Pub Date : 2018-07-01DOI: 10.1109/JCSSE.2018.8457177
Benjarat Tirasirichai, Peeraya Thanomboon, Pimpaknat Soontorntham, Worapan Kusakunniran, M. Robinson
This paper proposes the research project, called “Bloom Balance” that aims to develop the calorie balancing application. It is an alternative assistance for people who are willing to improve their health by controlling eating habits and doing more exercises. In the application, the calorie counters are separated into the intake-calorie and burned-calorie counters. For the intake-calorie counter, the users can select the consumed food daily from the database of the application, For the burned-calorie, it is calculated from the numbers of walking/running steps worked out daily. The steps are tracked using the accelerometer of the mobile device. This in-app calculation of burned-calorie is carefully validated scientifically in the sport science laboratory. Then, the tracked intake and burned calories can be visualized in days, weeks, or months. The Bloom Balance can provide the users with the health profiles (e.g., BMI, BMR and TDEE) and comes up with the suggested calorie balance plan which includes an expected calorie consumption and expected burnt calorie from the work out. It also daily notifies the users, regarding to their pre-set configuration, in order to encourage them to do more exercises.
{"title":"Bloom Balance: Calorie Balancing Application With Scientific Validation","authors":"Benjarat Tirasirichai, Peeraya Thanomboon, Pimpaknat Soontorntham, Worapan Kusakunniran, M. Robinson","doi":"10.1109/JCSSE.2018.8457177","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457177","url":null,"abstract":"This paper proposes the research project, called “Bloom Balance” that aims to develop the calorie balancing application. It is an alternative assistance for people who are willing to improve their health by controlling eating habits and doing more exercises. In the application, the calorie counters are separated into the intake-calorie and burned-calorie counters. For the intake-calorie counter, the users can select the consumed food daily from the database of the application, For the burned-calorie, it is calculated from the numbers of walking/running steps worked out daily. The steps are tracked using the accelerometer of the mobile device. This in-app calculation of burned-calorie is carefully validated scientifically in the sport science laboratory. Then, the tracked intake and burned calories can be visualized in days, weeks, or months. The Bloom Balance can provide the users with the health profiles (e.g., BMI, BMR and TDEE) and comes up with the suggested calorie balance plan which includes an expected calorie consumption and expected burnt calorie from the work out. It also daily notifies the users, regarding to their pre-set configuration, in order to encourage them to do more exercises.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"47 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":"115874463","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.2018.8457176
Chayawat Pechwises, K. Chanchio
A cluster of virtual machines is a common platform for running MPI applications in cloud computing environments. However, most traditional methods to provide fault tolerance to these applications are not fully transparent and require specific, checkpointing-enabled MPI software. This paper presents a novel Transparent Hypervisor-level Checkpoint-Restart mechanism, namely the Virtual Cluster Checkpoint-Restart (VCCR), to perform checkpoint and restart operations at hypervisor-level. VCCR is highly transparent to MPI applications and guest OS. In VCCR, a software framework consisting of a controller and agent processes is created to perform checkpoint and restart operations for the entire cluster. The checkpoint and restart protocols of VCCR are designed based on the principles of barrier synchronization and virtual time to maintain global consistency and efficiency. We have developed a prototype of VCCR on top the QEMU-KVM software and conducted two preliminary experiments using NAS Parallel Benchmark. Experimental results confirm that VCCR can correctly and efficiently checkpoint and restart a cluster of virtual machines.
{"title":"A Transparent Hypervisor-level Checkpoint-Restart Mechanism for a Cluster of Virtual Machines","authors":"Chayawat Pechwises, K. Chanchio","doi":"10.1109/JCSSE.2018.8457176","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457176","url":null,"abstract":"A cluster of virtual machines is a common platform for running MPI applications in cloud computing environments. However, most traditional methods to provide fault tolerance to these applications are not fully transparent and require specific, checkpointing-enabled MPI software. This paper presents a novel Transparent Hypervisor-level Checkpoint-Restart mechanism, namely the Virtual Cluster Checkpoint-Restart (VCCR), to perform checkpoint and restart operations at hypervisor-level. VCCR is highly transparent to MPI applications and guest OS. In VCCR, a software framework consisting of a controller and agent processes is created to perform checkpoint and restart operations for the entire cluster. The checkpoint and restart protocols of VCCR are designed based on the principles of barrier synchronization and virtual time to maintain global consistency and efficiency. We have developed a prototype of VCCR on top the QEMU-KVM software and conducted two preliminary experiments using NAS Parallel Benchmark. Experimental results confirm that VCCR can correctly and efficiently checkpoint and restart a cluster of virtual machines.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"8 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":"120963318","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}
The number of smartphone users increases continuously since the smartphone enables people to access data from anywhere and at any time. However, data accessibility for cosmetic and skincare products to check safety, information, and allergies of each cosmetic ingredient is quite difficult. Many people hesitate to try new products or new brands as they have no knowledge of cosmetic ingredients, the chemical characteristics of each ingredient, and quality. Moreover, they may be unsure that the product is appropriate for their skin. Therefore, to alleviate these problems, this research project proposes SkinProf, which is an Android application for providing knowledge about cosmetic and skin care products to smart consumers. SkinProf will help cosmetic users have convenient access to check the safety level of each ingredient and realize which ingredient they should avoid. SkinProf allows them to compare the ingredients between products and select the most appropriate product.
