strong key sensitivity,high safety and short encryption *** is suitable for medical image encryption of mobile devices with high real-time requirements.

关键词： Patient privacy DICOM medical image encryption scrambling degree cross-diffusion

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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