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arXiv 2024年

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates AI Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China Department of CSE The Hong Kong University of Science and Technology China

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Inconsistency Distillation For Consistency:Enhancing Multi-View Clustering via Mutual Contrastive Teacher-Student Leaning

Inconsistency Distillation For Consistency:Enhancing Multi-V...

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IEEE International Conference on Data Mining (ICDM)

作者： Dunqiang Liu Shu-Juan Peng Xin Liu Lei Zhu Zhen Cui Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Key Lab. of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen China School of Information Sci. and Eng. Shandong Normal University Jinan China School of Computer Sci. and Eng. Nanjing University of Science and Technology Nanjing China

Multi-view clustering has attracted more attention recently since many real-world data are comprised of different representations or views. Recent multi-view clustering works mainly exploit the instance consistency to obtain the shared representations across different views, and apply a single-view clustering method to perform data partitions. However, these existing methods often ignore the inconsistency of instance associations within the views, which may enlarge the intra-class diversity among the views and therefore degrade the clustering performance. To address this issue, this paper proposes an efficient mutual contrastive teacher-student leaning (MC-TSL) model to enhance the multi-view clustering, which is the first attempt to study the inconsistency distillation for consistency learning. First, the proposed MC-TSL approach exploits a view-specific encoder with two heads, an instance encoding head and a semantic distillation head, respectively, for capturing the consistent and discriminative feature representations. To be specific, the former head exploits a cross-view contrastive learning method to obtain a redundancy-free consistent representation at the instance level, while the latter head designs a mutual teacher-student learning module to capture the intra-view information at semantic level. By training these two heads in an end-to-end manner, the discriminative multi-view embeddings are efficiently obtained and refined by minimizing the weighted sum of the reconstruction loss, contrastive loss and contrast distillation loss. Extensive experiments verify the superiorities of the proposed MC-TSL framework and show its competitive clustering performances.

关键词： Training learning systems Clustering methods Semantics Encoding Data mining

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BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

arXiv

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arXiv 2022年

作者： Zheng, Kaizhong Yu, Shujian Li, Baojuan Jenssen, Robert Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam and the Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The School of Biomedical Engineering Fourth Military Medical University Xi’an China

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learning-based diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 3 baselines and 7 state-of-the-art brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at GitHub repository (https://***/SJYuCNEL/brain-and-Information-Bottleneck/). Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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learning Physics-Informed Neural Networks without Stacked Back-propagation

arXiv

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arXiv 2022年

作者： He, Di Li, Shanda Shi, Wenlei Gao, Xiaotian Zhang, Jia Bian, Jiang Wang, Liwei Liu, Tie-Yan National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research Center for Data Science Peking University China

Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE). But, facing high-dimensional second-order PDE problems, PINN will suffer from severe scalability issues since its loss includes second-order derivatives, the computational cost of which will grow along with the dimension during stacked back-propagation. In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks. In particular, we parameterize the PDE solution by the Gaussian smoothed model and show that, derived from Stein's Identity, the second-order derivatives can be efficiently calculated without back-propagation. We further discuss the model capacity and provide variance reduction methods to address key limitations in the derivative estimation. Experimental results show that our proposed method can achieve competitive error compared to standard PINN training but is significantly faster. Our code is released at https://***/LithiumDA/PINN-without-Stacked-BP. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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Using Cloud Computing Based Crowdsourcing for Security and Privacy Settings of Android Users

Using Cloud Computing Based Crowdsourcing for Security and P...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuzhu Sun Xuanyu Liu Tianchi Wu Xiao Fu Bin Luo Xiaojiang Du Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (纸本)9781665435413

With the widespread use of mobile devices, security and privacy concerns have been attracting increasing attention. However, it is challenging for Android users to apply the appropriate settings to protect their devices. Therefore, a cloud-based solution is proposed in this paper, which works based on crowdsourcing to generate recommended security and privacy settings for Android users. Following the suggestions of security experts, ordinary users can readily understand how to properly set up their devices for security enforcement. Our solution collects security-related data from experts, and a weight-based algorithm is introduced to determine data priority and credibility. Finally, the recommended security and privacy settings are created based on these data. The evaluation results demonstrate that our solution can effectively improve the robustness and reliability of Android devices.

关键词： Crowdsourcing Privacy Cloud computing Data privacy Protocols Robustness Mobile security

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Reliable neighbors-based collaborative filtering for recommendation systems 1

引用

1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collaborative filtering

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Semi-supervised feature selection using sparse laplacian support vector machine 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Zheng, Xiaohan Xu, Zhiqiang School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

Semi-supervised feature selection is an active topic in machine learning and data mining. Laplacian support vector machine (LapSVM) has been successfully applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the 1-norm regularization, which means the solution of SLapSVM has sparsity. In addition, the training procedure of SLapSVM can be formulated as solving a quadratic programming problem, which indicates that the solution of SLapSVM is unique and global. SLapSVM can perform feature selection and classification at the same time. Experimental results on semi-supervised classification problems show the feasibility and effectiveness of the proposed semi-supervised learning algorithms. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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A Decision Support Method for Interdisciplinary Major in Colleges and Universities Based on Natural Language Processing and Data Mining

A Decision Support Method for Interdisciplinary Major in Col...

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Big Data, Artificial Intelligence and Risk Management (ICBAR), International Conference on

作者： Lida Zou School of Computer Science and Technology Shandong University of Finance and Economics Jinan China Key Laboratory of Machine Learning and Financial Data Mining in Universities of Shandong Jinan China

ISBN: (纸本)9781665495660

With the development of science and technology, many disciplines reveal the general trends of crossing and blending, and new interdisciplinary subjects emerge. For decision makers in colleges and universities, it is crucial and difficult to choose and construct a proper interdisciplinary major. In the paper we simulate the phenomenon of current conduction to improve natural language processing and data mining. An assistant decision-making method of setting up interdisciplinary majors for colleges and universities is designed, which could not only intelligently and automatically recommend the most suitable interdisciplinary majors, but also simplify and correct the work of decision-makers. Our experimental analysis demonstrates that similarity ratio of interdisciplinary majors recommended by the proposed assistant decision-making method runs at more than 70 percent compared with manual recommendation of specialists.

关键词： Technological innovation Education Decision making Manuals Big Data Market research Natural language processing

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Extended $T$T: learning With Mixed Closed-Set and Open-Set Noisy Labels

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IEEE Transactions on Pattern Analysis and machine Intelligence 2022年第3期45卷 3047-3058页

作者： Xiaobo Xia Bo Han Nannan Wang Jiankang Deng Jiatong Li Yinian Mao Tongliang Liu Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney Camperdown NSW Australia Department of Computer Science Hong Kong Baptist University Hong Kong State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Xi'an Shaanxi China Department of Computing Imperial College London London U.K. Meituan Beijing China

The noise transition matrix $T$ , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data have true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Therefore, when considering a more realistic situation, i.e., both closed-set and open-set label noises occur, prior works will give unbelievable solutions. Besides, the traditional transition matrix is mostly limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning with the mixed closed-set and open-set noisy labels. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better combat the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended $T$ -estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive experiments validate that our method can better cope with realistic label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.

关键词： Noise measurement Training Training data Data models Search engines Face recognition computer science

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