Dissertations / Theses on the topic 'CORE learning model'

This is a study of teacher professional development at the school level using teacher research as a strategy for both teacher professional learning and school change. A qualitative study was conducted to determine the conditions that would develop and sustain teachers researching their own practice in a culture of inquiry. Participant observation in one school over a two year period was used to investigate the issue of how to embed teacher research as a central feature of teachers' work. As a result of working with teachers as they researched their practice I have developed a model to explain and understand the complexities of schools and their cultures. Teachers researching their practice provided the driving force in the interplay of the elements of the model and had the potential to change school culture. Relationships, structures and processes are central to this model. Social and professional relationships between the teachers and the university partner developed and were supported by structures and processes. As the research continued these relationships changed and evolved. These relationships help develop a culture of inquiry in schools. The school/university partnership in this study evolved from an initial symbiotic-cooperative partnership (in which I shared my expertise and supported the work of teachers) into a later organic-collaborative partnership (one based on mutual and shared goals and benefits). The existing team of four teachers and the allocation of time for them to meet provided the essential structures for the teachers to research their practice. The collective leadership style instigated by the school Principal provided important human and financial support for the development of inquiry cultures. Collaboration and collegiality as forms of association enabled teachers to conduct research which challenged their individual and collective beliefs and assumptions about students' learning and their classroom practice. The content and form of teacher culture mediated the effects of teachers researching their practice. There are critical and transformational effects when teachers research their practice as part of their core work. Introducing these teachers to research was not without its difficulties. There were events and factors in the school relating to relationships, structures and processes which hindered the development of teacher research in a culture of inquiry. Because of the time frame of this study there is no evidence that school culture change is permanent. This could be the subject of future research.

This is a study of teacher professional development at the school level using teacher research as a strategy for both teacher professional learning and school change. A qualitative study was conducted to determine the conditions that would develop and sustain teachers researching their own practice in a culture of inquiry. Participant observation in one school over a two year period was used to investigate the issue of how to embed teacher research as a central feature of teachers� work. As a result of working with teachers as they researched their practice I have developed a model to explain and understand the complexities of schools and their cultures. Teachers researching their practice provided the driving force in the interplay of the elements of the model and had the potential to change school culture. Relationships, structures and processes are central to this model. Social and professional relationships between the teachers and the university partner developed and were supported by structures and processes. As the research continued these relationships changed and evolved. These relationships help develop a culture of inquiry in schools. The school/university partnership in this study evolved from an initial symbiotic�cooperative partnership (in which I shared my expertise and supported the work of teachers) into a later organic�collaborative partnership (one based on mutual and shared goals and benefits). The existing team of four teachers and the allocation of time for them to meet provided the essential structures for the teachers to research their practice. The collective leadership style instigated by the school Principal provided important human and financial support for the development of inquiry cultures. Collaboration and collegiality as forms of association enabled teachers to conduct research which challenged their individual and collective beliefs and assumptions about students� learning and their classroom practice. The content and form of teacher culture mediated the effects of teachers researching their practice. There are critical and transformational effects when teachers research their practice as part of their core work. Introducing these teachers to research was not without its difficulties. There were events and factors in the school relating to relationships, structures and processes which hindered the development of teacher research in a culture of inquiry. Because of the time frame of this study there is no evidence that school culture change is permanent. This could be the subject of future research.

In recent decades, various software systems have gradually become the basis of our society. Programmers search existing code snippets from time to time in their daily life. It would be beneficial and meaningful to have better solutions for the task of semantic code search, which is to find the most semantically relevant code snippets for a given query. Our approach is to introduce tree representations by multi-modal learning. The core idea is to enrich semantic information for code snippets by preparing data of different modalities, and meanwhile ignore syntactic information. We design one novel tree structure named Simplified Semantic Tree and then extract RootPath representations from that. We utilize RootPath representation to complement the conventional sequential representation, namely the token sequence of the code snippet. Our multi-modal model receives code-query pair as input and computes similarity score as output, following the pseudo-siamese architecture. For each pair, besides the ready-made code sequence and query sequence, we extra one extra tree sequence from Simplified Semantic Tree. There are three encoders in our model, and they respectively encode these three sequences as vectors of the same length. Then we combine the code vector with the tree vector for one joint vector, which is still of the same length, as the multi-modal representation for the code snippet. We introduce triplet loss to ensure vectors of code and query in the same pair be close at the shared vector space. We conduct experiments in one large-scale multi-language corpus, with comparisons of strong baseline models by specified performance metrics. Among baseline models, the simplest Neural Bag-of-Words model is with the most satisfying performance. It indicates that syntactic information is likely to distract complex models from critical semantic information. Results show that our multi-modal representation approach performs better because it surpasses baseline models by far in most cases. The key to our multi-modal model is that it is totally about semantic information, and it learns from data of multiple modalities.<br>Under de senaste decennierna har olika programvarusystem gradvis blivit basen i vårt samhälle. Programmerare söker i befintliga kodavsnitt från tid till annan i deras dagliga liv. Det skulle vara fördelaktigt och meningsfullt att ha bättre lösningar för uppgiften att semantisk kodsökning, vilket är att hitta de mest semantiskt relevanta kodavsnitten för en given fråga. Vår metod är att introducera trädrepresentationer genom multimodal inlärning. Grundidén är att berika semantisk information för kodavsnitt genom att förbereda data med olika modaliteter och samtidigt ignorera syntaktisk information. Vi designar en ny trädstruktur med namnet Simplified Semantic Tree och extraherar sedan RootPath-representationer från det. Vi använder RootPath-representation för att komplettera den konventionella sekvensrepresentationen, nämligen kodsekvensens symbolsekvens. Vår multimodala modell får kodfrågeställningar som inmatning och beräknar likhetspoäng som utgång efter den pseudo-siamesiska arkitekturen. För varje par, förutom den färdiga kodsekvensen och frågesekvensen, extrager vi en extra trädsekvens från Simplified Semantic Tree. Det finns tre kodare i vår modell, och de kodar respektive tre sekvenser som vektorer av samma längd. Sedan kombinerar vi kodvektorn med trädvektorn för en gemensam vektor, som fortfarande är av samma längd som den multimodala representationen för kodavsnittet. Vi introducerar tripletförlust för att säkerställa att vektorer av kod och fråga i samma par är nära det delade vektorn. Vi genomför experiment i ett storskaligt flerspråkigt korpus, med jämförelser av starka baslinjemodeller med specificerade prestandametriker. Bland baslinjemodellerna är den enklaste Neural Bag-of-Words-modellen med den mest tillfredsställande prestanda. Det indikerar att syntaktisk information sannolikt kommer att distrahera komplexa modeller från kritisk semantisk information. Resultaten visar att vår multimodala representationsmetod fungerar bättre eftersom den överträffar basmodellerna i de flesta fall. Nyckeln till vår multimodala modell är att den helt handlar om semantisk information, och den lär sig av data om flera modaliteter.

