Effects of inbreeding on aversive learning in Drosophila.

Inbreeding adversely affects life history traits as well as various other fitness-related traits, but its effect on cognitive traits remains largely unexplored, despite their importance to fitness of many animals under natural conditions. We studied the effects of inbreeding on aversive learning (avoidance of an odour previously associated with mechanical shock) in multiple inbred lines of Drosophila melanogaster derived from a natural population through up to 12 generations of sib mating. Whereas the strongly inbred lines after 12 generations of inbreeding (0.75<F<0.93) consistently showed reduced egg-to-adult viability (on average by 28%), the reduction in learning performance varied among assays (average=18% reduction), being most pronounced for intermediate conditioning intensity. Furthermore, moderately inbred lines (F=0.38) showed no detectable decline in learning performance, but still had reduced egg-to-adult viability, which indicates that overall inbreeding effects on learning are mild. Learning performance varied among strongly inbred lines, indicating the presence of segregating variance for learning in the base population. However, the learning performance of some inbred lines matched that of outbred flies, supporting the dominance rather than the overdominance model of inbreeding depression for this trait. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts a trade-off observed in previous selection experiments and suggests that much of the genetic variation for learning is owing to pleiotropic effects of genes affecting functions related to survival. These results suggest that genetic variation that affects learning specifically (rather than pleiotropically through general physiological condition) is either low or mostly due to alleles with additive (semi-dominant) effects.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Natural variation in learning ability in "Drosophila melanogaster"

