Konstantinos Papageorgiadis, Konstantinos Georgiou, Konstantinos Charmanas, N. Mittas, L. Angelis
As the world is still recovering from the detrimental effects of the COVID-19 pandemic, one key aspect of the pandemic era were the global efforts for containment, case tracking and several other factors. While the scientific and governmental initiatives were largely successful and effective, a notable surge was observed in contributions from individuals and programming communities that developed their own software for COVID-19 by using data retrieval and analysis along with visualization methodologies. To achieve their goals, they turned their attention to knowledge exchange portals and asked questions regarding technological queries. In this paper, we present a collective platform that retrieves such questions from a well-known Q&A portal and visualizes the contained information. This platform serves as a useful tool for assessing programming and technological interest in COVID-19 related software development efforts while also promoting the open science principles.
{"title":"COVID-Vis: Visualizing knowledge exchange on scientific software development in the COVID-19 era","authors":"Konstantinos Papageorgiadis, Konstantinos Georgiou, Konstantinos Charmanas, N. Mittas, L. Angelis","doi":"10.1145/3575879.3576019","DOIUrl":"https://doi.org/10.1145/3575879.3576019","url":null,"abstract":"As the world is still recovering from the detrimental effects of the COVID-19 pandemic, one key aspect of the pandemic era were the global efforts for containment, case tracking and several other factors. While the scientific and governmental initiatives were largely successful and effective, a notable surge was observed in contributions from individuals and programming communities that developed their own software for COVID-19 by using data retrieval and analysis along with visualization methodologies. To achieve their goals, they turned their attention to knowledge exchange portals and asked questions regarding technological queries. In this paper, we present a collective platform that retrieves such questions from a well-known Q&A portal and visualizes the contained information. This platform serves as a useful tool for assessing programming and technological interest in COVID-19 related software development efforts while also promoting the open science principles.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116491534","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}
During the Covid-19 lockdowns, e-learning and e-assessment became immensely popular worldwide. Exam Wizard is a web-based online e-assessment platform which provides a variety of features including question pool creation, automatic grading, automatic resumption, reusability, simplicity, exam monitoring and scheduling, weighted questions, a count-down timer, and automatic generation of statistics. Exam Wizard supports three kinds of users: administrators, instructors and students. This paper describes some new features of Exam Wizard, as well as the responsibilities and the User Interfaces (UI) of each user role. In addition, this paper describes the instructor’s experience from the use of Exam Wizard in pilot tests and midterm exams of technical courses offered by a higher education institution. The instructor found the tool useful because it produces immediate results, saving time and effort; when the instructor’s time is limited or in the case of large classes, e-assessment is the only viable option. During the evaluation of Exam Wizard the students appreciated the fact that they received immediate feedback, and the instructor happily stated that as long as there are questions banks available, it is a matter of minutes to create a new exam.
{"title":"Exam Wizard e-assessment platform: new features, field test results and instructor’s experience","authors":"A. Andreatos, Duke Vomvyras, C. Douligeris","doi":"10.1145/3575879.3575993","DOIUrl":"https://doi.org/10.1145/3575879.3575993","url":null,"abstract":"During the Covid-19 lockdowns, e-learning and e-assessment became immensely popular worldwide. Exam Wizard is a web-based online e-assessment platform which provides a variety of features including question pool creation, automatic grading, automatic resumption, reusability, simplicity, exam monitoring and scheduling, weighted questions, a count-down timer, and automatic generation of statistics. Exam Wizard supports three kinds of users: administrators, instructors and students. This paper describes some new features of Exam Wizard, as well as the responsibilities and the User Interfaces (UI) of each user role. In addition, this paper describes the instructor’s experience from the use of Exam Wizard in pilot tests and midterm exams of technical courses offered by a higher education institution. The instructor found the tool useful because it produces immediate results, saving time and effort; when the instructor’s time is limited or in the case of large classes, e-assessment is the only viable option. During the evaluation of Exam Wizard the students appreciated the fact that they received immediate feedback, and the instructor happily stated that as long as there are questions banks available, it is a matter of minutes to create a new exam.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727338","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}
Dimitrios Theodoropoulos, Dimitra Ioannou, C. Katsanos
Difficulties of people with Autism Spectrum Disorder (ASD) in recognizing and expressing emotions, responding appropriately to them as well as in Theory of Mind (ToM) skills are a core symptom of the disorder. An important gap in the literature concerns structured training in emotions for the development of ToM and subsequently social reciprocity. This paper presents ToMtool, a software tool that systematically supports special education practitioners in helping people with ASD to improve perception of emotional states of themselves and others as well as thoughts and intentions that derive from them and choose an appropriate social response. The application promotes playful learning, personalized to the particular needs of each child. ToMtool focuses on the 4 basic emotions (happiness, sadness, anger, fear) with the possibility of expansion to more complex ones (e.g., surprise, anxiety) and concerns children of developmental age of 4 years and older. The application was developed following a user-centered design approach, involving speech and language therapists, psychologists, and special educators in its development process. To this end, semi-structured interviews and formative usability evaluations of intermediate versions of the application were carried out. A preliminary evaluation study of the ToMtool final version found that it met the users’ expectations and also identified issues for further improvement.