A tecnologia de agentes surge como uma solução que fornece flexibilidade e robustez para lidar com domínios dinâmicos e complexos. Tal flexibilidade pode ser alcançada através da adoção de abordagens já existentes baseadas em agentes, como a arquitetura BDI, que provê agentes com características mentais de crenças, desejos e intenções. Essa arquitetura é altamente personalizável, deixando lacunas a serem preenchidas de acordo com aplicações específicas. Uma dessas lacunas é o algoritmo de seleção de planos, responsável por selecionar um plano para ser executado pelo agente buscando atingir um objetivo, e tendo grande influência no desempenho geral do agente. Grande parte das abordagens existentes requerem considerável esforço para personalização e ajuste a fim de serem utilizadas em aplicações específicas. Nessa dissertação, propomos uma abordagem para seleção de planos apta a aprender quais planos possivelmente terão os melhores resultados, baseando-se no contexto atual e nas preferências do agente. Nossa abordagem é composta por um meta-modelo, que deve ser instanciado a fim de especificar metadados de planos, e uma técnica que usa tais metadados para aprender e predizer resultados da execução destes planos. Avaliamos nossa abordagem experimentalmente e os resultados indicam que ela é efetiva. Adicionalmente, fornecemos uma ferramenta para apoiar o processo de desenvolvimento de agentes de software baseados em nosso trabalho. Essa ferramenta permite que desenvolvedores modelem e gerem código-fonte para agentes BDI com capacidades de aprendizado. Um estudo com usuários foi realizado para avaliar os benefícios de um método de desenvolvimento baseado em agentes BDI auxiliado por ferramenta. Evidências sugerem que nossa ferramenta pode auxiliar desenvolvedores que não sejam especialistas ou que não estejam familiarizados com a tecnologia de agentes.<br>Agent technology arises as a solution that provides flexibility and robustness to deal with dynamic and complex domains. Such flexibility can be achieved by the adoption of existing agent-based approaches, such as the BDI architecture, which provides agents with the mental attitudes of beliefs, desires and intentions. This architecture is highly customisable, leaving gaps to be fulfilled in particular applications. One of these gaps is the plan selection algorithm that is responsible for selecting a plan to be executed by an agent to achieve a goal, having an important influence on the overall agent performance. Most existing approaches require considerable effort for customisation and adjustment to be used in particular applications. In this dissertation, we propose a plan selection approach that is able to learn plans that provide possibly best outcomes, based on current context and agent’s preferences. Our approach is composed of a meta-model, which must be instantiated to specify plan metadata, and a technique that uses such metadata to learn and predict plan outcomes. We evaluated our approach experimentally, and results indicate it is effective. Additionally, we provide a tool to support the development process of software agents based on our work. This tool allows developers to model and generate source code for BDI agents with learning capabilities. A user study was performed to assess the improvements of a tool-supported BDI-agent-based development method, and evidences suggest that our tool can help developers that are not experts or are unfamiliar with the agent technology.

A lo largo del presente trabajo de investigación se exploró el uso de herramientas de inteligencia artificial (Machine Learning) en la predicción del precio de cobre. Este proyecto de investigación se desarrolla dentro de las actividades del Grupo de investigación en finanzas aplicadas (GIFA) de la PUCP. En el cual, a partir de conocimientos interdisciplinarios se busca explotar metodologías de Inteligencia Artificial aplicando Machine Learning en el ámbito de inversión financiera. En el capítulo 1, se exponen trabajos previos para el pronóstico de acciones, índices bursátiles y commodities, pudiendo comparar y contrastar los resultados obtenidos al aplicar diversos algoritmos. De esta forma, se emplean los estudios previos presentados como base para la ejecución y selección del modelo en esta tesis. Asimismo, de manera más detallada se presentan los factores más importantes en el comportamiento del precio de un commodity. En el capítulo 2, se estructura la metodología a emplear en el desarrollo de la investigación. Se especifica el tipo de investigación y diseño, así como las métricas de evaluación a emplear. El tercer capítulo corresponde al modelo de predicción basado en herramientas estadísticas tradicionales. Se presenta la metodología Box Jenkins como punto de partida para la ejecución del modelo ARIMA, posteriormente se evalúan los resultados obtenidos con este. A partir del cuarto capítulo se introducen los conceptos de Machine Learning. Inicialmente se presenta un flujograma base para la elaboración de un algoritmo, y con este se estructuran dos modelos: regresión lineal y SVR. A lo largo de este capítulo se construyen ambos algoritmos de manera básica, desde la categorización del problema hasta la validación, según el flujo de procesos presentado. El quinto capítulo tiene como objetivo evaluar la importancia de realizar un análisis de selección de atributos con el fin de mejorar el modelo. De esta forma, se utilizan dos algoritmos de selección y posteriormente se comparan los resultados obtenidos. El sexto y último capítulo del cuerpo de esta investigación se centra en optimizar el modelo SVR a través de la implementación de algoritmos de selección de hiperparámetros.

2013 - 2014<br>How the brain controls movement is a question that has fascinated researchers from different areas as neuroscience, robotics and psychology. To understand how we move is not only an intellectual challenge, but it is important for finding new strategies for nursing people with movement diseases, for rehabilitation and to develop new robotic technology. While there is an agreement about the role of the primary motor cortex (M1) in the execution of voluntary movements, it is still debated what (and how) is encoded by the neural activity of the motor cortex. To unveil the "code" used for executing voluntary movements we investigated the interaction between the motor cortex and the spinal cord, the main recipient of the descending signals departing from M1 neurons. In particular, the research presented in this thesis aims at understanding how primary motor cortex and spinal cord cooperate to execute a reaching movement, and whether a modular organization of the spinal cord can be exploited for controlling the movement. On the basis of physiological studies about the primary motor cortex organization, we have hypothesized that this brain area encodes both movement's parameters and patterns of muscle activation. We argue that the execution of voluntary movements results from the cooperation of different clusters of neurons distributed in the rostral and caudal regions of primary motor cortex, each of which represents different aspects of the ongoing movement. In particular, kinetic aspects of movement are directly represented by the caudal part of primary motor cortex as activations of alpha motoneurons, while kinematic aspects of the movement are encoded by the rostral region and are translated by spinal cord interneurons into alpha motoneurons activation. The population of corticomotoneuron (CM) cells in the caudal part of M1 creates muscle synergies for a direct control of muscle activity, useful to execute highly novel skills that require a direct control of multijoint and single joint movements by the central nervous system (CNS). On the other side, clusters of neurons in the rostral M1 are devoted to the activation of different subpopulations of interneurons in the spinal cord organized in functional modules. Each spinal module implements hardwired muscle synergies regulating the activity of a subset of muscles working around one or more joints. The way a module regulates the muscles activations is related to its structural properties. One area recruits the hard-wired motor primitives hosted in the spinal cord as spatiotemporal synergies, while the other one has direct access to the alpha motoneurons and may build new synergies for the execution of very demanding movements. The existence of these two areas regulating directly and indirectly the muscle activity can explain the controversy about what kind of parameter is encoded by the brain. In order to validate our conjecture about the coexistence of an explicit representation of both kinetic and kinematics aspects of the movement, we have developed and implemented the computational model of the spinal cord and its connections with supraspinal brain. The model incorporates the key anatomical and physiological features of the neurons in the spinal cord (interneurons Ia, Ib and PN and Renshaw cells, and their interconnections). The model envisages descending inputs coming from both rostral and caudal M1 motor cortex and cerebellum (through the rubro- and reticulo-spinal tracts), local inputs from both Golgi tendon organs and spindles, and its output is directed towards alfa motoneurons, which also receive descending inputs from the cortex and local inputs from spindles. The musculoskeletal model used in this study is a one degree-of-freedom arm whose motion is restricted to the extension/flexion of the elbow. The musculoskeletal model includes three muscles: Biceps Short, Brachialis and Triceps Lateral. Our simulations show that the CNS may produce elbow flexion movements with different properties by adopting different strategies for the recruitment and the modulation of interneurons and motoneurons. The results obtained using our computational model confirm what has been hypothesized in literature: modularity may be the organizational principle that the central nervous system exploits in motor control. In humans, the central nervous system can execute motor tasks by recruiting the motor primitives in the spinal cord or by learning new collections of synergies essential for executing novel skills typical of our society. To get more insights about how brain encodes movements and to unveil the role played by the different areas of the brain we verified if the movement generated by our model satisfied the trade-off between speed and accuracy predicted by the Fitts’ law. An interesting result is that the speed-accuracy tradeoff does not follow from the structure of the system, that is capable of performing fast and precise movements, but arises from the strategy adopted to produce faster movements, by starting from a prelearned set of motor commands useful to reach the target position and by modifying only the activations of alfa motoneurons. These results suggest that the brain may use the clusters of neurons in the rostral M1 for encoding the direction of the movement and the clusters of CM cells in the caudal M1 for regulating the tradeoff between speed and accuracy. The simulation performed with our computational model have shown that the activation of an area cannot exclude the activation of the other one but, on the contrary, both the activations are needed to have a simulated behaviour that fits the real behavior. [edited by Author]<br>XIII n.s.

