2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)最新文献

{"title":"Prevention and Control of Emerging Infectious Diseases in Human Populations","authors":"Sophie Khaddaj, Hussain Chrief","doi":"10.1109/DCABES50732.2020.00092","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00092","url":null,"abstract":"The prevention, control and prediction of emerging infectious diseases are vital in order to effectively manage their spread and impact. Over the years many modelling techniques have been developed for the management of infectious diseases. However, emerging diseases are linked to selective pressures caused by humans, for example environmental pressure such as urbanisation and habitat fragmentation. In this paper we present a new approach, which combines human behavioural factors together with advanced mathematical modelling and machine learning, for preventing, monitoring and predicting future epidemics. This will help medical professionals and policy makers to optimize, in real-time, response efforts to major outbreaks.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115088688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientific Committee DCABES 2020","authors":"Dcabes, C. Douglas, Jianzhong Chen, Zirong Yang, Lishan Kang, Yi Pan, Peter Sloot, A. Shafarenko, Wei Hai, Shan Dong","doi":"10.1109/dcabes50732.2020.00008","DOIUrl":"https://doi.org/10.1109/dcabes50732.2020.00008","url":null,"abstract":"Craig C. Douglas, University of Wyoming, Yale University, USA Jianzhong Chen, Guizhou University of Finance and Economics, China Zirong Yang, Guizhou University of Finance and Economics, China C.-H. Lai, University of Greenwich, UK Q.P. Guo, Wuhan University of Technology, Wuhan, China Frederic Magoules, Applied Mathematics and Systems, Ecole Centrale Paris, France Albert Y. Zomaya, The University of Sydney, Australia Hai Jin, HUST, Wuhan, China Maurício Vieira Kritz, National Laboratory for Scientific Computation, Petropolis, RJ, Brasil Peter Jimack, University of Leeds W.B. Xu, Jiangnan University, Wuxi, China Xiao-Chuan Cai, University of Colorado Boulder, USA Jianwen CAO, Institute of Software Chinese Academy of Sciences, Beijing, China Chi XueBing, Chinese Academy of Sciences, China Yakup Paker, Queen Mary University of London, London, UK Turgay Altilar, Istanbul Technical University, Istanbul, Turkey Souheil Khaddaj, Kingston University, UK Andrew A. Chien, University of California, SAN DIEGO, USA Pui-Tak Ho, The University of Hong Kong, HK John W. T. Lee, The Hong Kong Polytechnic University, HK Lishan Kang, Department of Computer Science and Technology, China; University of Geosciences, China David Keyes, King Abdullah University of Science and Technology, USA Chen, Wei, Wuhan University of Technology, Wuhan, China Xiao, Xinping, Wuhan University of Technology, China Wenjing Li, Guangxi Normal University, Nanning, Guangxi, China Shesheng Zhang, Wuhan University of Technology, China Ping Lin, University of Dundee, Dundee Michael K. Ng, The University of Hong Kong, HK SUN Jiachang, Institute of Software, Academy of Science, China Alfred Loo, Lingnan University, HK Man Leung Wong, Lingnan University, HK Peter KACSUK, Hungarian Academy of Sciences, HU Stefan Vandewalle, Katholieke Universiteit Leuven, Belgium Robert Lovas, Hungarian Academy of Sciences, Hungary Faouzi Alaya Cheikh, Gjovik University College, Norway NIKOS CHRISTAKIS, University of Crete, Heraklion, Greece Haixin Lin, Delft University of Technology, Netherlands Anne Trefethen, University of Oxford, UK David Keyes, Columbia University, America Rassul Ayani, Royal Institute of Technology (KTH), Sweden Zhihui Du, Tsinghua University, China Meiqing Wang, Fuzhou University, Fuzhou, China Yuhua Liu, Central China Normal University, Wuhan, China Rongcong Xu, Fuzhou University, Fuzhou, China Youwei Yuan, Hangzhou Dianzi University, Hangzhou, China","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Architecture Design of A UUV Swarm System Based on Distributed Operation Theory","authors":"Yabo Zheng, Lixia Wang, Xiaoming Dong, Linzhou Xu","doi":"10.1109/DCABES50732.2020.00013","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00013","url":null,"abstract":"In order to improve the anti-submarine warfare capability, an anti-submarine UUV swarm system based on distributed operation theory is proposed. This paper describes the basic concept of the distributed operation theory and the anti-submarine operational mode of the UUV swarm, designs the system architecture, basic composition and control structure of the UUV swarm, and finally describes the operational workflow of the UUV swarm in detail. The proposed system integrates artificial intelligence technology, distributed operation concept and swarm tactics, which can effectively promote the diversification of the underwater battlefield awareness and the systematization of anti-submarine warfare so as to improve the naval cooperative anti-submarine warfare effectiveness and undersea control capability.