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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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Crowd Counting for Static Images: A Survey of Methodology

Crowd Counting for Static Images: A Survey of Methodology

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第三十九届中国控制会议

作者： Ying Luo Jinhu Lu Baochang Zhang School of Automation Science and Electrical Engineering Beihang University Shenyuan Honors College Beihang University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang University

Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. This paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.

关键词： Crowd counting crowd analysis pattern recognition computer vision

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Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks

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Computers & Geosciences 2025年 204卷

作者： Yecheng Liu Diquan Li Jin Li Yanfang Hu Zijie Liu Xian Zhang School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor Ministry of Education Central South University Changsha 410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha 410081 China Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha 410205 China

Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference.

关键词： Controlled-source electromagnetic method Deep learning Noise Signal detection

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

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IEEE International Conference on Services computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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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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Asymptotic properties of zeros for discrete-time linear systems with backward triangle sampled-and-hold using delta operator

Asymptotic properties of zeros for discrete-time linear syst...

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Chinese automation Congress (CAC)

作者： Minghui Ou Shan Liang Xiaobin Wu Zhiyong Yang Hehuan Ran School of Big Data and Internet of Things of Chongqing Vocational Institute of Engineering College of Automation of Chongqing University Chongqing China College of Automation of Chongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing China State Grid Chongqing Shiqu Electric Power Supply Branch Chongqing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This article focuses on a well-studied theoretical problem that, for rapid sampling, a continuous-time system with minimum-phase properties may become a non-minimum phase discrete-time system in the discretization process. The properties of zeros for discrete-time linear models in the field of digital control when using the special shift operator was considered in this paper. From the former relevant studies one can know that the backward triangle sample-and-hold (BTSH) as an alternative hold method to discretize the continuous-time systems has an advantage over ZOH in stabilizing sampling zeros for linear system with classical forward shift operator. In this article, we deduce the exact sampled-data model of linear system using delta operator to replace forward shift operator in the case of BTSH. Furthermore, the expressions and properties of zeros for the corresponding discrete-time system are derived.

关键词： Control systems Linear systems Discrete-time systems Process control Numerical models Signal reconstruction Internet of Things

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SG-PBFT: A secure and highly efficient blockchain PBFT Consensus Algorithm for internet of vehicles

arXiv

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arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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IEEE International Conference on Networking, Sensing and Control

作者： Xuke Wu Hang Gou Hao Wu Juan Wang Minzhi Chen Siyu Lai Computer School China West Normal University Nanchong China Computer School of China West Normal University Nanchong Sichuan China University of Chinese Academy of Sciences Beijing China 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 North Sichuan Medical College Nanchong China Medical College Nanchong Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.

关键词： Tensors Conferences Predictive models data models Sensors data mining

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Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, Dan Ma, Chunyan Xu, Lihua Liu, Xu Cui, Huiru Wei, Yanyan Zheng, Wensi Hong, Yawen Xie, Yuou Qian, Zhenying Hu, Yegang Tang, Yingying Li, Chunbo Liu, Zhi Chen, Tao Liu, Haichun Zhang, Tianhong Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine People's Republic of China Shanghai China First Clinical Medical College of Nanjing Medical University People's Republic of China Nanjing China Shanghai Institute for Advanced Communication and Data Science Shanghai University People's Republic of China Shanghai China School of Communication and Information Engineering Shanghai University People's Republic of China Shanghai China Labor and Worklife Program Harvard University Cambridge Massachusetts United States Big Data Research Laboratory University of Waterloo Waterloo ON Canada Niacin (Shanghai) Technology Co. Ltd. People's Republic of China Shanghai China Department of Automation Shanghai Jiao Tong University People's Republic of China Shanghai China Shanghai Mental Health Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai Jiaotong University School of Medicine People's Republic of China Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences People's Republic of China Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University People's Republic of China Shanghai China

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli. Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments. We compared group differences in the overall entropy score, as well as entropy scores under various conditions. We also investigated the correlations between entropy scores and symptoms and cognitive function. Results: Increased overall entropy scores were noted in the FES and CHR groups compared with the HC group, and these differences were already apparent within 0 to 2.5 seconds. In addition, the CHR group exhibited higher entropy than the HC group when viewing low-meaning images. Moreover, the entropy within 0 to 2.5 seconds showed significant correlations with negative symptoms in the FES group, attention/vigilance scores in the CHR group, and speed of processing and attention/vigilance scores across all 3 groups. Conclusions: The results indicate that individuals with FES and those at CHR scanned pictures more randomly and less strategically than HCs. These patterns also correlated with clinical symptoms and neurocognition. The current study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis. © 2024 Society of Biological Psychiatry

关键词： Biomarker Clinical high risk for psychosis Cognitive impairments Early psychosis Eye movement Schizophrenia

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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