In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simplication and explicitation. To this end, a multilingual learning framework is designed and implemented for the identification of translational language. The framework is modelled as a categorisation task, the learning techniques having the major goal to automatically learn to distinguish between translated and non-translated texts. The second and third major goals of this research are the retrieval of the recurring patterns that are revealed in the process of solving the task of categorisation, as well as the ranking of the most in uential characteristics used to accomplish the learning task. These aims are ful lled by implementing a system that adopts the machine learning methodology proposed in this research. The learning framework proves to be an adaptable multilingual framework for the investigation of the nature of translational language, its adaptability being illustrated in this thesis by applying it to the investigation of two languages: Spanish and Romanian. In this thesis, di erent research scenarios and learning models are experimented with in order to assess to what extent translated texts can be diff erentiated from non-translated texts in certain contexts. The findings show that machine learning algorithms, aggregating a large set of potentially discriminative characteristics for translational language, are able to diff erentiate translated texts from non-translated ones with high scores. The evaluation experiments report performance values such as accuracy, precision, recall, and F-measure on two datasets. The present research is situated at the con uence of three areas, more precisely: Descriptive Translation Studies, Machine Learning and Natural Language Processing, justifying the need to combine these elds for the investigation of translationese and translational hypotheses.

The purpose of this study was to explore and describe the organizational structure of computer user groups and what roles they play in adult computer learning. The research consisted of two stages that used triangulation methodology.The pilot study involved a survey, which used a 34 item questionnaire, answered by 128 persons. The second stage consisted of a nationwide survey, which used a 57 item questionnaire, telephone interviews, and content analysis of newsletters. The nationwide survey was answered by 72 groups, representing 55,222 members.Findings1. Computer user groups were a major provider of computer learning for adults in the United States.2. Computer user groups were one form of autonomous learning groups.3. Computer user groups existed in all parts of the United States and tended to have a formal organization.4. Members of computer user groups were typically over 35, had a high family income, were college graduates, and were male.5. Members of computer user groups preferred self-directed learning and peer led learning to classes at educational institutions.Recommendations1. Adult and community educators should study other autonomous learning groups to discover the role that they play in adult learning.2. Practical research should be conducted to determine if and how educational institutions can best assist autonomous learning groups.3. Adult and community education practitioners should survey their local communities to identify autonomous learning groups and determine methods that can be used to cooperate with these groups.4. Adult and community educators should identify local computer user groups and determine how their educational institutions could cooperate with computer user groups.5. More innovative programming should be conducted by adult and community educators that takes advantage of the learning model provided by computer user groups.6. Training and development personnel should examine the potential benefits of implementing computer user groups and other autonomous learning groups within their organizations to promote learning new knowledge and skills.ConclusionComputer user groups were organized primarily to assist their members in learning more about computers. In addition to satisfying the learning needs of members, the groups provided supplemental services that supported other computer related needs of the members. Computer user groups were one form of autonomous learning groups and the results of this study suggested that the role of such groups in adult learning may be far more important than was generally recognized by adult and community educators.

Gli alti costi in termini di gestione, tempo e lavoro legati all'uso dei mammiferi nella ricerca biomedica hanno reso sempre più urgente la necessità di individuare modelli alternativi più economici e semplici ma altrettanto efficaci. Lo scopo della mia tesi è stata la caratterizzazione della chiocciola di stagno Lymnaea stagnalis come modello per le Neuroscienze Traslazionali. Gli studi bioinformatici, molecolari e comportamentali condotti hanno dimostrato la validità e la versatilità di questo modello e hanno permesso di esaminare fenomeni finora osservati solo nei mammiferi. In particolare, è emerso che: (1) L. stagnalis consente di approfondire il dialogo tra il sistema nervoso e immunitario, inclusi gli effetti dell’infiammazione sulle funzioni cognitive. Il trattamento con uno stimolo infiammatorio (lipopolisaccaride - LPS) è stato in grado di modulare l’espressione degli enzimi della pathway delle chinurenine nel sistema nervoso centrale di L. stagnalis. Questa pathway è altamente conservata tra vertebrati e invertebrati e metabolizza il triptofano in diversi cataboliti neuroattivi, i quali possono essere sia neurotossici che neuroprotettivi. Lo stesso stimolo è stato in grado di alterare il comportamento e le performance cognitive delle chiocciole, come osservato in organismi più complessi, uomo incluso. Questi effetti possono essere eliminati trattando preventivamente gli animali con un composto antinfiammatorio come l’aspirina. (2) L. stagnalis è in grado di formare il Garcia effect, una forma di apprendimento complesso che consiste in un’avversione condizionata a un sapore nuovo a seguito della sua associazione a uno stimolo avverso che induce un malessere viscerale, incontrato fino a 48h ore dopo. Come precedentemente dimostrato nel modello di roditore e nell’uomo, è stata sufficiente una singola presentazione dei due stimoli per generare una duratura e specifica avversione per questo sapore. L. stagnalis rappresenta quindi un modello semplificato per lo studio dei meccanismi alla base del Garcia effect negli organismi più complessi. (3) L. stagnalis può essere utilizzata per studiare gli effetti del riscaldamento globale sulla resilienza, i comportamenti adattativi e le funzioni cognitive degli organismi. L'esposizione giornaliera a uno shock termico ha aumentato la sensibilità alle temperature delle chiocciole di laboratorio (mantenute a temperature costanti per generazioni), ma non è stato un fattore di stress per le chiocciole appena raccolte nei loro habitat naturali (dove sono state esposte a considerevoli escursioni termiche), né per la loro progenie nata e allevata in laboratorio. Questi risultati hanno suggerito un duplice ruolo della genetica e della plasticità fisiologica nella termo-tolleranza. (4) L. stagnalis permette di studiare gli effetti di composti naturali bioattivi su apprendimento e memoria. L'esposizione acuta al flavonoide quercetina si è dimostrata in grado di migliorare la formazione della memoria a lungo termine, mentre a livello molecolare ha indotto un’up-regolazione della via serotoninergica e di CREB1 (cAMP response element-binding protein 1), i quali svolgono un ruolo chiave e altamente conservato nei processi di plasticità sinaptica. Anche se i modelli animali non potranno mai riassumere l'intero fenotipo dei cervelli umani, i risultati presentati nella mia tesi hanno illustrato che, se spostata dallo stagno al banco di laboratorio, L. stagnalis rappresenta un modello valido per aprire nuove frontiere nelle Neuroscienze Traslazionali. L'obiettivo finale di questo progetto è quello di fornire un ulteriore strumento per promuovere lo spostamento della ricerca dal banco di laboratorio al letto dei pazienti, 'traducendo' i dati ottenuti nelle chiocciole ai mammiferi.<br>The high costs in time and efforts associated with the use of mammals in biomedical research are creating a pressing demand for alternative models that are cheaper and simpler, but still effective. The aim of my thesis was the characterization of the pond snail Lymnaea stagnalis as a model for Translational Neuroscience. Different bioinformatics, molecular, and behavioural studies have been performed to show the validity and versatility of this model to study phenomena so far demonstrated only in mammals. In particular, it has been shown that (1) L. stagnalis can be used to elucidate the conserved dialogue between the immune and nervous systems and to study the effects of inflammation on cognitive functions. An immune challenge (i.e., injection of lipopolysaccharide – LPS) affected the transcriptional levels of the enzymes of the kynurenine pathway in L. stagnalis’ central nervous ganglia. This conserved pathway in vertebrates and invertebrates catabolizes the aminoacid tryptophan into several neuroactive metabolites which can exert both neuroprotective and neurotoxic effects. The same immune challenge was able to alter snails’ adaptive behaviours and to obstruct their ability to form or show long-term memory (LTM), as observed for more complex organisms. These behavioural effects were prevented by exposing L. stagnalis to an anti-inflammatory compound, like aspirin, before the LPS injection, suggesting the involvement of immune-related molecules in mediating LPS-induced sequelae. The results of these studies gave important translational contributions for elucidating the effects of inflammation on the central nervous system. (2) L. stagnalis is capable of the Garcia effect, a higher form of learning, consisting of a conditioned aversion to an appetitive food stimulus consumed hours before the exposure to an aversive, nausea-inducing stimulus. As previously demonstrated in rodents and humans, a single paired presentation of these stimuli was sufficient to create a long-lasting and taste-specific gustatory aversion. This study allowed to elucidate the causal underpinnings of the Garcia effect in higher animals. (3) L. stagnalis can be used to predict the effects of the current global warming on animal resilience, adaptive behaviours, and cognitive functions. Daily exposure to a thermal shock increased the thermal sensitivity of laboratory-reared snails, which have been maintained under constant laboratory temperatures for generations. However, this ‘habitat-related challenge’ did not appear to be a stressor in freshly collected snails, that had experienced severe thermal fluctuations in their natural environment, nor in their progeny born and raised in lab conditions. These results allowed a better understanding of the role of genetic changes and physiological plasticity on thermotolerance. (4) L. stagnalis is a versatile model to examine the effects of bioactive natural compounds on learning and memory. Exposure to the flavonoid quercetin upregulated the serotoninergic pathway and CREB1 (cAMP response element-binding protein 1), a key regulator of synaptic plasticity in several in vivo models, in L. stagnalis’ central ganglia. This molecular effect was accompanied by an enhancement of LTM acquisition, consolidation, recall, and reconsolidation. Although animal models can never summarize the full phenotype of human brains, findings presented in my thesis illustrated that, when moved from pond to bench, L. stagnalis represents a valid model to open new frontiers in Translational Neuroscience. The ultimate goal of this project is to provide an additional tool to promote and sustain the rational and move research from bench to bedside, ‘translating’ data from snails to mammals, and maybe to humans.

