Alfaba, presented in this paper, is a low-cost, multi-sensory educational tool tailored to enhance literacy in underprivileged children. It leverages neuroplasticity principles, employing a tactile, interactive approach to develop essential neural connections for reading and writing. Tested with 11 children aged 7 to 10 years, its usability evaluation demonstrated effectiveness and user-friendliness, particularly in socially vulnerable contexts. Its cost-effectiveness makes Alfaba accessible in resource-limited settings, aiming to reduce educational disparities. Alfaba's innovative design focuses on providing equal learning opportunities for all children, regardless of socioeconomic background, making it a significant step toward educational equity and demonstrating the inclusive integration of technology in education for widespread impact.

The Covid-19 pandemic has driven students out of schools around the world. In Brazil, a developing country, this dropout has damaged the literacy of students between the ages of 5 and 9. We are running against the clock, and solutions to develop skills to promote reading and writing are fundamental. For students with learning difficulties, the damage is even greater. Dyslexic students have difficulties that naturally make this step even more complex for them. This work presents Alfaba: a tangible solution developed with low-cost hardware that stimulates literacy skills. Alfaba got evaluated by professionals and teachers and prototyped to support not just dyslexic students, but every student that needs to be supported at this stage of their learning journey. Our results show that Alfaba meets the needs of students and that its functions are consistent with the skills to be worked on in the reading and writing process.

In the context of Environmental Education, games help understand a particular theme and allow the observation of the approach from a player's perspective. This study presents an analysis of the use of feedback in RPGs (Role-Playing Games) and the importance of carefully analyzing the concepts being used. Furthermore, we observe the difficulty of individuals in understanding their roles in the RPG in the water resources context. The main contribution of this work is an empirical and qualitative study of the motivation of individuals. As a result, we present the individual's speech through a semistructured interview based on the Collective Subject Discourse (CSD) technique.

In the context of Environmental Education, games can help in understanding a particular theme and allow the observation of the approach from the perspective of a player. In this way, each person can develop strategies that she/he considers relevant in the game. We analyze players' strategies in an RPG (Role-Playing Game) in the water resources domain. This study analyzes the importance of water for survival and the problems of role representation, i.e., the lack of connection between reality and the game. In addition, we also analyze the importance of using the game to understand the pollution problem and the complexity of water management. As a result, we present speeches through a semi-structured interview based on the Collective Subject Discourse (CSD) technique.

This paper's main contribution is analyzing players' strategies and investigating the impact of gaming actions on players' lives. The games help understand a particular theme and allow the observation of the approach from a player's perspective. The sustainable management of water resources can be achieved when only the amount of water available locally is extracted from the available water resources. These resources are being recharged, naturally or artificially. Furthermore, we observe individuals related to the problem of water management and poor water management. As a result, we present the speeches through a semi-structured interview based on the Collective Subject Discourse (CSD) technique. The CSD consists of analyzing verbal material collected from the speeches (such as interviews or questionnaires), the ideas or central anchors, and the fundamental key expressions of these speeches. We compose the statements in the first person singular by the key expressions. Key Expressions (KE) are the most significant passages, and the Central Ideas (CI) synthesize the discursive content manifested in the KE. The case study presented in this work refers to the participatory negotiation of conflicts in the Lagoa Mirim and São Gonçalo Canal hydrographic basin, located in southern Brazil.

Diabetic retinopathy affects millions of working-age people worldwide. Of the countries in Latin America, Brazil has the highest incidence of cases. Diabetic retinopathy is detected through images of the fundus that contain lesions such as hard exudates, soft exudates, microaneurysms, and hemorrhages. Early identification of these lesions prevents the progression of the disease, which leads to a decrease in visual capacity. In addition, the early identification of these lesions allows the screening of patients who need priority care. The detection of these lesions occurs through the processing and analysis of fundus images using deep learning models. In this work, we present a new method that uses the You Only Learn One Representation with Cross Stage Partial Network (YOLOR-CSP) architecture combined with the Slicing Aided Hyper Inference (SAHI) framework to detect lesions. The proposed method was trained, adjusted, and evaluated using the Dataset for Diabetic Retinopathy (DDR) and the Indian Diabetic Retinopathy Image Dataset (IDRiD). The proposed method obtained in the data set DDR mAP equal to 38.08%, in the validation set, and 22.25% in the test set with SGD optimizer. The presented results were superior in the detection of eye fundus lesions in comparison with similar works found in the state-of-the-art literature.

This article presents a novel approach to identify two species of fruit insect pests as part of a network of intelligent traps designed to monitor the population of these insects in a plantation. The proposed approach uses a simple Digital Image Processing technique to detect regions in the image that are likely the monitored pests and an Artificial Neural Network to classify the regions into the right class given their characteristics. This identification is done essentially by a Convolutional Neural Network (CNN), which learns the characteristics of the insects based on their images made from the adhesive floor inside a trap. We have trained several CNN architectures, with different configurations, through a data set of images collected in the field. We aimed to find the model with the highest precision and the lowest time needed for the classification. The best performance in classification was achieved by ResNet18, with a precision of 93.55% and 91.28% for the classification of the pests focused on this study, named Ceratitis capitata and Grapholita molesta , respectively, and a 90.72%overall accuracy. Yet, the classification must be embedded on a resource-constrained system inside the trap, then we exploited SqueezeNet, MobileNet, and MNASNet architectures to achieve a model with lesser inference time and small losses in accuracy when compared to the models we assessed. We also attempted to quantize our highest precision model to reduce even more inference time in embedded systems, which achieved a precision of 88.76% and 89.73% for C. capitata and G. molesta , respectively; notwithstanding, a decrease of roughly 2% on the overall accuracy was endured. According to the expertise of our partner company, our results are worthwhile for a real-world application, since general human laborers have a precision of about 85%.

