Computer interaction is one of the significant challenges that elderly and disabled people face in today's computerized world. In recent years, hand gesture recognition has emerged as one of the most natural human-machine interactions in software development, particularly for facilitating friendly and flexible human-computer interaction. This article proposed a system architecture and software configuration for developing a real-time volume control hand gesture on a Raspberry Pi equipped with a camera programmed in Python using the Open-Source Computer Vision (OpenCV) library. The hand gesture recognition system's primary objective is to establish communication between humans and computerized systems in order to control volume. According to the experimental results, the hand gesture recognition system performs well when controlling volume. The system is capable of operating in real-time for any individual.
{"title":"Development of Real-Time Hand Gesture for Volume Control Application using Python on Raspberry Pi","authors":"P. Netinant, Yannakorn Tuaktao, Meennapa Rukhiran","doi":"10.1145/3520084.3520085","DOIUrl":"https://doi.org/10.1145/3520084.3520085","url":null,"abstract":"Computer interaction is one of the significant challenges that elderly and disabled people face in today's computerized world. In recent years, hand gesture recognition has emerged as one of the most natural human-machine interactions in software development, particularly for facilitating friendly and flexible human-computer interaction. This article proposed a system architecture and software configuration for developing a real-time volume control hand gesture on a Raspberry Pi equipped with a camera programmed in Python using the Open-Source Computer Vision (OpenCV) library. The hand gesture recognition system's primary objective is to establish communication between humans and computerized systems in order to control volume. According to the experimental results, the hand gesture recognition system performs well when controlling volume. The system is capable of operating in real-time for any individual.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115368160","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}
Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.
{"title":"Dementia Detection Using Language Models and Transfer Learning","authors":"Mondher Bouazizi, Chuheng Zheng, T. Ohtsuki","doi":"10.1145/3520084.3520108","DOIUrl":"https://doi.org/10.1145/3520084.3520108","url":null,"abstract":"Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931358","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}
Architectural pattern is the one of the most important parts in the development of a well-structured software, in this case, the code should be maintainable, readable, traceable, and modifiable. There are several architectural patterns that are available to be used by the developers. One of the most widely used is MVC (Model View Controller). The MVC pattern is also widely adopted in the development of applications in the iOS platform. However, according to the literature, the implementation of the MVC pattern violates the SOLID principles especially Single Responsibility Principle (SRP) - in this case of the implementation of the controller layer. In this paper, we propose the modification of the MVC architectural pattern in iOS application development in order to comply to the SOLID principle and reducing complexity of a particular module (layer). A comparative study was conducted by using a tool called coherent-swift (using LCOM4 as a method of measuring cohesion) to compare the level of cohesion as a measure of SRP. The result of the study shows that the modified MVC get a better cohesion level rather than the original one.
{"title":"Analysis and Implementation of Presenter Layer on MVC Architecture iOS Application Development∗","authors":"Agus Adi Pranata, N. Selviandro, M. Adrian","doi":"10.1145/3520084.3520097","DOIUrl":"https://doi.org/10.1145/3520084.3520097","url":null,"abstract":"Architectural pattern is the one of the most important parts in the development of a well-structured software, in this case, the code should be maintainable, readable, traceable, and modifiable. There are several architectural patterns that are available to be used by the developers. One of the most widely used is MVC (Model View Controller). The MVC pattern is also widely adopted in the development of applications in the iOS platform. However, according to the literature, the implementation of the MVC pattern violates the SOLID principles especially Single Responsibility Principle (SRP) - in this case of the implementation of the controller layer. In this paper, we propose the modification of the MVC architectural pattern in iOS application development in order to comply to the SOLID principle and reducing complexity of a particular module (layer). A comparative study was conducted by using a tool called coherent-swift (using LCOM4 as a method of measuring cohesion) to compare the level of cohesion as a measure of SRP. The result of the study shows that the modified MVC get a better cohesion level rather than the original one.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127461902","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 increasing availability of behavioral data about consumers offers great promise for understanding their shifting preferences over time. This poses an important challenge for business practitioners - particularly entrepreneurs and investors – who wish to be the first to identify and satisfy consumers’ unmet needs. Here, we introduce the Consumer Deviation Index (CDI) as a means of generating forecasts from historical time series data in order to isolate emerging behaviors that fall outside the realm of expectation. These deviations serve as leading indicators of market opportunities to fulfill pockets of “latent demand” before they fully manifest across a consumer population. We illustrate the application of this methodology to behavior change during the height of COVID-19 stay-at-home restrictions, and to the initial reopening of the U.S. economy post-pandemic. We discuss implications for optimizing product development and innovation to better serve consumers’ ever-changing needs.
