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IEEE International Conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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Graph convolutional module for temporal action localization in videos

arXiv

引用

arXiv 2021年

作者： Zeng, Runhao Huang, Wenbing Tan, Mingkui Rong, Yu Zhao, Peilin Huang, Junzhou Gan, Chuang The School of Software Engineering South China University of Technology The Pazhou Laboratory Guangzhou China China The School of Software Engineering South China University of Technology The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Tencent AI Laboratory Shenzhen China The MIT-IBM Watson AI Lab CambridgeMA02142 United States

Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision. The main challenge of temporal action localization lies in that videos are usually long and untrimmed with diverse action contents involved. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods

关键词： Convolution

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A Deep Learning Framework for Stock Prediction Using LSTM 1

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International Conference on Decision Economics, DECON 2019

作者： Lin, Yaohu Liu, Shancun Yang, Haijun Wu, Harris School of Economics and Management Beihang University Beijing China Key Laboratory of Complex System Analysis Management and Decision Beihang University Ministry of Education Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Strome College of Business Old Dominion University NorfolkVA United States

ISBN: (数字)9783030382278

ISBN: (纸本)9783030382261

In order to test the predictive power of the deep learning model, several machine learning methods were introduced for comparison. Empirical case results for the period of 2000 to 2017 show the forecasting power of deep learning technology. With a series of linear regression indicator measurement, we find LSTM networks outperform traditional machine learning methods, i.e., Linear Regression, Auto ARIMA, KNN. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Correction to: Core sample consensus method for two-view correspondence matching

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Multimedia Tools and Applications 2024年第8期83卷 24631-24631页

作者： Ding, Xintao Li, Boquan Zhou, Wen Zhao, Cheng School of Computer and Information Anhui Normal University Wuhu China Anhui Province Key Laboratory of Network and Information Security Wuhu China Anhui Engineering Research Center of Medical Big Data Intelligent System Wuhu China School of Mathematics and Statistics Anhui Normal University Wuhu China

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Medical-VLBERT: Medical visual language BERT for COVID-19 CT report generation with alternate learning

arXiv

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arXiv 2021年

作者： Liu, Guangyi Liao, Yinghong Wang, Fuyu Zhang, Bin Zhang, Lu Liang, Xiaodan Wan, Xiang Li, Shaolin Li, Zhen Zhang, Shuixing Cui, Shuguang The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Sun Yat-sen University Guangzhou510275 China Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou510630 China Jinan University Guangzhou510632 China Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai519000 China

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further finetuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at at https://***/. Copyright © 2021, The Authors. All rights reserved.

关键词： Diagnosis

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Larger Receptive Field and Context Information for Pose Estimation: Larger Gaussian Kernel

Larger Receptive Field and Context Information for Pose Esti...

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High Performance big data and intelligent Systems (HPBD&IS)

作者： Junxiao Ma Kai Yang Zunwang Ke Gang Wang Yugui Zhang Fengcai Cao Qi Zhou Yang Fei School of Information Beijing Forestry University Beijing China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Semiconductor Science and Technology South China Normal University GuangZhou China

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints' missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method.

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach

Deep Learning Strategies for Addressing Anomalous Exposure i...

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High Performance big data and intelligent Systems (HPBD&IS)

作者： Qi Zhou Kai Yang Zunwang Ke Gang Wang Yugui Zhang Yizhen Jia Fengcai Cao Junxiao Ma Changlin Liu Kaijie Zhang Min Wu School of Semiconductor Science and Technology South China Normal University GuangZhou China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China CGNPC Uranium Industry Development Co. Ltd. Beijing China School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China Zhongke Shangyi Health Technology (Beijing) Co. Ltd. Beijing China School of Information Beijing Forestry University Beijing China

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers' integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets.

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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