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2023 IEEE International Conference on Image Processing and computer Applications, ICIPCA 2023

作者： Wu, Yuhong Lv, Yunshan Zhang, Ling School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350314670

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system;then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency. © 2023 IEEE.

关键词： Network security

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SpeAr: A Spectral Approach for Zero-Shot Node Classification 38

SpeAr: A Spectral Approach for Zero-Shot Node Classification

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Ting Wang, Da Liang, Jiye Zhang, Kaihan Zeng, Jianchao Data Science and Technology North University of China Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China

Zero-shot node classification is a vital task in the field of graph data processing, aiming to identify nodes of classes unseen during the training process. Prediction bias is one of the primary challenges in zero-shot node classification, referring to the model's propensity to misclassify nodes of unseen classes as seen classes. However, most methods introduce external knowledge to mitigate the bias, inadequately leveraging the inherent cluster information within the unlabeled nodes. To address this issue, we employ spectral analysis coupled with learnable class prototypes to discover the implicit cluster structures within the graph, providing a more comprehensive understanding of classes. In this paper, we propose a Spectral Approach for zero-shot node classification (SpeAr). Specifically, we establish an approximate relationship between minimizing the spectral contrastive loss and performing spectral decomposition on the graph, thereby enabling effective node characterization through loss minimization. Subsequently, the class prototypes are iteratively refined based on the learned node representations, initialized with the semantic vectors. Finally, extensive experiments verify the effectiveness of the SpeAr, which can further alleviate the bias problem. © 2024 Neural information processing systems foundation. All rights reserved.

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ICL: Iterative Continual Learning for Multi-domain Neural Machine Translation

ICL: Iterative Continual Learning for Multi-domain Neural Ma...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Man, Zhibo Huang, Kaiyu Zhang, Yujie Chen, Yuanmeng Chen, Yufeng Xu, Jinan Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798891761681

In a practical scenario, multi-domain neural machine translation (MDNMT) aims to continuously acquire knowledge from new domain data while retaining old *** work separately learns each new domain knowledge based on parameter isolation methods, which effectively capture the new ***, task-specific parameters lead to isolation between models, which hinders the mutual transfer of knowledge between new *** the scarcity of domain-specific corpora, we consider making full use of the data from multiple new ***, our work aims to leverage previously acquired domain knowledge when modeling subsequent *** this end, we propose an Iterative Continual Learning (ICL) framework for multi-domain neural machine ***, when each new domain arrives, (1) we first build a pluggable incremental learning model, (2) then we design an iterative updating algorithm to continuously update the original model, which can be used flexibly for constructing subsequent domain ***, we design a domain knowledge transfer mechanism to enhance the fine-grained domain-specific representation, thereby solving the word ambiguity caused by mixing domain *** results on the UM-Corpus and OPUS multi-domain datasets show the superior performance of our proposed model compared to representative baselines. © 2024 Association for Computational Linguistics.

关键词： Neural machine translation

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GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning 12th

GrammarGPT: Exploring Open-Source LLMs for Native Chinese G...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Fan, Yaxin Jiang, Feng Li, Peifeng Li, Haizhou School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology Soochow University Suzhou China Shenzhen Research Institute of Big Data Guangdong Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783031446986

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error correction. However, the potential of open-source LLMs remains unexplored. In this paper, we introduced GrammarGPT, an open-source LLM, to preliminary explore its potential for native Chinese grammatical error correction. The core recipe of GrammarGPT is to leverage the hybrid dataset of ChatGPT-generated and human-annotated. For grammatical errors with clues, we proposed a heuristic method to guide ChatGPT to generate ungrammatical sentences by providing those clues. For grammatical errors without clues, we collected ungrammatical sentences from publicly available websites and manually corrected them. In addition, we employed an error-invariant augmentation method to enhance the ability of the model to correct native Chinese grammatical errors. We ultimately constructed about 1k parallel data and utilized these data to fine-tune open-source LLMs (e.g., Phoenix, released by The Chinese University of Hong Kong, Shenzhen) with instruction tuning. The experimental results show that GrammarGPT outperforms the existing SOTA system significantly. Although model parameters are 20x larger than the SOTA baseline, the required amount of data for instruction tuning is 1200x smaller, illustrating the potential of open-source LLMs on native CGEC. Our GrammarGPT ranks 3 rd on NLPCC2023 SharedTask1, demonstrating our approach’s effectiveness. The code and data are available at https://***/FreedomIntelligence/GrammarGPT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Error correction

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Efficient Dual-Confounding Eliminating for Weakly-supervised Temporal Action Localization 24

Efficient Dual-Confounding Eliminating for Weakly-supervised...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ao Liu, Huijun Sheng, Jinrong Chen, Zhongming Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798400706868

Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous works have attempted to distinguish these ambiguous snippets from action snippets without investigating the underlying causes of their formation, thus failing to effectively eliminate the bias on both action-context and action-content. In this paper, we revisit WTAL from the perspective of structural causal model to identify the true origins of confounding, and propose an efficient dual-confounding eliminating framework to alleviate these biases. Specifically, we construct a Substituted Confounder Set (SCS) to eliminate the confounding bias on action-context by leveraging the modal disparity between RGB and FLOW. Then, a Multi-level Consistency Mining (MCM) method is designed to mitigate the confounding bias on action-content by utilizing the consistency between discriminative snippets and corresponding proposals at both the feature and label levels. Notably, SCS and MCM could be seamlessly integrated into any two-stream models without additional parameters by Expectation-Maximization (EM) algorithm. Extensive experiments on two challenging benchmarks including THUMOS14 and ActivityNet-1.2 demonstrate the superior performance of our method. © 2024 ACM.

关键词： Expectation maximization algorithm

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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Privacy Preservation Fully Homomorphic Encryption for Cloud-assisted Biometric Identification with Multi-key

Privacy Preservation Fully Homomorphic Encryption for Cloud-...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Peng, Shenghui Jia, Peiheng Xiong, Jinbo Zhu, Liehuang Liu, Ximeng Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Fujian Normal University College of Computer and Cyber Security Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350351255

Biometrics is a technology that utilizes individual physiological or behavioral characteristics for identity authentication and identification, ensuring secure and accurate identity verification by comparing biometric information with samples in a database. However, traditional biometric technology raises concerns about privacy leakage, which has led to the emergence of privacy-preserving biometric technology. This study introduces a cloud-assisted privacy-preserving biometric authentication system based on multi-key full homomorphic encryption. Our scheme employs a TFHE encryption scheme to encrypt user biometrics and utilizes a cloud server to assist in computation. We design secure computing protocols based on multi-key TFHE and develop a privacy-preserving biometric system based on a single cloud server. Privacy analysis and performance evaluation demonstrate that the proposed solution is efficient and feasible. Our experimental findings indicate that the proposed scheme exhibits a superior accuracy rate to current schemes. Compared to the best solution, our method has increased the iris recognition accuracy by approximately 68.42%. Although the experimental results demonstrate high accuracy and feasibility, implementing TFHE in biometric models could be improved by its execution time, presenting challenges for broad adoption. Nevertheless, through optimization and technological advancements, this protocol is anticipated to elevate privacy protection standards in practical biometric recognition systems moving forward. © 2024 IEEE.

关键词： Differential privacy

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment 38

DFA-GNN: Forward Learning of Graph Neural Networks by Direct...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Gongpei Wang, Tao Lang, Congyan Jin, Yi Li, Yidong Ling, Haibin Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science Stony Brook University United States

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks. © 2024 Neura

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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