Pub Date : 2018-07-11DOI: 10.1109/JCSSE.2018.8457347
Noppanat Phumkaew, V. Visoottiviseth
Many hospitals and stock-and-trade mobile applications are developed in Thailand to fulfill business requirements. These applications normally handle user’s sensitive data, such as the identification, financial data, and health records. Thus, the objective of this research is to investigate whether these applications can expose the sensitive data over thecommunication channel and whether the sensitive data can be retrieved from the lost or stolen mobile phones. We conduct the forensic investigation and security assessment toward these mobile applications by considering the OWASP Mobile Security Top Ten Risks 2016. In our experiment, Android forensics was conducted over three hospital applications in Thailandand five stock-and-trade applications. The analysis techniques include both static analysis and dynamic analysis.From our results, we found that each application has its own vulnerability reflecting to OWASP’s risk, thus the user must use them with caution. Moreover, the Android application developers must take security awareness into their account.
{"title":"Android Forensic and Security Assessment for Hospital and Stock-and-Trade Applications in Thailand","authors":"Noppanat Phumkaew, V. Visoottiviseth","doi":"10.1109/JCSSE.2018.8457347","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457347","url":null,"abstract":"Many hospitals and stock-and-trade mobile applications are developed in Thailand to fulfill business requirements. These applications normally handle user’s sensitive data, such as the identification, financial data, and health records. Thus, the objective of this research is to investigate whether these applications can expose the sensitive data over thecommunication channel and whether the sensitive data can be retrieved from the lost or stolen mobile phones. We conduct the forensic investigation and security assessment toward these mobile applications by considering the OWASP Mobile Security Top Ten Risks 2016. In our experiment, Android forensics was conducted over three hospital applications in Thailandand five stock-and-trade applications. The analysis techniques include both static analysis and dynamic analysis.From our results, we found that each application has its own vulnerability reflecting to OWASP’s risk, thus the user must use them with caution. Moreover, the Android application developers must take security awareness into their account.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124928425","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}
Pub Date : 2018-07-11DOI: 10.1109/JCSSE.2018.8457359
Ratchanon Toncharoen, M. Piantanakulchai
Traffic state prediction methods have been considered by many researchers since accurate traffic prediction is an important part of the successful implementation of the Intelligent Transportation System (ITS). This study develops the traffic prediction model based on real traffic data in congested hours of expressways in Bangkok, Thailand. Unlike most studies, this model utilizes data from 40 nodes along the expressway instead of a single sensor. A Convolutional Neural Network (CNN) model was applied and compared to other widely used models. The result shows that the accuracy of CNN model is higher than other models.
{"title":"Traffic State Prediction Using Convolutional Neural Network","authors":"Ratchanon Toncharoen, M. Piantanakulchai","doi":"10.1109/JCSSE.2018.8457359","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457359","url":null,"abstract":"Traffic state prediction methods have been considered by many researchers since accurate traffic prediction is an important part of the successful implementation of the Intelligent Transportation System (ITS). This study develops the traffic prediction model based on real traffic data in congested hours of expressways in Bangkok, Thailand. Unlike most studies, this model utilizes data from 40 nodes along the expressway instead of a single sensor. A Convolutional Neural Network (CNN) model was applied and compared to other widely used models. The result shows that the accuracy of CNN model is higher than other models.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323151","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}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457326
V. Phartchayanusit, S. Rongviriyapanish
In recent years, the number of Internet of Things (IoT) systems has been increasing. Through design and analysis, IoT systems can be verified and monitored. However, it is difficult to find safety property with general-use models which we are familiar with such as UML model. In this paper, we proposed safety property analysis of service-oriented IoT based on Interval timed coloured Petri Nets (ITCPN). We model IoT design with StateMate which is easy to use and is similar to UML diagram. Then, transforming this diagram to ITCPN model which can be analysed and verified by model checking with Linear Temporal Logic (LTL). We also illustrated the usefulness of our approach with an example of infusion Pump.
