Pub Date : 2022-06-01DOI: 10.1109/COMPSAC54236.2022.00022
Nektarios Moumoutzis, Marios Christoulakis, C. Xanthaki, Yiannis Maragkoudakis, S. Christodoulakis, D. Paneva-Marinova, Lilia Pavlova
eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.
{"title":"eShadow+: Mixed Reality Storytelling Inspired by Traditional Shadow Theatre","authors":"Nektarios Moumoutzis, Marios Christoulakis, C. Xanthaki, Yiannis Maragkoudakis, S. Christodoulakis, D. Paneva-Marinova, Lilia Pavlova","doi":"10.1109/COMPSAC54236.2022.00022","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00022","url":null,"abstract":"eShadow is a digital storytelling platform inspired by traditional Shadow Theatre. It enables the creation of digital stories within a project-based approach that may start from scenario development and include the creation of digital puppets and sceneries, the set-up and recording of story scenes and the final assembly of a digital story. This paper presents how eShadow can be enhanced to solve the problem of creating mixed reality installations to offer rich learning experiences in informal learning settings. This enhanced version is eShadow+ and it is evaluated via two installations which are described and compared. The evaluation results demonstrate the effectiveness of the approach thus offering new learning opportunities that are aligned with current trends in the use of mixed reality technologies.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"57 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":"131903490","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/COMPSAC54236.2022.00146
B. Hnatkowska, Adrianna Kozierkiewicz-Hetmanska, Marcin Pietranik
The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.
{"title":"Compatibility Checking of Compound Business Rules Expressed in Natural Language Against Domain Specification","authors":"B. Hnatkowska, Adrianna Kozierkiewicz-Hetmanska, Marcin Pietranik","doi":"10.1109/COMPSAC54236.2022.00146","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00146","url":null,"abstract":"The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"387 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":"115852781","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/COMPSAC54236.2022.00026
Rui Zhao, Harvey P. Siy, Chulwoo Pack, Leen-Kiat Soh, Myoungkyu Song
Computer science students have difficulty understanding correct usages of an Application Programming Interface (API) and programming violations that cause compilation or runtime errors. Despite high-quality documentation for programming, the students typically need an instructor's feedback when their programs cause bugs, crashes, and vulnerabilities. This paper presents a pedagogical approach that is based on an Intelligent Tutoring System called INTTuToR. Briefly, INTTUTOR provides novice students with instant feedback to fix their programming issues or vulnerabilities. We have implemented our approach as a plug-in application in the Integrated Development Environment (IDE) for an interactive educational environment. In our proposed evaluation, we plan to perform empirical studies with CS students to assess how effectively INTTUTOR improves their ability to identify and fix potential bugs or vulnerabilities in the cryptography-related programming assignments.
{"title":"An Intelligent Tutoring System for API Misuse Correction by Instant Quality Feedback","authors":"Rui Zhao, Harvey P. Siy, Chulwoo Pack, Leen-Kiat Soh, Myoungkyu Song","doi":"10.1109/COMPSAC54236.2022.00026","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00026","url":null,"abstract":"Computer science students have difficulty understanding correct usages of an Application Programming Interface (API) and programming violations that cause compilation or runtime errors. Despite high-quality documentation for programming, the students typically need an instructor's feedback when their programs cause bugs, crashes, and vulnerabilities. This paper presents a pedagogical approach that is based on an Intelligent Tutoring System called INTTuToR. Briefly, INTTUTOR provides novice students with instant feedback to fix their programming issues or vulnerabilities. We have implemented our approach as a plug-in application in the Integrated Development Environment (IDE) for an interactive educational environment. In our proposed evaluation, we plan to perform empirical studies with CS students to assess how effectively INTTUTOR improves their ability to identify and fix potential bugs or vulnerabilities in the cryptography-related programming assignments.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"115 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":"124809833","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/COMPSAC54236.2022.00019
Holland Schutte, Chase Phelps, Aniruddha Marathe, T. Islam
Cost and power efficiency considerations have driven High Performance Computing (HPC) system design inno-vations in accelerator-based heterogeneous computing. Complex interactions between applications and heterogeneous hardware make it difficult for users to extract maximum performance out of these systems. While there is a plethora of performance measurement and analysis tools for CPU s, the same is not the case for GPUs. Existing tools either provide too high-level information or are overly complicated to setup, impeding performance profiling. While NVIDIA's CUPTI profiling library enables basic kernel-level measurements on NVIDIA's GPUs, it does not provide root-causes of performance slowdown. This paper presents a low-overhead, flexible, and user-friendly tool, LIBNV CD, built on top of CUPTI to simplify performance measurement and analysis of NVIDIA GPUs. LIBNVCD simplifies obtaining fine-grained measurements, requiring only three function calls in source, while masking changes and complexities of CUPTI. By automatically discovering performance event groups, LIBNV CD reduces data collection overhead significantly as many events (not all) can be measured at once. This user-friendly multi-GPU performance measurement tool incurs a mean overhead of less than 1% as compared to CUPTI, and has been released publicly.
