2019 11th International Conference on Knowledge and Systems Engineering (KSE)最新文献_第5页

A Hierarchical Deep Deterministic Policy Gradients for Swarm Navigation 群体导航的层次深度确定性策略梯度

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919269

H. Nguyen, Tung D. Nguyen, Do-Van Nguyen, T. Le

{"title":"A Hierarchical Deep Deterministic Policy Gradients for Swarm Navigation","authors":"H. Nguyen, Tung D. Nguyen, Do-Van Nguyen, T. Le","doi":"10.1109/KSE.2019.8919269","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919269","url":null,"abstract":"The problem of swarm navigation recently becomes a significant topic because of its suitability for various applications like search and rescue with autonomous systems. However, when it comes to a swarm of learning agents, one has to face the challenges from huge state spaces and the lack of scalability when the size of the swarm increases. Reinforcement learning (RL) approaches, which allow agents to interact not only with each other but also with their operational environment to obtain optimal policies, are considered as promising techniques for swarm navigation problems. Different RL algorithms have been used to solve these problems but most of them are limited to discrete state spaces and/or do not scale well with an increase of number of learning agents in the swarm. In this paper, we propose a Swarm Hierarchical Deep Deterministic Policy Gradients (SH-DDPGs) framework to address both drawbacks above in the context of leader-follower swarm navigation. By decomposing the navigation task of the swarm into two primitive sub-tasks: leader-following and collision avoidance, we can guarantee the convergence of the training processes of these sub-tasks in a continuous environment before combining output actions produced from those trained models to complete the entire task. Moreover, our method represents scalability as it is independent to the size of the swarm. Firstly, when training a follower, we only use information of its neighbors within its local view and the leader. Secondly, the trained model of one follower can be reapplied for the remaining followers. Training results show that the proposed SH-DDPGs algorithm is able to converge quickly and allow followers agent to learn an optimal policy for the whole group to navigate through the environment without colliding with each other and flexibly optimize their formation so that the distances among agents are minimized.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130907428","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

Prediction car prices using quantify qualitative data and knowledge-based system 利用定量定性数据和基于知识的系统预测汽车价格

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919408

D. Thai, Luong Ngoc Son, Pham Vu Tien, Nguyen Nhat Anh, T. Nguyen

引用次数: 10

On Analyzing Rule-Dependencies to Generate Test Cases for Model Transformations 分析规则依赖性以生成模型转换测试用例

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919486

Thi-Hanh Nguyen, Duc-Hanh Dang, Quang-Trung Nguyen

引用次数: 2

Systems Analysis of Acoustic Underwater Sensor Networks: A Taxonomy 声学水下传感器网络的系统分析:一个分类

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919353

Sandeep Dhakal, A. Alsadoon, P. Prasad, A. Elchouemi, V. Q. Nguyen

{"title":"Systems Analysis of Acoustic Underwater Sensor Networks: A Taxonomy","authors":"Sandeep Dhakal, A. Alsadoon, P. Prasad, A. Elchouemi, V. Q. Nguyen","doi":"10.1109/KSE.2019.8919353","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919353","url":null,"abstract":"Underwater seismic and volcanic activity can have catastrophic consequences which are often fatal for human and animal life. Scientists have development instruments to measure the onset of such events and a substantial number of systems are in use around the globe. However, most of these focus on some aspects of detection but are not covering all aspects of detection, measurement, power consumption and data communication. This research aims to evaluate existing frameworks and develop a taxonomy of systems components necessary for a fully functioning comprehensive underwater activity detection system. The proposed framework has sensor, positioning and mobility as main components and is a centralized underwater node localization method using range based multilateral accumulation (RBMAM). The mobility component is used for identification, improvisation and tracking of the position of the sensors, which have been placed under water for the purpose of wireless communication networking and nodes localization to detect the possibilities of seaquakes. This research enhances the underwater localization of the nodes using acoustic wireless sensor networking which through which earlier detection of potential seaquakes could be identified and the location of the nodes could be tracked. The communication underwater will be improved, and accurate results could be achieved for underwater navigation.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122673677","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

An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine 利用深度学习和基于词汇语义的Twitter数据情感分析方法了解远程医疗人口统计趋势分析

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919363

Harshvadan Talpada, M. Halgamuge, N. Q. Vinh

{"title":"An Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine","authors":"Harshvadan Talpada, M. Halgamuge, N. Q. Vinh","doi":"10.1109/KSE.2019.8919363","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919363","url":null,"abstract":"Technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. This data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. The aim of the research is to understand (i) the correlation between Deep Learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitterbased sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research; Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep Learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). The dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods; when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128691515","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: 11

Combining feature selection, feature learning and ensemble learning for software fault prediction 结合特征选择、特征学习和集成学习进行软件故障预测

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919292

Hung Duy Tran, L. Hạnh, N. Binh

引用次数: 11

A Solution for Building Motion Tracking System with Model Predictive Control for Autonomous Vehicles 一种基于模型预测控制的自动驾驶汽车运动跟踪系统的构建方案

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919385

Quach Hai Tho, Pham Anh Phuong, Huynh Cong Phap

引用次数: 0

Toward a Model for Verification of Business Logic Layer in 3-Layer Architecture: CPN-ECA Model 面向三层体系结构的业务逻辑层验证模型:CPN-ECA模型

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919341

N. T. Tuan, L. Nhan, Hoanh Thi Thanh Ha

引用次数: 3

Review: Data Security Models Developed by Blockchain Technology for Different Business Domains 综述:区块链技术为不同业务领域开发的数据安全模型

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919268

M. Hirani, M. Halgamuge, Pham Duong Thu Hang

{"title":"Review: Data Security Models Developed by Blockchain Technology for Different Business Domains","authors":"M. Hirani, M. Halgamuge, Pham Duong Thu Hang","doi":"10.1109/KSE.2019.8919268","DOIUrl":"https://doi.org/10.1109/KSE.2019.8919268","url":null,"abstract":"This study investigates a range of data security models developed to achieve data security using blockchain technology for different industrial domains. Current industries utilize data-driven mechanisms for decision making with concern focussed on ensuring data security. Blockchain has the potential to secure data by integrating different information systems since data is decentralized, encrypted and validated by the whole network. This study includes an analysis of blockchain security models using data extracted from 30 peer-reviewed scientific publications over two years (2017–2019). This study analyzed three components of the publications, including the process involved in securing data, the stage of development for securing data and in which industry a model is best applied. Results of the research show that the majority of articles (51.11%) cited Blockchain as a key feature of data security for improvising the data sharing process in industries. This study also finds that the stage of implementation most commonly featured is the proposal stage with potential architectures yet to be implemented (30%). Finally, this study shows that models are applied in industrial domains such as enterprises using data analytics, finance, Internet of Things (IoT), healthcare, education, and cloud service providers. This study finds that security models are most often applied to industries and supply chain management models (28.13%). It is recommended that industry professionals conduct further research to customize the data security models in their own domain. This study gives clear guidelines to researchers of suitable frameworks, processes and consensus mechanism to utilize Blockchain in Industries for data security.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115881627","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

Using Z-score to Extract Human Readable Logic Rules from Physiological Data 利用Z-score从生理数据中提取人类可读逻辑规则

2019 11th International Conference on Knowledge and Systems Engineering (KSE) Pub Date : 2019-10-01 DOI: 10.1109/KSE.2019.8919473

N. Costadopoulos, M. Islam, D. Tien

引用次数: 3