Editor i disseny d'interiors

L’OpenGL és un motor 3D que s’utilitza com a lligam entre el software i el hardware gràfic. Actualment és una de les tecnologies més utilitzades en el disseny d’aplicacions 3D. El treball està realitzat amb el programa Visual C++, que és el més recomanat per al desenvolupament d’aplicacions OpenGL. L’objectiu principal d’aquest treball és aprendre a programar amb aquest tipus de tecnologia que no hem estudiat durant el període de carrera. Un altre objectiu del treball era trobar una funció útil i pràctica per a l’aplicació i ens vam decantar per a realitzar un editor d’habitacions per un botiga o empresa de mobles. L’usuari pot de forma molt ràpida i senzilla dibuixar com és l’habitació que vol decorar de forma totalment personalitzada. El programa li generarà l’habitació en tres dimensions i amb els materials que s’han escollit (terra, parets, portes…). Després pot editar-hi mobles personalitzats o pertanyents a la llibreria del programa. El programa incorpora també una base de dades per a l’empresa que ens portarà la gestió de clients, habitacions, textures i mobles (permet ampliar la llibreria del programa). Un cop acabada l’habitació el programa ens hi permet fer una visita de forma interactiva i generar-ne la factura entre altres funcions. La conclusió principal després d’haver acabat aquest projecte, és que a part d’haver après OpenGL, hem aconseguit realitzar una aplicació molt pràctica de cares al disseny d’interiorisme.

Improving and User Interface to E-Procurement Software Used in Asset Accounting and Investigating Effects to Efficiency

This diploma thesis has been done to international organization which takes care from the accounting actions of two major companies. In this organization are used three different purchasing tools which are used when new asset master data is wanted to input to SAP R/3- system. The aim of this thesis is to find out how much changing the user interface of one of these three e-procurement programs will affect to overall efficiency in asset accounting. As an addition will be introduced project framework which can be used in future projects and which help to avoid certain steps in the development process. At the moment data needs to be inputted manually with many useless mouse clicks and data needs to be searched from many various resources which slow down the process. Other organization has better tools at the moment than the myOrders system which is under investigation Research was started by exploring the main improvement areas. After this possible defects were traced. Suggested improvements were thought by exploring literature which has been written from usability design and research. Meanwhile also directional calculations from the benefits of the project were done alongside with the analysis of the possible risks and threats. After this NSN IT approved the changes which they thought was acceptable. The next step was to program them into tool and test them before releasing to production environment. The calculations were made also from implemented improvements and compared them to planned ones From whole project was made a framework which can be utilized also to other similar projects. The complete calculation was not possible because of time schedule of the project. Important observation in the project was that efficiency is not improved not only by changing the GUI but also improving processes without any programming. Feedback from end user should be also listened more in development process. End-user is after all the one who knows the best how the program should look like.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Partnership for arts integration : exploring the experiences of teachers and artists working with integrated arts programs

This research acknowledges the difficulties experienced by teachers presenting integrated arts curricula. Instructional support is offered by arts organizations that provide arts partnerships with local schools boards. The study focuses on the experiences of 8 teachers from a Catholic school board in southern Ontario who participated in integrated arts programs offered by The Royal Conservatory of Music's Learning Through the Arts™ (LTTATM) program and a local art gallery's Art Based Integrated Learning (ABIL) program and examines their responses to the programs and their perception of personal and professional development through this association. Additionally, questions were posed to the . "aftisfs"from-tneSe]Jfograrrrs;-and"they liiscus·sed·how"participating in-collaboration with teachers in the development of in-school programs enabled them to experience personal and professional development as well. Seven themes emerged from the data. These themes included: teachers' feelings of a lack of preparedness to teach the arts; the value of the arts and arts partnerships in schools; the role of the artists in the education of teachers; professional development for both teachers and artists; the development of collegiality; perceptions of student engagement; and the benefits and obstacles of integrating the arts into the curriculum. This document highlights the benefits to both teachers and artists of arts partnerships between schools and outside arts organizations.