{"title":"LIBNVCD: An Extendable and User-friendly Multi-GPU Performance Measurement Tool","authors":"Holland Schutte, Chase Phelps, Aniruddha Marathe, T. Islam","doi":"10.1109/COMPSAC54236.2022.00019","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00019","url":null,"abstract":"Cost and power efficiency considerations have driven High Performance Computing (HPC) system design inno-vations in accelerator-based heterogeneous computing. Complex interactions between applications and heterogeneous hardware make it difficult for users to extract maximum performance out of these systems. While there is a plethora of performance measurement and analysis tools for CPU s, the same is not the case for GPUs. Existing tools either provide too high-level information or are overly complicated to setup, impeding performance profiling. While NVIDIA's CUPTI profiling library enables basic kernel-level measurements on NVIDIA's GPUs, it does not provide root-causes of performance slowdown. This paper presents a low-overhead, flexible, and user-friendly tool, LIBNV CD, built on top of CUPTI to simplify performance measurement and analysis of NVIDIA GPUs. LIBNVCD simplifies obtaining fine-grained measurements, requiring only three function calls in source, while masking changes and complexities of CUPTI. By automatically discovering performance event groups, LIBNV CD reduces data collection overhead significantly as many events (not all) can be measured at once. This user-friendly multi-GPU performance measurement tool incurs a mean overhead of less than 1% as compared to CUPTI, and has been released publicly.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"37 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":"125032313","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/COMPSAC54236.2022.00218
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/COMPSAC54236.2022.00231
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/COMPSAC54236.2022.00185
Adrian F. Spataru, Gabriel Iuhasz, S. Panica
A Distributed Application Topology is a valuable commodity built on the strength of a long and iterative design process. A topology is generally refined over time, other topologies can use it as a component, and the community may share it. To reproduce a deployment, several properties must be recorded such as data origin, processing steps, configuration settings, and hardware requirements. This paper presents an extension to the TOSCA specification that allows for the definition of accelerator-aware services that can span from Cloud to Edge. Additionally, we introduce the concept of Abstract Applications that contain at least one abstract service definition. The process of Service Optimization replaces the abstract sertvices, creating an explicit topology deployable under hybrid deployment models (Virtual Machines, Containers, HPC) residing on the Cloud Continuum spectrum.
{"title":"TUFA: A TOSCA extension for the specification of accelerator-aware applications in the Cloud Continuum","authors":"Adrian F. Spataru, Gabriel Iuhasz, S. Panica","doi":"10.1109/COMPSAC54236.2022.00185","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00185","url":null,"abstract":"A Distributed Application Topology is a valuable commodity built on the strength of a long and iterative design process. A topology is generally refined over time, other topologies can use it as a component, and the community may share it. To reproduce a deployment, several properties must be recorded such as data origin, processing steps, configuration settings, and hardware requirements. This paper presents an extension to the TOSCA specification that allows for the definition of accelerator-aware services that can span from Cloud to Edge. Additionally, we introduce the concept of Abstract Applications that contain at least one abstract service definition. The process of Service Optimization replaces the abstract sertvices, creating an explicit topology deployable under hybrid deployment models (Virtual Machines, Containers, HPC) residing on the Cloud Continuum spectrum.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"100 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":"133690184","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}
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