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Entropy evaluation method of hierarchical clustering","authors":"Quan Tu, Tianyang Xu, Tingting Fang, Wen Wang, Jie Jiang, Ping Zhu","doi":"10.1109/DCABES50732.2020.00066","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00066","url":null,"abstract":"Based on the agglomerative hierarchical clustering algorithm, this paper proposes a new information entropy evaluation indicator-Average Discriminant Entropy(ADE), to measure the stability of cluster structure. After that, We designed the corresponding algorithm. In order to verify the validity of the indicator, six heterogeneous artificial data sets were used to simulate. By comparing ADE with other classic evaluation indicators, we found that ADE can obtain the best results under various data sets. Finally, a Monte Carlo experiment on the data with different noise levels proved the robust of ADE.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125845350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Automatic Control System for The Touchdown Pressure of Disc Brush Based on Fuzzy Sensing Technology","authors":"Jiaming Feng","doi":"10.1109/DCABES50732.2020.00049","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00049","url":null,"abstract":"In the daily cleaning process, the workload of the cleaning vehicle is large, leading to fast wear of the disc brush, so that the touchdown pressure of disc brush decreases. The staffs need to adjust the hydraulic oil cylinder by hand frequently according to the wear of the disc brush to compensate it. The operation of this process is comparatively complicated, it is unfavorable to improving the efficiency of the work. This study combines the structural characteristics of the cleaning vehicle and the application of sensor technology, making disc brush adjust the touchdown pressure autonomously to reduce the poor cleaning effect because of the lack of the touchdown pressure when it can't work normally due to a large change in the touchdown pressure and enhance the cleaning efficiency of cleaning vehicle. The result shows that the self-control of the touchdown pressure of disc brush system can greatly improve the cleaning efficiency of the cleaning vehicle when the continuous working time of the cleaning vehicle exceeds 4h.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Comparison of Small Object Detection Algorithms of UAV based Aerial Images","authors":"Hao Xu, Yuan Cao, Qian Lu, Qiang Yang","doi":"10.1109/DCABES50732.2020.00014","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00014","url":null,"abstract":"Traffic controls in modern society are part of urban management. With the assistance of unmanned aerial vehicles (UAVs) equipped with mounted cameras, researchers could capture aerial (bird-view) images from appropriate altitude. The perspective in aerial images makes appearances of objects squat, although aerial images can supply more contextual information about the environment by a broader view angle, the object instances may be detected by mistake. This fact diminishes the aerial images that can be fed to a network with higher dimensions that increases the computational cost to prevent the diminishing of pixels belonging to small objects. To compare model performance on small objects with aerial images, this study trains and tests two object detectors, i.e. YOLOv4 and YOLOv3, on the AU-AIR dataset, and exploited the parameterization of YOLO based models for small object detection. Finally, the key numerical results and observations are presented.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132120257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast cancer diagnosis and prediction model based on improved PSO-SVM based on gray relational analysis","authors":"Chang Shuran, L. Yian","doi":"10.1109/DCABES50732.2020.00067","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00067","url":null,"abstract":"Early breast cancer diagnosis and prediction models use image data as input, which is likely to cause a large possibility of error in the conversion process of image data. Therefore, this paper proposes a PSO-SVM diagnostic prediction model called GP-SVM based on gray relational analysis (GRA) of a data set consisting of conventional sign data and blood analysis data. First of all, the original data set is optimized by gray relational analysis (GRA) to obtain a new data set. Secondly, the GP-SVM model composed of improved PSO and SVM, and uses the obtained data set as its input. The improvement point of its PSO algorithm is to dynamically adjust the inertial weights and learning factors to make the improved PSO The algorithm optimizes the parameters of SVM and balances the globality and speed of PSO algorithm convergence. On the breast cancer Coimbra data set in UCI, compared with other prediction models, the performance of the GP-SVM prediction model has better.