Pub Date : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00233
Amina Jandoubi, M. Bennani, A. E. Fazziki
Nowadays, the Internet of Things touches all areas of our daily life, such as industry, economy, energy and agriculture. If we extend these domains to solutions related to smart homes and cars, we will count more than 50 billion connected devices in 2020. These applications transmit a high amount of data on the internet through IoT communication protocols. In some cases, the security aspect is required as the exchanged data can be sensitive. Therefore, it is necessary to develop a means to assess the confidence we can assign to such transmission protocols. In this context, the fault injection characterization mechanism speeds up the fault introduction into a transmission protocol to observe its reaction and to assess its resilience to application conditions with risks of errors occurring. This paper presents a systematic approach to identifying the moment of fault injection in the messaging protocol Message Queuing Telemetry Transport (MQTT). MQTT protocol handles exchanged messages across a distributed system where the injection instant cannot be defined through a time value as the synchronization of the distributed components is not guaranteed. New algorithms are introduced: (1) extract the send/receive messages' pairs, (2) timestamp the communication events using the vector clock, (3) filter the sending events and (4) generate alternate sent messages sequences. Events models for the publisher/broker provided services are generated. These services are: connect, disconnect and publish, obeying some required properties for services' quality.
{"title":"Faultload time model of the MQTT protocol publish service","authors":"Amina Jandoubi, M. Bennani, A. E. Fazziki","doi":"10.1109/COMPSAC54236.2022.00233","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00233","url":null,"abstract":"Nowadays, the Internet of Things touches all areas of our daily life, such as industry, economy, energy and agriculture. If we extend these domains to solutions related to smart homes and cars, we will count more than 50 billion connected devices in 2020. These applications transmit a high amount of data on the internet through IoT communication protocols. In some cases, the security aspect is required as the exchanged data can be sensitive. Therefore, it is necessary to develop a means to assess the confidence we can assign to such transmission protocols. In this context, the fault injection characterization mechanism speeds up the fault introduction into a transmission protocol to observe its reaction and to assess its resilience to application conditions with risks of errors occurring. This paper presents a systematic approach to identifying the moment of fault injection in the messaging protocol Message Queuing Telemetry Transport (MQTT). MQTT protocol handles exchanged messages across a distributed system where the injection instant cannot be defined through a time value as the synchronization of the distributed components is not guaranteed. New algorithms are introduced: (1) extract the send/receive messages' pairs, (2) timestamp the communication events using the vector clock, (3) filter the sending events and (4) generate alternate sent messages sequences. Events models for the publisher/broker provided services are generated. These services are: connect, disconnect and publish, obeying some required properties for services' quality.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132810592","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00070
Wook-Sung Yoo, Hwapyeong Song, Hyun-Duck Kim
Periodontitis is a chronic inflammatory disease of the tissue around the teeth. The early detection of periodontitis before it manifests undesirable irreversible destruction of periodontal tissues has been an important issue in public dental health. The clinical examination is a traditional way of the diagnosis of periodontal diseases but is often insufficient and does not provide information on the current activity of periodontitis or its progression. After intensive clinical studies in the Department of Preventive and Social Dentistry at Seoul National University (SNU) in South Korea, the salivary matrix-metalloproteinase (MMP)-9 was identified as one of the major enzymes responsible for the initiation of periodontitis. SNU developed a point-of-care (POC) kit for a lateral flow test (LFT) using MMP-9 and created a diagnostic model based on a patient's personal information for screening periodontitis. After the successful clinical studies, the Rapid Periodontitis screening Tool (RPT), a database-driven web application, was developed to measure the risk of periodontitis online with the collaboration between the School of Dentistry at SNU and the Computer Science Program at Marshall University in the United States. The web interface in RPT allows anyone to enter their personal data and the value of LFT test results to receive the screening result immediately. The RPT also provides member pages to track down the test results in the long run. Once commercialized, the RPT will help early detection of periodontitis to enhance public health. This paper describes details of the tool and future research direction.