{"title":"Safety Property Analysis of Service-Oriented IoT Based on Interval Timed Coloured Petri Nets","authors":"V. Phartchayanusit, S. Rongviriyapanish","doi":"10.1109/JCSSE.2018.8457326","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457326","url":null,"abstract":"In recent years, the number of Internet of Things (IoT) systems has been increasing. Through design and analysis, IoT systems can be verified and monitored. However, it is difficult to find safety property with general-use models which we are familiar with such as UML model. In this paper, we proposed safety property analysis of service-oriented IoT based on Interval timed coloured Petri Nets (ITCPN). We model IoT design with StateMate which is easy to use and is similar to UML diagram. Then, transforming this diagram to ITCPN model which can be analysed and verified by model checking with Linear Temporal Logic (LTL). We also illustrated the usefulness of our approach with an example of infusion Pump.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613977","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}
Pub Date : 2018-07-01DOI: 10.1109/jcsse.2018.8457357
{"title":"JCSSE 2018 Title Page","authors":"","doi":"10.1109/jcsse.2018.8457357","DOIUrl":"https://doi.org/10.1109/jcsse.2018.8457357","url":null,"abstract":"","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163313","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}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457182
Apirak Hoonlor, Varodom Charoensawan, S. Srisuma
With the rises of the AI technology in Healthcare, researchers have been using the technology to develop a computational system to aid diagnosis, commonly known as 'Clinical Decision Support Systems (CDSSs)'. The CDSS applications currently available are usually neither free, nor optimized for treating Thai patients. In this work, we propose a new CDSS platform intended as an open platform for the CDSS application in Thailand. As a prototype and proof of concept, we developed the Mahidol Snake Envenomation Support System (MSESS), as the first C DSS a pplication u sing o ur n ew p latform. MSESS was designed to help its user formulate a treatment plan for the patient with snake bite found in Thailand, particularly in rural areas, and guide the user through the treatment flow. The treatments suggested by MSESS strictly follows the Snake Envenomation guideline provided by the Ramathibodi Poison Center. The targeted user is the medical personnel such as general practitioner seeking a medical advice from specialists. The medical personnel will first e nter t he p atient information to the CDSS. The system will then retrieve the information, submit it to the inference engine unit hosted at our central computing facilities, and display the suggested actions to the medical personnel via our application. We discuss our lesson learn from the development of MSESS for the future development of CDSS applications on our platform.
随着人工智能技术在医疗保健领域的兴起，研究人员一直在使用该技术开发一种辅助诊断的计算系统，通常称为“临床决策支持系统(cdss)”。目前可用的CDSS应用程序通常既不是免费的，也不是为治疗泰国患者而优化的。在这项工作中，我们提出了一个新的CDSS平台，旨在作为泰国CDSS应用的开放平台。作为原型和概念验证，我们开发了Mahidol Snake Envenomation Support System (messs)，这是我们在新平台上使用的第一个cdss应用程序。mess的目的是帮助用户为泰国，特别是农村地区的蛇咬伤患者制定治疗计划，并指导用户完成治疗流程。mess建议的治疗方法严格遵循Ramathibodi中毒中心提供的蛇中毒指南。目标用户是向专家寻求医疗建议的全科医生等医务人员。医务人员将首先把病人的资料传送给社会保障系统。然后，系统将检索信息，将其提交给托管在我们中央计算设施上的推理引擎单元，并通过我们的应用程序向医务人员显示建议的操作。我们讨论了从mssss开发中获得的经验教训，以便将来在我们的平台上开发CDSS应用程序。
{"title":"The Clinical Decision Support System for the Snake Envenomation in Thailand","authors":"Apirak Hoonlor, Varodom Charoensawan, S. Srisuma","doi":"10.1109/JCSSE.2018.8457182","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457182","url":null,"abstract":"With the rises of the AI technology in Healthcare, researchers have been using the technology to develop a computational system to aid diagnosis, commonly known as 'Clinical Decision Support Systems (CDSSs)'. The CDSS applications currently available are usually neither free, nor optimized for treating Thai patients. In this work, we propose a new CDSS platform intended as an open platform for the CDSS application in Thailand. As a prototype and proof of concept, we developed the Mahidol Snake Envenomation Support System (MSESS), as the first C DSS a pplication u sing o ur n ew p latform. MSESS was designed to help its user formulate a treatment plan for the patient with snake bite found in Thailand, particularly in rural areas, and guide the user through the treatment flow. The treatments suggested by MSESS strictly follows the Snake Envenomation guideline provided by the Ramathibodi Poison Center. The targeted user is the medical personnel such as general practitioner seeking a medical advice from specialists. The medical personnel will first e nter t he p atient information to the CDSS. The system will then retrieve the information, submit it to the inference engine unit hosted at our central computing facilities, and display the suggested actions to the medical personnel via our application. We discuss our lesson learn from the development of MSESS for the future development of CDSS applications on our platform.