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Explicit Unsupervised Feature Selection Based on Structured Graph and Locally Linear Embedding

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SSRN

SSRN 2023年

作者： Miao, Jianyu Zhao, Jingjing Yang, Tiejun Fan, Chao Tian, Yingjie Shi, Yong Xu, Mingliang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Numerous redundant and irrelevant features, and outliers are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Feature selection as a trending topic in a great deal of domains has been greatly concerned, which can effectively deal with noise and outlier. In this work, we propose a novel unsupervised method that unifies the explicit feature selective matrix learning and structured graph learning into a framework. An explicit selection matrix only consisting of 0 and 1 are designed, which can be integrated into locally linear embedding, and can be effectively and efficiently solved with closed-form solution. Furthermore, we take into account the local consistency between the samples so that the low dimensional manifold structures of samples can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both explicit selection matrix and intrinsic structure are learned in low-dimensional feature subspace, and thus the proposed methodology is more straightforward. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis results. Extensive experiments verify that our proposed approach can significantly outperform advanced methods. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

Multi-Temporal Relationship Inference in Urban Areas

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning

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arXiv 2024年

作者： Liu, Saichao Sun, Geng Li, Jiahui Liang, Shuang Wu, Qingqing Wang, Pengfei Niyato, Dusit The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Information Science and Technology Northeast Normal University Changchun130117 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Exploring linguistic features and user engagement in Chinese online mental health counseling

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Heliyon 2024年第19期10卷 e38042页

作者： Zhang, Liyuan Liu, Dexi Li, Jing Wan, Changxuan Liu, Xiping School of Computer and Artificial Intelligence Jiangxi University of Finance and Economics JiangXi Nanchang 330013 China School of Mathematics and Computer YuZhang Normal College Jiangxi Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Jiangxi Nanchang 330013 China School of Electronic Management Science Fujian Jiangxia University Fujian Fuzhou 350108 China

With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of which problem disclosures and social support on these platforms can attract more user attention and engagement. Previous research has primarily focused on social media forums. Our work concentrates on the professional mental health platform, intending to understand the linguistic features present in posts that promote user engagement and interaction. We employ text mining and deep learning techniques to analyze posts consisting of 22,250 questions from help-seekers and 78,328 answers providing social support extracted from the Chinese online mental health counseling platform. Initially, we analyze the high-frequency words and topics of the questions and answers to gain insights into the primary focal points and the range of topics covered in these posts. The results indicate that work-related issues are the most concerning and troublesome for help-seekers, and the topics that users follow are approximately 8 types, including growth, family, in-love, marriage, emotions, human-relations, behavioral-therapy and career. Subsequently, we analyze the language usage in question-and-answer posts with different engagement from three aspects: vocabulary categories, linguistic style matching, and language modeling, aiming to identify which linguistic features can attract more user attention and engagement. The results reveal that high-engagement answer posts exhibit a higher degree of linguistic style matching with the corresponding questions, and the use of vocabulary categories also influences the attention and engagement of the posts. By exploring the linguistic features and patterns displayed in posts with different levels of engagement on the professional online mental health platform, this study offers deep insights into user behavior and the factors that impact

关键词： Language modeling Linguistic style matching Online mental health counseling The topics of questions and answers Vocabulary categories

HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models

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HSR-Diff: Hyperspectral Image Super-Resolution via Condition...

International Conference on Computer Vision (ICCV)

作者： Chanyue Wu Dong Wang Yunpeng Bai Hanyu Mao Ying Li Qiang Shen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University U.K.

Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.

关键词：

Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

Research Method of Blind Path Recognition Based on DCGAN 6th

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Research Method of Blind Path Recognition Based on DCGAN

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Ling Zhang, Ping-Jun Hu, Peng-Jun Yang, Liu Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030586683

In order to solve the problem that there are few blind path data sets and a lot of manual data collection work in the current blind guide system, computer vision algorithm is used to automatically generate blind path images in different environments. Methods a blind path image generation method based on the depth convolution generative adversary network (DCGAN) is proposed. The method uses the characteristics of typical blind path, which is the combination of depression and bulge. The aim of long short memory network (LSTM) is to encode the depression part, and the aim of convolution neural network (CNN) is to encode the bulge part. The two aspects of information are combined to generate blind path images in different environments. It can effectively improve the blind path recognition rate of the instrument and improve the safe travel of the visually impaired. Conclusion generative adversarial networks (GANs) can be used to generate realistic blind image, which has certain application value in expanding blind channel recognition data, but it still needs to be improved in some details. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives

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arXiv 2024年

作者： Wu, Xuan Wang, Di Wen, Lijie Xiao, Yubin Wu, Chunguo Wu, Yuesong Yu, Chaoyu Maskell, Douglas L. Zhou, You The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin university Changchun130012 China The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore The School of Software Tsinghua University Beijing100084 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing surveys did not cover the state-of-the-art (SOTA) NCO solvers emerged recently. More importantly, to provide a comprehensive taxonomy of NCO solvers with up-to-date coverage, based on our thorough review of relevant publications and preprints, we divide all NCO solvers into four distinct categories, namely Learning to Construct, Learning to Improve, Learning to Predict-Once, and Learning to Predict-Multiplicity solvers. Subsequently, we present the inadequacies of the SOTA solvers, including poor generalization, incapability to solve large-scale VRPs, inability to address most types of VRP variants simultaneously, and difficulty in comparing these NCO solvers with the conventional Operations Research algorithms. Simultaneously, we propose promising and viable directions to overcome these inadequacies. In addition, we compare the performance of representative NCO solvers from the Reinforcement, Supervised, and Unsupervised Learning paradigms across both small- and large-scale VRPs. Finally, following the proposed taxonomy, we provide an accompanying web page as a live repository for NCO solvers. Through this survey and the live repository, we hope to make the research community of NCO solvers for VRPs more thriving. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

PSSL: Self-supervised learning for personalized search with contrastive sampling

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arXiv 2021年

作者： Zhou, Yujia Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Université de Montréal MontréalQC Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing *** Codes 68P20 Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding