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 the "Letter Explorers" platform, an educational tool designed to support preschool children in their writing acquisition process. The platform was designed based on recognized pedagogical guidelines, structuring its activities into progressive levels of difficulty. The main focus is on learning letters and encouraging independent writing, respecting the motor and cognitive development of young children. Its features include real-time visual and auditory feedback, which helps correct and reinforce learning, as well as the possibility of personalizing activities based on the child’s name, a factor that contributes to motivation and engagement. The proposal aims to serve, especially, educational contexts in the interior of emerging countries, where access to quality digital solutions may be limited. Preliminary results indicate the potential of the tool to promote an interactive, inclusive and contextualized environment for learning to write in preschool education.

Context: Spectrum-based defect localization uses code coverage information obtained during testing to abstract patterns that help identify faulty statements. Since the number of test cases that reveal the presence of defects is typically lower than that which do not, such imbalance hinders the discovery of these patterns and, therefore, adds cost to defect localization. Objective: A better balanced test suite is necessary to achieve efficiency in software fault localization. This article explores the following questions: (1) Does balancing test cases reduce the effort involved in software fault localization? (2) If two or more test cases are redundant with respect to code coverage, does eliminating this redundancy improve the effectiveness of fault localization heuristics? Method: The Defects4J benchmark was used and the Smote technique (Synthetic Minority Over-sampling Technique) was applied to balance the test cases with and without removing redundant test cases with respect to code coverage. After balancing, the effectiveness of software fault localization was evaluated using some heuristics. Results: It was revealed that the removal of redundancy with respect to code coverage promoted the effectiveness of the fault localization heuristics when applied after the test case balancing procedure implemented by the Smote technique. Conclusions: The analyses presented in the article revealed the positive possibility of exploring research into the use of test case balancing techniques as a way of improving the effectiveness of fault localization heuristics.

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 presents a method for interacting with video players through real-time hand gesture recognition, implemented using the MediaPipe library and the Random Forest algorithm. The main goal was to develop a lightweight and accessible system capable of controlling playback, time navigation, and volume adjustment without requiring physical contact with the screen. The prototype was developed for Android devices and evaluated through an experiment conducted under real usage conditions, where users interacted with the system on their own mobile devices. Usability was assessed using the System Usability Scale (SUS), which resulted in an average score of 83.38, classified as "Good." The user experience was further evaluated using the short version of the User Experience Questionnaire (UEQ-S), which yielded "Good" results in the pragmatic dimension and "Excellent" in the hedonic dimension. The results indicate that the system is intuitive, functional, and pleasant to use, even among users with little prior experience with gesture-based interaction. The model demonstrated good generalization capabilities, even with a reduced training dataset. The proposed approach proved to be viable and promising for future applications in various contexts, such as entertainment, education, and accessibility.

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.

Smartwatches are wearable devices of emerging mobile technology and are increasingly in people's daily lives. They allow you to perform various tasks; however, the interaction with these devices still proves to be a significant challenge. Several studies carried out interaction studies with smartwatches using gesture recognition. Thus, this work presents a comprehensive Systematic Review of Literature (SRL) on interaction with smartwatches using gesture recognition, showing what has already been developed and what is state of the art. We searched in four databases with relevant scientific scope: ACM Digital Library, IEEE Xplore Digital Library, Science Direct, and Scopus. Work on this theme is diverse; gesture recognition is used to perform operations on smartwatches itself or even control other devices or virtual environments. We hope that this work will provide a rich base on the methods developed and that it will become a source of research for future researchers and that can support the development of other methods and applications.

Identifying the phase or stage of children's spelling development is a regular activity in literacy classes. Usually, teachers assume some developmental theory and perform paper-and-pencil based tests. Existing digital tools often do not consider any of these known theories, nor do they capture the child's handwriting. Therefore, some teachers prefer to continue applying the tests manually. Accordingly, this study's research question is how to promote the performance of spelling tests on mobile devices, simulating the interaction between manual tests and the theoretical models already used by teachers. Through the Design Science Research Methodology (DSRM), we propose a method to apply child spelling tests in an automated way. In this work, we present the results of the child's interface usability evaluation in the developed computer artifact, using guidelines of Touchscreen Interaction Design Recommendations for Children (TIDRC). The results indicate adequacy to the recommendations of 88% of items in visual and audio features (cognitive dimension), 75% in the physical dimension, and 47% in socio-emotional dimension. These results are promising and relevant compared to previous studies that evaluated apps using the TIDRC framework.