Responses of crayfish to a reflective environment /

This thesis examines how dominance status of crayfish alters responses to their own reflection. Crayfish are social animals that fight to develop a dominance hierarchy consisting of dominant and subordinate members. After socialization, crayfish were videotaped in an aquarium with mirrors on one half of the tank and a non-reflective plastic on the other half. Dominants paired for 14 days perform more cornering, turning, crossing and spent more time in a reflective environment versus a non-reflective environment. Subordinate crayfish exhibit more reverse walking in a mirrored environment while isolated crayfish show no preference for reflection. This change in behaviour occurs immediately for dominants paired for 30 min while subordinates require 3 days of pairing to exhibit the same behaviour as subordinate crayfish paired for 14 days. Thus, 30 min of pairing is required to enhance responses to a reflection observed in dominant crayfish while 3 days is required to decrease subordinate responses to a reflection. These findings propose that male socialized crayfish respond to their mirror image as they do a male conspecific. Their responses depend on both their dominance status and the length of socialization which suggests that crayfish are learning to behave in a characteristic manner as a result of their social experience.

Shop-floor narratives : how six workers and a researcher describe learning activities

I am a part-time graduate student who works in industry. This study is my narrative about how six workers and I describe shop-floor learning activities, that is learning activities that occur where work is done, outside a classroom. Because this study is narrative inquiry, you wilileam about me, the narrator, more than you would in a more conventional study. This is a common approach in narrative inquiry and it is important because my intentions shape the way that I tell these six workers' stories. I developed a typology of learning activities by synthesizing various theoretical frameworks. This typology categorizes shop-floor learning activities into five types: onthe- job training, participative learning, educational advertising, incidental learning, and self-directed learning. Although learning can occur in each of these activities in isolation, it is often comprised of a mixture of these activities. The literature review contains a number of cases that have been developed from situations described in the literature. These cases are here to make the similarities and differences between the types of learning activities that they represent more understandable to the reader and to ground the typology in practice as well as in theory. The findings are presented as reader's theatre, a dramatic presentation of these workers' narratives. The workers tell us that learning involves "being shown," and if this is not done properly they "learn the hard way." I found that many of their best case lean1ing activities involved on-the-job training, participative learning, incidentalleaming, and self-directed learning. Worst case examples were typically lacking in properly designed and delivered participative learning activities and to a lesser degree lacking carefully planned and delivered on-the-job training activities. Included are two reflective chapters that describe two cases: Learning "Engels" (English), and Learning to Write. In these chapters you will read about how I came to see that my own shop-floor learning-learning to write this thesis-could be enhanced through participative learning activities. I came to see my thesis supervisor as not only my instructor who directed and judged my learning activities, but also as a more experienced researcher who was there to participate in this process with me and to help me begin to enter the research community. Shop-floor learning involves learners and educators participating in multistranded learning activities, which require an organizational factor of careful planning and delivery. As with learning activities, which can be multi-stranded, so too, there can be multiple orientations to learning on the shop floor. In our stories, you will see that these six workers and I didn't exhibit just one orientation to learning in our stories. Our stories demonstrate that we could be behaviorist and cognitivist and humanist and social learners and constructivist in our orientation to learning. Our stories show that learning is complex and involves multiple strands, orientations, and factors. Our stories show that learning narratives capture the essence of learning-the learners, the educators, the learning activities, the organizational factors, and the learning orientations. Learning narratives can help learners and educators make sense of shop-floor learning.

The association of faculty's teaching philosophy and teaching behaviour, and critical thinking and self- direction in learning

The intent in this study was to investigate in what ways teachers· beliefs about education and teaching are expressed in the specific teaching behaviours they employ, and whether teaching behaviours, as perceived by their students, are correlated with students· critical thinking and self-directed learning. To this end the relationships studied were: among faCUlty members· philosophy of teaching, locus of control orientation, psychological type, and observed teaching behaviour; and among students· psychological type, perceptions of teaching behaviour, self-directed learning readiness, and critical thinking. The overall purpose of the study was to investigate whether the implicit goals of higher education, critical thinking and self-direction, were actually accounted for in the university classroom. The research was set within the context of path-goal theory, adapted from the leadership literature. Within this framework, Mezirow·s work on transformative learning, including the influences of Habermas· writings, was integrated to develop a theoretical perspective upon which to base the research methodology. Both qualitative and quantitative methodologies were incorporated. Four faCUlty and a total of 142 students participated in the study. Philosophy of teaching was described through faCUlty interviews and completion of a repertory grid. Faculty completed a descriptive locus of control scale, and a psychological type test. Observations of their teaching behaviour were conducted. Students completed a Teaching Behaviour Assessment Scale, the Self-Directed Learning Readiness Scale, a psychological type test, and the Watson-Glaser Critical Thinking Appraisal. A small sample of students were interviewed. Follow-up discussions with faculty were used to validate the interview, observation, teaching behaviour, and repertory grid data. Results indicated that some discrepancies existed between faculty's espoused philosophy of teaching and their observed teaching behaviour. Instructors' teaching behaviour, however, was a function of their personal theory of practice. Relationships were found between perceived teaching behaviour and students· self-directed learning and critical thinking, but these varied across situations, as would be predicted from path-goal theory. Psychological type of students and instructor also accounted for some of the variability in the relationships studied. Student psychological type could be shown as a partial predictor of self-directed learning readiness. The results were discussed in terms of theory development and implications for further research and practice.