{"title":"ToMtool: An Interactive Multimedia Application to Support Training of Emotion Recognition and Theory of Mind Skills to Children with Autism Spectrum Disorder","authors":"Dimitrios Theodoropoulos, Dimitra Ioannou, C. Katsanos","doi":"10.1145/3575879.3575997","DOIUrl":"https://doi.org/10.1145/3575879.3575997","url":null,"abstract":"Difficulties of people with Autism Spectrum Disorder (ASD) in recognizing and expressing emotions, responding appropriately to them as well as in Theory of Mind (ToM) skills are a core symptom of the disorder. An important gap in the literature concerns structured training in emotions for the development of ToM and subsequently social reciprocity. This paper presents ToMtool, a software tool that systematically supports special education practitioners in helping people with ASD to improve perception of emotional states of themselves and others as well as thoughts and intentions that derive from them and choose an appropriate social response. The application promotes playful learning, personalized to the particular needs of each child. ToMtool focuses on the 4 basic emotions (happiness, sadness, anger, fear) with the possibility of expansion to more complex ones (e.g., surprise, anxiety) and concerns children of developmental age of 4 years and older. The application was developed following a user-centered design approach, involving speech and language therapists, psychologists, and special educators in its development process. To this end, semi-structured interviews and formative usability evaluations of intermediate versions of the application were carried out. A preliminary evaluation study of the ToMtool final version found that it met the users’ expectations and also identified issues for further improvement.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965214","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}
Creating a well-integrated IoT management system requires a usable user interface. Usability, which is the outcome of interface design and is affected by the experience provided when using the system, is a critical component that influences how successful an interface is and how well users accept a system. With IoT devices spreading around us at an enormous pace in the last years, a growing research interest concerns how to design efficient interaction between users and smart devices. The experimental process in this study includes the pilot design of two interface variations of a smart home dashboard, a flat and a skeuomorphic design, with the purpose of examining which is better in terms of performance and aesthetics. The results indicate that participants performed better in the flat design environment as they managed to execute the assigned tasks easier and faster based on a set of metrics that comprised time to complete the task, as well as eye-tracking metrics (Time to First Fixation, Total Fixation Duration, Total Visit Duration, Visit Count, and Time to First Click). Moreover, users claimed that icons and controls in the skeuomorphic design took more time to recognize and use, an observation confirmed by recorded eye-tracking data. Overall, flat design is preferable in terms of user performance while skeuomorphism is preferable in terms of aesthetics as users consider it more visually appealing.