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130564189","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}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457390
Piyawat Maneenual, S. Vasupongayya
A logging mechanism for NETPIE in the patient medical device monitoring task is proposed in this work. The logging mechanism aims to collect the communication log between things and NETPIE such that any communication issue or any attack can be detected. There are three main components in the proposed logging mechanism including the raw data, the analysis part, and the final result. The raw data is approximately less than 256 bytes per each communication. The raw data will be analyzed to generate the final result which will contain only the suspicion events including communication issue (i.e., packet lost) and possible attack (i.e., reply attack). The cost of the proposed mechanism includes an extra communication per each communication path and a storage space for the collected data at NETPIE, and things.
{"title":"Logging mechanism for Internet of Things: A Case Study of Patient Monitoring System","authors":"Piyawat Maneenual, S. Vasupongayya","doi":"10.1109/JCSSE.2018.8457390","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457390","url":null,"abstract":"A logging mechanism for NETPIE in the patient medical device monitoring task is proposed in this work. The logging mechanism aims to collect the communication log between things and NETPIE such that any communication issue or any attack can be detected. There are three main components in the proposed logging mechanism including the raw data, the analysis part, and the final result. The raw data is approximately less than 256 bytes per each communication. The raw data will be analyzed to generate the final result which will contain only the suspicion events including communication issue (i.e., packet lost) and possible attack (i.e., reply attack). The cost of the proposed mechanism includes an extra communication per each communication path and a storage space for the collected data at NETPIE, and things.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125596563","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}
Generally, a traditional methodology to assess the aesthetics (appreciating beauty) of a photograph involves a number of professional photographers rating the photo based on given criteria and providing ensemble feedback minimize bias. Such a traditional photo assessment method, however, is not applicable to massive users, especially in real-time. To mitigate such an issue, recent studies have devoted on developing algorithms to automatically provide feedback to photo takers. Most of such algorithms train variants of neural networks using ground-truth photos assessed by professional photographers. Regardless, most existing photo assessment algorithms provide the aesthetic score as a single number. From our observation, users typically use multiple criteria to justify the beautifulness of a photo, and hence a single rating score may not be informative. In this paper, we propose a novel Fine-tuned Inception with Fully Connected and Regression Layers model which gives five attribute scores: vivid colour, colour harmony, lighting, balance of elements, and depth of field. T his s olution i ncorporates t he p re-trained inception model which is the state-of-the-art model for processing images. Our proposed algorithm enhances the existing state-of-the-art by fine-tuning the parameters, introducing fully connected layers, and attaching the regression layers to compute the numeric score for each focus attribute. The experimental results show that our model helps to decrease the mean absolute error (MAE) to 0.211, benchmarking on the aesthetics and attributes datasets provided in the previous studies.
