Reyes Juárez-Ramírez

This study reports the results of an empirical study aimed at assessing usability and effectiveness of a block-based visual programming language (VPL) designed to facilitate social robot script creation by novice with basic programming knowledge users. Through a controlled experiment involving novice and expert participants, two assisted use cases were implemented: medication reminder and relaxation therapy, each with three levels of complexity. Quantitative metrics including efficiency, correctness, and assistance frequency were analyzed, along with qualitative feedback. The results indicate that novice with basic programming knowledge users demonstrated high adaptability and achieved near-expert performance levels, with minimal differences in task completion times. Novice with basic programming knowledge users was able to complete the tasks effectively, though some required additional guidance and exhibited small errors, such as disconnected nodes or partial scripts, indicating opportunities to improve the intuitiveness of the interface. Qualitative feedback also emphasized the importance of intuitive visual design, clearer instructions, and better node organization to enhance the user experience. These findings underscore the potential of VPLs to democratize robotics programming by lowering technical barriers, making social robot customization accessible to a wider audience. Future work will focus on refining the VPL interface based on user feedback and expanding the study to diverse user groups and additional use cases.

This paper compares the statistical properties of several modern pseudorandom number generation algorithms. A method is also proposed for using various hash functions to improve the statistical properties of the considered pseudorandom sequence generations. Using cryptographic hash functions MD5, SHA3, Streebog, Blake3 to create pseudorandom number generators allows combining high generation speed with excellent statistical properties of output sequences. Due to the ability of hash functions to effectively “mix” data, eliminating any patterns, even if there are patterns in the internal state of the generator, the output is a statistically independent sequence. Modern optimized hash algorithms demonstrate data processing speed at the level of gigabytes per second even on conventional processors for example, GE + Streebog – up to 8.03 MB/s per core, which makes them suitable for modeling, rendering, statistical research and cryptography. At the same time, they often require fewer resources than traditional cryptographic generators based on block ciphers, providing comparable data quality. This approach is optimal for modern computing systems, where the balance between speed, randomness quality and energy efficiency is important.

The term citizen science, also referred to as public participation in scientific research and knowledge production, is gaining increasing recognition as a well-developed and valued approach. This approach has a wide global reach and is utilized across a broad range of scientific fields and disciplines. However, the current literature highlights several challenges associated with citizen science data and processes, which often result in subpar quality and, as a result, lead to undesirable outcomes in citizen science projects. These issues have a direct impact on the sustainability of such projects. To mitigate these negative effects, we propose that standardizing best practices for citizen science projects could enhance process performance and, in turn, improve their outcomes. One of the most important concerns in any kind of process is the quality of the data used at the various stages of the data life cycle, from its generation by participants (researchers and citizens) to the usage and exploitation of the data. The main contribution of this work is twofold: first, it identifies the best practices associated with citizen science projects, and second, it explores how these practices can be enhanced by incorporating principles of data quality management and data governance. As a result, we produced CI.SCI.FORM, a framework that can be used to support institutions to better propose and execute their citizen science projects. This framework consists of two main components: a process reference model (PRM) and a process assessment model. In this paper, we are going to first introduce the CI.SCI.FORM PRM, which includes 16 processes, which are organized into four distinct blocks.

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), autism spectrum disorder (ASD) is characterized by a neurodevelopmental disorder. Strengthening these skills is essential for daily living and is a key factor in improving the quality of life of individuals with ASD, as well as promoting their inclusion in the labor market. In this context, two mobile applications have been developed to promote the development of fine motor skills in children with this disorder. The study analyzes the usability of mobile applications designed to support children with ASD. These apps, developed with a user-centered design approach, seek to strengthen motor, social, and cognitive skills through interactive and engaging games. The evaluation was conducted through heuristic analysis based on Nielsen's principles and using the System Usability Scale (SUS) applied to 15 adolescents. The study concludes that, although both applications offer potential, they require improvements in usability, accessibility, and instruction, proposing future mixed research that integrates quantitative and qualitative metrics to optimize their pedagogical effectiveness.

