2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)最新文献_第4页

An Economical and High-Quality Encryption Scheme for Cloud Servers with GPUs 一种经济、高质量的gpu云服务器加密方案

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00048

Xiongwei Fei, Kenli Li, Shui Yu, Kuan-Ching Li

引用次数: 0

Right Ventricle Segmentation of Cine MRI Using Residual U-net Convolutinal Networks 残差u网卷积网络在MRI右心室分割中的应用

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00072

Zexiong Liu, Yuhong Feng, Xuan S. Yang

引用次数: 1

Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data 基于CNN和LSTM结构的智能手机传感器人体活动识别混合模型

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00055

S. Deep, Xi Zheng

{"title":"Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data","authors":"S. Deep, Xi Zheng","doi":"10.1109/PDCAT46702.2019.00055","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00055","url":null,"abstract":"The traditional methods of recognizing human activities involve typical machine learning (ML) algorithms which uses heuristic engineered features. Human activities are dynamic in nature and are encoded with a sequence of actions. ML methods are able to perform activity recognition tasks but may not exploit the temporal correlations of the input data. Therefore, in this paper, we proposed and showed the effectiveness of employing a new combination of deep learning (DL) methods for human activity recognition (HAR). DL methods are capable of extracting discriminative features automatically from the raw sensor data. Specifically, in this paper, we proposed a hybrid architecture which features a combination of Convolutional neural networks (CNN) and Long short-term Memory (LSTM) networks for HAR task. The model is tested on UCI HAR dataset which is a benchmark dataset and comprises of accelerometer and gyroscope data obtained from a smartphone. Our experimental results showed that our proposed method outperformed the recent results which used pure LSTM and bidirectional LSTM networks on the same dataset.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"237 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132702839","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: 22

A Map-Reduce-Based Relation Inference Algorithm for Autonomous System 基于映射约简的自治系统关系推理算法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00091

Ting Lv, Donghong Qin, Lina Ge, Song Wen

引用次数: 0

Faithful Multi-Hop Qubit Transmission Based on GHZ States 基于GHZ态的忠实多跳量子比特传输

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00042

Na Chen, B. Yan, Shuangshuang Shuai, Changxing Pei

引用次数: 0

D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting 基于能量收集的移动边缘计算系统的d2d辅助计算卸载

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00028

Molin Li, Tong Chen, Jiaxin Zeng, Xiaobo Zhou, Keqiu Li, Heng Qi

{"title":"D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting","authors":"Molin Li, Tong Chen, Jiaxin Zeng, Xiaobo Zhou, Keqiu Li, Heng Qi","doi":"10.1109/PDCAT46702.2019.00028","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00028","url":null,"abstract":"In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132216490","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: 3

Modeling Power Consumption of The Code Execution Using Performance Counters Statistics 使用性能计数器统计对代码执行的功耗进行建模

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00075

Guang Wei, D. Qian, Hailong Yang, Zhongzhi Luan

{"title":"Modeling Power Consumption of The Code Execution Using Performance Counters Statistics","authors":"Guang Wei, D. Qian, Hailong Yang, Zhongzhi Luan","doi":"10.1109/PDCAT46702.2019.00075","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00075","url":null,"abstract":"This paper presents an empirical model to classify the programs according to their power consumption by using the performance counter statistics. The programs with similar power consumption are put into the same group. The difference in power data between two adjacent groups is 5 watts. A power model is generated based on the performance data that the program generated. Discriminant analysis is adopted to generate the power consumption model upon the data from the performance counter statistics. We use discriminant analysis to determine the power category (i.e., the number of the group) that is derived from the independent variable. By using the performance counter variables as the input to the power model, we can predict the level of power consumption of the code, that is, the group that this code belongs to. The experiment results in modeling and validation show that this power model can predict power group membership of a code with an accuracy of more than 96.5%, with the difference of original and predicted group numbers being smaller than 2.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438438","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: 1

Imbalanced Data Classification Using Improved Clustering Algorithm and Under-Sampling Method 基于改进聚类算法和欠采样方法的不平衡数据分类

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00071

Lu Cao, Hong Shen

{"title":"Imbalanced Data Classification Using Improved Clustering Algorithm and Under-Sampling Method","authors":"Lu Cao, Hong Shen","doi":"10.1109/PDCAT46702.2019.00071","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00071","url":null,"abstract":"Imbalanced classification problem is a hot issue in data mining and machine learning. Traditional classification algorithms are proposed based on some form of symmetry hypothesis of class distribution, whose main purpose is to improve the overall classification performance. It is difficult to obtain ideal classification result when handling imbalanced datasets. In order to improve the classification performance of imbalanced datasets, this paper proposes a cluster-based under-sampling algorithm (CUS) according to the important characteristic of support vector machines (SVM) classification relying on support vector. Firstly, majority class is divided into different clusters using improved clustering by fast search and find of density peaks (CFSFDP) algorithm. The improved clustering algorithm can realize automatic selection of clustering centers, which overcomes the limitation of the original algorithm. Then the minority class and each cluster of the majority class are used to construct training set to get the support vector of each cluster by support vector machine. Retaining support vectors for each cluster and deleting non-support vectors are to construct a new majority class sample points to obtain relatively balanced datasets. Finally, the new datasets are classified by support vector machines and the performance is evaluated by cross validation sets. The experimental results show that CUS algorithm is effective.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126121333","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: 9

A CloudSim-Extension for Simulating Distributed Functions-as-a-Service 模拟分布式功能即服务的cloudsim扩展

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00076

H. Jeon, Chunglae Cho, Seungjae Shin, Seunghyun Yoon

引用次数: 15

A Model Driven-Based Approach for Managing Unanticipated Runtime Adaptation of RTE Systems 基于模型驱动的RTE系统非预期运行时适应性管理方法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00064

Nissaf Fredj, Y. H. Kacem, M. Abid

{"title":"A Model Driven-Based Approach for Managing Unanticipated Runtime Adaptation of RTE Systems","authors":"Nissaf Fredj, Y. H. Kacem, M. Abid","doi":"10.1109/PDCAT46702.2019.00064","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00064","url":null,"abstract":"Nowadays, Real-Time Embedded Systems (RTES) and particularly adaptive ones are gaining more use in several domains. They must be always consistent and available to preserve their usefulness and feasibility, as a response to environmental conditions, resources limitations and user requirements. For this reason, they are subject to anticipated and unanticipated runtime reconfigurations. The design of such systems requires an abstract representation of the execution context independently from low-level details, with lower complexity. In this context, designers adopt runtime models which need to be checked and validated at early stages of development. Besides, these models must be updated according to unpredictable execution changes. In the present paper, we propose an MDE-based approach that allows managing runtime adaptive RTES. Our proposal starts by specifying the behavior and structure of runtime adaptive systems. Then Model-to-Text (M2T) transformations allows us to generate configuration files to simulate adaptive systems behavior and evaluate their real-time constraints. An additional feature offered by our approach consists to monitor the execution context and update runtime models according to unanticipated reconfigurations.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124584686","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: 1