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Incomplete multi-view clustering via efficient anchor tensor recovery framework

Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107652页

作者： Jintian Ji Songhe Feng Jie Huang Taotao Wei Xiang Feng Peiwu Lv Bing Li School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing 100044 China. Electronic address: 22120385@***. Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing 100044 China. Electronic address: shfeng@***. Jiangsu Railway Group Co. Ltd. Nanjing 210012 China Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: huangj@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: weitt@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: fengx@***. Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China. Electronic address: lvpw@***. The National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing 100190 China. Electronic address: bli@***.

Tensor-based Incomplete Multi-view Clustering (TIMC) methods have received widespread attention due to the powerful data recovery capability of capturing cross-view high-order correlation. Although such methods have achieved remarkable progress, they still suffer from the following problems: (1) The extremely high computational complexity makes it hard for tensor-based methods to handle large-scale multi-view data. (2) Geometric structure constraints in the sample space often lead to high computational complexity and redundancy of structural information. (3) The commonly used Tensor Nuclear Norm (TNN) over-penalizes the primary rank components, leading to a sub-optimal representation tensor. Being aware of these, we propose Incomplete Multi-View Clustering with Efficient Anchor Tensor Recovery Framework (EATER). Specifically, it learns a group of anchors to construct a low-rank anchor tensor to recover the missing data with the high-order correlation among views and the geometric structure in the learned representation tensor is enhanced by an Anchor Laplacian Regularization (ALR). Moreover, instead of employing TNN, we adopt a tighter Non-convex Tensor Rank (NTR) to capture the multi-view high-order correlation effectively. An efficient iterative optimization algorithm is designed to solve the EATER, which is time-economical and enjoys favorable convergence. Extensive experimental results on various datasets demonstrate the superiority of the proposed algorithm as compared to state-of-the-art methods.

关键词： Geometric structure constraint High-order correlation Incomplete multi-view clustering Non-convex tensor rank

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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

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Efficient dynamical system resource management method in cloud computing

Efficient dynamical system resource management method in clo...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Wu, Tianshu Wang, Shuai Shi, Xiaoyu College of Computer Science Chongqing University Chongqing400044 China China West Noraml Univerisity 637002 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

The resource management of virtualisation system is a core issue in the field of cloud computing. For achieving the goal of efficient manage system resource without performance loss in clouding environment, the authors propose an optimal load balancing controller to assign the resource to different virtual machines, which run on a single physical server. Here, the authors focus on dynamical web workloads that the system resource management problem can be formulated as a dynamical optimisation problem. In the face of dynamical and robust workloads, the authors present an alternative approach based on the constrained boundary linear quadratic control (cBLQC) method, the optimal resource allocation scheme is solved by minimising the quadratic cost function with constrains on the system input and output. Different with existing works that assume a particular distribution for system noise or ignore the noise directly, the authors loosen the restrictions on system noise, which simply assume that system noise belongs with some reasonable norm-bounded set. Experiments on the Xen-based platform with a set of workload patterns show that the efficiency of authors’ method in terms of control precision and stability. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Cloud computing

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Distribution Pattern of Soil Organic Carbon and Its Regional Humification Constant in the Coastal Monsoon Region of Eastern China

Research Square

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Research Square 2021年

作者： Liu, Shutian Xie, Xiansheng Wang, Xiaochuan Feng, Xinxin Hou, Xianda Wang, Shuojin Lin, keyu Huang, Mei Jia, Shugang Hou, Yanlin Dou, Sen Guangxi Geographical Indication Crops Research Centre of Big Data Mining and Experimental Engineering Technology Nanning Normal University Nanning530001 China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning530001 China School of Geography and Planning Nanning Normal University Nanning530001 China College of Resource and Environmental Science Jilin Agricultural University Changchun130118 China

