Results of a mindfulness-based social-emotional learning program on portuguese elementary students and teachers: a quasi-experimental study

Recently, mindfulness-based social-emotional learning (SEL) approaches have been taught to children in some schools. Due to deficient methodological consistency observed in most studies, their results should be interpreted with caution. Moreover, research on how mindfulness-based SEL approaches benefit teachers is scarce, and the majority of these studies have been conducted in English-speaking countries; therefore, it is uncertain whether these approaches are suited to other cultural backgrounds. The aim of the present study was to evaluate the efficacy of the MindUp curriculum, an SEL program through mindfulness practice for Portuguese students and teachers. Participants included 454 3rd and 4th grade students and 20 teachers from state schools. A quasiexperimental (pre- and post-test) study compared outcomes for an experimental group with a waitlist control group. Data were collected from teachers and children through self-report measures. Results showed that over 50 % of the children who participated in the MindUp program scored above the control group mean in their ability to regulate emotions, to experience more positive affect, and to be more self-compassionate, and over 50 % scored lower in negative affect. In the group of teachers, over 80 % scored above the control group mean in observing, in personal accomplishment, and in self-kindness. Our results contribute to the recent research on the potential added value of mindfulness practices to a SEL program and strengthen the importance for teachers and students of adding to the academic curriculum a SEL program through mindfulness practices.

Action and perception: Neural indices of learning in infants

Early human development offers a unique perspective in investigating the potential cognitive and social implications of action and perception. Specifically, during infancy, action production and action perception undergo foundational developments. One essential component to examine developments in action processing is the analysis of others’ actions as meaningful and goal-directed. Little research, however, has examined the underlying neural systems that may be associated with emerging action and perception abilities, and infants’ learning of goal-directed actions. The current study examines the mu rhythm—a brain oscillation found in the electroencephalogram (EEG)—that has been associated with action and perception. Specifically, the present work investigates whether the mu signal is related to 9-month-olds’ learning of a novel goal-directed means-end task. The findings of this study demonstrate a relation between variations in mu rhythm activity and infants’ ability to learn a novel goal-directed means-end action task (compared to a visual pattern learning task used as a comparison task). Additionally, we examined the relations between standardized assessments of early motor competence, infants’ ability to learn a novel goal-directed task, and mu rhythm activity. We found that: 1a) mu rhythm activity during observation of a grasp uniquely predicted infants’ learning on the cane training task, 1b) mu rhythm activity during observation and execution of a grasp did not uniquely predict infants’ learning on the visual pattern learning task (comparison learning task), 2) infants’ motor competence did not predict infants’ learning on the cane training task, 3) mu rhythm activity during observation and execution was not related to infants’ measure of motor competence, and 4) mu rhythm activity did not predict infants’ learning on the cane task above and beyond infants’ motor competence. The results from this study demonstrate that mu rhythm activity is a sensitive measure to detect individual differences in infants’ action and perception abilities, specifically their learning of a novel goal-directed action.

Perceived benefits, motivations and preferences for foreign language learning by older adults. Insights from an initiative in Cuenca, Ecuador

El presente estudio analiza las percepciones y actitudes que tienen los adultos mayores de la ciudad de Cuenca, Ecuador hacia el aprendizaje del inglés. Un total de 151 adultos mayores (con edad promedio de 70.3 años) respondió a un cuestionario con 50 ítems. Se llevó a cabo análisis factoriales, de regresión múltiple y cluster con el propósito de definir las dimensiones subyacentes en las percepciones, motivaciones y ambiciones de los adultos mayores para aprender un idioma extranjero, y su relación con las características sociodemográficas de los participantes. Los resultados señalan que el interés por estudiar un idioma extranjero está basado en la percepción de que aquello mejora la interacción social de las personas, su desarrollo personal, el funcionamiento y mantenimiento de la mente y memoria, y que activa y vuelve su vida más dinámica. Los resultados además revelaron que la principal motivación de los participantes para tomar un curso de inglés está relacionada con el potencial de usar este idioma en la vida diaria y el de leer profusamente en esa lengua extranjera. La duración del curso y la obtención de un certificado fueron factores determinantes que permitieron agrupar a los participantes en función de sus preferencias en lo que respecta al diseño práctico de un curso de inglés. Adicionalmente, la edad y el nivel de instrucción fueron variables determinantes de motivación que influyeron en la mayor parte de las respuestas dadas por los participantes.

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^

Balance ability of 7 and 10 year old children in the population:results from a large UK birth cohort study

OBJECTIVE: The literature contains many reports of balance function in children, but these are often on atypical samples taken from hospital-based clinics and may not be generalisable to the population as a whole. The purpose of the present study is to describe balance test results from a large UK-based birth cohort study. METHODS: Data from the Avon Longitudinal Study of Parents and Children (ALSPAC) were analysed. A total of 5402 children completed the heel-to-toe walking test at age 7 years. At age 10 years, 6915 children underwent clinical tests of balance including beam-walking, standing heel-to-toe on a beam and standing on one leg. A proportion of the children returned to the clinic for retesting within 3 months allowing test-retest agreement to be measured. RESULTS: Frequency distributions for each of the balance tests are given. Correlations between measures of dynamic balance at ages 7 and 10 years were weak. The static balance of 10 year old children was found to be poorer with eyes closed than with eyes open, and poorer in boys than in girls for all measures. Balance on one leg was poorer than heel-to-toe balance on a beam. A significant learning effect was found when first and second attempts of the tests were compared. Measures of static and dynamic balance appeared independent. Consistent with previous reports in the literature, test-retest reliability was found to be low. CONCLUSIONS: This study provides information about the balance ability of children aged 7 and 10 years and provides clinicians with reference data for balance tests commonly used in the paediatric clinic.

