2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)最新文献_第7页

A Situation-Centric Approach to Identifying New User Intentions Using the MTL Method 使用MTL方法识别新用户意图的情境中心方法

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.36

Jingwei Yang, Carl K. Chang, Ming Hua

{"title":"A Situation-Centric Approach to Identifying New User Intentions Using the MTL Method","authors":"Jingwei Yang, Carl K. Chang, Ming Hua","doi":"10.1109/COMPSAC.2017.36","DOIUrl":"https://doi.org/10.1109/COMPSAC.2017.36","url":null,"abstract":"Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this paper focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect human intentions by inferring their desires through monitoring environmental and human behavioral contexts prior to or after system deployment. Researchers have reported that Situ is able to infer users' desires with high accuracy using the Conditional Random Fields method. However, manual analysis is still needed for new intention identification and new requirements elicitation. This work attempts to find a computable way to identify users' new intentions with minimal help from human oracle. We discuss the feasibility of implementing the concept of DIKW (Data, Information, Knowledge, Wisdom) to bridge the gap between user behavioral & contextual data and requirements, and propose a situation-centric approach using the Multi-strategy, Task-adaptive Learning (MTL) method. A case study shows that the proposed approach is able to identify users' new intentions, and is especially effective to capture alternatives of low-level task.","PeriodicalId":6556,"journal":{"name":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)","volume":"12 1","pages":"347-356"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81053862","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: 10

An Intelligent Tracking System: Application to Acute Respiratory Tract Infection (TrackARTI) 智能跟踪系统在急性呼吸道感染中的应用

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.58

Duygu Çelik Ertugrul, Atilla Elçi, Y. Bi̇ti̇ri̇m

{"title":"An Intelligent Tracking System: Application to Acute Respiratory Tract Infection (TrackARTI)","authors":"Duygu Çelik Ertugrul, Atilla Elçi, Y. Bi̇ti̇ri̇m","doi":"10.1109/COMPSAC.2017.58","DOIUrl":"https://doi.org/10.1109/COMPSAC.2017.58","url":null,"abstract":"This article proposes an Intelligent Tracking System for Acute Respiratory Tract Infection (TrackARTI) via a smart mobile for monitoring disease term of 0-6 age group child patients remotely (e.g. home, clinics). It is possible to maximize the quality of life of the child patients and decrease parental anxiety by keeping the child under control during monitoring stage and achieve a proper distant diagnose by the patient's clinician. This is possible with the designs of intelligent M-Health systems that can be used for diagnosing and monitoring the child patients away from hospitals by presenting the instant medical data to their registered doctors. Intelligent M-Health systems require strong knowledge management technology and ease of extension to provide information from additional medical tools. With the contribution of intelligent M-Health systems, it is possible to infer new facts from the certain gathered medical data during examination from child patients. This article mentions an intelligent and easy medical data gathering system that can be used by pediatricians or parents any time. In addition, the system has its own inferencing mechanism that involves two main steps, inferencing on image processing and Semantic Web rule knowledge base.","PeriodicalId":6556,"journal":{"name":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)","volume":"9 1","pages":"137-142"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88664600","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: 4

Cardinality Counting Circuit for Real-Time Abnormal Traffic Detection 用于实时异常流量检测的基数计数电路

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.81

Shuji Sannomiya, Akira Sato, K. Yoshida, H. Nishikawa

引用次数: 4

Finding Extract Method Refactoring Opportunities by Analyzing Development History 通过分析开发历史发现提取方法重构的机会

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.129

Ayaka Imazato, Yoshiki Higo, Keisuke Hotta, S. Kusumoto

{"title":"Finding Extract Method Refactoring Opportunities by Analyzing Development History","authors":"Ayaka Imazato, Yoshiki Higo, Keisuke Hotta, S. Kusumoto","doi":"10.1109/COMPSAC.2017.129","DOIUrl":"https://doi.org/10.1109/COMPSAC.2017.129","url":null,"abstract":"Refactoring is an important technique to improve maintainability of software, and developers often use this technique during a development process. Before now, researchers have proposed some techniques finding refactoring opportunities for developers. Finding refactoring opportunities means identifying locations to be refactored. However, there are no specific criteria for developers to determine where they should refactor because the criteria differ from project to project and from developer to developer. In this study, we propose a technique to find refactoring opportunities in source code by using machine learning techniques. Machine learning techniques enable to flexibly find refactoring opportunities by the characteristics of target projects and developers. Our proposed technique learns information on the features of refactorings conducted in the past. Then, based on this information, it suggests some refactorings on given the source code to developers. We investigated three research questions with five open source projects. As a result, we confirmed that the proposed technique was able to find refactorings with high accuracy.","PeriodicalId":6556,"journal":{"name":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)","volume":"2 1","pages":"190-195"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88132511","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