Soils are an important pool for storing organic carbon. Soil organic carbon (SOC) content is generally considered as an important indicator to evaluate farmland soil quality. The loss of SOC causes soil degradation and reduces the sustainability of farmland. In order to reveal the distribution pattern of SOC in the coastal monsoon area of eastern China, and to clarify the macro dominant factors of SOC accumulation caused by temperature and precipitation, this paper analyzed the distribution pattern of SOC in the coastal monsoon area of eastern China by using the SOC data collected from the national soil testing and formula fertilization data set, and discussed the effects of temperature and precipitation on SOC content. According to the provincial administrative divisions, the distribution of SOC in the coastal monsoon areas of eastern China from Heilongjiang Province to Hainan Province was calculated. According to the annual average temperature 20.95℃, annual average precipitation 0~400 mm, 400~800 mm, > 800 mm, the study area was divided into different regions, and the effects of annual average temperature and annual average precipitation on SOC content were studied. In the region with annual average temperature less than 10.18℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region of 10.18℃~20.95℃, the annual average temperature and annual average precipitation had a significant positive correlation, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC;In the region >20.95℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature was not related to the accumulation of SOC. In the range of 0 ~ 400 mm of annual average precipitation, the temperature had a positive correlation with the accumulation

关键词： Temperature

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Cost-Driven Off-Loading for DNN-Based Applications Over Cloud, Edge, and End Devices

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5456-5466页

作者： Lin, Bin Huang, Yinhao Zhang, Jianshan Hu, Junqin Chen, Xing Li, Jun Fujian Normal Univ Coll Phys & Energy Fujian Prov Key Lab Quantum Manipulat & New Energ Fuzhou 350117 Peoples R China Fujian Prov Collaborat Innovat Ctr Optoelect Semi Xiamen 361005 Peoples R China Fujian Prov Univ Engn Res Ctr Big Data Applicat Private Hlth Med Putian 351100 Fujian Peoples R China Fujian Prov Collaborat Innovat Ctr Adv High Field Fuzhou 350117 Fujian Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350118 Peoples R China Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350118 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Natl Res Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk 634050 Russia

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.

关键词： Cloud computing cost-driven off-loading deep neural networks (DNNs) edge computing workflow scheduling

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Multi-Granularity Power Prediction for data Center Operations via Long Short-Term Memory Network

Multi-Granularity Power Prediction for Data Center Operation...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Ziyu Shen Xusheng Zhang Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.

关键词： Power demand data centers Task analysis Predictive models data models Time series analysis Servers

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Repeated-root constacyclic codes with optimal locality

arXiv

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arXiv 2021年

作者： Zhao, Wei Shum, Kenneth W. Yang, Shenghao The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China University of Science and Technology of China China The School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

A code is called a locally repairable code (LRC) if any code symbol is a function of a small fraction of other code symbols. When a locally repairable code is employed in a distributed storage systems, an erased symbol can be recovered by accessing only a small number of other symbols, and hence alleviating the network resources required during the repair process. In this paper we consider repeated-root constacyclic codes, which is a generalization of cyclic codes, that are optimal with respect to a Singleton-like bound on minimum distance. An LRC with the structure of a constacyclic code can be encoded efficiently using any encoding algorithm for constacyclic codes in general. In this paper we obtain optimal LRCs among these repeated-root constacyclic codes. Several infinite classes of optimal LRCs over a fixed alphabet are found. Under a further assumption that the ambient space of the repeated-root constacyclic codes is a chain ring, we show that there is no other optimal LRC. © 2021, CC BY.

关键词： Multiprocessing systems

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基于大语言模型的糖尿病管理:潜力与展望

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Science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Convergence Analysis of an SLF-NMU Algorithm for Non-negative Latent Factor Analysis on a High-Dimensional and Sparse Matrix

Convergence Analysis of an SLF-NMU Algorithm for Non-negativ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhigang Liu Xin Luo Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection. An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is highly efficient to build NLF model. However, its convergence ability on such a matrix is still unveiled in theory. This paper presents the convergence property of an SLF-NMU algorithm. We theoretically prove that it can guarantee its convergence to a Karush-Kuhn-Tucher (KKT) stationary point. Empirical studies on two HiDS matrices from practical applications indicate that an SLF-NMU algorithm make an NLF model to converge at a relatively steady state.

关键词： Sparse matrices Convergence Computational modeling Analytical models Linear programming data models Technological innovation

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Localization of Deep Inpainting Using High-Pass Fully Convolutional Network 17

Localization of Deep Inpainting Using High-Pass Fully Convol...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Li, Haodong Huang, Jiwu Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China

ISBN: (纸本)9781728148038

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an upsampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： High pass filters

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