Orientador: Lucia Galvão de Albuquerque<br>Resumo: Registros de características quantitativas e informações genotípicas cole- tadas para cada animal são utilizados para identificar regiões do genoma associadas à variação fenotípica. No entanto, essas investigações são, geralmente, realizadas com base em testes estatísticos de correlação ou associação, que não implicam em causalidade. A fim de explorar amplamente essas informações, métodos poderosos de inferência causal foram desenvolvidos para estimar os efeitos causais entre as variáveis estudadas. Apesar do progresso significativo neste campo, inferir os efeitos causais entre variáveis aleatórias contínuas ainda é um desafio e poucos estudos têm explorado as relações causais em genética quantitativa e no melhoramento animal. Neste contexto, dois estudos foram realizados com os seguintes objetivos: 1) Buscar as relações causais entre as características de carcaça e qualidade de carne usando um modelo de equação estrutural (MEE), sob modelo linear misto em bovinos da raça Nelore, e 2) Reconstruir redes de genes-fenótipos e realizar análise de rede causal por meio da integração de dados fenotípicos, genotípicos e transcriptômicos em bovinos da raça Nelore. Para o primeiro estudo, um total de 4.479 animais com informação fenotípica para o peso da carcaça quente (PCQ), área de olho lombo (AOL), espessura de gordura subcutânea (EGS), força de cisalhamento (FC) e marmoreio (MAR) foram usados. Os animais foram genotipados usando os painéis BovineHD Bead- Chip e GeneSeek Genomic Pro... (Resumo completo, clicar acesso eletrônico abaixo)<br>Abstract: Quantitative traits and genotypes information have been collected for each animal and used to identify genome regions related to phenotypes variation. However, these investigations are, usually, performed based on correlation or association statistical tests, which do not imply in causation. In order to fully explore these information, powerful causal inference methods have been developed to estimate causal effects among the variables under study. Despite significant progress in this field infer causal effect among random variables remains a challenge and some few studies have explored causal relationships in quantitative genetics and animal breeding. In this context, two studies were performed with the following objectives: 1) Search for the causal relationship among carcass yield and meat quality traits using a structural equation model (SEM), under linear mixed model context in Nelore cattle, and 2) Reconstruct gene-phenotype networks and perform causal network analysis through the integrating of phenotypic, genotypic, and transcriptomic data in Nelore cattle. For the first study, a total of 4,479 animals with phenotypic information for hot carcass weight (HCW), longissimus muscle area (LMA), backfat thickness (BF), Warner-Bratzler shear force (WBSF), and marbling score (MB) traits were used. Animals were genotyped using BovineHD BeadChip and GeneSeek Genomic Profiler Indicus HD - GGP75Ki. For causal inference using SEM a multistep procedure methodology was used as follow:... (Complete abstract click electronic access below)<br>Doutor

In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation using multiple datasets from various application domains were conducted. In addition, CloneTM, an LDA-based working prototype for code clone detection was developed. Results showed that, if calibrated properly, topic modeling can deliver a satisfactory performance in capturing different types of code clones, showing particularity good performance in detecting Type III clones. CloneTM also achieved levels of performance comparable to already existing practical tools that adopt different clone detection strategies.

Le coût élevé de la maintenance des logiciels exige de travailler sur leur maintenabilité. Bien qu’émergente de la structure du code source, son évaluation est subjective, car elle dépend des développeurs et du contexte. Les modèles de maintenabilité actuels tendent à réduire la maintenabilité à un score unidimensionnel basé sur des métriques, souvent mal définies, qui représentent mal la structure du code. Nos travaux se sont basés sur l’analyse statique des graphes de code pour évaluer la maintenabilité. Ils ont permis de développer Javanalyser, un outil libre qui génère automatiquement le graphe de code d’un programme Java. Ces graphes ont permis de formaliser 33 métriques statiques sous forme de requêtes déclarative et ont permis de reproduire avec succès une étude de Schnappinger et al. Notre extension de l’étude confirme l’importance de la taille du programme comme facteur influençant la maintenabilité, sans toutefois négliger l’impact des autres métriques. Ce travail ouvre la voie à une compréhension plus approfondie de la maintenabilité, grâce à une représentation multidimensionnelle permettant de tenir compte de la variabilité entre les développeurs<br>The high cost of software maintenance requires a focus on software maintainability. Although it emerges from the structure of the source code, its evaluation is subjective, as it depends on developers and the context. Current maintainability models tend to reduce maintainability to a one-dimensional score based on metrics, often poorly defined, which inadequately represent the structure of the code. Our work is based on the static analysis of code graphs to evaluate maintainability. It led to the development of Javanalyser, an open-source tool that automatically generates the code graph of a Java program. These graphs enabled the formalization of 33 static metrics as declarative queries, and allowed the successful replication of a study by Schnappinger et al. Our extension of the study confirmed the importance of size as a factor influencing maintainability, while also recognizing the impact of other metrics. This work opens the way to a deeper understanding of maintainability through a multidimensional representation that takes into account the variability between developers