Learning is the ability of an organism to adapt to the changes of its environment in response to its past experience. It is a widespread ability in the animal kingdom, but its evolutionary aspects are poorly known. Learning ability is supposedly advantageous under some conditions, when environmental conditions are not too stable - because in this case there is no need to learn to predict any event in the environment - and not changing too fast - otherwise environmental cues cannot be used because they are not reliable. Nevertheless, learning ability is also known to be costly in terms of energy needed for neuronal synthesis, memory formation, initial mistakes. During my PhD, I focused on the study of genetic variability of learning ability in natural populations. Genetic variability is the basis on which natural selection and genetic drift can act. How does learning ability vary in nature? What are the roles of additive genetic variation or maternal effects in this variation? Is it involved in evolutionary trade-offs with other fitness-related traits?¦I investigated a natural population of fruit fly, Drosophila melanogaster, as a model organism. Its learning ability is easy to measure with associative memory tests. I used two research tools: multiple inbred and isofemale lines derived from a natural population as a representative sample. My work was divided into three parts.¦First, I investigated the effects of inbreeding on aversive learning (avoidance of an odor previously associated with mechanical shock). While the inbred lines consistently showed reduced egg-to-adult viability by 28 %, the effects of inbreeding on learning performance was 18 % and varied among assays, with a trend to be most pronounced for intermediate conditioning intensity. Variation among inbred lines indicates that ample genetic variance for learning was segregating in the base population, and suggests that the inbreeding depression observed in learning performance was mostly due to dominance rather than overdominance. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts previous studies which observed a trade-off between learning ability and lifespan or larval competitive ability. It suggests that much of the genetic variation for learning is due to pleiotropic effects of genes affecting other functions related to survival. Together with the overall mild effects of inbreeding on learning performance, this suggests that genetic variation specifically affecting learning is either very low, or is due to alleles with mostly additive (semi-dominant) effects. It also suggests that alleles reducing learning performance are on average partially recessive, because their effect does not appear in the outbred base population. Moreover, overdominance seems unlikely as major cause of the inbreeding depression, because even if the overall mean of the inbred line is smaller than the outbred base population, some of the inbred lines show the same learning score as the outbred base population. If overdominance played an important part in inbreeding depression, then all the homozygous lines should show lower learning ability than¦outbred base population.¦In the second part of my project, I sampled the same natural population again and derived isofemale lines (F=0.25) which are less adapted to laboratory conditions and therefore are more representative of the variance of the natural population. They also showed some genetic variability for learning, and for three other fitness-related traits possibly related with learning: resistance to bacterial infection, egg-to-adult viability and developmental time. Nevertheless, the genetic variance of learning ability did not appear to be smaller than the variance of the other traits. The positive correlation previously observed between learning ability and egg- to-adult viability did not appear in isofemale lines (nor a negative correlation). It suggests that there was still genetic variability within isofemale lines and that they did not fix the highly deleterious pleiotropic alleles possibly responsible for the previous correlation.¦In order to investigate the relative amount of nuclear (additive and non-additive effects) and extra-nuclear (maternal and paternal effect) components of variance in learning ability and other fitness-related traits among the inbred lines tested in part one, I performed a diallel cross between them. The nuclear additive genetic variance was higher than other components for learning ability and survival to learning ability, but in contrast, maternal effects were more variable than other effects for developmental traits. This suggests that maternal effects, which reflects effects from mitochondrial DNA, epigenetic effects, or the amount of nutrients that are invested by the mother in the egg, are more important in the early stage of life, and less at the adult stage. There was no additive genetic correlation between learning ability and other traits, indicating that the correlation between learning ability and egg-to-adult viability observed in the first pat of my project was mostly due to recessive genes.¦Finally, my results showed that learning ability is genetically variable. The diallel experiment showed additive genetic variance was the most important component of the total variance. Moreover, every inbred or isofemale line showed some learning ability. This suggested that alleles impairing learning ability are eliminated by selection, and therefore that learning ability is under strong selection in natural populations of Drosophila. My results cannot alone explain the maintenance of the observed genetic variation. Even if I cannot eliminate the hypothesis of pleiotropy between learning ability and the other fitness-related traits I measured, there is no evidence for any trade-off between these traits and learning ability. This contradicts what has been observed between learning ability and other traits like lifespan and larval competitivity.¦L'apprentissage représente la capacité d'un organisme à s'adapter aux changement de son environnement au cours de sa vie, en réponse à son expérience passée. C'est une capacité très répandue dans le règne animal, y compris pour les animaux les plus petits et les plus simples, mais les aspects évolutifs de l'apprentissage sont encore mal connus. L'apprentissage est supposé avantageux dans certaines conditions, quand l'environnement n'est ni trop stable - dans ce cas, il n'y a rien à apprendre - ni trop variable - dans ce cas, les indices sur lesquels se reposer changent trop vite pour apprendre. D'un autre côté, l'apprentissage a aussi des coûts, en terme de synthèse neuronale, pour la formation de la mémoire, ou de coûts d'erreur initiale d'apprentissage. Pendant ma thèse, j'ai étudié la variabilité génétique naturelle des capacités d'apprentissage. Comment varient les capacités d'apprentissage dans la nature ? Quelle est la part de variation additive, l'impact des effets maternel ? Est-ce que l'apprentissage est impliqué dans des interactions, de type compromis évolutifs, avec d'autres traits liés à la fitness ?¦Afin de répondre à ces questions, je me suis intéressée à la mouche du vinaigre, ou drosophile, un organisme modèle. Ses capacités d'apprentissage sont facile à étudier avec un test de mémoire reposant sur l'association entre un choc mécanique et une odeur. Pour étudier ses capacités naturelles, j'ai dérivé de types de lignées d'une population naturelle: des lignées consanguines et des lignées isofemelles.¦Dans une première partie, je me suis intéressée aux effets de la consanguinité sur les capacités d'apprentissage, qui sont peu connues. Alors que les lignées consanguines ont montré une réduction de 28% de leur viabilité (proportion d'adultes émergeants d'un nombre d'oeufs donnés), leurs capacités d'apprentissage n'ont été réduites que de 18%, la plus forte diminution étant obtenue pour un conditionnement modéré. En outre, j'ai également observé que les capacités d'apprentissage était positivement corrélée à la viabilité entre les lignées. Cette corrélation est surprenante car elle est en contradiction avec les résultats obtenus par d'autres études, qui montrent l'existence de compromis évolutifs entre les capacités d'apprentissage et d'autres traits comme le vieillissement ou la compétitivité larvaire. Elle suggère que la variation génétique des capacités d'apprentissage est due aux effets pleiotropes de gènes récessifs affectant d'autres fonctions liées à la survie. Ces résultats indiquent que la variation pour les capacités d'apprentissage est réduite comparée à celle d'autres traits ou est due à des allèles principalement récessifs. L'hypothèse de superdominance semble peu vraisemblable, car certaines des lignées consanguines ont obtenu des scores d'apprentissage égaux à ceux de la population non consanguine, alors qu'en cas de superdominance, elles auraient toutes dû obtenir des scores inférieurs.¦Dans la deuxième partie de mon projet, j'ai mesuré les capacités d'apprentissage de lignées isofemelles issues de la même population initiale que les lignées consanguines. Ces lignées sont issues chacune d'un seul couple, ce qui leur donne un taux d'hétérozygosité supérieur et évite l'élimination de lignées par fixation d'allèles délétères rares. Elles sont ainsi plus représentatives de la variabilité naturelle. Leur variabilité génétique est significative pour les capacités d'apprentissage, et trois traits liés à la fois à la fitness et à l'apprentissage: la viabilité, la résistance à l'infection bactérienne et la vitesse de développement. Cependant, la variabilité des capacités d'apprentissage n'apparaît cette fois pas inférieure à celle des autres traits et aucune corrélation n'est constatée entre les capacité d'apprentissage et les autres traits. Ceci suggère que la corrélation observée auparavant était surtout due à la fixation d'allèles récessifs délétères également responsables de la dépression de consanguinité.¦Durant la troisième partie de mon projet, je me suis penchée sur la décomposition de la variance observée entre les lignées consanguines observée en partie 1. Quatre composants ont été examinés: la variance due à des effets nucléaires (additifs et non additifs), et due à des effets parentaux (maternels et paternels). J'ai réalisé un croisement diallèle de toutes les lignées. La variance additive nucléaire s'est révélée supérieure aux autres composants pour les capacités d'apprentissage et la résistance à l'infection bactérienne. Par contre, les effets maternels étaient plus importants que les autres composants pour les traits développementaux (viabilité et vitesse de développement). Ceci suggère que les effets maternels, dus à G ADN mitochondrial, à l'épistasie ou à la quantité de nutriments investis dans l'oeuf par la mère, sont plus importants dans les premiers stades de développement et que leur effet s'estompe à l'âge adulte. Il n'y a en revanche pas de corrélation statistiquement significative entre les effets additifs des capacités d'apprentissage et des autres traits, ce qui indique encore une fois que la corrélation observée entre les capacités d'apprentissage et la viabilité dans la première partie du projet était due à des effets d'allèles partiellement récessifs.¦Au, final, mes résultats montrent bien l'existence d'une variabilité génétique pour les capacités d'apprentissage, et l'expérience du diallèle montre que la variance additive de cette capacité est importante, ce qui permet une réponse à la sélection naturelle. Toutes les lignées, consanguines ou isofemelles, ont obtenu des scores d'apprentissage supérieurs à zéro. Ceci suggère que les allèles supprimant les capacités d'apprentissage sont fortement contre-sélectionnés dans la nature Néanmoins, mes résultats ne peuvent pas expliquer le maintien de cette variabilité génétique par eux-même. Même si l'hypothèse de pléiotropie entre les capacités d'apprentissage et l'un des traits liés à la fitness que j'ai mesuré ne peut être éliminée, il n'y a aucune preuve d'un compromis évolutif pouvant contribuer au maintien de la variabilité.