{"title":"Collaborative Research on Rapid Periodontitis Test","authors":"Wook-Sung Yoo, Hwapyeong Song, Hyun-Duck Kim","doi":"10.1109/COMPSAC54236.2022.00070","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00070","url":null,"abstract":"Periodontitis is a chronic inflammatory disease of the tissue around the teeth. The early detection of periodontitis before it manifests undesirable irreversible destruction of periodontal tissues has been an important issue in public dental health. The clinical examination is a traditional way of the diagnosis of periodontal diseases but is often insufficient and does not provide information on the current activity of periodontitis or its progression. After intensive clinical studies in the Department of Preventive and Social Dentistry at Seoul National University (SNU) in South Korea, the salivary matrix-metalloproteinase (MMP)-9 was identified as one of the major enzymes responsible for the initiation of periodontitis. SNU developed a point-of-care (POC) kit for a lateral flow test (LFT) using MMP-9 and created a diagnostic model based on a patient's personal information for screening periodontitis. After the successful clinical studies, the Rapid Periodontitis screening Tool (RPT), a database-driven web application, was developed to measure the risk of periodontitis online with the collaboration between the School of Dentistry at SNU and the Computer Science Program at Marshall University in the United States. The web interface in RPT allows anyone to enter their personal data and the value of LFT test results to receive the screening result immediately. The RPT also provides member pages to track down the test results in the long run. Once commercialized, the RPT will help early detection of periodontitis to enhance public health. This paper describes details of the tool and future research direction.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128304616","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00226
Boya Li, Jianqiang Li, Zhichao Zhu, Linna Zhao, Wen-fang Cheng
Microscopic imaging plays an important role in the biomedical field. Existing deep learning based methods rely on high-quality data. However, there is a lot of noise (such as bubbles and impurities) in the microscopic images of biological samples collected outdoors, which may lead to significant interference in the microscopic objects identification task. To solve this problem, this paper proposes a deep learning based method for microscopic object localization and classification. Firstly, the whole slide image is preprocessed to obtain the microscopic images after preliminary filtering bubbles and impurities. Then, the sensitized pollen grains are located based on the deep learning model to remove the interference of remaining impurities, and the microscopic images of sensitized pollen grains are classified. This method can effectively suppress the interference of noise in microscopic images on object classification and improve the accuracy and reliability of model. The proposed method is verified by experiments based on real data and the results show that the proposed method achieves the highest accuracy compared with other deep learning methods.
{"title":"A Deep Learning based Method for Microscopic Object Localization and Classification","authors":"Boya Li, Jianqiang Li, Zhichao Zhu, Linna Zhao, Wen-fang Cheng","doi":"10.1109/COMPSAC54236.2022.00226","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00226","url":null,"abstract":"Microscopic imaging plays an important role in the biomedical field. Existing deep learning based methods rely on high-quality data. However, there is a lot of noise (such as bubbles and impurities) in the microscopic images of biological samples collected outdoors, which may lead to significant interference in the microscopic objects identification task. To solve this problem, this paper proposes a deep learning based method for microscopic object localization and classification. Firstly, the whole slide image is preprocessed to obtain the microscopic images after preliminary filtering bubbles and impurities. Then, the sensitized pollen grains are located based on the deep learning model to remove the interference of remaining impurities, and the microscopic images of sensitized pollen grains are classified. This method can effectively suppress the interference of noise in microscopic images on object classification and improve the accuracy and reliability of model. The proposed method is verified by experiments based on real data and the results show that the proposed method achieves the highest accuracy compared with other deep learning methods.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133282055","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00196
Mohamed Zouidine, Mohammed Khalil
In this paper, we conduct a series of experiments to systematically study both context-independent and context-dependent word embeddings for the purpose of Arabic sentiment analysis. We use pretrained word embeddings as fixed features extractors to provide input features for a CNN model. Experimental results with two different Arabic sentiment analysis datasets indicate that the pre-trained contextualized AraBERT model is the most suitable for such tasks. AraBERT reaches an accuracy score of 91.4% and 95.49% on the large Arabic book reviews dataset (LABR) and the hotel Arabic-reviews dataset (HARD), respectively.