A qualitative journey into cyberspace /

Educators should movefrom teacher-centered learning to student-centered learning, from isolated work to collaborative work, andfromfactual knowledgebased instructions to critical thinking and informed decision-making. The high tech classroom should be more interactive and encourage active, exploratory, inquiry-based learning, as opposed to the didactic mode in which teachersfeed students information. (Valenti,2000, p. 85) The influence of technology in schools is growing as quickly as the students it impacts. As a pioneer in an e-leaming high school, I hoped to better understand the effects and influences of this learning tool in the English classroom. Using interpretive ethnography as my main frame of reference, I examined the role of technology in a grade 9 Academic English class environment. My role was participant observer as I worked with 4 students in the Laptop Program at St. Augustine Catholic High School. Through interview, observation, joumaling, and thick description, I undertook a journey into cyberspace. I documented the experiences, the frustrations, and the highlights of being in e-leaming along with my students. In this study, I specifically considered the issues of teacher training, administrative support, technology support personnel, resource availability, the role of the teacher in a constructivist classroom, and the benefits of the laptop computer as a learning tool in classroom and school.

Writing and science connections : integrated instruction and assessment

Learning to write is a daunting task for many young children. The purpose of this study was to examine the impact of a combined approach to writing instruction and assessment on the writing performance of students in two grade 3 classes. Five forms and traits of writing were purposefully connected during writing lessons while exhibiting links to the four strands of the grade 3 Ontario science curriculum. Students then had opportunities to engage in the writing process and to self-assess their compositions using either student-developed (experimental group/teacher-researcher's class) or teachercreated (control group/teacher-participant's class) rubrics. Paired samples t-tests revealed that both the experimental and control groups exhibited statistically significant growth from pretest to posttest on all five integrated writing units. Independent samples t-tests showed that the experimental group outperformed the control group on the persuasive + sentence fluency and procedure + word choice writing tasks. Pearson product-moment correlation r tests revealed significant correlations between the experimental group and the teacher-researcher on the recount + ideas and report + organization tasks, while students in the control group showed significant correlations with the teacher-researcher on the narrative + voice and procedure + word choice tasks. Significant correlations between the control group and the teacher-participant were evident on the persuasive + sentence fluency and procedure + word choice tasks. Qualitative analyses revealed five themes that highlighted how students' self-assessments and reflections can be used to guide teachers in their instructional decision making. These findings suggest that educators should adopt an integrated writing program in their classrooms, while working with students to create and utilize purposeful writing assessment tools.

Handwriting Intervention: Impact on the Reading and Writing Abilities of High School Students

Handwriting is a functional task that is used to communicate thoughts using a written code. Research findings have indicated that handwriting is related to learning to read and learning to write. The purposes of this research project were to determine if a handwriting intervention would increase abilities in reading and writing skills, in graphomotor and visual-motor integration skills, and improve the participants’ self-perceptions and self-descriptions pertaining to handwriting enjoyment, competence, and effort. A single-subject research design was implemented with four struggling high school students who each received 10.5 to 15.5 hours of cursive handwriting intervention using the ez Write program. In summary, the findings indicated that the students showed significant improvements in aspects of reading and writing; that they improved significantly in their cursive writing abilities; and that their self-perceptions concerning their handwriting experience and competence improved. The contribution of handwriting to academic achievement and vocational success can no longer be neglected.

