A programming environment having three levels of complexity for mobile robotics = Entorno de programación con tres niveles de complejidad para robótica móvil

This paper presents a programming environment for supporting learning in STEM, particularly mobile robotic learning. It was designed to maintain progressive learning for people with and without previous knowledge of programming and/or robotics. The environment was multi platform and built with open source tools. Perception, mobility, communication, navigation and collaborative behaviour functionalities can be programmed for different mobile robots. A learner is able to programme robots using different programming languages and editor interfaces: graphic programming interface (basic level), XML-based meta language (intermediate level) or ANSI C language (advanced level). The environment supports programme translation transparently into different languages for learners or explicitly on learners’ demand. Learners can access proposed challenges and learning interfaces by examples. The environment was designed to allow characteristics such as extensibility, adaptive interfaces, persistence and low software/hardware coupling. Functionality tests were performed to prove programming environment specifications. UV BOT mobile robots were used in these tests

Emerging paradigms and questions on pro-angiogenic bone marrow-derived myelomonocytic cells.

Cancer-related inflammation has emerged in recent years as a major event contributing to tumor angiogenesis, tumor progression and metastasis formation. Bone marrow-derived and inflammatory cells promote tumor angiogenesis by providing endothelial progenitor cells that differentiate into mature endothelial cells, and by secreting pro-angiogenic factors and remodeling the extracellular matrix to stimulate angiogenesis though paracrine mechanisms. Several bone marrow-derived myelonomocytic cells, including monocytes and macrophages, have been identified and characterized by several laboratories in recent years. While the central role of these cells in promoting tumor angiogenesis, tumor progression and metastasis is nowadays well established, many questions remain open and new ones are emerging. These include the relationship between their phenotype and function, the mechanisms of pro-angiogenic programming, their contribution to resistance to anti-angiogenic treatments and to metastasis and their potential clinical use as biomarkers of angiogenesis and anti-angiogenic therapies. Here, we will review phenotypical and functional aspects of bone marrow-derived myelonomocytic cells and discuss some of the current outstanding questions.

Python as a programming language for the introductory programming courses

Tämä kandidaatintyö tutkii tietotekniikan perusopetuksessa keskeisen aiheen,ohjelmoinnin, alkeisopetusta ja siihen liittyviä ongelmia. Työssä perehdytään ohjelmoinnin perusopetusmenetelmiin ja opetuksen lähestymistapoihin, sekä ratkaisuihin, joilla opetusta voidaan tehostaa. Näitä ratkaisuja työssä ovat mm. ohjelmointikielen valinta, käytettävän kehitysympäristön löytäminen sekä kurssia tukevien opetusapuvälineiden etsiminen. Lisäksi kurssin läpivientiin liittyvien toimintojen, kuten harjoitusten ja mahdollisten viikkotehtävien valinta kuuluu osaksitätä työtä. Työ itsessään lähestyy aihetta tutkimalla Pythonin soveltuvuutta ohjelmoinnin alkeisopetukseen mm. vertailemalla sitä muihin olemassa oleviin yleisiin opetuskieliin, kuten C, C++ tai Java. Se tarkastelee kielen hyviä ja huonoja puolia, sekä tutkii, voidaanko Pythonia hyödyntää luontevasti pääasiallisena opetuskielenä. Lisäksi työ perehtyy siihen, mitä kaikkea kurssilla tulisi opettaa, sekä siihen, kuinka kurssin läpivienti olisi tehokkainta toteuttaa ja minkälaiset tekniset puitteet kurssin toteuttamista varten olisi järkevää valita.

A Virtual Laboratory Structure for Developing Programming Labs

Virtual Laboratories are an indispensablespace for developing practical activities in a Virtual Environment. In the field of Computer and Software Engineering different types of practical activities have tobe performed in order to obtain basic competences which are impossible to achieve by other means. This paper specifies an ontology for a general virtual laboratory.The proposed ontology provides a mechanism to select the best resources needed in a Virtual Laboratory once a specific practical activity has been defined and the maincompetences that students have to achieve in the learning process have been fixed. Furthermore, the proposed ontology can be used to develop an automatic and wizardtool that creates a Moodle Classroom using the practical activity specification and the related competences.