Engaging students in an e-learning experience : how to manage information resources through wikis and web blogs

The purpose of this paper is to describe the collaboration between librarians and scholars, from a virtual university, in order to facilitate collaborative learning on how to manage information resources. The personal information behaviour of e-learning students when managing information resources for academic, professional and daily life purposes was studied from 24 semi-structured face-to-face interviews. The results of the content analysis of the interview' transcriptions, highlighted that in the workplace and daily life contexts, competent information behaviour is always linked to a proactive attitude, that is to say, that participants seek for information without some extrinsic reward or avoiding punishment. In the academic context, it was observed a low level of information literacy and it seems to be related with a prevalent uninvolved attitude.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Nursing student-patient relationship and associated factors

The purpose of this study was to analyse the nursing student-patient relationship and factors associated with this relationship from the point of view of both students and patients, and to identify factors that predict the type of relationship. The ultimate goal is to improve supervised clinical practicum with a view to supporting students in their reciprocal collaborative relationships with patients, increase their preparedness to meet patients’ health needs, and thus to enhance the quality of patient care. The study was divided into two phases. In the first phase (1999-2005), a literature review concerning the student-patient relationship was conducted (n=104 articles) and semi-structured interviews carried out with nursing students (n=30) and internal medicine patients (n=30). Data analysis was by means of qualitative content analysis and Student-Patient Relationship Scales, which were specially developed for this research. In the second phase (2005-2007), the data were collected by SPR scales among nursing students (n=290) and internal medicine patients (n=242). The data were analysed statistically by SPSS 12.0 software. The results revealed three types of student-patient relationship: a mechanistic relationship focusing on the student’s learning needs; an authoritative relationship focusing on what the student assumes is in the patient’s best interest; and a facilitative relationship focusing on the common good of both student and patient. Students viewed their relationship with patients more often as facilitative and authoritative than mechanistic, while in patients’ assessments the authoritative relationship occurred most frequently and the facilitative relationship least frequently. Furthermore, students’ and patients’ views on their relationships differed significantly. A number of background factors, contextual factors and consequences of the relationship were found to be associated with the type of relationship. In the student data, factors that predicted the type of relationship were age, current year of study and support received in the relationship with patient. The higher the student’s age, the more likely the relationship with the patient was facilitative. Fourth year studies and the support of a person other than a supervisor were significantly associated with an authoritative relationship. Among patients, several factors were found to predict the type of nursing student-patient relationships. Significant factors associated with a facilitative relationship were university-level education, several previous hospitalizations, admission to hospital for a medical problem, experience of caring for an ill family member and patient’s positive perception of atmosphere during collaboration and of student’s personal and professional growth. In patients, positive perceptions of student’s personal and professional attributes and patient’s improved health and a greater commitment to self-care, on the other hand, were significantly associated with an authoritative relationship, whereas positive perceptions of one’s own attributes as a patient were significantly associated with a mechanistic relationship. It is recommended that further research on the student-patient relationship and related factors should focus on questions of content, methodology and education.