{"title":"Flat vs Skeuomorphic Design for Smart Home Devices: An Exploratory Eye-Tracking Study","authors":"Dimitrios Krallis, Stefanos Balaskas, Maria Rigou","doi":"10.1145/3575879.3575965","DOIUrl":"https://doi.org/10.1145/3575879.3575965","url":null,"abstract":"Creating a well-integrated IoT management system requires a usable user interface. Usability, which is the outcome of interface design and is affected by the experience provided when using the system, is a critical component that influences how successful an interface is and how well users accept a system. With IoT devices spreading around us at an enormous pace in the last years, a growing research interest concerns how to design efficient interaction between users and smart devices. The experimental process in this study includes the pilot design of two interface variations of a smart home dashboard, a flat and a skeuomorphic design, with the purpose of examining which is better in terms of performance and aesthetics. The results indicate that participants performed better in the flat design environment as they managed to execute the assigned tasks easier and faster based on a set of metrics that comprised time to complete the task, as well as eye-tracking metrics (Time to First Fixation, Total Fixation Duration, Total Visit Duration, Visit Count, and Time to First Click). Moreover, users claimed that icons and controls in the skeuomorphic design took more time to recognize and use, an observation confirmed by recorded eye-tracking data. Overall, flat design is preferable in terms of user performance while skeuomorphism is preferable in terms of aesthetics as users consider it more visually appealing.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134372660","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}
Andreas Komninos, Angeliki Tsiouma, Georgia Gogoulou, J. Garofalakis
Transcription tasks have been long used as the de-facto evaluation method in mobile text entry research. Evaluations use memorable phrase sets, in order to prevent participants from devoting more attention to the stimulus phrase than the bare minimum. We present evidence from an eye-tracking study, demonstrating that the attention devoted to the stimulus phrase is much higher than might be expected. In fact, attention to the stimulus phrase takes up almost 50% of participant attention spent outside the keyboard area, and overall 25% of participant attention throughout any single transcription task. We explore a modification to the transcription task aimed at reducing this level of visual attention, without finding any statistically significant differences. These findings raise important questions on the continued use of the transcription task as the mainstream evaluation method for mobile text entry research.
{"title":"Don’t Look Up: The Cost of Attention to Stimulus Phrases in Mobile Text Entry Evaluations","authors":"Andreas Komninos, Angeliki Tsiouma, Georgia Gogoulou, J. Garofalakis","doi":"10.1145/3575879.3576015","DOIUrl":"https://doi.org/10.1145/3575879.3576015","url":null,"abstract":"Transcription tasks have been long used as the de-facto evaluation method in mobile text entry research. Evaluations use memorable phrase sets, in order to prevent participants from devoting more attention to the stimulus phrase than the bare minimum. We present evidence from an eye-tracking study, demonstrating that the attention devoted to the stimulus phrase is much higher than might be expected. In fact, attention to the stimulus phrase takes up almost 50% of participant attention spent outside the keyboard area, and overall 25% of participant attention throughout any single transcription task. We explore a modification to the transcription task aimed at reducing this level of visual attention, without finding any statistically significant differences. These findings raise important questions on the continued use of the transcription task as the mainstream evaluation method for mobile text entry research.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128272488","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}
Accessing objects in software applications usually breaks down to four basic operations: Create, Read, Update, and Delete (CRUD). The latter is a well-known pattern in software development and web application domains. CRUD has been applied primarily to relational database-backed systems, object-relational mappers (ORMs), and relevant tools since the early ’80s. In the era of cloud computing, though, relational databases are not always the most efficient service to store application data due to the application requirements shifting towards non-functional requirements such as observability. Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) are a couple of alternative patterns on which one can build applications. However, there is a lack of tooling and guidance, especially for inexperienced practitioners. In addition, as reported in the literature, this approach requires a thorough understanding of the application domain. In this paper, we investigate the possibility of bridging CRUD modeling technics with the CQRS-ES patterns systematically and generically. Upon success, we will be able to build new event-sourced applications in the same manner as we now utilize ORMs and tools to accelerate the process. Moreover, legacy systems might also benefit by enhancing their current operation with an event-source component and, if needed, gradually replacing obsolete parts.