eLab, a Personal Learning Environment for the Green Open Innovation Platform

Open Innovation is a relatively new concept which involves a change of paradigm in the R+D+i processes of companies whose aim is to create new technologies or new processes. If to this change, we add the need for innovation in the new green and sustainability economy, and we set out to create a collaborative platform with a learning space where this can happen, we will be facing an overwhelming challenge which requires the application of intelligent programming technologies and languages at the service of education.The aim of the Green IDI (Green Open Innovation) ¿ Economic development and job creation vector in SMEs, based on the environment and sustainability project is to create a platform where companies and individual researchers can perform open innovation processes in the field of sustainability and the environment.The Green IDI (Green Open Innovation) project is funded under the program INNPACTO by the Ministry of Science and Innovation of Spain and is being developed through a consortium formed by the following institutions: GRUPO ICA; COMPARTIA; GRUPO INTERCOM; CETAQUA and the Instituto de Investigación en Inteligencia Artificial (IIIA) from Consejo Superior de Investigaciones Científicas (CSIC). Also the consortium include FUNDACIÓ PRIVADA BARCELONA DIGITAL; PIMEC and UNIVERSITAT OBERTA DE CATALUNYA (UOC).Sustainability and positive action for the environment are considered the principle vector of economic development for companies. As Nicolás Scoli says (2007) ¿in short, preventing unnecessary consumption and the efficient consumption of resources means producing greater wealth with less. Both effects lead to reduced pollution linked to production and consumption¿.The Spanish Sustainable Development Strategy (EEDS) plan defends consumption and sustainable production linked to social and economic development by adhering to the commitment not to endanger ecosystems and abolishing the idea that economic growth is directly proportional to the deterioration of the environment.Uniting the Open Innovation and New Green Economy concepts leads to the "Green Open Innovation¿ Platform creation project.This article analyses the concept of open innovation and defines the importance of the new green and sustainable economy. Lastly, it proposes the creation of eLab. The eLab is defined as an Open Green Innovation Platform personal and collaborative education space which is fed by the interactions of users and which enables innovation processes based on new green economy concepts to be carried out.The creation of a personal learning environment such as eLab on the Green Open Innovation Platform meets the need to offer a collaborative space where platform users can improve their skills regarding the environment and sustainability based on collaborative synergies through Information and Communication Technologies.

Learning Robot Motions from Visual Sensing

Learning from demonstration becomes increasingly popular as an efficient way of robot programming. Not only a scientific interest acts as an inspiration in this case but also the possibility of producing the machines that would find application in different areas of life: robots helping with daily routine at home, high performance automata in industries or friendly toys for children. One way to teach a robot to fulfill complex tasks is to start with simple training exercises, combining them to form more difficult behavior. The objective of the Master’s thesis work was to study robot programming with visual input. Dynamic movement primitives (DMPs) were chosen as a tool for motion learning and generation. Assuming a movement to be a spring system influenced by an external force, making this system move, DMPs represent the motion as a set of non-linear differential equations. During the experiments the properties of DMP, such as temporal and spacial invariance, were examined. The effect of the DMP parameters, including spring coefficient, damping factor, temporal scaling, on the trajectory generated were studied.

Teaching mathematics and programming : new approaches with empirical evaluation

Programming and mathematics are core areas of computer science (CS) and consequently also important parts of CS education. Introductory instruction in these two topics is, however, not without problems. Studies show that CS students find programming difficult to learn and that teaching mathematical topics to CS novices is challenging. One reason for the latter is the disconnection between mathematics and programming found in many CS curricula, which results in students not seeing the relevance of the subject for their studies. In addition, reports indicate that students' mathematical capability and maturity levels are dropping. The challenges faced when teaching mathematics and programming at CS departments can also be traced back to gaps in students' prior education. In Finland the high school curriculum does not include CS as a subject; instead, focus is on learning to use the computer and its applications as tools. Similarly, many of the mathematics courses emphasize application of formulas, while logic, formalisms and proofs, which are important in CS, are avoided. Consequently, high school graduates are not well prepared for studies in CS. Motivated by these challenges, the goal of the present work is to describe new approaches to teaching mathematics and programming aimed at addressing these issues: Structured derivations is a logic-based approach to teaching mathematics, where formalisms and justifications are made explicit. The aim is to help students become better at communicating their reasoning using mathematical language and logical notation at the same time as they become more confident with formalisms. The Python programming language was originally designed with education in mind, and has a simple syntax compared to many other popular languages. The aim of using it in instruction is to address algorithms and their implementation in a way that allows focus to be put on learning algorithmic thinking and programming instead of on learning a complex syntax. Invariant based programming is a diagrammatic approach to developing programs that are correct by construction. The approach is based on elementary propositional and predicate logic, and makes explicit the underlying mathematical foundations of programming. The aim is also to show how mathematics in general, and logic in particular, can be used to create better programs.

