This work presents a comparison between techniques for recognizing inorganic molecular structures from hand-drawn sketches. The approach used combines Convolutional Neural Networks (CNN) and the Histogram of Oriented Gradients (HOG) method to classify and recognize these sketches. For the tests, we used images drawn by high school students. The comparison of the results obtained with the CNN and HOG techniques was carried out in detail. Additionally, we developed an application capable of generating three-dimensional visualizations of the molecular structures, allowing their representation in a virtual environment. This 3D visualization provides a more intuitive understanding of the molecular structures. This work highlights the effectiveness of machine learning technologies in education, by offering an accessible and interactive educational tool that complements traditional chemistry teaching. In this way, it provides students with a deeper understanding of the physical and chemical properties of inorganic molecules, enriching the learning process and making it more engaging.

This paper presents a computational approach for the machine translation of comic book texts, aiming to promote linguistic accessibility, particularly in emerging countries. The proposed system performs end-to-end processing: it detects text regions, extracts content using Optical Character Recognition (OCR), translates the text into the target language, and reinserts the translated content into the original image, preserving its visual structure. Built with open-source libraries, the system is lightweight and suitable for low-resource computational environments. The methodology was validated through an experiment involving 1,000 pages of comics in four languages—English, French, Spanish, and Japanese. Results demonstrated high accuracy in the OCR and translation stages for Latin-based languages, and satisfactory performance for Japanese, despite its right-to-left reading layout and ideographic characters. The tool also showed potential as an assistive reading solution, with applications in educational and inclusive contexts. This work contributes to the development of accessible technologies aligned with the United Nations Sustainable Development Goals (SDGs), particularly in promoting quality education and reducing inequalities.

This work investigated the usability of two interaction techniques in low-cost virtual environments using smart-watches: on-screen continuous gesture recognition and the MDDTW algorithm for touchless gestures. While continuous gesture recognition requires direct interaction on the device's screen, MDDTW allows users to perform gestures in the air without the need for physical touch. Although users initially preferred continuous gesture recognition, the results revealed that MDDTW achieved slightly higher scores. This underscores the crucial importance of considering user experience in the development of new technologies. The comparative analysis between the two approaches contributes to the design and implementation of interactions in accessible virtual environments, especially concerning the integration of physical gestures as part of the interaction. This work contributes to understanding the factors influencing usability in virtual environments and highlights the need for user-centered approaches in designing interactive technologies.

In this work, we propose a method for recognizing percussive gestures using smartwatches with accelerometers and the MDDTW algorithm, incorporating an activation threshold to identify the beginning of gestures. We developed a system that allows simulating drum percussion in a musical duet, providing an interactive and engaging experience for users. Our method utilizes an activation threshold based on the value of gravity to identify the onset of percussive gestures, enabling precise and efficient detection of user movements. We conducted a controlled experiment where participants were instructed to perform predefined percussive gestures, which were captured by the smartwatch sensor and processed by the system. The results demonstrated good accuracy, with a consistent recall rate, indicating the system's ability to correctly identify performed gestures. The Fl-score, as a combined measure of precision and recall, confirmed the overall good performance of the method.

Strategies aimed at keeping the user's interest in using computer applications are being studied to provide greater user engagement, and can influence how people interact with computers. One of the approaches that can promote user engagement is Affective Computing (AC), based on the premise of recognizing the user's emotional state and adjusting the computer application to respond to such state in real-time. Although it is a relatively new area, over the past few years many research works have investigated the use of AC in various activities and objectives. To provide an overview on the use of AC in computer applications, this article presents a systematic literature review based on available articles on the main scientific databases of the Computer Science area. The main contribution of this review is the analysis of different types of applications. Based on the 58 articles analyzed, the main emotion recognition techniques and approaches to the adaptation of computer applications, as well as the limitations and challenges to be overcome were compiled. Our conclusions present the limitations and challenges still to be overcome in the area of automatic adaptation of computer applications by means of AC.

Temporal data visualization supports planning and decision-making processes as it helps understanding patterns and relationships among time-based data. In the Healthcare area, the anamnesis procedure offers to physicians a large volume of valuable information, which is usually analyzed considering temporal aspects. Contributing to overcome the limited use of three-dimensional (3D) space, in this article we present a VR approach named 3D Block ARL to support interactive visualization of medical temporal data where the interface design is based on VA concepts. Additionally, we use a rule-based learning method to associate users' preferences to graphical elements aiming to personalize the proposed 3D visualization interface. Our results indicate that VA can be a valuable resource to improve the design of Information Visualization interface tools in the context of temporal medical data as well as to personalize the visualizations according to the preferences of users.

Although many studies investigate the automatic adaptation in serious games with the goal to improve the users motivation, the majority of Affective Computing approaches requires a high development cost and usually does not consider the intervention of health professionals in adapting the game. This paper describes an approach to enable affective adaptation in serious games for motor rehabilitation with physiotherapists aid. Our approach consists of the definition and implementation of a framework. Its architecture reduces the development cost of a game with affective adaptation whilist enabling physiotherapists to configure adaptations in it according to patients profile. The results of an experiment with physiotherapists show that the system presents a high level of acceptance.