Neurodegenerative diseases (NDDs) cause, among other symptoms, motor impairment. Given the incurable nature of the NDDs, several studies have investigated gait using artificial intelligent models as non-invasive alternative methods to assist in the diagnosis of these diseases. This work proposes a novel method using an Encoder-Only Transformer to detect NDDs, a multi-classification task, through gait signal analysis. The approach comprises data preprocessing, windowing technique, a modified transformer architecture and cross-validation evaluation. The results indicate the transformer-based architecture can be a promising alternative to accomplish this goal.

Edge bundling is a technique commonly used to reduce visual clutter and improve the comprehension of the drawings of large graphs. Here, we model edge bundling as a multi-objective optimization problem and employ clustering strategies, metaheuristic and Pareto analysis to identify non-dominated solutions for some classical graphs from the literature.

Celtic knots, an ancient art form often linked to Celtic heritage, have been used historically in the decoration of monuments and manuscripts, often symbolizing the notions of eternity and interconnectedness. This paper introduces the framework CelticGraph designed for illustrating graphs in the style of Celtic knots and links. The process of creating these drawings raises interesting combinatorial concepts in the theory of circuits in planar graphs. Further, CelticGraph uses a novel algorithm to represent edges as Bézier curves, aiming to show each link as a smooth curve with limited curvature. We also show that with our production mechanisms we can compute any 4-regular plane graph and thereby any celtic knot or link. The CelticGraph framework for drawing graphs as celtic knots and links is implemented as an add-on of Vanted, a network visualization and analysis tool.

The solution space of an optimization problem consists of all its feasible solutions. In this work, we present a systematic literature review on the application of Information Visualization (IV) techniques for understanding and exploring such solution spaces. The review was conducted on several search databases, and we identified 264 papers that satisfied our inclusion criteria. A performance filter was applied to these papers, and we further analyzed and extracted data from 65 of them. Our analysis shows that there are a variety of solution space visualization approaches and provides useful references to support further studies on the subject.

One of the commonly used techniques to improve the readability of large graph drawings is called edge bundling, which groups edges in such a way that reduces the visual complexity of the drawing. This paper proposes to treat this task as a clustering problem, using compatibility metrics to evaluate the generated solutions in an optimization pipeline, combined with a clustering ensemble approach. The goal was to solve the General-based Edge Bundling (GBEB) problem with relatively low computational costs using a method called Clustering Ensemble-based Edge Bundling (CEBEB) and evaluate the results. CEBEB proved to be a very promising alternative to solve GBEB, since it is capable of generating relatively good solutions with shorter run-times compared to an existing, well-established GBEB method.

Amyotrophic Lateral Sclerosis (ALS) is a neurode-generative disease characterized by the progressive and specific loss of motor neurons in the brain, causing a variety of symptoms, including weakness of muscles and changes in gait. Currently, there is no cure for ALS, nor there is a definitive diagnostic test that can detect whether someone has ALS. Therefore, there is still a need for alternative and non-invasive methods to aid the diagnosis of ALS. This article proposes an automatic method to aid the diagnosis of ALS. A feature extraction technique based on metrics of fluctuation magnitude and fluctuation dynamics, followed by a machine learning algorithm to separate subjects with ALS from healthy ones, was used. The results showed that the proposed approach is comparable to others in the literature even though a simpler and smaller feature set was considered. Five different machine learning classifiers were compared and evaluated using the leave-one-out cross-validation method. A comparison and discussion of the results based on the foot from which the data were extracted and the phases of the gait were also carried out.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects the nervous system responsible for muscle movement and eventually compromising one's ability to walk. Diagnosing ALS is a difficult task since no test can provide a definite diagnosis. In this sense, automatic methods that aid the diagnosis of ALS have an essential role in helping to reach a diagnose. However, most of the existing approaches that use gait dynamics are based on a 5-minute observation, which can be exhausting and demanding for a patient with ALS seeking the diagnosis. This paper proposes an automated method to aid the diagnosis of ALS using information obtained from one minute gait observation. The GaitNDD database, which provides gait data recorded for 5 minutes from people with ALS and from healthy subjects, was used to support and validate this study. Results are reported and evaluated for different machine learning classifiers. Features extracted from either 1-min or 5-min observations are evaluated. Our results show that 96.6% of accuracy was achieved for data derived from either the first or the 5-minute walking, with excellent sensitivity and specificity, thus showing that our method can help aid the diagnosis of ALS while reducing the time required for the walking experiment.

A disparity map is a key component of stereo vision systems. Autonomous navigation, 3D reconstruction and mobility are examples of areas that use disparity maps as an important element. Although much work has been done in the stereo vision field, it is not easy to build stereo systems with concepts such as reuse and extensible scope. In the present paper, we contribute to reducing this gap by presenting a software architecture that can accommodate different stereo methods through a new standard structure. Firstly, we introduce scenarios that illustrate use cases of disparity maps, and we show a novel architecture that foments code reuse. A Disparity Computation Framework (DCF) is presented and how its components are structured regarding compartmentalization are discussed. Then, we introduce a prototype that closely follows our proposal, and we describe some test cases that were performed. We conclude that the DCF can satisfy different on-demand scenarios and that it can support new stereo methods, functions, and evaluations for different applications without much effort.

The analysis of affinity or similarity between people is an important task in the study of social dynamics. Traditional methods for determining similarity depends on considerable amount of data regarding people's preferences and features. Those methods present limitations when the data is scarce and/or changes constantly. This paper introduces a new method for determining people similarity that does not suffer from the same problems. The method can learn a customized similarity function based on social variables of friends that attend the same events (concerts, parties, conferences etc), collected from social networks. Two types of optimization algorithms for learning a similarity function are presented: The universal function approximator modelling, which relays on the relationship of social attributes and a friends' importance ranking; and the populational evolutionary modelling, which linearly combines social variables. Both models were tested in a generalist and in a specialist approach. The results show that the specialist approach exceeded in almost 38 % the generalist approach using populational evolutionary methods and in almost 69 % when using the universal function approximator methods. Among the implemented optimization algorithms employed inside the methods for learning similarity, Genetic Algorithm and Particle Swarm Optimization presented better performance for the populational evolutionary methods and the Artificial Neural Network presented the best performance overall using the universal function approximator modelling.

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.

Emerging technology on mobile and wearable market, smartwatches have embedded movement sensors whose potential is yet to be fully explored. This paper proposes an interaction method with smart TVs via gestures performed by person's wrist using a smartwatch. Detailed architecture and implementation for a complete prototype, named Wrist Player, is presented. A user study is also conducted, in order to evaluate the prototype performance and the user's interest on the proposal. Results show that the method works very well, with participants reporting having a good experience with the prototype. We present our insights on the concept, challenges faced in our research and ideas for future studies.