Enabling constructive alignment in programming instruction

In this thesis, simple methods have been sought to lower the teacher’s threshold to start to apply constructive alignment in instruction. From the phases of the instructional process, aspects that can be improved with little effort by the teacher have been identified. Teachers have been interviewed in order to find out what students actually learn in computer science courses. A quantitative analysis of the structured interviews showed that in addition to subject specific skills and knowledge, students learn many other skills that should be mentioned in the learning outcomes of the course. The students’ background, such as their prior knowledge, learning style and culture, affects how they learn in a course. A survey was conducted to map the learning styles of computer science students and to see if their cultural background affected their learning style. A statistical analysis of the data indicated that computer science students are different learners than engineering students in general and that there is a connection between the student’s culture and learning style. In this thesis, a simple self-assessment scale that is based on Bloom’s revised taxonomy has been developed. A statistical analysis of the test results indicates that in general the scale is quite reliable, but single students still slightly overestimate or under-estimate their knowledge levels. For students, being able to follow their own progress is motivating, and for a teacher, self-assessment results give information about how the class is proceeding and what the level of the students’ knowledge is.

Developing an introductory object-oriented programming course

The state of the object-oriented programming course in Lappeenranta University of Technology had reached the point, where it required changes to provide better learning opportunities and thus the learning outcomes. Based on the student feedback the course was partially dated and ineffective. The components of the course were analysed and the ineffective elements were removed and new methods were introduced to improve the course. The major changes included the change from traditional teaching methods to reverse classroom method and the use of Java as the programming language. The changes were measured by the student feedback, lecturer’s observations and comparison to previous years. The feedback suggested that the changes were successful; the course received higher overall grade than before.

Perceived effectiveness of alternative programming : a case study /

The topic of this research was alternative programming in secondary public education. The purpose of this research was to explore the perceived effectiveness of two public secondary programs that are aJternative to mainstream or "regular" education. Two case study sites were used to research diverse ends of the aJtemative programming continuum. The first case study demonstrated a gifted program and the second demonstrated a behavioral program. Student needs were examined in terms of academic needs, emotional needs, career needs, and social needs. Research conducted in these sites examined how the students, teachers, onsite staff, and program administrators perceived that individual needs were met and unmet in these two programs. The study was qualitative and exploratory, using deductive and inductive research techniques. Similar themes of best practice that were identified in the case study sites aided in the development of a teaching and learning model. Four themes were identified as important within the case study sites. These themes included the commitment and motivation of teachers and the support of administration in the gifted program, and the importance of location and the flow of information and communication in the behavior program. Six themes emerged that were similar across the case study sites. These themes included the individual nature of programming, recognition of student achievement, the alternative program as a place of safety and community, importance of interpersonal capacity, priority of basic needs, and, finally, matching student capacity with program expectations. The model incorporates these themes and is designed as a resource for teachers, program administrators, parents, and policy makers of alternative educational programs.

Learning to be literate : parental empowerment in early literacy interventions

Considerable research has focused on the success of early intervention programs for children. However, minimal research has focused on the effect these programs have on the parents of targeted children. Many current early intervention programs champion family-focused and inclusive programming, but few have evaluated parent participation in early interventions and fewer still have evaluated the impact of these programs on beliefs and attitudes and parenting practices. Since parents will continue to play a key role in their child's developmental course long after early intervention programs end, it is vital to examine whether these programs empower parents to take action to make changes in the lives of their children. The goal of this study was to understand parental influences on the early development of literacy, and in particular how parental attitudes, beliefs and self efficacy impact parent and child engagement in early literacy intervention activities. A mixed method procedure using quantitative and qualitative strategies was employed. A quasi-experimental research design was used. The research sample, sixty parents who were part of naturally occurring community interventions in at- risk neighbourhoods in a south-western Ontario city participated in the quantitative phase. Largely individuals whose home language was other than English, these participants were divided amongst three early literacy intervention groups, a Prescriptive Interventionist type group, a Participatory Empowering type group and a drop-in parent- child neighbourhood Control group. Measures completed pre and post a six session literacy intervention, on all three literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs. groups, were analyzed for evidence of change in parental attitudes and beliefs about early literacy and evidence of change in parental empowerment. Parents in all three groups, on average, held beliefs about early literacy that were positive and that were compatible with current approaches to language development and emergent literacy. No significant change in early literacy beliefs and attitudes for pre to post intervention was found. Similarly, there was no significant difference between groups on empowerment scores, but there was a significant change post intervention in one group's empowerment score. There was a drop in the empowerment score for the Prescriptive Interventionist type group, suggesting a drop in empowerment level. The qualitative aspect of this study involved six in-depth interviews completed with a sub-set of the sixty research participants. Four similar themes emerged across the groups: learning takes place across time and place; participation is key; success is achieved by taking small steps; and learning occurs in multiple ways. The research findings have important implications for practitioners and policy makers who target at risk populations with early intervention programming and wish to sustain parental empowerment. Study results show the value parents place on early learning and point to the importance of including parents in the development and delivery of early intervention programs.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.