Leadership and Pedagogical Interaction as Predictors of Learning Outcomes in Physical Education

This research aims to understand the relative contribution of leadership styles and teacher-student and student-student pedagogical interaction concerning learning performance and academic achievement in Physical Education. A quantitative methodology was implemented, comprising a sample of 447 students attending a school grouping located in the coastal region of central Portugal. In order to verify the nature, the strength and the direction of the relations among the variables, correlation and multiple regression analyses were used. For this, scales already validated and used in other researches were applied. The results show that the learning performance and the academic achievement are significantly associated with teacher leadership styles and teacher-student and student-student pedagogical interaction. A stronger association was obtained with leadership styles, especially the democratic one. It should be mentioned that these factors provide a higher relative contribution to the learning performance than to the academic achievement. The analysis conducted highlights the importance of the democratic teacher leadership style and of the pedagogical interaction established within the classroom towards the improvement of students’ ability to understand the gains and the effort made in learning.

Leadership and Pedagogical Interaction as Predictors of Learning Outcomes in Physical Education

Problem Statement: This research aims to understand the relative contribution of leadership styles and teacher-student and student-student pedagogical interaction concerning the learning performance and academic achievement in physical education. Research Questions: Are the teacher leadership style and the teacher-student and student-student pedagogical interaction related to the learning performance and academic achievement in physical education in basic schooling? Purpose of Study: There are several factors that contribute for the explanation of learning outcomes, namely teacher leadership styles in the classroom, as well as teacher-student and student-student pedagogical interactions. These factors are considered to be essential in the teaching-learning process and in the subsequent improvement of educational outcomes. Research Methods: A quantitative methodology was implemented, comprising a sample of 447 students attending a School Grouping located in the Central Region of Portugal. In order to verify the nature, the strength and the direction of the relations among the variables, correlation and multiple regression analyses were used. For this, scales already validated and used in other researches were applied. Findings: The results show that the learning performance and the academic achievement are significantly associated with teacher leadership styles and teacher-student and student-student pedagogical interaction. A stronger association was obtained with leadership styles, especially the democratic one. It should be mentioned that these factors provide a higher relative contribution to the learning performance than to the academic achievement. Conclusions: This study sought to deepen the understanding of the explanatory factors of academic success concerning the teaching-learning process in physical education. The analysis conducted highlights the importance of the democratic teacher leadership style and of the pedagogical interaction established within the classroom towards the improvement of students' ability to understand the gains and the effort made in learning.

Learning curves: analysing pace and challenge in four successful puzzle games

The pace at which challenges are introduced in a game has long been identified as a key determinant of both the enjoyment and difficulty experienced by game players, and their ability to learn from game play. In order to understand how to best pace challenges in games, there is great value in analysing games already demonstrated as highly engaging. Play-through videos of four puzzle games (Portal, Portal 2 Co-operative mode, Braid and Lemmings), were observed and analysed using metrics derived from a behavioural psychology understanding of how people solve problems. Findings suggest that; 1) the main skills learned in each game are introduced separately, 2) through simple puzzles that require only basic performance of that skill, 3) the player has the opportunity to practice and integrate that skill with previously learned skills, and 4) puzzles increase in complexity until the next new skill is introduced. These data provide practical guidance for designers, support contemporary thinking on the design of learning structures in games, and suggest future directions for empirical research.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

The Students’ Perspective of Geometry Teaching and Learning in High School

The purpose of this article is to present the results obtained from a questionnaire applied to Costa Rican high school students, in order to know their perspectives about geometry teaching and learning. The results show that geometry classes in high school education have been based on a traditional system of teaching, where the teacher presents the theory; he presents examples and exercises that should be solved by students, which emphasize in the application and memorization of formulas. As a consequence, visualization processes, argumentation and justification don’t have a preponderant role. Geometry is presented to students like a group of definitions, formulas, and theorems completely far from their reality and, where the examples and exercises don’t possess any relationship with their context. As a result, it is considered not important, because it is not applicable to real life situations. Also, the students consider that, to be successful in geometry, it is necessary to know how to use the calculator, to carry out calculations, to have capacity to memorize definitions, formulas and theorems, to possess capacity to understand the geometric drawings and to carry out clever exercises to develop a practical ability.

Reinforcement learning over encrypted data

Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinforcement learning in particular has never been applied to such cases before. Being able to successfully make use of reinforcement learning in an encrypted scenario would allow us to create an agent that efficiently controls a system without providing it with full knowledge of the environment it is operating in, leading the way to many possible use cases. Therefore, we have decided to apply the reinforcement learning paradigm to encrypted data. In this project we have applied one of the most well-known reinforcement learning algorithms, called Deep Q-Learning, to simple simulated environments and studied how the encryption affects the training performance of the agent, in order to see if it is still able to learn how to behave even when the input data is no longer readable by humans. The results of this work highlight that the agent is still able to learn with no issues whatsoever in small state spaces with non-secure encryptions, like AES in ECB mode. For fixed environments, it is also able to reach a suboptimal solution even in the presence of secure modes, like AES in CBC mode, showing a significant improvement with respect to a random agent; however, its ability to generalize in stochastic environments or big state spaces suffers greatly.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.