Feature Representation Extraction Method of Hotel Reviews Using Co-occurrence Restriction and Dependency Graph 基于共现约束和依赖图的酒店评论特征表示提取方法

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.126

Koji Tanaka, Koichi Tsujii, T. Ikoma, Akiyuki Sekiguchi, K. Tsuda

引用次数: 1

Adaptive Cost Efficient Framework for Cloud-Based Machine Learning 基于云的机器学习自适应成本效益框架

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.42

Rezvan Pakdel, J. Herbert

{"title":"Adaptive Cost Efficient Framework for Cloud-Based Machine Learning","authors":"Rezvan Pakdel, J. Herbert","doi":"10.1109/COMPSAC.2017.42","DOIUrl":"https://doi.org/10.1109/COMPSAC.2017.42","url":null,"abstract":"Machine learning is an increasingly important form of cognitive computing, making progress in several application areas. Machine learning often involves big data sets and is computationally challenging, requiring efficient use of resources. The use of cloud computing as the platform for machine learning offers advantages of scalability and efficient use of hardware. It may, however, be difficult to provision appropriate cost-effective resources for a machine learning task. Our experiments have shown that there can be radical differences between different datasets and different algorithms on the same dataset. The cloud-based machine learning framework presented here aims to provide multiple levels of efficient use of resources and uses a high-level cost model to deal with overall cost-efficiency with respect to cloud service providers. The cost model allows evaluation of trade-offs and supports the choice of appropriate provider resources based on user-defined criteria. A user may choose to prioritize performance, prioritize cost or specify a cost-performance balance. An Amazon AWS cost model for instances is used to illustrate the practical benefits of using the approach - it is seen that large savings can be made by employing this job-specific monitoring and cost-performance analysis. The method can provide all the information for a comparison across different cloud service providers as well as comparisons across the Amazon AWS offerings.","PeriodicalId":6556,"journal":{"name":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)","volume":"1 1","pages":"155-160"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89772755","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: 7

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.35

Xiaobing Zhang, Qiliang Yang, Jianchun Xing, Deshuai Han, Ying Chen

{"title":"A Similarity-Based Approach to Recognizing Voice-Based Task Goals in Self-Adaptive Systems","authors":"Xiaobing Zhang, Qiliang Yang, Jianchun Xing, Deshuai Han, Ying Chen","doi":"10.1109/COMPSAC.2017.35","DOIUrl":"https://doi.org/10.1109/COMPSAC.2017.35","url":null,"abstract":"With the development of the natural language processing (NLP) technologies, users tend to directly input their goals via natural language to a task system. Thus, how to input informal voice-based task goals to self-adaptive systems (SASs) has become a challenge issue. Our previously proposed framework V-SFSA (voice-driven software fuzzy self-adaptation) can realize to input voice-based task goals to SAS. However, it still suffers from low efficiency of recognition. In this paper, in order to improve on our previous V-SFSA framework, we propose a similarity-based NLP approach to recognizing the voice-based task goals in SASs. It uses the verb of the raw voice inputs to preselect the semantic relevant commands, and then to compute the similarity between the preselected commands and predefined featured commands in a SAS. The command with the highest similarity score is accepted as the intended goals to drive a SAS. We establish the improved V-SFSA, and implement the algorithm of similarity-based fuzzy adaptation. In addition, we construct a prototype to conduct a case study. The result shows that our approach is effective.","PeriodicalId":6556,"journal":{"name":"2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)","volume":"116 1","pages":"536-542"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89411951","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

Research on Integrated Authentication Using Passwordless Authentication Method 基于无密码认证方法的集成认证研究

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.198

Michitomo Morii, Hiroki Tanioka, K. Ohira, M. Sano, Yosuke Seki, Kenji Matsuura, T. Ueta

引用次数: 13

Executable Domain-Specific Modelling Based on Domain Spaces 基于领域空间的可执行领域特定建模

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.122

Qing Duan, Junhui Liu, Dong-dai Zhou, Feng Yu, Hongji Yang

引用次数: 0

Towards Requirements Reuse by Implementing Traceability in Agile Development 通过在敏捷开发中实现可追溯性实现需求重用

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) Pub Date : 2017-07-01 DOI: 10.1109/COMPSAC.2017.250

R. Elamin, Rasha Osman

引用次数: 13