Tipo de publicación: Conference Paper

Publicado en: Ubiquitous Computing and Ambient Intelligence

Tipo de publicación: Conference Paper

Publicado en: Internet Technology and Secured Transactions (ICITST), 2014 9th International Conference for

Tipo de publicación: Journal Article

Publicado en: Journal of Ambient Intelligence and Humanized Computing

Tipo de publicación: Book Chapter

Publicado en: Ubiquitous Computing and Ambient Intelligence

Tipo de publicación: Conference Paper

Publicado en: IEEE Frontiers in Education Conference

Tipo de publicación: Book Chapter

Publicado en: Product-Focused Software Process Improvement

Tipo de publicación: Book Chapter

Publicado en: ICT for Promoting Human Development and Protecting the Environment

Tipo de publicación: Conference Paper

Publicado en: Latin American Computing Conference

Tipo de publicación: Book Chapter

Publicado en: Historias de las TIC en América Latina y el Caribe: inicios, desarrollos y rupturas

Tipo de publicación: Journal Article

Publicado en: Novática

Tipo de publicación: Journal Article

Publicado en: Anuario Centro de Investigación y Estudios Políticos

Tipo de publicación: Book Chapter

Publicado en: Human Computer Interaction

Tipo de publicación: Journal Article

Publicado en: INTERNET TECHNOLOGIES & SOCIETY (ITS 2012)

Tipo de publicación: Journal Article

Publicado en: CLEI Electronic Journal

Tipo de publicación: Conference Paper

Publicado en: Proceedings of the Conferencia Latinoamericana de Informática, CLEI

Descripción:

Smart agriculture is an active research field. Currently, many researchers are working on the construction of platforms directed to improve efficiency, crop processes, and data awareness. However, it is common that developers focus on data monitoring instead of the data relevance for decision making or the costs associated with the creation of monitoring platforms. In this paper, we present a classification of the data required by researchers on the construction of decision-support systems applied to smart agriculture processes. By using this classification, the user can define which data are relevant according to the characteristics of the problem that needs to be solved.We have applied the classification to data recollected in a study case conducted by the end of last year. Besides, we identify a list of agronomic and climatic variables commonly used in the construction of decision support systems. We apply the classification to this list of variables as an example for researchers. As a conclusion, this typification permits the researcher to identify data that has to be monitored and controlled, and data that does not have to be measured, the later based on the data characteristics and utility for the farmer.

Tipo de publicación: Conference Paper

Publicado en: 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Descripción:

Metaphors of ordinary concepts are intensively used in computer science for naming abstract concepts and for designing users interfaces. Metaphors are mappings from a source domain (e.g., a stream) to a target domain (e.g., a sequence of bytes). Typically computing phenomena are explained using incoherent discourses at the source domain, formed by metaphors taken from a diversity of origins. Nonetheless, versatility of metaphors allows for the creation of coherent discourses in the origin domain that map the target computing discourse, and we call them allegories. The limited number of existing studies about this topic has compared only textual allegories, visual allegories, and the traditional unconnected metaphors. Although their findings are heterogeneous, equal effectiveness is the most frequent empirical result between these three types of metaphors. Furthermore, we have not found any study about oral allegories, in spite of oral being the most used modality for metaphors in computer science education. This article experimentally compares the effects of oral allegories and oral metaphors on a complex problem‐solving task. As in previous studies, our results did not find any significant differences on support or detriment of oral allegories. Our results support new metaphor theories posing that the context significantly influences the metaphors’ efficacy, and encourages future research about the interaction between context and allegories.

Tipo de publicación: Journal Article

Publicado en: Computer Applications in Engineering Education

Descripción:

One of the biggest challenges for farmers is the prevention of disease appearance on crops. Governments around the world control border product entry to reduce the number of foreign diseases affecting local producers. Evenmore, it is also important to reduce the spread of crop diseases as quickly as possible and in early stages of propagation, to enable farmers to attack them on time, or to remove the affected plants. In this research, we propose the use of convolutional neural networks to detect diseases in horticultural crops. We compare the results of disease classification in images of plant leaves, in terms of performance, time execution, and classifier size. In the analysis, we implement two distinct classifiers, a densenet-161 pre-trained model and a custom created model. We concluded that for disease detection in tomato crops, our custom model has better execution time and size, and the classification performance is acceptable. Therefore, the custom model could be useful to use to create a solution that helps small farmers in rural areas in resource-limited mobile devices.

Tipo de publicación: Book Chapter

Publicado en: Advances in Sustainability Science and Technology

Descripción:

Technical debt is a concept used to describe technical decisions that can benefit companies in the short term but can produce costs and software quality issues in the long term. Technical debt management can help enterprise profitability, sustainability, and the software industry's credibility. This paper presents a replication of the InsighTD survey (a globally distributed family of industrial surveys on causes and effects of TD), focusing on Costa Rica and comparing other regional countries. In total, 145 practitioners from the Costa Rican IT industry participated. Results show that the leading cause of technical debt is not technical (not only in Costa Rica but also in the region). On the other hand, the main effects reported are delivery delay and general dissatisfaction of the parties involved. A comparative study of InsighTD survey results in various countries is also included.