{"title":"SkinProf: An Android Application for Smart Cosmetic and Skincare Users","authors":"Songsri Tangsripairoj, Kwanchanok Khongson, Pitchapa Puangnak, Yada Boonserm","doi":"10.1109/JCSSE.2018.8457178","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457178","url":null,"abstract":"The number of smartphone users increases continuously since the smartphone enables people to access data from anywhere and at any time. However, data accessibility for cosmetic and skincare products to check safety, information, and allergies of each cosmetic ingredient is quite difficult. Many people hesitate to try new products or new brands as they have no knowledge of cosmetic ingredients, the chemical characteristics of each ingredient, and quality. Moreover, they may be unsure that the product is appropriate for their skin. Therefore, to alleviate these problems, this research project proposes SkinProf, which is an Android application for providing knowledge about cosmetic and skin care products to smart consumers. SkinProf will help cosmetic users have convenient access to check the safety level of each ingredient and realize which ingredient they should avoid. SkinProf allows them to compare the ingredients between products and select the most appropriate product.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"91 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":"122527871","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.2018.8457376
Chanatip Saetia, P. Vateekul
Hierarchical text categorization is a task that aims to assign predefined categories to text documents with hierarchical constraint. Recently, deep learning techniques has shown many success results in various fields, especially, in text categorization. In our previous work called Shared Hidden Layer Neural Network (SHL-NN), it has shown that sharing information between levels can improve a performance of the model. However, this work is based on a sequence of unsupervised word embedding vectors, so the performance should be limited. In this paper, we propose a supervised document embedding specifically designed for hierarchical text categorization based on Autoencoder, which is trained from both words and labels. To enhance the embedding vectors, the document embedding strategies are invented to utilize the class hierarchy information in the training process. To transfer the prediction result from the parent classes, the shared information technique has been improved to be more flexible and efficient. The experiment was conducted on three standard benchmarks: WIPO-C, WIPO-D and Wiki comparing to two baselines: SHL-NN and a top-down based SVM framework with TF-IDF inputs called “HR-SVM.” The results show that the proposed model outperforms all baselines in terms of F1 macro.
{"title":"Enhance Accuracy of Hierarchical Text Categorization Based on Deep Learning Network Using Embedding Strategies","authors":"Chanatip Saetia, P. Vateekul","doi":"10.1109/JCSSE.2018.8457376","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457376","url":null,"abstract":"Hierarchical text categorization is a task that aims to assign predefined categories to text documents with hierarchical constraint. Recently, deep learning techniques has shown many success results in various fields, especially, in text categorization. In our previous work called Shared Hidden Layer Neural Network (SHL-NN), it has shown that sharing information between levels can improve a performance of the model. However, this work is based on a sequence of unsupervised word embedding vectors, so the performance should be limited. In this paper, we propose a supervised document embedding specifically designed for hierarchical text categorization based on Autoencoder, which is trained from both words and labels. To enhance the embedding vectors, the document embedding strategies are invented to utilize the class hierarchy information in the training process. To transfer the prediction result from the parent classes, the shared information technique has been improved to be more flexible and efficient. The experiment was conducted on three standard benchmarks: WIPO-C, WIPO-D and Wiki comparing to two baselines: SHL-NN and a top-down based SVM framework with TF-IDF inputs called “HR-SVM.” The results show that the proposed model outperforms all baselines in terms of F1 macro.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"29 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":"129460258","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.2018.8457328
Myint Zaw, Pichaya Tandayya
Social network platforms allow the customers to feedback and complain about their opinions on products and services. Normally, users' feedbacks on social networks are unstructured data usually involving an enormous size of texts, called Social Big Data. Even though Social Big Data supports marketers by giving the information about the customers' sentiments, a lot of organizations suffer with labor intensive and time-consuming tasks in extracting the customers' satisfaction from Social Big Data manually. Therefore, an automatic process to extract the information from Social Big Data is required by marketers and decision-makers. To deal with this requirement, this paper proposes a new sentiment information extraction algorithm, called the Contrast Rule-based Sentiment Analysis algorithm that intends to extract the information automatically. We prove the validity of our proposed algorithm through comparison with the well-known sentiment information extraction algorithms, general word counting and SentiStrength. Applying on the labelled customer feedbacks on the Amazon dataset, our algorithm extracted sentiments more correctly than the general word counting and SentiStrength algorithms, especially in the negative cases. The processing time is also faster than the SentiStrength algorithm. This algorithm can be applied in a marketing system to help extract the customers' satisfaction, especially work as an alarming tool for negative comments.