Made available in DSpace on 2016-12-12T17:32:59Z (GMT). No. of bitstreams: 1 resumo ANAMARIA.pdf: 111799 bytes, checksum: 1fd65c3285e10d1c7098ccfa70716c7e (MD5) Previous issue date: 2015-07-20<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Background. Training aimed at restoring and / or improving motor impairments after a spinal cord injury have shown importance in the neurofunctional rehabilitation process, as well as the relationship with biochemical markers involved in neuroplasticity. Objective. verify the effects of two training protocols in sensorimotor recovery and neural plasticity in an experimental model of incomplete LME Methods: 32 adult Wistar rats divided into four groups: Sham, NT, BCT and GT, subject to weekly reviews of locomotor and sensory-motor recovery of the hind limbs. Brain structures and lumbar spinal cord were dissected and processed for biochemical analysis quantification of the expression of BDNF, MAP-2 and Synaptophysin. Results: In the motor cortex, for synaptophysin the NT showed lower rates compared to the sham group. The BCT showed higher rates compared to the GT and NT group. For MAP-2 NT showed lower rates compared to the sham group and BCT showed higher rates compared to the NT group. For BDNF, BCT and GT groups showed higher rates compared to the NT and SHAM group. In the cerebellum for synaptophysin GT group showed higher rates compared to the NT and SHAM group, BCT showed higher rates compared to the NT. For MAP-2 the BCT group showed higher rates compared to the NT. In the SC, for synaptophysin the NT, BCT and GT showed lower rates compared to the sham group.Conclusions. Gait training played key role in sensory-motor recovery of the hind limbs. And both training modulate synaptic and structural proteins, playing an important role in exercise dependent plasticity after experimental spinal cord injury.<br>O treinamento motor tem sido utilizado para restabelecer e/ou melhorar deficiências após uma lesão medular e têm mostrado importância no processo de reabilitação neurofuncional, bem como a sua relação com os marcadores bioquímicos envolvidos na neuroplasticidade. Objetivo: verificar os efeitos de dois protocolos de treinamento na recuperação funcional e plasticidade neural em um modelo lesão medular incompleta. Métodos: 32 ratas Wistar adultos divididos em quatro grupos: Controle da lesão (Sham), Não-treinado (NT), Balance and coordenation trainning (BCT) e o grupo gait trainning (GT), sujeitos a avaliações semanais de recuperação locomotora e coordenação sensório-motora dos membros posteriores. O córtex motor, cerebelo e medula espinal lombar foram dissecados e processados para análise da quantificação bioquímica da expressão do BDNF, MAP-2 e sinaptofisina. Resultados: No córtex motor, para sinaptofisina o NT mostrou valores menores em comparação com o grupo Sham. O BCT mostraram níveis mais elevadas em comparação com o grupo GT e NT. Para MAP-2, NT mostrou menores níveis em comparação com o grupo Sham e BCT mostraram níveis mais elevados em comparação com o grupo NT. Para BDNF, grupos BCT e GT mostraram maiores níveis em comparação ao grupo SHAM e NT. No cerebelo para sinaptofisina, o grupo GT mostrou maiores níveis em comparação com o grupo NT e SHAM, BCT mostrou taxas mais elevadas em comparação com o NT. Para MAP-2 do grupo BCT mostraram taxas mais elevadas em comparação com o NT. Na medula, para sinaptofisina o NT, BCT e GT mostraram menores em comparação com os grupo Sham. Conclusão: Treino de marcha exerceu papel fundamental na recuperação sensório-motora dos membros posteriores. E ambos os treinamentos modularam a expressão de proteínas sinápticas e estruturais, desempenhando um importante papel na plasticidade exercício-dependente após lesão medular experimental.

The core concern of my thesis is with shifting the focus from the description on how innovation is done (predominantly STS accounts of innovation in-the-making) to what designers do with conceptions of innovation. The thesis is based on ethnographic fieldwork within a group of interaction designers of Milan. Despite the different conceptions and traditions of innovation that these designers bring in – the artistic and technological ones – I observed that a design-centered conception of innovation is reproduced, as well as the idea that plans and intentions precede things. However, another key idea of my fieldwork is the importance designers give to imagining things as they might be, rather than focusing on how things are. This is where different models of action, planned and open ones coexist in creative ways: it is these processes that the ethnography details.

Structure learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One of them is to generate multiple approximate structures and then to reduce the ensemble to a representative structure. It is possible to use the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this doctoral research, it was made an analogy with an adapted one-dimensional random-walk for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across benchmark datasets applying the Matthews Correlation Coefficient as the performance metric. In the experiments using a recent medical dataset, the BN resulting from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. In literature, the feature accounted along of the perturbed structures has been the edges and not the directed edges (arcs) as in this thesis. That modified strategy still was applied to an elderly dataset to identify potential relationships between variables of medical interest but using an increased threshold instead of the predict by the proposed formula - such prudence is due to the possible social implications of the finding. The motivation behind such an application is that in spite of the proportion of elderly individuals in the population has increased substantially in the last few decades, the risk factors that should be managed in advance to ensure a natural process of mental decline due to ageing remain unknown. In the learned structural model, it was graphically investigated the probabilistic dependence mechanism between two variables of medical interest: the suspected risk factor known as Metabolic Syndrome and the indicator of mental decline referred to as Cognitive Impairment. In this investigation, the concept known in the context of BNs as D-separation has been employed. Results of the carried out study revealed that the dependence between Metabolic Syndrome and Cognitive Variables indeed exists and depends on both Body Mass Index and age.<br>O aprendizado da estrutura de uma Rede Bayesiana (BN) é um problema NP-difícil, e o uso de estratégias sub-ótimas é essencial em domínios que envolvem muitas variáveis. Uma delas consiste em gerar várias estruturas aproximadas e depois reduzir o conjunto a uma estrutura representativa. É possível usar a frequência de ocorrência (no conjunto de estruturas) como critério para aceitar um arco dominante entre dois nós e assim obter essa estrutura única. Nesta pesquisa de doutorado, foi feita uma analogia com um passeio aleatório unidimensional adaptado para deduzir analiticamente um limiar de decisão apropriado para essa frequência de ocorrência. A expressão de forma fechada obtida foi validada usando bases de dados de referência e aplicando o Coeficiente de Correlação de Matthews como métrica de desempenho. Nos experimentos utilizando dados médicos recentes, a BN resultante da frequência de corte analítica capturou as associações esperadas entre os nós e também obteve melhor desempenho de predição do que as BNs aprendidas com limiares vizinhos ao calculado. Na literatura, a característica contabilizada ao longo das estruturas perturbadas tem sido as arestas e não as arestas direcionadas (arcos) como nesta tese. Essa estratégia modificada ainda foi aplicada a um conjunto de dados de idosos para identificar potenciais relações entre variáveis de interesse médico, mas usando um limiar aumentado em vez do previsto pela fórmula proposta - essa cautela deve-se às possíveis implicações sociais do achado. A motivação por trás dessa aplicação é que, apesar da proporção de idosos na população ter aumentado substancialmente nas últimas décadas, os fatores de risco que devem ser controlados com antecedência para garantir um processo natural de declínio mental devido ao envelhecimento permanecem desconhecidos. No modelo estrutural aprendido, investigou-se graficamente o mecanismo de dependência probabilística entre duas variáveis de interesse médico: o fator de risco suspeito conhecido como Síndrome Metabólica e o indicador de declínio mental denominado Comprometimento Cognitivo. Nessa investigação, empregou-se o conceito conhecido no contexto de BNs como D-separação. Esse estudo revelou que a dependência entre Síndrome Metabólica e Variáveis Cognitivas de fato existe e depende tanto do Índice de Massa Corporal quanto da idade.