{"title":"A Comparative Study of Pre-trained Word Embeddings for Arabic Sentiment Analysis","authors":"Mohamed Zouidine, Mohammed Khalil","doi":"10.1109/COMPSAC54236.2022.00196","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00196","url":null,"abstract":"In this paper, we conduct a series of experiments to systematically study both context-independent and context-dependent word embeddings for the purpose of Arabic sentiment analysis. We use pretrained word embeddings as fixed features extractors to provide input features for a CNN model. Experimental results with two different Arabic sentiment analysis datasets indicate that the pre-trained contextualized AraBERT model is the most suitable for such tasks. AraBERT reaches an accuracy score of 91.4% and 95.49% on the large Arabic book reviews dataset (LABR) and the hotel Arabic-reviews dataset (HARD), respectively.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435301","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00218
Zhang Liu, Liang Xiao, Jianxia Chen, He Yu, Yunlong Ye
Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge graph. Objective data was extracted from semi-structured online medical service interfaces, and subjective emotional data from patient review pages. A prototype system was designed and implemented to demonstrate the feasibility of the method. The system can recommend a ranked list of doctors with the best matched clinical background as well as patient preferences. An evaluation was conducted via carrying out a survey of user groups upon the medical guidance options of a human nurse, the “We Doctor” system, and our prototype system.
{"title":"An Emotion-fused Medical Knowledge Graph and its Application in Decision Support","authors":"Zhang Liu, Liang Xiao, Jianxia Chen, He Yu, Yunlong Ye","doi":"10.1109/COMPSAC54236.2022.00218","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00218","url":null,"abstract":"Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge graph. Objective data was extracted from semi-structured online medical service interfaces, and subjective emotional data from patient review pages. A prototype system was designed and implemented to demonstrate the feasibility of the method. The system can recommend a ranked list of doctors with the best matched clinical background as well as patient preferences. An evaluation was conducted via carrying out a survey of user groups upon the medical guidance options of a human nurse, the “We Doctor” system, and our prototype system.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131746774","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00232
Emna Souidi, Lamia Zouhaier, Y. Hlaoui
With the evolution of mobile devices such as laptops, tablets, smartphones, etc., the number of users has also increased, but it is difficult for some people due to their limited abilities. The Internet of Things (IoT) can make people's lives more convenient, and especially important for people with disabilities. This paper presents an approach to semantic representation using ontology. This approach is based on IoT to facilitate the real-time populating of the ontology in the context of adapting User Interfaces (UI) to people with disabilities such as sensory impairments (hearing and vision). IoT technology offers a great service to gain more autonomy and independence for people with disabilities.
{"title":"IoT for Real-time Accessibility Ontology Population to Context-awareness Adapt User Interfaces","authors":"Emna Souidi, Lamia Zouhaier, Y. Hlaoui","doi":"10.1109/COMPSAC54236.2022.00232","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00232","url":null,"abstract":"With the evolution of mobile devices such as laptops, tablets, smartphones, etc., the number of users has also increased, but it is difficult for some people due to their limited abilities. The Internet of Things (IoT) can make people's lives more convenient, and especially important for people with disabilities. This paper presents an approach to semantic representation using ontology. This approach is based on IoT to facilitate the real-time populating of the ontology in the context of adapting User Interfaces (UI) to people with disabilities such as sensory impairments (hearing and vision). IoT technology offers a great service to gain more autonomy and independence for people with disabilities.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130309740","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00220
S. Rahman, Swetha Variyath, Nabeel Al-Yateem, Sheikh Iqbal Ahamed, A. A. Al Marzouqi, M. Subu, J. Dias, A. Saifan, F. Ahmed
Introduction: The spread of the COVID-19 pandemic has overwhelmed the overall world causing not only a health crisis but affecting multiple industries and institutions like businesses, health care, transportation, economy, tourism, employment, and foremost education and students regardless of their age and educational level (Anaya, 2020). Students of almost all countries all over the world confined to those attending school online are currently facing lots of challenges and opportunities. we have decided to initiate such a research study focusing on the online learning experience since it has taken over the traditional learning pattern causing a lot of challenges and many more opportunities to students. Objective: To explore and grasp the challenges and opportunities of online learning that are encountered by University of Sharjah students. Results: The majority of the students (59%) found that online learning has affected their academic performance and 45% felt it was extremely stressful. 75% of students had concerns about their health & financial status. Around 43% do not feel engaged in their online course. 68% prefer paper-based exams. During in-depth interview most statements included “it's hard to stay motivated while you're at home as you can always get distracted”, “not as effective as traditional classes”, “I dread online learning; I'm not used to it … it's a very bad experience. Conclusion: Based on the results acquired the online learning experience was not the best experience for the university of Sharjah students a lot associated online learning with a very bad and stressful experience, many issues were addressed in the discussion regarding the technical issues, lack of face-to-face communication, lack of appropriate study environment, lack of motivation and passion for studying and keeping up with online courses and a lot more.