{"title":"Multi-level Sentiment Information Extraction Using the CRbSA Algorithm","authors":"Myint Zaw, Pichaya Tandayya","doi":"10.1109/JCSSE.2018.8457328","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457328","url":null,"abstract":"Social network platforms allow the customers to feedback and complain about their opinions on products and services. Normally, users' feedbacks on social networks are unstructured data usually involving an enormous size of texts, called Social Big Data. Even though Social Big Data supports marketers by giving the information about the customers' sentiments, a lot of organizations suffer with labor intensive and time-consuming tasks in extracting the customers' satisfaction from Social Big Data manually. Therefore, an automatic process to extract the information from Social Big Data is required by marketers and decision-makers. To deal with this requirement, this paper proposes a new sentiment information extraction algorithm, called the Contrast Rule-based Sentiment Analysis algorithm that intends to extract the information automatically. We prove the validity of our proposed algorithm through comparison with the well-known sentiment information extraction algorithms, general word counting and SentiStrength. Applying on the labelled customer feedbacks on the Amazon dataset, our algorithm extracted sentiments more correctly than the general word counting and SentiStrength algorithms, especially in the negative cases. The processing time is also faster than the SentiStrength algorithm. This algorithm can be applied in a marketing system to help extract the customers' satisfaction, especially work as an alarming tool for negative comments.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"12 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":"128302002","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}
Traffic congestion is occasionally caused by an unusual traffic incident such as a road accident or a big sporting event. The congestion could have been avoided if the traffic authority had detected and responded to it quickly and appropriately. This article explores a machine learning approach for detecting anomalous traffic incidents in real-time using GPS data collected from thousands of taxicabs in Bangkok Metropolitan area. The detection model is based on applying Principal Component Analysis (PCA) on various features extracted from overlapping fixed-length time windows over a target region. After the model has been trained, it is validated on past data and is able to discover meaningful anomalous incidents that have been verified by cross-checking with other information sources. Our approach does not require any street layout information, is computationally efficient, and can be deployed to monitor realtime traffic over large areas at scales.
{"title":"A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection","authors":"Majeed Thaika, Songwong Tasneeyapant, Sunsern Cheamanunkul","doi":"10.1109/JCSSE.2018.8457338","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457338","url":null,"abstract":"Traffic congestion is occasionally caused by an unusual traffic incident such as a road accident or a big sporting event. The congestion could have been avoided if the traffic authority had detected and responded to it quickly and appropriately. This article explores a machine learning approach for detecting anomalous traffic incidents in real-time using GPS data collected from thousands of taxicabs in Bangkok Metropolitan area. The detection model is based on applying Principal Component Analysis (PCA) on various features extracted from overlapping fixed-length time windows over a target region. After the model has been trained, it is validated on past data and is able to discover meaningful anomalous incidents that have been verified by cross-checking with other information sources. Our approach does not require any street layout information, is computationally efficient, and can be deployed to monitor realtime traffic over large areas at scales.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"15 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":"122991869","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.2018.8457340
V. Visoottiviseth, Pongnapat Jutadhammakorn, Natthamon Pongchanchai, Pongjarun Kosolyudhthasarn
As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.
{"title":"Firmaster: Analysis Tool for Home Router Firmware","authors":"V. Visoottiviseth, Pongnapat Jutadhammakorn, Natthamon Pongchanchai, Pongjarun Kosolyudhthasarn","doi":"10.1109/JCSSE.2018.8457340","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457340","url":null,"abstract":"As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"100 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":"121255329","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.2018.8457371
Nutcha Chayanurak, Chakrit Watcharopas
In this paper we propose a particle-based approach mto ice melting simulation using heat received from the air assuming to be around an ice object using Newton’s law of cooling. We use the SPH method for handling flowing motion of water coming off from the melting ice surface. However, for melted water that is sparsely generated and flowing on the ice surface, thin features are difficult to simulate only with the SPH-based approach. To avoid unnatural appearance of a few water particles flowing on ice surface, we extract an isosurface from the density distribution of the desired characteristic calculated from melted ice volume transferring to water volume, based on the ratio between current heat of the ice and latent heat of the ice fusion. We also explain a simplicity of our simulation method that reduces computational cost during the heat transfer process.
{"title":"Ice Melting Simulation using SPH and Heat Transfer with Constant Ambient Temperature","authors":"Nutcha Chayanurak, Chakrit Watcharopas","doi":"10.1109/JCSSE.2018.8457371","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457371","url":null,"abstract":"In this paper we propose a particle-based approach mto ice melting simulation using heat received from the air assuming to be around an ice object using Newton’s law of cooling. We use the SPH method for handling flowing motion of water coming off from the melting ice surface. However, for melted water that is sparsely generated and flowing on the ice surface, thin features are difficult to simulate only with the SPH-based approach. To avoid unnatural appearance of a few water particles flowing on ice surface, we extract an isosurface from the density distribution of the desired characteristic calculated from melted ice volume transferring to water volume, based on the ratio between current heat of the ice and latent heat of the ice fusion. We also explain a simplicity of our simulation method that reduces computational cost during the heat transfer process.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 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":"129254379","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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