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.

With the appearance of the Internet, open to everyone in 1991, criminals saw a big opportunity in moving their organisations to the World Wide Web, taking advantage of these infrastructures as it allowed higher mobility and scalability. Later on, in the year 2000, the first system appeared, creating what is known today as the Dark Web. This layer of the World Wide Web became quickly the option to go when criminals wanted to sell and deliver content such as match-fixing, children pornography, drugs market, guns market, etc. This obscure side of the Dark Web, makes it a relevant topic to study in order to tackle this huge network and help to identify these malicious activities and actors. In this master thesis, it is shown through the study of two datasets from the Dark Web, that we are surrounded by capable technologies that can be applied to these types of problems in order to increase our knowledge about the data and reveal interesting characteristics in an interactive and useful way. One dataset has 10 000 relations from domains living in the Dark Web, and the other dataset has thousands of data from just 11 specific domains from the Dark Web. We reveal detailed information about each dataset by applying di↵erent analysis and data mining algorithms. For the first dataset we studied domains availability patterns with temporal analysis, we categorised domains with machine learning neural networks and we reveal the network topology and nodes relevance with social networks analysis and core-periphery model. Regarding the second dataset, we created a cross matching information web graph and applied a name entity recognition algorithm which ended in a tool for identifying entities within dark web’s domains. All of these approaches culminated in an interactive web application where we publicly not only display the entire research but also the tools developed along with the project (https://darkor.org).<br>Com o surgimento da Internet, aberta a todos em 1991, os criminosos viram uma grande oportunidade em passar as suas organizações para a World Wide Web, aproveitando-se assim dessas infraestruturas que permitiam uma maior mobilidade e escalabilidade. Mais tarde, no ano 2000, surgiu o primeiro sistema, criando o que hoje é conhecido como a Dark Web. Essa camada da World Wide Web tornou-se rapidamente a opção a seguir quando os criminosos queriam vender e entregar conteúdo como combinação de resultados, pornografia infantil, mercado de drogas, mercado de armas, etc. Este lado obscuro da Dark Web, torna-a num tema relevante de estudo a fim de ajudar a identificar atividades e atores maliciosos. Nesta dissertação de mestrado é mostrado, através do estudo de dois conjuntos de dados da Dark Web, que estamos rodeados de tecnologias que podem ser aplicadas neste tipo de problemas de forma a aumentar o nosso conhecimento sobre os dados e revelar características interessantes de forma interativa e útil. Um conjunto de dados tem 10 000 relações de domínios que vivem na Dark Web enquanto que o outro conjunto de dados tem milhares de dados de apenas 11 domínios específicos da Dark Web. Neste estudo revelamos informações detalhadas sobre cada conjunto de dados aplicando diferentes análises e algoritmos de data mining. Para o primeiro conjunto de dados, estudamos padrões de disponibilidade de domínios com análise temporal, categorizamos domínios com o auxílio de redes neuronais e revelamos a topologia da rede e a relevância dos nós com análise de redes sociais e a aplicação de um modelo núcleo-periferia. Em relação ao segundo conjunto de dados, criamos um grafo da rede com cruzamento de dados e aplicamos um algoritmo de reconhecimento de entidades que resultou em uma ferramenta para identificar entidades dentro dos domínios da Dark Web estudados. Todas estas abordagens culminaram em uma aplicação web interativa onde exibimos publicamente não apenas todo o estudo, mas também as ferramentas desenvolvidas ao longo do projeto (https://darkor.org).

This qualitative study was guided by the framework of Intercultural Communicative Competence (ICC). In Canada, although researchers recognize that learning French through a cultural context will promote understanding and acceptance of cultural diversity, as well as the learning of the target language, there is little evidence to support the ways in which this methodology may influence the learning of elementary Core French students. The purpose of this study was to investigate the ways in which a group of Grade 4 students, from an English community, perceived their peers from a French community, and to describe any changes in their attitudes toward learning French as a Second Language in response to the opportunity to learn through a cultural context. This action research study used a questionnaire, an observation checklist of task-related behaviour, and semi-structured group photo-interviews to explore 15 Grade 4 students’ perceptions and attitudes in response to a new research-based teaching approach. As the cultural exchange unfolded, I sought evidence of the three savoirs (that according to the ICC model, primary students have the ability to develop), in order to promote interculturality: savoir être (related to students’ attitudes towards the other students), savoirs (related to knowledge of the other culture), and savoir apprendre/faire (related to behaviour toward the other culture). Results indicated that students showed evidence of two savoirs: savoir être and savoirs. Savoir apprendre/faire, however, was not shown. In addition to the two savoirs, the exchange promoted the learning of French in the classroom, and supported the development of students’ confidence in communicating in French. The findings of this study contribute to the teaching of Elementary Core French in Ontario, by providing valuable insights into the possible ways that learning French through an intercultural experience holds potential in developing students’ communication abilities as well as awareness and acceptance of otherness, which is the bedrock upon one can develop effective communicators in the target language.<br>Thesis (Master, Education) -- Queen's University, 2014-04-23 18:35:56.721

碩士<br>國立虎尾科技大學<br>資訊管理研究所<br>102<br>The frequency of using PowerPoint is estimated a huge amount of times every day in university. Prior studies indicated that most students prefer PowerPoint, and using PowerPoint can enhance their learning motivation. However, there are few studies to further discuss about the effects of using PowerPoint integrating with mobile assessment method. The aim of this study is to propose a new assessment activity that applying mobile technology as well as two-dimensional barcode, and integrating PowerPoint learning material. The new learning activity is named code-embedded mobile assessment (CEMA) activity. This study developed an assessment system based on the CEMA activity. Additionally, to understand the effectiveness, the learning activity and the PowerPoint learning material were designed based on the ARCS model proposed by Keller. A total of thirty undergraduate students were invited in this experiment, and randomly assigned into experimental group and control group. The experiment was conducted on a Java programming course and took seven weeks. Students of experimental group learned with CEMA learning activity. On the contrary, students of control group learned with traditional learning activity. In addition, this study conducted pre-test, post-test, questionnaire and interview to investigate the effect of CEMA learning activity. After experiment, the result showed CEMA learning activity can improve students’ learning performance, and they have positive learning motivation toward the CEMA activity. Overall, the activity has positive feasibility and effect toward students’ learning.