Observational learning of motor skills : Looking for optimal models

L’observation d’un modèle pratiquant une habileté motrice promeut l’apprentissage de l’habileté en question. Toutefois, peu de chercheurs se sont attardés à étudier les caractéristiques d’un bon modèle et à mettre en évidence les conditions d’observation pouvant optimiser l’apprentissage. Dans les trois études composant cette thèse, nous avons examiné les effets du niveau d’habileté du modèle, de la latéralité du modèle, du point de vue auquel l’observateur est placé, et du mode de présentation de l’information sur l’apprentissage d’une tâche de timing séquentielle composée de quatre segments. Dans la première expérience de la première étude, les participants observaient soit un novice, soit un expert, soit un novice et un expert. Les résultats des tests de rétention et de transfert ont révélé que l’observation d’un novice était moins bénéfique pour l’apprentissage que le fait d’observer un expert ou une combinaison des deux (condition mixte). Par ailleurs, il semblerait que l’observation combinée de modèles novice et expert induise un mouvement plus stable et une meilleure généralisation du timing relatif imposé comparativement aux deux autres conditions. Dans la seconde expérience, nous voulions déterminer si un certain type de performance chez un novice (très variable, avec ou sans amélioration de la performance) dans l’observation d’une condition mixte amenait un meilleur apprentissage de la tâche. Aucune différence significative n’a été observée entre les différents types de modèle novices employés dans l’observation de la condition mixte. Ces résultats suggèrent qu’une observation mixte fournit une représentation précise de ce qu’il faut faire (modèle expert) et que l’apprentissage est d’autant plus amélioré lorsque l’apprenant peut contraster cela avec la performance de modèles ayant moins de succès. Dans notre seconde étude, des participants droitiers devaient observer un modèle à la première ou à la troisième personne. L’observation d’un modèle utilisant la même main préférentielle que soi induit un meilleur apprentissage de la tâche que l’observation d’un modèle dont la dominance latérale est opposée à la sienne, et ce, quel que soit l’angle d’observation. Ce résultat suggère que le réseau d’observation de l’action (AON) est plus sensible à la latéralité du modèle qu’à l’angle de vue de l’observateur. Ainsi, le réseau d’observation de l’action semble lié à des régions sensorimotrices du cerveau qui simulent la programmation motrice comme si le mouvement observé était réalisé par sa propre main dominante. Pour finir, dans la troisième étude, nous nous sommes intéressés à déterminer si le mode de présentation (en direct ou en vidéo) influait sur l’apprentissage par observation et si cet effet est modulé par le point de vue de l’observateur (première ou troisième personne). Pour cela, les participants observaient soit un modèle en direct soit une présentation vidéo du modèle et ceci avec une vue soit à la première soit à la troisième personne. Nos résultats ont révélé que l’observation ne diffère pas significativement selon le type de présentation utilisée ou le point de vue auquel l’observateur est placé. Ces résultats sont contraires aux prédictions découlant des études d’imagerie cérébrale ayant montré une activation plus importante du cortex sensorimoteur lors d’une observation en direct comparée à une observation vidéo et de la première personne comparée à la troisième personne. Dans l’ensemble, nos résultats indiquent que le niveau d’habileté du modèle et sa latéralité sont des déterminants importants de l’apprentissage par observation alors que le point de vue de l’observateur et le moyen de présentation n’ont pas d’effets significatifs sur l’apprentissage d’une tâche motrice. De plus, nos résultats suggèrent que la plus grande activation du réseau d’observation de l’action révélée par les études en imagerie mentale durant l’observation d’une action n’induit pas nécessairement un meilleur apprentissage de la tâche.