Tipo de publicación: Conference Paper

Publicado en: 2021 XLVII Latin American Computing Conference (CLEI)

Descripción:

Smart farming solutions seek to help farmers in their daily activities. Their use has shown that it is beneficial for farmers to be aware of the distinct variables affecting the production. For this reason, having alerts and notifications in monitoring and control platforms is crucial. However, in some circumstances, farmers cannot attend to the messages delivered through traditional mechanisms, making it impossible for them to be informed at the right moment. In this paper, we present the design evaluation of an intelligent context-aware smart notifications system for precision agriculture. We consider using distinct notification mechanisms to improve the delivery of notifications to the farmers. We carry out an anticipated user experience evaluation to assess the system’s design and validate the use of the notification mechanisms in distinct scenarios. A total of 48 potential users from Spain and Costa Rica participated in the evaluation. The results show that our proposed system can be very helpful in supporting farmers to be aware of the state of crops. In addition, non-traditional notification mechanisms can potentially keep the farmers informed without affecting their daily activities. Costa Rican potential users value the system’s novelty more than Spanish users.

Tipo de publicación: Conference Paper

Publicado en: Proceedings of the International Conference on Ubiquitous Computing Ambient Intelligence (UCAmI 2022)

Descripción:

Context: Technical debt (TD) is a challenge for companies who develop software on which their critical operations depend. To properly manage TD, it is necessary to make it visible to the different stakeholders involved to support informed decisions. Objective: To validate a TD visualization approach based on hybrid Sankey diagrams that makes the TD visible by showing (a) technical and business aspects, and (b) the flow of value and TD impacts. This approach regards visualizations as boundary objects. Method: We performed a multi-case study in a large multi-industry state-owned company. The objective was to validate the effectiveness of such visualizations and to explore their possible uses in TD management. We first used a retrospective case study on a TD decision-making scenario and, later, visualization usage scenarios using focus groups to evaluate its usefulness. Results: The results suggest that the proposed approach: (a) provides a structured process for systematic TD visualization to help the decision-making process; (b) enables the communication at knowledge boundaries between stakeholders to make informed decisions; (c) uses flow representations that are important for assessing the impact in multiple functional areas; and (d) enables documentation and reuse. Conclusion: The study results suggest that TD decision-making events can benefit from using our TD visualizations based on hybrid Sankey diagrams as boundary objects to portray the impact of TD in business, services, and technical aspects.

Tipo de publicación: Journal Article

Publicado en: International Journal of Software Engineering and Knowledge Engineering

Descripción:

Malicious URLs are often used by phishing campaigns, botnets and other attacks. Indeed, DNS traffic is necessary for the Internet to function correctly, which means that this data flow cannot be blocked. For these reasons, detecting malicious URLs is both important, challenging and still an open research problem. There are two types of techniques used to detect malicious URLs: rules-based and machine learning-based. The traditional, rules-based techniques rely on blacklists and heuristics. These techniques struggle to keep up with a rapidly changing array of malicious URLs. Therefore, machine learning-based techniques have emerged. Both detection techniques rely on URL characteristics such as length, number of vowels and others to classify them as legitimate or malicious. The main contribution of this paper is to propose a taxonomy of detection techniques and to point out which URL characteristics are used by each method. While surveys on the topic exist, a precise mapping between the detection methods and the characteristics is not available. We also compare these techniques, highlighting that machine learning-based techniques are more complex to implement but better at keeping up with rapidly incoming new malicious URLs. In contrast, rules-based techniques are simpler and easier to implement, but they struggle to update fast enough to identify new malicious URLs.

Tipo de publicación: Book Chapter

Publicado en: Information Technology and Systems

Descripción:

Smart farming technologies have the potential to revolutionize agriculture by enhancing resource efficiency, productivity, and sustainability. However, small-scale farmers in Latin America face challenges in adopting these technologies due to limited resources and technological constraints. In response, we have developed an affordable smart farming solution that empowers small-scale farmers with a comprehensive system that strategically integrates sensors into monitoring stations allowing control over specific actuators. This solution enables precise and resource-efficient production, by using monitoring stations deployed in crop fields, accessible visualization dashboards for farmers to review collected crop data, and a context-sensitive alert module.

Tipo de publicación: Journal Article

Publicado en: Communications of the ACM

Descripción:

The agricultural sector is facing significant challenges. Producing more food while consuming fewer resources like water, fertilizers, and arable land is probably the most important. Today more than ever, farmers need specialized technological tools to achieve this goal. It is possible to improve agricultural processes by practicing technology-supported precision agriculture. However, more technology development to optimize crop yield is needed worldwide. In this research, we conducted a systematic literature review in which we studied different articles that propose optimization for agricultural processes. We identified some proposals made from multiple researchers at distinct latitudes, focused on optimizing the irrigation and management of pests and diseases. Nevertheless, there are only a few studies that focus on optimizing the crop yield. They do that by controlling the different factors that can affect it. We also found out that only a few studies have been conducted in the Latin American region. Thus, these findings show areas in which there is still a lot of opportunities to contribute to the agro sector in Latin American countries.

Tipo de publicación: Conference Paper

Publicado en: 2020 XLVI Latin American Computing Conference (CLEI)

Descripción:

This systematic review depicts potential uses, tools, and strategies to enhance programming learning in university students pursuing technology-related fields like systems engineering or computer science. The review explores the technical aspects, including types of tools, programs, and Artificial Intelligence (AI) specifications proposed for technology-based university programs. A total of 113 records from 2018 to 2023 were analyzed from IEEE Xplore database, with 41 records meeting the inclusion criteria based on language, document completeness, and the use of AI in higher education, A study published in Dialnet on the experience of a Costa Rican distance learning university is also included. The study highlights that AI support in higher education is utilized in academic performance prediction systems, evaluation and personalization of the learning process, and data analytics. The conclusions emphasize the importance of ethically and responsibly integrating technological tools for AI in education, as well as reevaluating the role of educators and pedagogical mediation strategies.

Tipo de publicación: Conference Paper

Publicado en: JoCICI 2023: IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)