2020 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献

Kubernetes-based Workload Allocation Optimizer for Minimizing Power Consumption of Computing System with Neural Network 基于kubernetes的神经网络计算系统功耗最小化的工作负载分配优化器

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00238

Ryuki Douhara, Ying-Feng Hsu, T. Yoshihisa, Kazuhiro Matsuda, Morito Matsuoka

引用次数: 3

A Low Cost LoRa-based IoT Big Data Capture and Analysis System for Indoor Air Quality Monitoring 基于lora的低成本物联网室内空气质量监测大数据采集与分析系统

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00070

Matthew Meli, E. Gatt, O. Casha, I. Grech, J. Micallef

引用次数: 1

Clustering County-Wise COVID-19 Dynamics in North Carolina 北卡罗来纳州县级COVID-19动态聚集

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00157

M. Park, Seong‐Tae Kim

引用次数: 0

Anomalous Detection System in Crowded Environment using Deep Learning 基于深度学习的拥挤环境异常检测系统

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00012

D. Esan, P. Owolawi, Chuling Tu

{"title":"Anomalous Detection System in Crowded Environment using Deep Learning","authors":"D. Esan, P. Owolawi, Chuling Tu","doi":"10.1109/CSCI51800.2020.00012","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00012","url":null,"abstract":"In recent years, surveillance systems have become very important due to security concerns. These systems are widely used in many applications such as airports, railway stations, shopping malls, crowded sports arenas, military etc., [1]. The wide deployment of surveillance systems has made the detection of anomalous behavioral patterns in video streams to become increasingly important. An anomalous event can be considered as a deviation from the regular scene; however, the distribution of normal and anomalous events is severely imbalanced, since the anomalous behavior events do not frequently occur, hence it is imperative to accurately detect anomalous behavioral pattern from a normal pattern in a surveillance system. This paper proposes a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) technique. The CNN is used to extract the features from the image frames and the LSTM is used as a mechanism for remembrance to make quick and accurate detection. Experiments are done on the University of California San Diego dataset using the proposed anomalous behavioral pattern detection system. Compared with other existing methods, experimental analysis demonstrates that CNN-LSTM technique has high accuracy with better parameters tuning. Different analyses were conducted using the publicly available dataset repository that has been used by many researchers in the field of computer vision in the detection of anomalous behavior. The results obtained show that CNN-LSTM outperforms the others with overall F1-score of 0.94; AUC of 0.891 and accuracy of 89%. This result shows that the deployment of the proposed technique in a surveillance detection system can assist the security personnel to detect an anomalous behavioral pattern in a crowded environment.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"121 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120851392","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}

引用次数: 2

Recursive MaxSquare: Cache-friendly, Parallel, Scalable in situ Rectangular Matrix Transposition 递归MaxSquare:缓存友好，并行，可伸缩的原位矩形矩阵转置

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00228

Claudio A. Parra, Travis Yu, Kyu Seon Yum, Arturo Garza, I. Scherson

{"title":"Recursive MaxSquare: Cache-friendly, Parallel, Scalable in situ Rectangular Matrix Transposition","authors":"Claudio A. Parra, Travis Yu, Kyu Seon Yum, Arturo Garza, I. Scherson","doi":"10.1109/CSCI51800.2020.00228","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00228","url":null,"abstract":"An in situ rectangular matrix transposition algorithm is presented based on recursively partitioning an original rectangular matrix into a maximum size square matrix and a remaining rectangular sub-matrix. To transpose the maximum size square sub-matrix, a novel cache-friendly, parallel (multithreaded) and scalable in-place square matrix transposition procedure is proposed: it requires a total of Θ(n2/2) simple memory swaps, a single element temporary storage per thread, and does not make use of complex index arithmetic in the main transposition loop. Recursion is used to transpose the remaining rectangular sub-matrix. Dubbed Recursive MaxSquare, the novel proposed rectangular matrix in-place transposition algorithm uses a generalization of the perfect shuffle/unshuffle data permutation to stitch together the recursively transposed square matrices. The shuffle/unshuffle permutations are shown to be efficiently decomposed using basic vector/segment swaps, exchanges and/or cyclic shifts (rotations). A balanced parallel cycles-based transposition is also proposed for comparison.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"18 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120989253","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}

引用次数: 0

ECG Signal Analysis for Patient with Metabolic Syndrome based on 1D-Convolution Neural Network 基于一维卷积神经网络的代谢综合征心电信号分析

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00134

Chhayly Lim, Jung-Yeon Kim, Yunyoung Nam

引用次数: 1

Applying an Energy-Aware Security Mechanism in Healthcare Internet of Things 能源感知安全机制在医疗物联网中的应用

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00192

M. Tavakolan, Ismaeel A. Faridi

引用次数: 0

Information Flow Control to Secure Data in the Cloud 信息流控制以保护云中的数据

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00241

F. Alqahtani, Salahaldeen Duraibi, Predrag T. Tosic, Frederick T. Sheldon

引用次数: 1

Performance Analysis of IoT Physical layer Security Using 3-D Stochastic Geometry 基于三维随机几何的物联网物理层安全性性能分析

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00191

H. Chamkhia, A. Al-Ali, Amr M. Mohamed, M. Guizani, A. Erbad, A. Refaey

引用次数: 1

Securing Three Dimensional Regions with Stereo Vision 用立体视觉保护三维区域

2020 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2020-12-01 DOI: 10.1109/CSCI51800.2020.00297

Leonard D. Litvak, L. Deligiannidis

引用次数: 0