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127864706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless sensor network node localization research based on improved wolves algorithm","authors":"Guojun Chen, P. Xu","doi":"10.1109/DCABES50732.2020.00055","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00055","url":null,"abstract":"DV-Hop is a typical node localization algorithm without ranging of wireless sensor network. Because of the shortcoming that this algorithm's accuracy for node positioning is not high, a sensor node localization algorithm based on improved wolves algorithm is proposed. The algorithm is based on the classic DV-Hop algorithm, converts the wireless sensor node localization problem into a multi-constrained optimization problem, and uses improved wolves algorithm to solve this problem, then obtains the optimal solution of unknown node coordinates. Matlab simulation show that, this algorithm can significantly improve the positioning accuracy of the node than other wireless sensor network node localization algorithm.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123092948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LFTD: A Light and Fast Text Detector","authors":"Guanghao Hu, Silu Chen, Jun Sun","doi":"10.1109/DCABES50732.2020.00073","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00073","url":null,"abstract":"Aiming at the problem that the existing text detection technology runs slowly on edge devices and terminal devices with limited storage space and low computing capacity, this paper proposes a method based on A Light and Fast Face Detector for Edge Devices (LFFD) and Connectionist Text Proposal Network. (CTPN) A Light and Fast Text Detector (LFTD). First of all, distinguishing from the current situation of large number of parameters and complex model structure of previous text detectors, this paper is based on the LFFD face detection model. It introduces the characteristics that it does not need to preset a large number of anchor boxes with different sizes and proportions, which makes the detection box network in this paper. The frame is lighter. Secondly, for the problem that the detection range of text and image detection frames is different, this paper improves the label part of LFFD and combines the CTPN method to divide the detection frame of this article into several detection frames according to the font size. The proposed method can theoretically detect Large continuous text scale with 100% coverage. Experiments were performed on popular benchmark datasets (ICDAR11, ICDAR13 and ICDAR15). The proposed method can obtain fast inference speed (NVIDIA TITAN 1080Ti: 131.45 FPS at 640 × 480; NVIDIA 2080Ti at 640 × 480: 136.99 FPS; 640 × 480 Raspberry Pi 3 Type B +: 8.44 FPS), the parameter amount of this model is 8 MB.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130735442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Last Utterance-Context Attention Model for Multi-Turn Response Generation","authors":"Guodong Zhang, Li-ting Mao, Jun Sun","doi":"10.1109/DCABES50732.2020.00057","DOIUrl":"https://doi.org/10.1109/DCABES50732.2020.00057","url":null,"abstract":"Recently, conversation response generation task is attracting the attention of more and more researchers. Different from single-turn response generation, multi-turn response generation not only focuses on fluency, but also needs to make use of contextual information. Therefore, we believe that an appropriate response should be coherent to the last utterance, and take conversation history into consideration at the same time. We propose a Last Utterance-Context Attention model. The last utterance attention calculates each word in last utterance and form them as a vector. Representation of each utterance is processed by the context attention and formed as a vector as well. Then the two vectors are concatenated as a context vector for decoding the response. In addition, we also apply the multi-head self-attention mechanism to focus more on the key words in each utterance. Both automatic and human evaluation results show that our model outperform baseline models for multi-turn response generation.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134062978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}