An ontology to automated learning scenarios? an approach to its knowledge domain

Automation or semi-automation of learning scenariospecifications is one of the least exploredsubjects in the e-learning research area. There isa need for a catalogue of learning scenarios and atechnique to facilitate automated retrieval of stored specifications. This requires constructing anontology with this goal and is justified inthis paper. This ontology must mainlysupport a specification technique for learning scenarios. This ontology should also be useful in the creation and validation of new scenarios as well as in the personalization of learning scenarios or their monitoring. Thus, after justifying the need for this ontology, a first approach of a possible knowledge domain is presented. An example of a concrete learning scenario illustrates some relevant concepts supported by this ontology in order to define the scenario in such a way that it could be easy to automate.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

Inter organizational learning within and by innovation networks

The thesis deals with the phenomenon of learning between organizations in innovation networks that develop new products, services or processes. Inter organizational learning is studied especially at the level of the network. The role of the network can be seen as twofold: either the network is a context for inter organizational learning, if the learner is something else than the network (organization, group, individual), or the network itself is the learner. Innovations are regarded as a primary source of competitiveness and renewal in organizations. Networking has become increasingly common particularly because of the possibility to extend the resource base of the organization through partnerships and to concentrate on core competencies. Especially in innovation activities, networks provide the possibility to answer the complex needs of the customers faster and to share the costs and risks of the development work. Networked innovation activities are often organized in practice as distributed virtual teams, either within one organization or as cross organizational co operation. The role of technology is considered in the research mainly as an enabling tool for collaboration and learning. Learning has been recognized as one important collaborative process in networks or as a motivation for networking. It is even more important in the innovation context as an enabler of renewal, since the essence of the innovation process is creating new knowledge, processes, products and services. The thesis aims at providing enhanced understanding about the inter organizational learning phenomenon in and by innovation networks, especially concentrating on the network level. The perspectives used in the research are the theoretical viewpoints and concepts, challenges, and solutions for learning. The methods used in the study are literature reviews and empirical research carried out with semi structured interviews analyzed with qualitative content analysis. The empirical research concentrates on two different areas, firstly on the theoretical approaches to learning that are relevant to innovation networks, secondly on learning in virtual innovation teams. As a result, the research identifies insights and implications for learning in innovation networks from several viewpoints on organizational learning. Using multiple perspectives allows drawing a many sided picture of the learning phenomenon that is valuable because of the versatility and complexity of situations and challenges of learning in the context of innovation and networks. The research results also show some of the challenges of learning and possible solutions for supporting especially network level learning.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Active teaching-learning methodologies: medical students' views of problem-based learning

The prevailing undergraduate medical training process still favors disconnection and professional distancing from social needs. The Brazilian Ministries of Education and Health, through the National Curriculum Guidelines, the Incentives Program for Changes in the Medical Curriculum (PROMED), and the National Program for Reorientation of Professional Training in Health (PRO-SAÚDE), promoted the stimulus for an effective connection between medical institutions and the Unified National Health System (SUS). In accordance to the new paradigm for medical training, the Centro Universitário Serra dos Órgãos (UNIFESO) established a teaching plan in 2005 using active methodologies, specifically problem-based learning (PBL). Research was conducted through semi-structured interviews with third-year undergraduate students at the UNIFESO Medical School. The results were categorized as proposed by Bardin's thematic analysis, with the purpose of verifying the students' impressions of the new curriculum. Active methodologies proved to be well-accepted by students, who defined them as exciting and inclusive of theory and practice in medical education.