This work proposes the development of a method that allows the use of smartwatch as a control for interactive films using continuous recognition of gestures and conducts a case study with the film "Black Mirror: Bandersnatch". We developed two prototypes to interact with interactive movies. The first one uses gesture recognition on the smartwatch screen. Thus, we created a set of gestures composed of straight lines that represent the actions in the interactive movie. In this prototype, the recognition of gestures is performed by the algorithm of continuous recognition of gestures, in this way, a gesture can be recognized before the user finalize, and action is sent quickly to the movie. The second prototype uses a touch of pressure in the smartwatch to control the film. Thus, the actions to be performed in the smartwatch that represents the actions in the interactive film is proposed, the recognition of the gestures is determined by the change of the values obtained by the accelerometer. The prototypes communicate with a simulation service responsible for running the movie. The prototypes were used in a study with users, as well as in usability and experience tests, and the results show that the method has the potential to be used in everyday users to control interactive films efficiently and effectively.

As an emerging technology that combines mobile and wearable markets, smartwatches are finding their place on consumers' daily lives. They allow tasks that used to be performed only by smartphones and tracking devices. Despite the increasing interest on them, a task that is still not fully covered by these devices is text entry, mainly due to their reduced screen size. Researchers have been working hard on solutions for this issue in the past years, and a number of methods for interactive text entry with smartphones now exist. The aim of this paper is to present a systematic review on these methods, showing what has been developed and what is the performance of the current state of art of the technology. The review focused on four databases. After applying a large selection criterion, it resulted in twenty-six approaches, which helped to answer questions that grounded this work. We hope to deliver a rich and useful foundation about methods, results, challenges and opportunities and to support new research on smartwatches.

This work proposes the development of a method for smartwatches that allows to control platform games using continuous recognition of gestures and conducts a case study as the game Super Mario World. Uses a set of gestures based on geometric shapes to send actions to the game. Gesture recognition is performed by the algorithm of continuous gesture recognition, as it is able to recognize a gesture before being finalized, allows an action to be performed quickly, improving feedback. The recognition process was paralleled to improve performance. A technique has been developed that allows the execution of several gestures in sequence, without the need for a signaling that a gesture has been finalized or initiated. It was also created a technique that allows the sending of special commands to the game using the pressure applied on the screen by the player. A prototype for smartwatches was developed that communicates with an emulation platform installed on a Raspberry PI 3. A user experiment was performed as well as usability and experience tests. The results show that the method has the potential to be used effectively and effectively by players.

This work proposes a method that allows the entry of text in smartwatches using gestures based on geometric forms. For this it is proposed the development of a prototype capable of inserting a letter with no more than two user interactions. Gesture recognition is performed using the incremental recognition algorithm. A set of gestures with lines and curves were created to be recognized by the incremental recognition algorithm, generated from the reduced equation of the line and the reduced equation of the circumference, respectively. After recognizing the gestures, they are sent to a classifier Naïve Bayes which is responsible for predicting the letter that will be inserted. The Naïve Bayes classifier was trained with a user gesture base that drew all the letters of the alphabet using only the gestures available in the set presented to them. Using the gesture base and the classifier Naïve Bayes a prototype was developed for smartwatches that automatically suggests the most likely letters to be inserted. The prototype was used to perform an experiment, during the experiment the users inserted the five most frequent letters and the five less frequent letters of the English language. The results of the experiment show that the prototype is able to recognize a letter with at most two interactions between the user and the smartwatch. The analysis of the usability and experience test shows that the prototype has generalized potential for use, since it allows the entry of text with up to two interactions and with a 100% hit rate for the most frequent letters and 95,14% For less frequent letters.