Marcello Messina, Ariane de Souza Stolfi, Luzilei Aliel, Ivan Simurra, Damián Keller
A recent initiative within ubimus research contemplates the development of an internet of musical stuff (IoMuSt) as a concept that interacts with and expands the pre-existing rubric of the internet of musical things (IoMusT). Opposed to the ontological fixedness of things, stuff is pliable, fairly amorphous, changeable depending on usage, context-reliant, either persistent or volatile. It encompasses adaptable and flexible temporalities, featuring non-allotable, non-monetisable and non-reifiable resources. Furthermore, IoMuSt highlights the distinction between object and subject, blurring this crisp separation. The IoMuSt rubric is sustained by aesthetic pliability, fostering an expansion of creative practices and a critical stance towards utilitarian human-computer interaction perspectives. The authors discuss key dimensions of aesthetic pliability as related to flexible infrastructures, open sources and methods, enhanced collaboration and a low ecological footprint. The properties of aesthetic pliability are explored within the realm of two case studies.
{"title":"The Internet of Musical Stuff","authors":"Marcello Messina, Ariane de Souza Stolfi, Luzilei Aliel, Ivan Simurra, Damián Keller","doi":"10.4018/ijsi.344018","DOIUrl":"https://doi.org/10.4018/ijsi.344018","url":null,"abstract":"A recent initiative within ubimus research contemplates the development of an internet of musical stuff (IoMuSt) as a concept that interacts with and expands the pre-existing rubric of the internet of musical things (IoMusT). Opposed to the ontological fixedness of things, stuff is pliable, fairly amorphous, changeable depending on usage, context-reliant, either persistent or volatile. It encompasses adaptable and flexible temporalities, featuring non-allotable, non-monetisable and non-reifiable resources. Furthermore, IoMuSt highlights the distinction between object and subject, blurring this crisp separation. The IoMuSt rubric is sustained by aesthetic pliability, fostering an expansion of creative practices and a critical stance towards utilitarian human-computer interaction perspectives. The authors discuss key dimensions of aesthetic pliability as related to flexible infrastructures, open sources and methods, enhanced collaboration and a low ecological footprint. The properties of aesthetic pliability are explored within the realm of two case studies.","PeriodicalId":507606,"journal":{"name":"International Journal of Software Innovation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109992","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}
Kuo Jong-Yih, Hsieh Ti-Feng, Yu-De Lin, Hui-Chi Lin
Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes. The goal of refactoring is to fix smells in the system. Fixing architectural smells requires more effort than other smells because it is tangled in multiple components in the system. Architecture smells refer to commonly used architectural decisions that negatively impact system quality. They cause high software coupling, create complications when developing new requirements, and are hard to test and reuse. This paper presented a tool to analyze the causes of architectural smells such as cyclic dependency and unstable dependency and included a priority metric that could be used to optimize the smell with the most refactoring efforts and simulate the most cost-effective refactoring path sequence for a developer to follow. Using a real case scenario, a refactoring path was evaluated with real refactoring execution, and the validity of the path was verified.
{"title":"The Study on Software Architecture Smell Refactoring","authors":"Kuo Jong-Yih, Hsieh Ti-Feng, Yu-De Lin, Hui-Chi Lin","doi":"10.4018/ijsi.339884","DOIUrl":"https://doi.org/10.4018/ijsi.339884","url":null,"abstract":"Maintenance and complexity issues in software development continue to increase because of new requirements and software evolution, and refactoring is required to help software adapt to the changes. The goal of refactoring is to fix smells in the system. Fixing architectural smells requires more effort than other smells because it is tangled in multiple components in the system. Architecture smells refer to commonly used architectural decisions that negatively impact system quality. They cause high software coupling, create complications when developing new requirements, and are hard to test and reuse. This paper presented a tool to analyze the causes of architectural smells such as cyclic dependency and unstable dependency and included a priority metric that could be used to optimize the smell with the most refactoring efforts and simulate the most cost-effective refactoring path sequence for a developer to follow. Using a real case scenario, a refactoring path was evaluated with real refactoring execution, and the validity of the path was verified.","PeriodicalId":507606,"journal":{"name":"International Journal of Software Innovation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425672","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}
Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very impressive performances. In general, deep architecture models suffer from the over-fitting problem when there is a small number of training data. In this article the attempt is made to remedy this problem in deep architecture with regularization techniques including overlap pooling and flipped image augmentation and dropout; the authors also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. The authors present a hybrid neural-network solution which compares favorably with other methods.
{"title":"Recommendation System for Hairstyle Based on Face Recognition Using AI and Machine Learning","authors":"Yogesh M. Kamble, Raj B. Kulkarni","doi":"10.4018/ijsi.309960","DOIUrl":"https://doi.org/10.4018/ijsi.309960","url":null,"abstract":"Many machine learning algorithms have been introduced to solve different types of problems. Recently, many of these algorithms have been applied to deep architecture models and showed very impressive performances. In general, deep architecture models suffer from the over-fitting problem when there is a small number of training data. In this article the attempt is made to remedy this problem in deep architecture with regularization techniques including overlap pooling and flipped image augmentation and dropout; the authors also compared a deep structure model (convolutional neural network (CNN)) with shallow structure models (support vector machine and artificial neural network with one hidden layer) on a small dataset. It was statistically confirmed that the shallow models achieved better performance than the deep model that did not use a regularization technique. Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. The authors present a hybrid neural-network solution which compares favorably with other methods.","PeriodicalId":507606,"journal":{"name":"International Journal of Software Innovation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140476491","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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