{"title":"Technology Utilization in Health Science Education during Covid-19: Experience from University of Sharjah","authors":"S. Rahman, Swetha Variyath, Nabeel Al-Yateem, Sheikh Iqbal Ahamed, A. A. Al Marzouqi, M. Subu, J. Dias, A. Saifan, F. Ahmed","doi":"10.1109/COMPSAC54236.2022.00220","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00220","url":null,"abstract":"Introduction: The spread of the COVID-19 pandemic has overwhelmed the overall world causing not only a health crisis but affecting multiple industries and institutions like businesses, health care, transportation, economy, tourism, employment, and foremost education and students regardless of their age and educational level (Anaya, 2020). Students of almost all countries all over the world confined to those attending school online are currently facing lots of challenges and opportunities. we have decided to initiate such a research study focusing on the online learning experience since it has taken over the traditional learning pattern causing a lot of challenges and many more opportunities to students. Objective: To explore and grasp the challenges and opportunities of online learning that are encountered by University of Sharjah students. Results: The majority of the students (59%) found that online learning has affected their academic performance and 45% felt it was extremely stressful. 75% of students had concerns about their health & financial status. Around 43% do not feel engaged in their online course. 68% prefer paper-based exams. During in-depth interview most statements included “it's hard to stay motivated while you're at home as you can always get distracted”, “not as effective as traditional classes”, “I dread online learning; I'm not used to it … it's a very bad experience. Conclusion: Based on the results acquired the online learning experience was not the best experience for the university of Sharjah students a lot associated online learning with a very bad and stressful experience, many issues were addressed in the discussion regarding the technical issues, lack of face-to-face communication, lack of appropriate study environment, lack of motivation and passion for studying and keeping up with online courses and a lot more.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132319200","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00231
F. Akhtar, Jianqiang Li, Z. Khand, Yu-Chih Wei, Khalid Hussain, Sana Fatima
Background: Classification of infants has always been considered a crucial task in the literature related to predicting small for gestational age (SGA) infants. Traditional medical guidance becomes increasingly unsatisfactory, as patients' care should be centered not only on clinical symptoms but also on socio-economic and demographic factors. Infants with excessive gestational weight exhibit serious maternal complications that require early intervention to stream-line the progression of the disease. Methods: This research proposes to use the Stacked Generalization Scheme (SGS) to predict Small for Gestational (SGA) Infants on the dataset collected from the National Pre-Pregnancy and Examination Program of China. A Cleaned Feature Vector (CFV) is created that entertains issues related to missing values, discretization of fields, and data imbalance. Later, Knowledge-Driven Data (KDD) Features are extracted from the obtained CFV, and the proposed scheme is applied to predict SGA infants. The proposed scheme superposed the existing baseline approaches by achieving the highest precision, recall, and AUC scores of 0.94, 0.85, and 0.89, respectively. Conclusion: The proposed SGS can predict SGA infants accurately compared to existing baseline schemes using KDD parameters, which can help pediatricians develop an efficient SGA Prognosis process.
{"title":"An Efficient Small for Gestational Age Prognosis System Using Stacked Generalization Scheme (SGS)","authors":"F. Akhtar, Jianqiang Li, Z. Khand, Yu-Chih Wei, Khalid Hussain, Sana Fatima","doi":"10.1109/COMPSAC54236.2022.00231","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00231","url":null,"abstract":"Background: Classification of infants has always been considered a crucial task in the literature related to predicting small for gestational age (SGA) infants. Traditional medical guidance becomes increasingly unsatisfactory, as patients' care should be centered not only on clinical symptoms but also on socio-economic and demographic factors. Infants with excessive gestational weight exhibit serious maternal complications that require early intervention to stream-line the progression of the disease. Methods: This research proposes to use the Stacked Generalization Scheme (SGS) to predict Small for Gestational (SGA) Infants on the dataset collected from the National Pre-Pregnancy and Examination Program of China. A Cleaned Feature Vector (CFV) is created that entertains issues related to missing values, discretization of fields, and data imbalance. Later, Knowledge-Driven Data (KDD) Features are extracted from the obtained CFV, and the proposed scheme is applied to predict SGA infants. The proposed scheme superposed the existing baseline approaches by achieving the highest precision, recall, and AUC scores of 0.94, 0.85, and 0.89, respectively. Conclusion: The proposed SGS can predict SGA infants accurately compared to existing baseline schemes using KDD parameters, which can help pediatricians develop an efficient SGA Prognosis process.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133806667","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00059
Ge Hangli, Lifeng Lin, Renhe Jiang, Takashi Michikata, N. Koshizuka
In this study, a method of multi-weighted graphs learning for passenger count prediction in railway networks, is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by various factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task because of the complexity of the railway networks. To achieve high learning performance of the models and discover the correlation between the models and features, we proposed various heterogenerous weighted graphs for the passenger count prediction. Six types of weight graphs, that is, connection graph, distance graph, correlation graph, and their fused weight graphs were proposed to fully construct the spatial and geometrical features within the entire railway network. Two representative types of graph neural networks, that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RSME), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Finally, the importance and effectiveness of the models with corresponding weight graphs were also investigated and explained. It also provides the interpretability of the traffic prediction tasks on the railway network.