abstract: Learning programming involves a variety of complex cognitive activities, from abstract knowledge construction to structural operations, which include program design,modifying, debugging, and documenting tasks. In this work, the objective was to explore and investigate the barriers and obstacles that programming novice learners encountered and how the learners overcome them. Several lab and classroom studies were designed and conducted, the results showed that novice students had different behavior patterns compared to experienced learners, which indicates obstacles encountered. The studies also proved that proper assistance could help novices find helpful materials to read. However, novices still suffered from the lack of background knowledge and the limited cognitive load while learning, which resulted in challenges in understanding programming related materials, especially code examples. Therefore, I further proposed to use the natural language generator (NLG) to generate code explanations for educational purposes. The natural language generator is designed based on Long Short Term Memory (LSTM), a deep-learning translation model. To establish the model, a data set was collected from Amazon Mechanical Turks (AMT) recording explanations from human experts for programming code lines. To evaluate the model, a pilot study was conducted and proved that the readability of the machine generated (MG) explanation was compatible with human explanations, while its accuracy is still not ideal, especially for complicated code lines. Furthermore, a code-example based learning platform was developed to utilize the explanation generating model in programming teaching. To examine the effect of code example explanations on different learners, two lab-class experiments were conducted separately ii in a programming novices’ class and an advanced students’ class. The experiment result indicated that when learning programming concepts, the MG code explanations significantly improved the learning Predictability for novices compared to control group, and the explanations also extended the novices’ learning time by generating more material to read, which potentially lead to a better learning gain. Besides, a completed correlation model was constructed according to the experiment result to illustrate the connections between different factors and the learning effect.<br>Dissertation/Thesis<br>Doctoral Dissertation Engineering 2020

碩士<br>國立雲林科技大學<br>電機工程系<br>106<br>In this thesis, we develop a hash code generation based on deep learning and visual attention model for image retrieval. This system is composed of several parts: object detection, saliency computation, and hash code generation. In the object detection part, a deep learning technique, Faster R-CNN, is used to detect and classify objects in images. A pre-trained model can classify 20 categories for image classification. In the saliency computation, the pre-training model proposed in [26] is to compute the saliency value of each object. According to category information and saliency value, the proposed system can generate the corresponding hash code. To evaluate the performance of the proposed system, the PASCAL VOC image set is used. There are 27088 images in the PASCAL VOC image set. For image retrieval, the nDCG value of the proposed system is higher than that of [29]. Experimental results show that the proposed system is superior to the existing method [29]. Keywords: object detection, visual attention, hash code

This qualitative study used a narrative inquiry approach to conduct in-depth interviews of eight distributed learning educators who designed and offered online English courses in British Columbia during the 2011/12 school year. There were three research questions: (1) How do teachers describe their professional experiences of teaching in a full time online environment? (2) What are the enablers and inhibitors for online teacher development? (3) Do teachers feel their teaching practice has changed over their career as online educators? Findings were examined through the lens of Korthagen’s (2004) Onion Model. Six themes that comprised this model, provided a framework for data analysis and insight into the process by which teachers made sense of their lived experience. The findings revealed that online educators valued their online experience because it removed the constraints of a regular classroom. They expressed frustration with some aspects of the current model of online education in BC because it prevented them from engaging in synchronous, highly connective learning projects with their students. Recognition of the fact that online educators work in a different milieu with a different set of environmental pressures is necessary to ensure the success of distributed learning in BC.

Investigating mechanisms that regulate genes and the genes' functions are essential to understand a biological system. This dissertation is consists of two specific research projects under these aims, which are for understanding piRNA's regulation mechanism and predicting genes' function computationally. The first project shows a piRNA regulation landscape in C. elegans. piRNAs (Piwi-interacting small RNAs) form a complex with Piwi Argonautes to maintain fertility and silence transposons in animal germlines. In C. elegans, previous studies have suggested that piRNAs tolerate mismatched pairing and in principle could target all transcripts. In this project, by computationally analyzing the chimeric reads directly captured by cross-linking piRNA and their targets in vivo, piRNAs are found to target all germline mRNAs with microRNA-like pairing rules. The number of targeting chimeric reads correlates better with binding energy than with piRNA abundance, suggesting that piRNA concentration does not limit targeting. Further more, in mRNAs silenced by piRNAs, secondary small RNAs are found to be accumulating at the center and ends of piRNA binding sites. Whereas in germline-expressed mRNAs, reduced piRNA binding density and suppression of piRNA-associated secondary small RNAs targeting correlate with the CSR-1 Argonaute presence. These findings reveal physiologically important and nuanced regulation of piRNA targets and provide evidence for a comprehensive post-transcriptional regulatory step in germline gene expression. The second project elaborates a computational model to predict gene function. Predicting genes involved in a biological function facilitates many kinds of research, such as prioritizing candidates in a screening project. Following the “Guilt By Association” principle, multiple datasets are considered as biological networks and integrated together under a multi-label learning framework for predicting gene functions. Specifically, the functional labels are propagated and smoothed using a label propagation method on the networks and then integrated using an “Error correction of code” multi-label learning framework, where a “codeword” defines all the labels annotated to a specific gene. The model is then trained by finding the optimal projections between the code matrix and the biological datasets using canonical correlation analysis. Its performance is benchmarked by comparing to a state-of-art algorithm and a large scale screen results for piRNA pathway genes in D.melanogaster. Finally, piRNA targeting's roles in epigenetics and physiology and its cross-talk with CSR-1 pathway are discussed, together with a survey of additional biological datasets and a discussion of benchmarking methods for the gene function prediction.

This study is a systematic, objective investigation of educator empowerment, where the researcher explores the perspective of ABET educators. This study is presented against the backdrop of striking a balance between the conventional curriculum and Curriculum 2005 (OBE) because they exhibit a variety of challenges, anomalies and imbalances which led to the Revised National Curriculum Statements which will be implemented in 2006. It is informed by the fact that educators, if empowered, form an integral and central feature of changes in educational centres, especially in teaching adult learners to be able to assist their school-leaving children so as to improve provincial matric results, for example. The researcher focused on educators of ABET in Limpopo Province as the unit of analysis in this study. Interactive, cooperative, peer teaching, dialogic meditation, group and team teaching, constructivism and human resource development, reflective and multi-level approaches, are discussed with a particular focus on educator empowerment, including in-service training programmes. The researcher regards ”church settings” (her term) as inappropriate for effective teaching as they reduce educators to preachers and learners to congregants. This occurred as a result of the failure to recognise the importance of educator-learner, learner-learner and educator-educator interaction in the past curriculum. The study argues that the approaches mentioned are appropriate for this study. Educator empowerment is a lived-in and continuous process, monitoring and evaluating of in-service training to ensure quality. It was therefore necessary to develop a research design that would make it possible to enable educators to implement the new curriculum. Qualitative research is based on an in-depth inquiry which captures an educator's personal perspectives and experiences. Focus groups and in-depth interviews, which exemplify qualitative methods, were seen to be the best research tools in gathering the data for this study. The respondents, were UNISA ABET certificate students, and also professional educators at formal schools. They showed zeal in improving the educational situation. Their responses led to the model which has been developed in the thesis, culminating in uplifting the teaching profession and handling its multi-level led dynamic in an interactive and cooperative manner and reflecting in their experiences so that purposes of the National Qualifications Framework can be attained.<br>Educational Studies<br>D.Ed. (Didactics)

碩士<br>大葉大學<br>管理學院碩士在職專班<br>106<br>This study investigated the middle and upper grade students of Cijin District Chung Chou Primary School in Kaohsiung City. It is aiming to explore the degree of intention to use the QR Code for innovative learning. The research tries to figure out the causal relationship between external variables (Mobility and convenience of leaning) that includes perceived usefulness, perceived ease of use, perceived playfulness, and usage intention through modified technology acceptance model. The study refered to prior literatures to construct research framework and conducted to questionnaires. A research tool “Questionnaire for usage intention of elementary school students to learn through QR Code innovation teaching” was used. This research adopts the tool of descriptive statistics, independent sample t-test, reliability analysis, factor analysis, Pearson product correlation analysis, multiple regression analysis, and path analysis to conduct statistical analysis. The results showed as follows : 1.Mobile learning has significant and positive effects on perceived usefulness, perceived ease of use and perceived playfulness. 2.Convenience of leaning has significant and positive effects on perceived usefulness, perceived ease of use and perceived playfulness. 3.Perceived usefulness, perceived ease of use and perceived playfulness had significant and positive effects on usage intention. Key Words : Technology acceptance model (TAM), QRCODE, Innovation teaching