Diabetic Retinopathy is one of the leading causes of vision loss and presents in its initial phase retinal lesions, such as microaneurysms, hemorrhages, and hard and soft exudates. Therefore, computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping define the best form of treatment. This work proposes a method based on deep neural network models that perform one-stage object detection, using state-of-the-art data augmentation and transfer learning techniques to present a model that aids in the medical diagnosis of fundus lesions. The model was trained, adjusted, and evaluated using the DDR Diabetic Retinopathy Dataset, and implemented based on the YOLOv5 architecture and the PyTorch framework, achieving values for mAP of 0.1040 and 0.0283 for IoU threshold of 0.5 and 0.5:0.95 respectively, in the validation set. The results obtained in the experiments demonstrate that the proposed method presented superior results to equivalent works found in the literature.

Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is completely-specified, the implementation accurately represents the function. If the function is incompletely-specified, the implementation has to be true only on the care set. While most of the algorithms in logic synthesis rely on SAT and Boolean methods to exactly implement the care set, we investigate learning in logic synthesis, attempting to trade exactness for generalization. This work is directly related to machine learning where the care set is the training set and the implementation is expected to generalize on a validation set. We present learning incompletely-specified functions based on the results of a competition conducted at IWLS 2020. The goal of the competition was to implement 100 functions given by a set of care minterms for training, while testing the implementation using a set of validation minterms sampled from the same function. We make this benchmark suite available and offer a detailed comparative analysis of the different approaches to learning.

Diabetic Retinopathy is a major cause of vision loss caused by retina lesions, including hard and soft exudates, microaneurysms, and hemorrhages. The development of a computational tool capable of detecting these lesions can assist in the early diagnosis of the most severe forms of the lesions and assist in the screening process and definition of the best treatment form. However, the detection of tiny objects of very different sizes and shapes makes the detection process more complicated. This paper proposes a computational model based on pre-trained convolutional neural networks capable of detecting fundus lesions to promote medical diagnosis support. We trained, adjusted, and evaluated the model using the DDR diabetic retinopathy dataset and implemented it based on a YOLOv4 architecture and Darknet framework, achieving an mAP of 7.26% and a mloU of 11.64%. The experimental results show that the proposed model presented results superior to those obtained in related works found in the literature.

This paper presents an analysis of the strategies used by the players of a Role-Playing Game (RPG) in the context of water resources. Our research is a theoretical framework that informs the practice and states the implications for educational practice with a focus on action. RPGs are widely used in several areas because individuals have strategies that come close to reality. In the context of water resources, RPG helps in the process of understanding the problem and how each player thinks, analyzes, observes a situation from his perspective, and thus elaborates strategies that s/he deems relevant to her/his role and the game as a whole. The RPG developed in this research refers to the participatory management of water resources, based on data from the state of Rio Grande do Sul/Brazil, and focusing on the pilot application of the work in the Lagoa Mirim and São Gonçalo Watershed Management Committee, which involves the cities of Rio Grande and Pelotas, in southern Brazil. The main contribution of this work is an empirical study on the RPG players' motivation and how they elaborated on their strategies. In this research, we measure the habits of RPG players trough a semi-structured interview that was applied to a group of these players to assess the habits and strategies of individuals during the RPG.

Controlling energy consumption of video encoders is a complex multi-objective optimization problem of great importance. In this work we propose the RDE-MOGA, an multi-objective genetic algorithm capable of finding energetically efficient configurations for the HEVC encoder and replacing the current sensitivity analysis methodologies in the development of energy controllers. The utilization of our algorithm improved its efficiency in 60% whereas increasing the range of achievable reductions of the controller in at least 50%. Furthermore, the algorithm proved capable of sustaining 30% energy reduction at a cost of 3.45 BD-BR loss.

An open problem in social simulation and MAS applications is the self-regulation of social exchange processes, aiming at the achievement/maintenance of equilibrated exchanges by the agents themselves, providing the continuation of the interactions in time. This paper faces this problem through an approach based on the proposed spatial and evolutionary Game of Self-Regulation of Social Exchange Processes. The agents, adopting different social exchange strategies, which take into account both the short and long-term aspects of interactions, evolve such strategies by themselves in time, in order to maximize their respective strategy-based fitness functions. In consequence, the agents happen to perform more equilibrated and fair interactions, increasing the number of successful exchanges.

This paper describes an extended Genetic Algorithm Approach for path planning of multiple mobile robots with obstacle detection and avoidance in static and dynamic scenarios. Through the software Netlogo, used in simulations of multi-agent applications, a model was developed for the given problem. The model, which contains multiple robots and a scenario with several dynamic and static obstacles, is responsible for determining the best path used by the robots to achieve the goal state in a shorter number of steps and avoiding collisions. Additionally, a performance evaluation of this model in comparison with A* algorithm is presented.

In geophysics, the appropriate subdivision of a region into segments is extremely important. ICTM (interval categorizer tesselation model) is an application that categorizes geographic regions using information extracted from satellite images. The categorization of large regions is a computational intensive problem, what justifies the proposal and development of parallel solutions in order to improve its applicability. Recent advances in multiprocessor architectures lead to the emergence of NUMA (non-uniform memory access) machines. In this work, we present NUMA-ICTM: a parallel solution of ICTM for NUMA machines. First, we parallelize ICTM using OpenMP. After, we improve the OpenMP solution using the MAI (memory affinity interface) library, which allows a control of memory allocation in NUMA machines. The results show that the optimization of memory allocation leads to significant performance gains over the pure OpenMP parallel solution.