{"title":"Multi-weighted Graphs Learning for Passenger Count Prediction on Railway Network","authors":"Ge Hangli, Lifeng Lin, Renhe Jiang, Takashi Michikata, N. Koshizuka","doi":"10.1109/COMPSAC54236.2022.00059","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00059","url":null,"abstract":"In this study, a method of multi-weighted graphs learning for passenger count prediction in railway networks, is presented. Traffic prediction can provide significant insights for railway system optimization, urban planning, smart city development, etc. However, affected by various factors, including spatial, temporal, and other external ones, traffic prediction on railway networks remains a critical task because of the complexity of the railway networks. To achieve high learning performance of the models and discover the correlation between the models and features, we proposed various heterogenerous weighted graphs for the passenger count prediction. Six types of weight graphs, that is, connection graph, distance graph, correlation graph, and their fused weight graphs were proposed to fully construct the spatial and geometrical features within the entire railway network. Two representative types of graph neural networks, that is, the graph convolutional network (GCN) and graph attention network (GAT) were implemented for evaluation. The evaluation results demonstrate that the proposed GAT model learning on the correlation graph achieves the best performance, as it can reduce the metrics of mean absolute error (MAE), root mean square error (RSME), and mean absolute percentage error metrics (MAPE) on average by 19.7%, 6.9%, 27.9% respectively. Finally, the importance and effectiveness of the models with corresponding weight graphs were also investigated and explained. It also provides the interpretability of the traffic prediction tasks on the railway network.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133825034","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 : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00133
Xiaocao Hu, Haibo Wang
Android has recently become one of the leading operating systems for mobile app development. The permission- based mechanism in Android forces app developers to determine permissions required by apps besides implementing the functionality, which increases the burden on developers. App permission recommendation becomes necessary and meaningful to assist developers determine appropriate needed permissions. Existing approaches for app permission recommendation have various limitations, such as suffering from the cold-start problem, needing to learn both of the app and permission embedding matrices. To address these issues, we define a sparse matrix factorization model, in which API categories are utilized as latent factors, app-API calls are applied for app representation, and only one sparse matrix is to be learned for permission representation. We further present an efficient approach by utilizing the Alternating Direction Method of Multipliers to solve the optimization problem. We conduct a comprehensive set of experiments on a real-world dataset, which show that our approach outperforms the state-of-the-art approaches in terms of four well-known metrics.
{"title":"Category-Aware App Permission Recommendation based on Sparse Linear Model","authors":"Xiaocao Hu, Haibo Wang","doi":"10.1109/COMPSAC54236.2022.00133","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00133","url":null,"abstract":"Android has recently become one of the leading operating systems for mobile app development. The permission- based mechanism in Android forces app developers to determine permissions required by apps besides implementing the functionality, which increases the burden on developers. App permission recommendation becomes necessary and meaningful to assist developers determine appropriate needed permissions. Existing approaches for app permission recommendation have various limitations, such as suffering from the cold-start problem, needing to learn both of the app and permission embedding matrices. To address these issues, we define a sparse matrix factorization model, in which API categories are utilized as latent factors, app-API calls are applied for app representation, and only one sparse matrix is to be learned for permission representation. We further present an efficient approach by utilizing the Alternating Direction Method of Multipliers to solve the optimization problem. We conduct a comprehensive set of experiments on a real-world dataset, which show that our approach outperforms the state-of-the-art approaches in terms of four well-known metrics.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"51 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114014409","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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