碩士<br>修平科技大學<br>人力資源管理與發展碩士班<br>105<br>This study topic is Experiential learning, using the study of the Training Transfer base on the Theory of Planned Behavior and Motivation to Transfer Model to investigate the influence between Autonomous Motivation to transfer, Controlled Motivation to transfer, Subjective norm, Perceived behavioral control and Participate in experiential learning intention; Perceived behavioral control, Participate in experiential learning intention and Training transfer of organize employees who participate in Experiential Learning class.The quantitative method was taken in this study, we gather1364 questionnaires to find the opinions of organize employees who will participate in experiential learning class.It is expected that the organize employees will be able to achieve the goal of sustainable development by means of experiential learning class, more integration, attention to organization, as member of the organization as pride, to reduce internal problems, enhace competitiveness and achieve sustainable development. But also for the experitential learning has not yet included in the education and training organizations to provide a basis for reference. 1. The results are as follow:High Autonomous Motivation to transfer and Perceived behavioral control will improve Participate in experiential learning intention. 2. High Controlled Motivation to transfer will reduce Participate in experiential learning intention. 3. High Perceived behavioral control and Participate in experiential learning intention will improve Training to transfer.

碩士<br>樹德科技大學<br>資訊管理系碩士班<br>107<br>In recent years, in order to attract students to enter, schools have tried to differentiate themselves from other schools to increase their attractiveness. Due to the developments of information technology and mobile learning, the traditional learning that limited in the classroom have gradually been replaced by multi-learning. Based on the above reasons, this study hopes to use the school-based standard as a starting point to establish a campus-specific eco-guided website dedicated to the school, so that students can find all the knowledge about the animals and plants in campus, and increase their identity to the school. Based on this website, combined with mobile devices, QR Code campus plant guided cards and field games, establish a mobile learning model of learning campus plant courses. And compare with the learning model that teaching with blackboard in the classroom, combined with the traditional teaching mode of computer and single-gun projector, explore whether there is better teaching effect? This study uses a quasi-experimental design, and the study is aimed at two classes of students in the fifth grade. According to the research results of this study, when the school establishes a school-based eco-guided website, it can indeed satisfy the students'' needs for the knowledge of animals and plants in campus. It not only helps the learners, but also brings great convenience to the educators. The campus plants mobile learning courses established on this website can achieve more effective learning results than traditional classroom teaching,and bringing more students diverse learning methods. Moreover, although the results of this study are aimed at the teaching of nature and life science and technology, they can also provide a reference for the establishment of mobil learning teaching models in other learning areas.

The purpose of this study was to examine the effect of using Lakatos’ heuristic method to teach the surface area of the cone (SAC) on students’ learning. The Lakatos (1976) heuristic framework and the Oh (2010) model of “the enhanced-conflict map” were employed as framework for the study. The first research question examined the impact of the Lakatosian heuristic method on students’ learning of the SAC, which was addressed in three sub-questions: the impact of the method on the students’ achievement, the impact of the method on their conceptual learning and the impact of the method on their higher order thinking skills. The second question examined whether the heuristic method of teaching the SAC helped students to sustain their learning better than the traditional method (Euclidean method). The third question examined whether the heuristic method of teaching SAC could change students’ readiness level, according to Bloom’s taxonomy. A pre-test and post-test quasi-experimental research design was used in the study that involved a total of 198 Grade 11 students (98 in the experimental group and 100 in the control group) from two schools in Cyprus. The instruments used for data collection were cognitive tests, lesson observations (video-recorded), interviews and questionnaire. Data was analysed using inferential statistics and the Oh (2010) model of the enhanced conflict map. Student achievement within time was the dependent variable and the method of training the independent variable. Therefore, time was the “within” factor and each group was measured three times (pre-test, post-test and delayed). The differences in students’ achievement within each group over time were examined. Results indicated that the average mean score achievement of the students in the experimental group was double that of the students in the control group. The Jun- Young Oh’s model of the enhanced conflict map showed that students in both groups changed from alternative conceptions to scientific conceptions with the experimental group showing greater improvement. It was also observed that from the post-test to delayed test, the Lakatosian method of teaching the SAC has a significant positive effect on students’ achievement at all levels of Bloom’s taxonomy, especially at the higher order thinking (HOT) levels (application and analysis-synthesis levels) as compared to the Euclidean method of teaching. In addition, the Lakatosian method helped the students to sustain their learning over time better than the Euclidean method did and also helped them to change their readiness level, especially at the HOT levels. The Lakatosian method helped students to foster skills that promote active learning. Of great importance was the use of mathematical language, as well as, the enhanced perception in the experimental group in comparison with the control group, through the use of the Lakatosian method. The results of this study are promising. It is recommended that pre-service teachers should be trained on how to effectively implement the Lakatosian heuristic method in their teaching.<br>Mathematics Education<br>D. Phil. (Mathematics, Science and Technology Education (Mathematics Education))

Since the beginning of the 21st century, service-oriented architecture (SOA) has emerged as an advancement of distributed computing. SOA is a framework where software modules are developed using straightforward interfaces, and each module serves a specific array of functions. It delivers enterprise applications individually or integrated into a more significant composite Web services. However, SOA implementation faces several challenges, hindering its broader adaptation. This thesis aims to highlight three significant challenges in the implementation of SOA. The abundance of functionally similar Web services and the lack of integrity with non-functional features such as Quality of Service (QoS) leads to the difficulties in the prediction of QoS. Thus, the first challenge to be addressed is to find an efficient scheme for the prediction of QoS. The use of software source code metrics is a widely accepted alternative solution. Source code metrics are measured at a micro level and aggregated at the macro level to represent the software adequately. However, the effect of aggregation schemes on QoS prediction using source code metrics remains unexplored. The inequality distribution model, the Theil index, is proposed in this research to aggregate micro level source code metrics for three different datasets and compare the quality of QoS prediction. The experiment results show that the Theil index is a practical solution for effective QoS prediction. The second challenge is to search and compose suitable Web services with- out the need for expertise in composition tools. Currently, the existing approaches need system engineers with extensive knowledge of SOA techniques. A keyword-based search is a common approach for information retrieval which does not require an understanding of a query language or the underlying data structure. The proposed framework uses a schema-based keyword search over the relational database for an efficient Web service search and composition. Experiments are conducted with the WS-Dream data set to evaluate Web service search and composition framework using adequate performance parameters. The results of a quality constraints experiments show that the schema-based keyword search can achieve a better success rate than the existing approaches. Building an efficient data architecture for SOA applications is the third challenge as real-world SOA applications are required to process a vast quantity of data to produce a valuable service on demand. Contemporary SOA data processing systems such as the Enterprise Data Warehouse (EDW) lack scalability. A data lake, a productive data environment, is proposed to improve data ingestion for SOA systems. The data lake architecture stores both structured and unstructured data using the Hadoop Distributed File System (HDFS). Experiment results compare the data ingestion time of data lake and EDW. In the evaluation, the data lake-based architecture is implemented for personalized medication suggestion system. The data lake shows that it can generate patient clusters more concisely than the current EDW-based approaches. In summary, this research can effectively address three significant challenges for the broader adaptation of SOAs. The Theil index-based data aggregation model helps QoS prediction without the dependence on the Web service registry. Service engineers with less knowledge of SOA techniques can exploit a schema-based keyword search for a Web service search and composition. The data lake shows its potential to act as a data architecture for SOA applications.