In some application fields, series are affected by two different types of effects: abrupt changes (or change-points) and functional effects. We propose here a Bayesian approach that allows us to estimate these two parts. Here, the underlying piecewise-constant part (associated to the abrupt changes) is expressed as the product of a lower triangular matrix by a sparse vector and the functional part as a linear combination of functions from a large dictionary where we want to select the relevant ones. This problem can thus lead to a global sparse estimation and a stochastic search variable selection approach is used to this end. The performance of our proposed method is assessed using simulation experiments. Applications to three real datasets from geodesy, agronomy and economy fields are also presented.

Exchange rates are determined by factors such as interest rates, political stability, confidence, the current account on balance of payments, government intervention, economic growth and relative inflation rates, among other variables. In October 2019, an increased climate of citizen discontent with current social policies resulted in a series of massive protests that ignited important political changes in Chile. This event along with the global COVID-19 pandemic were two major factors that affected the value of the US dollar and produced sudden changes in the typically stable USD/CLP (Chilean Peso) exchange rate. In this paper, we use a Bayesian approach to detect and locate change points in the currency exchange rate process in order to identify and relate these points with the important dates related to the events described above. The implemented method can successfully detect the onset of the social protests, the beginning of the COVID-19 pandemic in Chile and the economic reactivation in the US and Europe. In addition, we evaluate the performance of the proposed MCMC algorithms using a simulation study implemented in Python and R.

Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

We propose an efficient computational method to obtain the fractional derivative of a digital signal. The proposal consists of a new interpretation of the Grünwald–Letnikov differintegral operator where we have introduced a finite Cauchy convolution with the Grünwald–Letnikov dynamic kernel. The method can be applied to any signal without knowing its analytical form. In the experiments, we have compared the proposed Grünwald–Letnikov computational fractional derivative method with the Riemman–Louville fractional derivative approach for two well-known functions. The simulations exhibit similar results for both methods; however, the Grünwald–Letnikov method outperforms the other approach in execution time. Finally, we show an application of how our proposal can be useful to find the fractional relationship between two well-known biomedical signals.

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model's capability of generalization and certainty on its predictions, explaining its functionality.

Collaborative acts occur daily in every human activity. In the case of medicine, and particularly in the diagnostic decision process, these acts are very frequent and occur naturally. It is very important to properly understand how these collaborative acts are developed in order to provide tools that facilitate and support them. In this article, we describe this collaborative work process in the framework of a complex real medical case in the radiological field. Usually, complex cases require several specialists. In this work, we have analyzed the intervention of several specialists and the exchange and interaction of different reasoning strategies among specialists, while considering their temporal dimension. Two types of collaboration are presented in the case analysis (1) exchange between specialists from different specialties and (2) exchange between specialists from the same specialty. The method of analysis follows five steps: (1) Case synopsis, (2) Temporal representation of the case, (3) Analysis of the general decision in the case, (4) Analysis of the reasoning in the medical case using the different strategies, and (5) Analysis of radiological collaboration. We have presented different reasoning strategies, data, hypotheses and complementary tests from different sources in the diagnostic resolution process and we have shown that collaboration is present during the entire process. The temporality and the intervention of different specialists is shown using a graphical representation. We have focused special attention on radiological collaboration, and have shown how a radiological diagnosis is achieved. We have discussed different elements present in the collaboration process. Our study has produced meta-knowledge derived from these exchanges that is of value in the context of artificial intelligence progress, in particular for the comprehension of collaborative medical work.

Sex differences have been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to sex and the conditions that generate these effects. The present work studied the effects of a drill-and-practice mathematical game on primary students. The study focused on an analysis by sex, measuring motivation and learning in the practice activity. Also, two instructional mechanics were considered regarding the question answering to search for possible differences: a multiple-try feedback (MTF) condition and a single-try feedback (STF) condition. A total of 81 students from four courses and two schools participated in the intervention. The study’s main findings were as follows: (a) the girls outperformed the boys in terms of the students’ learning gains; (b) the girls presented lower levels of competence and autonomy than the boys; (c) under MTF, the girls presented lower levels of autonomy but no differences in competence contrasted with the boys; (d) under STF, the girls presented lower levels of competence but no differences in autonomy contrasted with the boys; (e) no sex differences existed in interest, effort, and value, in general, as per the instructional condition. This study enhances the knowledge of sex differences under diverse instructional settings, in particular providing insights into the possible differences by sex when varying the number of attempts provided to students.

This paper focuses on modeling and solving the Manufacturing Cell Design Problem (MCDP) by using the Harmony Search (HS) metaheuristic. The MDCP consists on grouping machines and parts that they process, into groups called cells. So, the idea is to identify an organization of cells such that the number of times that a piece is transported between these cells is minimized. To this end, we use the HS optimization algorithm, which is based on the process of improvisation performed by musicians to find a perfect musical harmony. The experimental results demonstrate the efficiency of the proposed approach which is able to reach all global optimums for a set of 90 well-known MDCP instances.

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED stimulator system explicitly designed for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED stimulator provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the stimulator utilizes direct digital synthesis and pulse width modulation techniques to control the LEDs. We validated its performance through four experiments: the first two measured LED light intensities directly, while the last two assessed the stimulator’s impact on EEG recordings. The results demonstrate that the stimulator can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. As an open source resource, we provide comprehensive documentation, including all necessary codes and electrical diagrams, which facilitates the system’s replication and adaptation for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience setups.

Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin β subunit ( β -HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of β -HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures. Longitudinal profiles of β -HCG can be used to distinguish between normal and abnormal pregnancies and to assist and guide the clinician in better management and monitoring of post-IVF pregnancies. Therefore, assessing the association between longitudinally measured β -HCG serum concentration and time to early miscarriage is of crucial interest to clinicians. A common joint modeling approach is to use the longitudinal β -HCG trajectory to determine the risk of miscarriage. This work was motivated by a follow-up study with normal and abnormal pregnancies where β -HCG serum concentrations were measured in 173 young women during a gestational age of 9–86 days in Santiago, Chile. Some women experienced a miscarriage event, and their exact event times were unknown, so we have interval-censored data, with the event occurring between the last time of the observed measurement and ten days later. However, for those women belonging to the normal pregnancy group; that is, carrying a pregnancy to a full-term event, right censoring data are observed. Estimation procedures are based on the Stochastic Approximation of the Expectation–Maximization (SAEM) algorithm.

Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.