{"title":"A Deep Learning Methodology for Automatic Assessment of Portrait Image Aesthetic Quality","authors":"Poom Wettayakorn, Siripong Traivijitkhun, Ponpat Phetchai, Suppawong Tuarob","doi":"10.1109/JCSSE.2018.8457381","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457381","url":null,"abstract":"Generally, a traditional methodology to assess the aesthetics (appreciating beauty) of a photograph involves a number of professional photographers rating the photo based on given criteria and providing ensemble feedback minimize bias. Such a traditional photo assessment method, however, is not applicable to massive users, especially in real-time. To mitigate such an issue, recent studies have devoted on developing algorithms to automatically provide feedback to photo takers. Most of such algorithms train variants of neural networks using ground-truth photos assessed by professional photographers. Regardless, most existing photo assessment algorithms provide the aesthetic score as a single number. From our observation, users typically use multiple criteria to justify the beautifulness of a photo, and hence a single rating score may not be informative. In this paper, we propose a novel Fine-tuned Inception with Fully Connected and Regression Layers model which gives five attribute scores: vivid colour, colour harmony, lighting, balance of elements, and depth of field. T his s olution i ncorporates t he p re-trained inception model which is the state-of-the-art model for processing images. Our proposed algorithm enhances the existing state-of-the-art by fine-tuning the parameters, introducing fully connected layers, and attaching the regression layers to compute the numeric score for each focus attribute. The experimental results show that our model helps to decrease the mean absolute error (MAE) to 0.211, benchmarking on the aesthetics and attributes datasets provided in the previous studies.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122098429","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}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457380
A. Prayote, Kallaya Songklang
This paper presents a technique to alternatively discover the temporal research topics correlation by using a topic model, Latent Dirichlet Allocation (LDA). LDA model assumes the documents as a mixture of topics that group the co-occurrence words with the certain probabilities. Hence the model is popularly used to extract the latent topics from document collections. However, LDA gives an independence assumption between topics and is unable to model the correlation between the topics. Motivated by above limitation, this study introduces a method for improving the topic correlation. The correlation of two topics from different time periods can occur when there exists a publication tagged by the two topics and these two topics are said to be co-occurred by this publication. LDA weights of these co-occurred topics are used in our model to calculate gross-correlation values. The number of publications in a topic co-occurrence is also used in the model. Therefore, we split dataset into groups with some common sub-dataset ordered by temporal timestamp of published year. The experiment results show the correlation between topics in different time periods and results can further support the research collaboration in future.
{"title":"Temporal Topic Correlation and Evolution","authors":"A. Prayote, Kallaya Songklang","doi":"10.1109/JCSSE.2018.8457380","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457380","url":null,"abstract":"This paper presents a technique to alternatively discover the temporal research topics correlation by using a topic model, Latent Dirichlet Allocation (LDA). LDA model assumes the documents as a mixture of topics that group the co-occurrence words with the certain probabilities. Hence the model is popularly used to extract the latent topics from document collections. However, LDA gives an independence assumption between topics and is unable to model the correlation between the topics. Motivated by above limitation, this study introduces a method for improving the topic correlation. The correlation of two topics from different time periods can occur when there exists a publication tagged by the two topics and these two topics are said to be co-occurred by this publication. LDA weights of these co-occurred topics are used in our model to calculate gross-correlation values. The number of publications in a topic co-occurrence is also used in the model. Therefore, we split dataset into groups with some common sub-dataset ordered by temporal timestamp of published year. The experiment results show the correlation between topics in different time periods and results can further support the research collaboration in future.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122380502","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}
Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457384
J. Ilao, M. Cordel
Algorithms that perform crowd estimation are dependent on crowd levels. The two approaches to crowd estimation discussed are the model-based and texture-based approaches. The aim of this work is to determine the precision, recall and F-measure of the two algorithms, Histogram of Oriented Gradients (HOG) with Support Vector Machines (SVM) and Region-Specific HOG, for estimating the number of people in high and low crowd levels, respectively, in an indoor area installed with a surveillance camera, while considering the camera’s position and its field of view.
{"title":"Crowd Estimation Using Region-Specific HOG With SVM","authors":"J. Ilao, M. Cordel","doi":"10.1109/JCSSE.2018.8457384","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457384","url":null,"abstract":"Algorithms that perform crowd estimation are dependent on crowd levels. The two approaches to crowd estimation discussed are the model-based and texture-based approaches. The aim of this work is to determine the precision, recall and F-measure of the two algorithms, Histogram of Oriented Gradients (HOG) with Support Vector Machines (SVM) and Region-Specific HOG, for estimating the number of people in high and low crowd levels, respectively, in an indoor area installed with a surveillance camera, while considering the camera’s position and its field of view.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126890230","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}
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