{"title":"The Consumer Deviation Index: A Method for Identifying Changing Behaviors and Market Opportunities","authors":"Jolie M. Martin, Fen Zhao, Jaden Hou","doi":"10.1145/3520084.3520118","DOIUrl":"https://doi.org/10.1145/3520084.3520118","url":null,"abstract":"The increasing availability of behavioral data about consumers offers great promise for understanding their shifting preferences over time. This poses an important challenge for business practitioners - particularly entrepreneurs and investors – who wish to be the first to identify and satisfy consumers’ unmet needs. Here, we introduce the Consumer Deviation Index (CDI) as a means of generating forecasts from historical time series data in order to isolate emerging behaviors that fall outside the realm of expectation. These deviations serve as leading indicators of market opportunities to fulfill pockets of “latent demand” before they fully manifest across a consumer population. We illustrate the application of this methodology to behavior change during the height of COVID-19 stay-at-home restrictions, and to the initial reopening of the U.S. economy post-pandemic. We discuss implications for optimizing product development and innovation to better serve consumers’ ever-changing needs.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129660449","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}
Companies worldwide have enabled their employees to work remotely as a consequence of the Covid 19 pandemic. Software development is a human-centered discipline and thrives on teamwork. Agile methods are focusing on several social aspects of software development. Software development teams in Germany were mainly co-located before the pandemic. This paper aims to validate the findings of existing studies by expanding on an existing multiple-case study. Therefore, we collected data by conducting semi-structured interviews, observing agile practices, and viewing project documents in three cases. Based on the results, we can confirm the following findings: 1) The teams rapidly adapted the agile practices and roles, 2) communication is more objective within the teams, 3) decreased social exchange between team members, 4) the expectation of a combined approach of remote and onsite work after the pandemic, 5) stable or increased (perceived) performance and 6) stable or increased well-being of team members.
{"title":"The Covid 19 Pandemic and its Effects on Agile Software Development","authors":"M. Neumann, Yevgen Bogdanov, Senol Sager","doi":"10.1145/3520084.3520093","DOIUrl":"https://doi.org/10.1145/3520084.3520093","url":null,"abstract":"Companies worldwide have enabled their employees to work remotely as a consequence of the Covid 19 pandemic. Software development is a human-centered discipline and thrives on teamwork. Agile methods are focusing on several social aspects of software development. Software development teams in Germany were mainly co-located before the pandemic. This paper aims to validate the findings of existing studies by expanding on an existing multiple-case study. Therefore, we collected data by conducting semi-structured interviews, observing agile practices, and viewing project documents in three cases. Based on the results, we can confirm the following findings: 1) The teams rapidly adapted the agile practices and roles, 2) communication is more objective within the teams, 3) decreased social exchange between team members, 4) the expectation of a combined approach of remote and onsite work after the pandemic, 5) stable or increased (perceived) performance and 6) stable or increased well-being of team members.","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124494919","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}
{"title":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","authors":"","doi":"10.1145/3520084","DOIUrl":"https://doi.org/10.1145/3520084","url":null,"abstract":"","PeriodicalId":444957,"journal":{"name":"Proceedings of the 2022 5th International Conference on Software Engineering and Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130133120","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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