{"title":"Mapping CRUD to Events - Towards an object to event-sourcing framework","authors":"Michail Pantelelis, Christos Kalloniatis","doi":"10.1145/3575879.3576006","DOIUrl":"https://doi.org/10.1145/3575879.3576006","url":null,"abstract":"Accessing objects in software applications usually breaks down to four basic operations: Create, Read, Update, and Delete (CRUD). The latter is a well-known pattern in software development and web application domains. CRUD has been applied primarily to relational database-backed systems, object-relational mappers (ORMs), and relevant tools since the early ’80s. In the era of cloud computing, though, relational databases are not always the most efficient service to store application data due to the application requirements shifting towards non-functional requirements such as observability. Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) are a couple of alternative patterns on which one can build applications. However, there is a lack of tooling and guidance, especially for inexperienced practitioners. In addition, as reported in the literature, this approach requires a thorough understanding of the application domain. In this paper, we investigate the possibility of bridging CRUD modeling technics with the CQRS-ES patterns systematically and generically. Upon success, we will be able to build new event-sourced applications in the same manner as we now utilize ORMs and tools to accelerate the process. Moreover, legacy systems might also benefit by enhancing their current operation with an event-source component and, if needed, gradually replacing obsolete parts.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130892547","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}
T. Kalampokas, G. Papakostas, V. Chatzis, S. Krinidis
With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation and many more. A very hot topic that is the playground of the proposed benchmark is visual human tracking in images acquired by a camera mounted on a UAV. This target application troubles CV and deep learning (DL) research community in recent years and it has created serious demands for visual tracking algorithms. Some of the most important demands are high performance under hard visual tracking conditions and deployment in edge devices with limited computation resources. These two challenges are the main motivation of the presented paper, where 37 tracking algorithms have been benchmarked in visual object tracking (VOT) images. For each tracking algorithm two metric categories, relative to detection performance and hardware resources consumption, have been considered. The objective of the proposed paper is to highlight the most lightweight and high performance tracking algorithms for usage in UAV based applications.
{"title":"Performance Benchmarking of Visual Human Tracking Algorithms for UAVs","authors":"T. Kalampokas, G. Papakostas, V. Chatzis, S. Krinidis","doi":"10.1145/3575879.3575880","DOIUrl":"https://doi.org/10.1145/3575879.3575880","url":null,"abstract":"With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation and many more. A very hot topic that is the playground of the proposed benchmark is visual human tracking in images acquired by a camera mounted on a UAV. This target application troubles CV and deep learning (DL) research community in recent years and it has created serious demands for visual tracking algorithms. Some of the most important demands are high performance under hard visual tracking conditions and deployment in edge devices with limited computation resources. These two challenges are the main motivation of the presented paper, where 37 tracking algorithms have been benchmarked in visual object tracking (VOT) images. For each tracking algorithm two metric categories, relative to detection performance and hardware resources consumption, have been considered. The objective of the proposed paper is to highlight the most lightweight and high performance tracking algorithms for usage in UAV based applications.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133527032","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}
Power quality is a critical parameter of modern power electrical systems, the complexity and decentralization of which are rapidly increasing. Indeed, the highest possible quality is a requirement of all the stakeholders of a power grid. In response to this demand, we introduce, in this article, a novel neuro-symbolic approach for the diagnosis (i.e., detection and classification) of the typical faults that a smart power grid encounters during its operation (that is, voltage interruptions, voltage sags, voltage swells, transients and harmonics). Heart of the implemented system is an Artificial Neural Network (ANN) that identifies with high fidelity the patterns of voltage-waveforms — for the sake of comparison, two ANNs were evaluated, namely, a conventional Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (CNN). The output of the ANN is passed through a symbolic reasoner, implemented by means of Answer Set Programming (ASP), which provides a final response on the condition of the power grid, taking into account the background knowledge of the domain, which is in turn encoded into appropriate symbolic rules. The proposed approach achieved very high classification-performance on the validation dataset ( the MLP and the CNN), and, thus, it constitutes a promising powerful tool that will contribute to the improved quality of future power grids.