Teaching Tips from a Recovered Autistic

An article by Grandin sharing tips for teaching and working with autistic children. The focus is on: Structured Environment, Learning to Talk, Rhythm, Sensory Problems, Reducing Arousal, Tactile Stimulation, Fixations, Visual Thinking. The conclusion of the article reads "I cannot over emphasize the important role that good teachers and therapists play in enabling autistics to lead a fuller life. A good autism program needs dedicated people and should use a variety of treatment methods in combination with an intense structured environment".

An empirical study of the impact of two antipatterns on program comprehension

Les antipatrons sont de “mauvaises” solutions à des problèmes récurrents de conception logicielle. Leur apparition est soit due à de mauvais choix lors de la phase de conception soit à des altérations et des changements continus durant l’implantation des programmes. Dans la littérature, il est généralement admis que les antipatrons rendent la compréhension des programmes plus difficile. Cependant, peu d’études empiriques ont été menées pour vérifier l’impact des antipatrons sur la compréhension. Dans le cadre de ce travail de maîtrise, nous avons conçu et mené trois expériences, avec 24 sujets chacune, dans le but de recueillir des données sur la performance des sujets lors de tâches de compréhension et d’évaluer l’impact de l’existence de deux antipatrons, Blob et Spaghetti Code, et de leurs combinaisons sur la compréhension des programmes. Nous avons mesuré les performances des sujets en terme : (1) du TLX (NASA task load index) pour l’éffort ; (2) du temps consacré à l’exécution des tâches ; et, (3) de leurs pourcentages de réponses correctes. Les données recueillies montrent que la présence d’un antipatron ne diminue pas sensiblement la performance des sujets alors que la combinaison de deux antipatrons les entrave de façon significative. Nous concluons que les développeurs peuvent faire face à un seul antipatron, alors que la combinaison de plusieurs antipatrons devrait être évitée, éventuellement par le biais de détection et de réusinage.

Advances in scaling deep learning algorithms

Les algorithmes d'apprentissage profond forment un nouvel ensemble de méthodes puissantes pour l'apprentissage automatique. L'idée est de combiner des couches de facteurs latents en hierarchies. Cela requiert souvent un coût computationel plus elevé et augmente aussi le nombre de paramètres du modèle. Ainsi, l'utilisation de ces méthodes sur des problèmes à plus grande échelle demande de réduire leur coût et aussi d'améliorer leur régularisation et leur optimization. Cette thèse adresse cette question sur ces trois perspectives. Nous étudions tout d'abord le problème de réduire le coût de certains algorithmes profonds. Nous proposons deux méthodes pour entrainer des machines de Boltzmann restreintes et des auto-encodeurs débruitants sur des distributions sparses à haute dimension. Ceci est important pour l'application de ces algorithmes pour le traitement de langues naturelles. Ces deux méthodes (Dauphin et al., 2011; Dauphin and Bengio, 2013) utilisent l'échantillonage par importance pour échantilloner l'objectif de ces modèles. Nous observons que cela réduit significativement le temps d'entrainement. L'accéleration atteint 2 ordres de magnitude sur plusieurs bancs d'essai. Deuxièmement, nous introduisont un puissant régularisateur pour les méthodes profondes. Les résultats expérimentaux démontrent qu'un bon régularisateur est crucial pour obtenir de bonnes performances avec des gros réseaux (Hinton et al., 2012). Dans Rifai et al. (2011), nous proposons un nouveau régularisateur qui combine l'apprentissage non-supervisé et la propagation de tangente (Simard et al., 1992). Cette méthode exploite des principes géometriques et permit au moment de la publication d'atteindre des résultats à l'état de l'art. Finalement, nous considérons le problème d'optimiser des surfaces non-convexes à haute dimensionalité comme celle des réseaux de neurones. Tradionellement, l'abondance de minimum locaux était considéré comme la principale difficulté dans ces problèmes. Dans Dauphin et al. (2014a) nous argumentons à partir de résultats en statistique physique, de la théorie des matrices aléatoires, de la théorie des réseaux de neurones et à partir de résultats expérimentaux qu'une difficulté plus profonde provient de la prolifération de points-selle. Dans ce papier nous proposons aussi une nouvelle méthode pour l'optimisation non-convexe.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character both of natural kinds and of artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies.