Music conveys both basic emotions, like joy and sadness, and complex ones, such as tenderness and nostalgia. Its effects on emotion regulation and reward have attracted much research attention, as the neural correlates of music-evoked emotions may inform neurorehabilitation interventions. Here, we used fMRI to decode and examine the neural correlates of perceived valence and arousal in music excerpts. Twenty participants were scanned while listening to 96 music excerpts, classified beforehand into four categories varying in valence and arousal. Music modulated activity in cortical regions, most noticeably in music-specific subregions of the auditory cortex, thalamus, and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model to decode the perceived valence and arousal of the music excerpts with above-chance accuracy. We further explored associations between musical features and brain activity in valence-, arousal-, reward-, and auditory-related networks. The results emphasize the involvement of distinct musical features, notably expressive features such as vibrato and tonal and spectral dissonance in valence, arousal, and reward brain networks. Using ecologically valid music stimuli, we contribute to delineating the neural correlates of music-evoked emotions with potential implications in the development of novel music-based neurorehabilitation strategies.

Adaptability and behavioral diversity are core components of social interactions between humans. Naturally, these are traits research should strive to achieve in social robotics so agents may be better accepted and engage with their user peers. In this paper, we propose a novel activity modulation to increase behavioral diversity, based on a surprise-exploration correlation model, in a social robot undergoing behavioral optimization to user state and preference. This framework was tested with 21 participants to assess preferences as well as the impact that action variability and persistence would have on user perception of the robot. Results indicate a positive effect of persistence and variability over robot likability as well as user engagement, contributing insight for future research in social robotics.

Code review is an essential practice in software engineering to spot code defects in the early stages of software development. Modern code reviews (e.g., acceptance or rejection of pull requests with Git) have become less formal than classic Fagan's inspections, lightweight, and more reliant on individuals (i.e., reviewers). However, reviewers may encounter mentally demanding challenges during the code review, such as code comprehension difficulties or distractions that might affect the code review quality. This work proposes a novel approach that evaluates the quality of code reviews in terms of bug-finding effectiveness and provides the reviewers with a clear message of whether the review should be repeated, indicating the code regions that may not have been well-reviewed. The proposed approach utilizes biometric information collected from the reviewer during the review process using non-intrusive biofeedback devices (e.g., smartwatches). Biometric measures such as Heart Rate Variability (HRV) and task-evoked pupillary response are captured as a surrogate of the cognitive state of the reviewer (e.g., mental workload) and inexpensive desktop eye-trackers compatible with the software development settings. This work uses Artificial Intelligence techniques to predict the cognitive load from the extracted biomarkers and classify each code region according to a set of features. The final evaluation considers various factors such as code complexity, time of the code review, the experience level of the reviewer, and other factors. Our experimental results show the approach could predict the review quality with 87.77%±4.65 accuracy and a Spearman correlation coefficient of 0.85 (p-value < 0.001) between the predicted and the actual review performance. This evaluation validates the cognitive load measurement using electroencephalography (EEG) signals as ground truth for the HRV and pupil signals.

Code reviews and software inspections are essential for building reliable software. However, current code reviews practice in the software industry (e.g., acceptance or rejection of pull requests with Git) deviates considerably from classic (and expensive) Fagan's inspections. Modern code reviews are lightweight and asynchronous and do not rely on a group of inspectors and inspection meetings any longer. The modern style of code reviews is much more flexible and cost-effective. Still, these advantages come with the price of reducing the quality of code reviews, as a single reviewer generally makes them with all the inherent and the very human limitations of one single look. The reviewer could be distracted, overloaded, under stress, or not even fully understand the code under review. This paper proposes a new tool (iReview) that evaluates the code review quality using biometric measures gathered from code reviewers (often called Biofeedback). Biometric measures such as Heart Rate Variability (HRV) and eye movement dynamics are used to assess the reviewer's comprehension of the code under review. iReview evaluates the quality of each review globally and indicates the code regions that have not been well-reviewed, explaining why those code regions should be reviewed again. The tool uses Artificial Intelligence techniques to classify the code regions into good and bad reviews based on various biometric and non-biometric features. The first results show that iReview can predict the review quality of medium or complex programs with an accuracy ranging from 75% to 87% in detecting bad reviews (i.e., code regions classified as bad reviewed still have undetected bugs). This tool is expected to improve software reliability by ensuring that good reviews have been carried out despite the current lightweight reviewing processes.

GameAAL - Gamification Supporting Active and Assisted Living is an innovative system for monitoring elderly daily life activities and provide neurocognitive stimulation games. It is focused at filling a gap on existing Ambient Assisted Living solutions by promoting social interaction and mobility of its end-users using gamification and machine learning techniques. As long as the goals of user are being achieved, the system unlocks rewards like digital badges, providing the user with the chance of winning free admissions in cultural events provided by community partners. A Proof of Concept is ongoing with 15 real end-users who interact with a TV interface, showing a good acceptability and willing to use the system.

Our research explores a recent paradigm called Biofeedback Augmented Software Engineering (BASE) that introduces a strong new element in the software development process: the programmers' biofeedback. In this Practical Experience Report we present the results of an experiment to evaluate the possibility of using pupillography to gather biofeedback from the programmers. The idea is to use pupillography to get meta information about the programmers' cognitive and emotional states (stress, attention, mental effort level, cognitive overload,...) during code development to identify conditions that may precipitate programmers making bugs or bugs escaping human attention, and tag the corresponding code locations in the software under development to provide online warnings to the programmer or identify code snippets that will need more intensive testing. The experiments evaluate the use of pupillography as cognitive load predictor, compare the results with the mental effort perceived by programmers using NASATLX, and discuss different possibilities for the use of pupillography as biofeedback sensor in real software development scenarios.

Recent studies have shown that programmers' cognitive load during typical code development activities can be assessed using wearable and low intrusive devices that capture peripheral physiological responses driven by the autonomic nervous system. In particular, measures such as heart rate variability (HRV) and pupillography can be acquired by nonintrusive devices and provide accurate indication of programmers' cognitive load and attention level in code related tasks, which are known elements of human error that potentially lead to software faults. This paper presents an experimental study designed to evaluate the possibility of using HRV and pupillography together with eye tracking to identify and annotate specific code lines (or even finer grain lexical tokens) of the program under development (or under inspection) with information on the cognitive load of the programmer while dealing with such lines of code. The experimental data is discussed in the paper to assess different alternatives for using code annotations representing programmers' cognitive load while producing or reading code. In particular, we propose the use of biofeedback code highlighting techniques to provide online programmer's warnings for potentially problematic code lines that may need a second look at (to remove possible bugs), and biofeedback-driven software testing to optimize testing effort, focusing the tests on code areas with higher bug probability.

In this paper, we imaged the retina of wild-type and the triple-transgenic mouse model of Alzheimer's disease (3xTg- AD) with optical coherence tomography to assess changes in the retinal tissue associated with the Alzheimer's disease. Texture analysis allowed to identify differences between groups at the age of four months, and to find biomarkers of disease progression. Furthermore, our findings suggest that specific layers of the retina may play a fundamental role in the assessment of early changes associated with the Alzheimer's disease.

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

This text presents the integration of touch attention mechanisms to improve the efficiency of the action-perception loop, typically involved in active haptic exploration tasks of surfaces by robotic hands . The progressive inference of regions of the workspace that should be probed by the robotic system uses information related with haptic saliency extracted from the perceived haptic stimulus map ( exploitation ) and a “curiosity”-inducing prioritisation based on the reconstruction's inherent uncertainty and inhibition-of-return mechanisms ( exploration ), modulated by top-down influences stemming from current task objectives, updated at each exploration iteration. This work also extends the scope of the top-down modulation of information presented in a previous work, by integrating in the decision process the influence of shape cues of the current exploration path. The Bayesian framework proposed in this work was tested in a simulation environment. A scenario made of three different materials was explored autonomously by a robotic system . The experimental results show that the system was able to perform three different haptic discontinuity following tasks with a good structural accuracy, demonstrating the selectivity and generalization capability of the attention mechanisms. These experiments confirmed the fundamental contribution of the haptic saliency cues to the success and accuracy of the execution of the tasks.

We propose that understanding functional patterns of activity in mapped brain regions associated with code comprehension tasks and, more specifically, to the activity of finding bugs in traditional code inspections could reveal useful insights to improve software reliability and to improve the software development process in general. This includes helping to select the best professionals for the debugging effort, improving the conditions for code inspections, and identify new directions to follow for training code reviewers. This paper presents an interdisciplinary study to analyze the brain activity during code inspection tasks using functional magnetic resonance imaging (fMRI), which is a well-established tool in cognitive neuroscience research. We used several programs where realistic bugs representing the most frequent types of software faults found in the field were injected. The code inspectors involved in the research include programmers with different levels of expertise and experience in real code reviews. The goal is to understand brain activity patterns associated with code comprehension tasks and, more specifically, the brain activity when the code reviewer identifies a bug in the code ('eureka' moment), which can be a true positive or a false positive. Our results confirmed that brain areas associated with language processing and mathematics are highly active during code reviewing and shows that there are specific brain activity patterns that can be related to the decision-making moment of suspicion/bug detection. Importantly, the activity at the anterior insula region that we find to play a relevant role in the process of identifying software bugs is positively correlated to the precision of bug detection by the inspectors. This finding provides a new perspective on the role of this region on error awareness and monitoring and of its potential predictive value in predicting the quality of bug removing.

This paper describes an automatic method for the optic disc localization in retinal images, which is effective and reliable with multiple datasets. Particularly, the described method reveals very effective dealing with retinal images with large pathological signs. The algorithm begins with a new vessel enhancement method based on a modified corner detector. Subsequently, a weighted version of the vessel enhancement is combined with morphological operators, to detect the four main vessels orientations {0(°), 45(°), 90(°), 135(°) }. These four image functions have all the necessary information to determine an initial optic disc localization, resulting in two images that are respectively divided along the vertical or horizontal orientations with different division sizes. Each division is averaged creating a 2-D step function, and a cumulative sum of the different sizes step functions is calculated in the vertical and horizontal orientations, resulting in an initial optic disc position. The final optic disc localization is determined by a vessel convergence algorithm using its two most relevant features; high vasculature convergence and high intensity values. The proposed method was evaluated in eight publicly available datasets, including the STARE and DRIVE datasets. The optic disc was localized correctly in 1752 out of the 1767 retinal images (99.15%) with an average computation time of 18.34 s.

In this paper, we present a Bayesian framework for the active multimodal perception of 3-D structure and motion. The design of this framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cyclopean geometry-based stereovision and auditory perception based only on binaural cues, modeled using a consistent formalization that allows their hierarchical use as building blocks for the multimodal sensor fusion framework. We will explicitly or implicitly address the most important challenges of sensor fusion using this framework, for vision, audition, and vestibular sensing. Moreover, interaction and navigation require maximal awareness of spatial surroundings, which, in turn, is obtained through active attentional and behavioral exploration of the environment. The computational models described in this paper will support the construction of a simultaneously flexible and powerful robotic implementation of multimodal active perception to be used in real-world applications, such as human-machine interaction or mobile robot navigation.