Towards adoption of eLearning technologies at Lappeenranta University of Technology: Understanding Teachers‟ Adoption of Blackboard Learning System

The electronic learning has become crucial in higher education with increased usage of learning management systems as a key source of integration on distance learning. The objective of this study is to understand how university teachers are influenced to use and adopt web-based learning management systems. Blackboard, as one of the systems used internationally by various universities is applied as a case. Semi-structured interviews were made with professors and lecturers who are using Blackboard at Lappeenranta University of Technology. The data collected were categorized under constructs adapted from Unified Theory of Acceptance and Use of Technology (UTAUT) and interpretation and discussion were based on reviewed literature. The findings suggest that adoption of learning management systems by LUT teachers is highly influenced by perceived usefulness, facilitating conditions and gained experience. The findings also suggest that easiness of using the system and social influence appear as medium influence of adoption for teachers at LUT.

Saksan opiskelijasta saksan opettajaksi. Aineenopettajaopiskelijan ammatillinen kasvu ohjatussa harjoitttelussa

Tutkimuksessani tarkastelin, miten ammatillinen kasvu ilmenee saksan opetusharjoittelijoiden näkemyksissä ja toiminnassa opettajan pedagogisiin opintoihin kuuluvan ohjatun harjoittelun aikana. Keräsin tutkimusaineiston lukuvuosina 2007–2010 kaikilta saksan opetusharjoittelijoilta, jotka suorittivat ohjatussa harjoittelussa vähintään 15 op Turun normaalikoulussa. Tutkimusaineistona käytin henkilökohtaisia harjoittelusuunnitelmia (HOPS) ja reflektiovihkoja, jotka ovat ohjatun harjoittelun normaaleja työvälineitä. Täydensin aineistoa ohjatun harjoittelun alussa toteutetulla kyselyllä ja harjoittelun päätteeksi tehdyllä puolistrukturoidulla teemahaastattelulla. Toimin tutkimuksen aikana Turun normaalikoulussa saksan opettajana ja aineryhmän harjoittelusta vastaavana opettajana. Tutkimuskysymykset tarkentuivat aineistolähtöisesti. Ensimmäinen tutkimuskysymys liittyi siihen, miten harjoittelijoiden ideaalit hyvästä vieraan kielen opetuksesta ja henkilökohtaiset tavoitteet toteutuivat ohjatun harjoittelun aikana. Alkukyselyn pohjalta ideaaleiksi nousivat vuorovaikutus ja monipuoliset työtavat, kulttuurin opettaminen, tavoitekielen käyttö luokkakielenä sekä selkeä kieliopin opetus. Tutkimuksessa kävi ilmi, että alkukyselyssä esiin tullut hyvän opettajan tai vieraan kielen opetuksen ideaali ei välttämättä näy opettajaksi opiskelevan HOPSeihin kirjatuissa henkilökohtaisissa tavoitteissa tai toteudu hänen harjoitustunneillaan. Parhaiten opetusharjoittelijat kokivat onnistuneensa vuorovaikutuksen luomisessa oppilaisiin ja opiskelijoihin sekä monipuolisten työtapojen käytössä. Eriyttäminen ja oppimaan oppimisen ohjaaminen koettiin hankalina. Suurin osa harjoittelijoista oli tyytyväisiä siihen, miten he onnistuivat tuomaan kulttuuria opetukseensa, kun taas tavoitekielen käyttö luokkakielenä ja kieliopin opetus koettiin haasteellisiksi. Toisessa tutkimuskysymyksessä tarkasteltiin, millainen ammatillinen näkemys vieraan kielen opiskelijalla on ohjatun harjoittelun jälkeen. Opetusharjoittelijat korostivat opettajan kasvatustyötä ja opettajien välistä yhteistyötä. Ajatus toimia saksan tai vieraan kielen opettajana oli vahvistunut pedagogisten opintojen aikana. Kolmas tutkimuskysymys kohdistui sen selvittämiseen, miten HOPS ja reflektiovihko toimivat harjoittelijoiden kasvun tukena. Harjoittelijoiden kirjallinen itsereflektio vaihteli syvällisistä pohdinnoista niukkiin merkintöihin. Suurimmassa osassa reflektiovihoista harjoittelijat olivat miettineet palautteissa esille tulleita asioita. HOPS ja reflektiovihko ovat tämän tutkimuksen perusteella toimivia harjoittelun ohjauksen välineitä, kunhan harjoittelijoita ohjataan niiden käytössä. Itsereflektion merkityksen puolesta puhuu se, että harjoittelijat, jotka olivat myös kirjallisesti pohtineet saksan käyttöä luokkakielenä, olivat muita tyytyväisempiä siihen, miten he kokivat onnistuneensa luokkakielen käytössä.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.