Deep vein thrombosis is a condition associated with substantial morbidity and a high risk of pulmonary embolism, underscoring the need for rapid and reliable diagnostic solutions. Although machine learning and deep learning techniques are increasingly being applied for clinical decision support, comprehensive analyses of their contributions to early detection, risk prediction, and monitoring remain limited. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, we conducted a systematic search in ScienceDirect, IEEE Xplore, Scopus, and Web of Science for studies published between January 2014 and March 2025. Eligible studies applied machine learning or deep learning approaches for the early prediction, monitoring, or risk assessment of deep vein thrombosis, or described reference datasets for algorithm development. Two authors independently extracted data and evaluated methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2 framework. The included studies were categorized into four domains: Early prediction, monitoring, risk assessment, and reference datasets. In total, 66 studies met the inclusion criteria. Recent advances include deep learning-assisted ultrasound interpretation and real-time implementation of machine learning algorithms. While most studies demonstrated a low overall risk of bias, recurring limitations were identified in terms of patient selection, reporting practices, and validation strategies. Dataset harmonization and external validation were infrequently performed, and documentation of data provenance and class imbalance handling was inconsistent. Machine learning and deep learning approaches demonstrate considerable potential to accelerate accurate diagnoses and facilitate individualized risk stratification; however, their translation into routine practice requires standardized datasets, rigorous external validation, and integration into existing clinical workflows. This review consolidates a decade of research, links methodological quality to clinical applicability, and provides a task-oriented roadmap for advancing machine learning-enabled diagnostics and monitoring in the context of deep vein thrombosis.

Deaf and Hearing-Impaired (DHH) readers often find it hard to understand what they read. Among others, differences in the ability to monitor comprehension could be one of the high-level processes producing the reading gap between DHH and hearing readers. This study aims to improve reading comprehension in DHH students with an intervention based on an online videogame focused on monitoring and self-regulation in reading comprehension. The study sample consisted of four Spanish DHH students in Year 6 of Primary Education from a special school for DHH students, with a mean age of 11.75 years and heterogeneous hearing characteristics. A randomised multiple-baseline experimental design was used. Baseline sessions included only reading-related play activities, while the intervention sessions focused on training monitoring and self-regulation of reading comprehension using a video game. Each session of the game included components of an intervention successfully implemented in a typical population: questions based on the title, reading, reflection on question responses, detection of comprehension difficulties, and proposition of strategies. One signer participant with very poor reading, but prior knowledge of texts, and comparatively good nonverbal intelligence benefited from the intervention. The study suggests interventions based on self-regulation could be used even with DHH children with poor reading skills but might require a number of supporting skills to be effective.

Generative AI has revolutionized numerous fields, including software engineering education. Tools like GitHub Copilot, ChatGPT, or Llama have reshaped learning by offering real-time suggestions, automating code generation, and enhancing debugging. However, software engineering encompasses far more than writing code—it spans requirements analysis, specification, design, implementation, testing, deployment, maintenance, configuration management, etc. The rise of generative AI raises critical questions about how to train software engineers who can master these principles in an era where AI automates many traditionally manual tasks. This chapter explores strategies for integrating AI into education while reinforcing fundamental software engineering principles, ensuring students develop critical thinking, ethical awareness, and a comprehensive understanding of the entire software development lifecycle.

This article explores the potential of chatbots to bridge the gap for students transitioning from structured programming to object-oriented programming (OOP). We delve into the advantages and disadvantages of using chatbots like ChatGPT 3.5 and 4.0, Gemini, and Bing to assist with OOP learning. While these tools offer benefits like providing code examples and explanations, limitations exist in accurate problem interpretation and adherence to best practices. Building upon the previous student-focused analysis, we present the results of a new survey targeting software engineering teachers: ‘Survey on the Use of AI Chatbots in Teaching Software Engineering’. This survey sheds light on how educators integrate chatbots into their curriculum and their perspectives on their effectiveness. The original student survey revealed positive impressions of chatbots as study aids, with a high percentage utilizing and finding them valuable. The teacher survey will provide further insights into their role in the teaching and learning process, ultimately contributing to the development of more efficient software engineers. Recognizing and addressing the limitations of chatbots remains crucial to maximizing their potential in OOP education. This paper is an extension of work originally presented in Conisoft 2023 [1].

Quantum mechanics as the basis of quantum computing imposes an inherent complexity for practitioners not formed on quantum physics, for understanding such new paradigm of computation and getting involved in the prominent quantum world. Strong technical skills on quantum topics, mathematics and related disciplines are required to attend job positions in the labor market. Furthermore, the multidisciplinary environment where quantum computing is immersed requires soft skills to work in integrative teams. This paper presents a literature review, supported by the systematic mapping methodology, to gather the core knowledge and technical skills, as well as soft skills required to prepare students and practitioners to work on quantum computing. The results allow us to have a wide spectrum of the competences involved, enabling academic institutions and educational bodies to design individual courses or curricula blocks to prepare the workforce.