Artículos y presentaciones en Congresos
URI https://digital.cic.gba.gob.ar/handle/11746/543
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- ArtículoAcceso AbiertoTeaching Modeling in the Time of Agile Development(2022)We discuss modeling in the context of agile software development and reflect on how we, as educators, can use modeling to help improve agile practices.
- ArtículoAcceso AbiertoA Domain-Specific Language for Modeling IoT System Architectures That Support Monitoring(2022)The Internet of Things (IoT) is a technological paradigm involved in a diversity of domains with favorable impacts on people's daily lives and the development of industry and cities. Nowadays, one of the most critical challenges is developing software for IoT systems since the traditional Software Engineering methodologies and tools are unproductive in the face of the complex requirements resulting from the highly distributed, heterogeneous, and dynamic scenarios in which these systems operate. Model-Driven Engineering (MDE) emerges as an appropriate approach to abstract the complexity of IoT systems. However, there are no domain-specific languages (DSLs) aligned to standardized reference architectures for IoT. Furthermore, existing DSLs have an incomplete language to represent the IoT entities that may be needed at the edge, fog, and cloud layers to monitor IoT environments. Therefore, this paper proposes a domain-specific language named Monitor-IoT, which supports developers in designing multi-layer monitoring architectures for IoT systems with high abstraction, expressiveness, and flexibility. Monitor-IoT consists of a high-level visual modeling language and a metamodel aligned with the ISO/IEC 30141:2018 reference architecture. In addition, it provides a language capable of modeling architectures with a wide variety of digital entities and dataflows (synchronous and asynchronous) between them across the edge, fog, and cloud layers to support the monitoring of a diversity of IoT scenarios. The empirical evaluation of Monitor-IoT through the application of an experiment, which contemplates the use of the Technology Acceptance Model (TAM), demonstrates the intention of the participants to use this tool in the future since they consider it easy to use and useful.
- Documento de conferenciaAcceso AbiertoA study on user experience of smart glasses for higher education students(2022)This paper reports a study on the User experience (UX) of STEM (e.g., Science, Technology, Engineering, and Mathematics) students using smart glasses and an interactive Augmented Reality (AR) educational app. The results show that the AR app provides a good UX, despite the presence of some form factor issues. Students’ usability ratings for both the AR app and the smart glasses, are positively correlated with the students’ perceived learning. The results of this study can be used as a guideline to design and develop further immersive e-learning technologies.
- ArtículoAcceso AbiertoPredicting Interaction Effort in Web Interface Widgets(2022)The product of good design should render a tool invisible for a user who is executing a task. Unfortunately, web applications are often far from invisible to users, who struggle with poor design of websites and processes in them. We are particularly interested in web processes that involve form filling, so we have been studying how people interact with web forms. Besides cataloguing user interaction problems that are common in web forms, we have noticed that, in many cases, there is a single form element or widget to blame for a certain interaction problem, because such widget is not the most appropriate one for the required input in that particular context. This unfitness of the widget causes an extra burden to the user, which we call interaction effort. In this work we propose measuring the interaction effort of a widget with a unified score based on micro-measures automatically captured from interaction logs. We present the micromeasures that were found relevant to predict the interaction effort in 6 different types of web forms widgets. We describe a large data collection process and prediction models, showing that it is indeed possible to automatically predict a widget interaction effort score by learning from expert human ratings. We consequently believe that the interaction effort could be used as an effective metric to compare small variations in a design in terms of user experience.
- Documento de conferenciaAcceso AbiertoExtending RCA algorithm to consider ternary relations(2022)Relational Concept Analysis (RCA) is a multirelational data mining method that aims to extract knowledge from multiple formal contexts (i.e., objects, attributes, and a binary relation between them) and the relations between them. One of the problems RCA has is the lack of the possibility of extracting knowledge directly from data that is represented with ternary relations. While there are some existing solutions towards this problem, either they require complex preprocessing of the input data, or they lose some capabilities of RCA such as the different meanings of the relations between concepts (∃, ∀, etc). In this work, we present an intuitive extension to RCA to be able to use it with data directly represented with ternary relations. As an example of its usage, we apply it to a dataset called Knomana which includes ternary relations