{"title":"A Neuro-Symbolic Approach for Fault Diagnosis in Smart Power Grids","authors":"T. Aravanis, I. Kabouris","doi":"10.1145/3575879.3575972","DOIUrl":"https://doi.org/10.1145/3575879.3575972","url":null,"abstract":"Power quality is a critical parameter of modern power electrical systems, the complexity and decentralization of which are rapidly increasing. Indeed, the highest possible quality is a requirement of all the stakeholders of a power grid. In response to this demand, we introduce, in this article, a novel neuro-symbolic approach for the diagnosis (i.e., detection and classification) of the typical faults that a smart power grid encounters during its operation (that is, voltage interruptions, voltage sags, voltage swells, transients and harmonics). Heart of the implemented system is an Artificial Neural Network (ANN) that identifies with high fidelity the patterns of voltage-waveforms — for the sake of comparison, two ANNs were evaluated, namely, a conventional Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (CNN). The output of the ANN is passed through a symbolic reasoner, implemented by means of Answer Set Programming (ASP), which provides a final response on the condition of the power grid, taking into account the background knowledge of the domain, which is in turn encoded into appropriate symbolic rules. The proposed approach achieved very high classification-performance on the validation dataset ( the MLP and the CNN), and, thus, it constitutes a promising powerful tool that will contribute to the improved quality of future power grids.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114816500","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}
Mohsan Ali, Ali Muhammad, Muhammad Asad, Makhdoom Sajawal, C. Alexopoulos, Y. Charalabidis
Social media users are growing daily, with hundreds of millions of active users per month on certain networking sites. For any administrative institution, the manual method for regulating user content is challenging. There are hundreds of languages through which you can direct your attention on the web. The Urdu language is among the most widely utilized languages in the world. We have proposed a quick way of detecting the content of Urdu language hate using machine learning models. We used the open data set and manually created instances to make this investigation viable on a balanced data set. Our experimental set-up has demonstrated that support vector machine in the detection of Urdu hatred detection is 81.87% accurate. The training time, testing time, and accuracy helped us select the best model for Urdu hate detection on social media sites. We also compared the training and testing times of various methods. Additionally, we demonstrated k and stratified folding via indexing to provide a better understanding of folding in machine learning. Finally, we compared our findings to those of previously published works in the field of Urdu hate detection.
{"title":"Towards Perso-Arabic Urdu Language Hate Detection Using Machine Learning: A Comparative Study Based on a Large Dataset and Time-Complexity","authors":"Mohsan Ali, Ali Muhammad, Muhammad Asad, Makhdoom Sajawal, C. Alexopoulos, Y. Charalabidis","doi":"10.1145/3575879.3576011","DOIUrl":"https://doi.org/10.1145/3575879.3576011","url":null,"abstract":"Social media users are growing daily, with hundreds of millions of active users per month on certain networking sites. For any administrative institution, the manual method for regulating user content is challenging. There are hundreds of languages through which you can direct your attention on the web. The Urdu language is among the most widely utilized languages in the world. We have proposed a quick way of detecting the content of Urdu language hate using machine learning models. We used the open data set and manually created instances to make this investigation viable on a balanced data set. Our experimental set-up has demonstrated that support vector machine in the detection of Urdu hatred detection is 81.87% accurate. The training time, testing time, and accuracy helped us select the best model for Urdu hate detection on social media sites. We also compared the training and testing times of various methods. Additionally, we demonstrated k and stratified folding via indexing to provide a better understanding of folding in machine learning. Finally, we compared our findings to those of previously published works in the field of Urdu hate detection.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122384574","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 development of human-computer interaction (HCI) systems that will efficiently capture the human brain, the so-called Brain-Computer Interaction (BCI) systems, will bring a new era in various disciplines (gaming, education, cultural heritage, etc). Actually, it is expected that the design and development of an electroencephalography (EEG) based-driven framework for intelligent real-time modelling of human cognitive abilities will provide groundbreaking technological advances in the delivery of human cognition-centred personalized systems and significantly advance the state-of-the-art research in human brain modelling. The aim of this paper is to make a concise and focused presentation of Signal Processing and Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), and how these fields may help to model and thus predict human behaviour, emotion, cognitive state in different tasks.
{"title":"A Survey on Signal Processing Methods for EEG-based Brain Computer Interface Systems","authors":"M. Trigka, Elias Dritsas, C. Fidas","doi":"10.1145/3575879.3575995","DOIUrl":"https://doi.org/10.1145/3575879.3575995","url":null,"abstract":"The development of human-computer interaction (HCI) systems that will efficiently capture the human brain, the so-called Brain-Computer Interaction (BCI) systems, will bring a new era in various disciplines (gaming, education, cultural heritage, etc). Actually, it is expected that the design and development of an electroencephalography (EEG) based-driven framework for intelligent real-time modelling of human cognitive abilities will provide groundbreaking technological advances in the delivery of human cognition-centred personalized systems and significantly advance the state-of-the-art research in human brain modelling. The aim of this paper is to make a concise and focused presentation of Signal Processing and Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), and how these fields may help to model and thus predict human behaviour, emotion, cognitive state in different tasks.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058043","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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