This study investigates Japanese high school teachers’ learning of grammar proceduralization strategies designed to promote oral fluency. It is a multiple case study of six Japanese EFL teachers who learn to use their declarative knowledge of L2 grammar while engaging in tasks that enable them to compare their oral output with a native English speaker’s reformulations of it. Past studies of language learning strategies have been primarily focused either on the learners’ general study habits toward the target language or on their skill-specific language learning strategies in the areas of listening, reading, speaking, writing, and vocabulary. Although the effectiveness of these strategies on learning outcomes is known to be highly constrained by learners’ prior linguistic knowledge, strategies to proceduralize grammar, a core component of one’s linguistic knowledge, have not been well researched. Therefore, little is known about how learners’ volitional efforts contribute to the proceduralization of L2 grammar. Research into oral fluency development has provided evidence that the use of formulas promotes fluency, but it has not revealed how formulas and other varieties of multiword units contribute to different aspects of oral fluency; namely, temporal, repair, and perceived fluency. This study fills these gaps in research by defining, investigating, and creating a set of grammar proceduralization strategies as a promising construct that sheds light on what learners can proactively do to proceduralize their knowledge of L2 grammar. The three main purposes of this study are to (a) investigate Japanese EFL teachers’ grammar proceduralization strategies for appropriating, refining, and using their grammar knowledge, (b) identify L2 morphosyntactic forms and multiword units that facilitate Japanese EFL teachers’ oral production during oral summary and personal anecdote tasks, and (c) investigate the possible relationships between the participants’ L2 grammar proceduralization strategies, their use of specific grammar forms, and their oral fluency development. The participants are six Japanese teachers of English who teach at public senior high schools in Japan. To gain a detailed understanding of the participants’ complex learning processes, their learning trajectories were investigated for a period of six months, using a longitudinal mixed-methods design, with detailed analyses of their English learning history, post-task protocols, linguistic measures, and rubric-based assessment of their oral fluency development. The results provide (a) a typology of L2 grammar proceduralization strategies created based on models of communicative competence and speech production, (b) 16 categories of grammar items that have potential impact on oral fluency development, with insights into factors that facilitate and debilitate the participants’ use of these grammar items, and (c) insights into how the participants’ goal orientation leads to their orchestration of L2 grammar proceduralization strategies, their use of 16 categories of grammar items, and to the different trajectories of their temporal, repair, and perceived fluency development. This study presents data to support the conclusion that a reverse-saliency strategy to learn L2 grammar in concepts, propositions, and discourse is a key to effective EFL pedagogy. / Teaching & Learning

Nowadays, we have millions of cameras in public places such as traffic junctions, railway stations etc., and capturing video data round the clock. This humongous data has resulted in an increased need for automation of visual surveillance. Analysis of crowd and traffic flows is an important step towards achieving this goal. In this work, we present our algorithms for identifying and segmenting dominant ows in surveillance scenarios. In the second part, we present our work aiming at predicting the visual saliency. The ability of humans to discriminate and selectively pay attention to few regions in the scene over the others is a key attentional mechanism. Here, we present our algorithms for predicting human eye fixations and segmenting salient objects in the scene. (i) Flow Analysis in Surveillance Videos: We propose algorithms for segmenting flows of static and dynamic nature in surveillance videos in an unsupervised manner. In static flows scenarios, we assume the motion patterns to be consistent over the entire duration of video and analyze them in the compressed domain using H.264 motion vectors. Our approach is based on modeling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments which are merged to obtain the final flow segments. This approach in compressed domain is shown to be both accurate and computationally efficient. In the case of dynamic flow videos (e.g. flows at a traffic junction), we propose a method for segmenting the individual object flows over long durations. This long-term flow segmentation is achieved in the framework of CRF using local color and motion features. We propose a Dynamic Time Warping (DTW) based distance measure between flow segments for clustering them and generate representative dominant ow models. Using these dominant flow models, we perform path prediction for the vehicles entering the camera's field-of-view and detect anomalous motions. (ii) Visual Saliency Prediction using Deep Convolutional Neural Networks: We propose a deep fully convolutional neural network (CNN) - DeepFix, for accurately predicting eye fixations in the form of saliency maps. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture visual semantics at multiple scales while taking global context into account. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We experimentally show that our network outperforms other recent approaches by a significant margin. In general, human eye fixations correlate with locations of salient objects in the scene. However, only a handful of approaches have attempted to simultaneously address these related aspects of eye fixations and object saliency. In our work, we also propose a deep convolutional network capable of simultaneously predicting eye fixations and segmenting salient objects in a unified framework. We design the initial network layers, shared between both the tasks, such that they capture the global contextual aspects of saliency, while the deeper layers of the network address task specific aspects. Our network shows a significant improvement over the current state-of-the-art for both eye fixation prediction and salient object segmentation across a number of challenging datasets.

Les études menées dans cette thèse portent sur le rôle de la couleur dans l'attention visuelle. Nous avons tenté de comprendre l'influence de l'information couleur dans les vidéos sur les mouvements oculaire, afin d'intégrer les attributs couleur dans un modèle de saillance visuelle. Pour cela, nous avons analysé différentes caractéristiques des mouvements oculaires d'observateurs regardant librement des vidéos en deux conditions: couleur et niveaux de gris. Nous avons également comparé les régions principales de regard sur des vidéos en couleur avec celles en niveaux de gris. Il est apparu que les informations de couleur modifient légèrement les caractéristiques de mouvement oculaire comme la position de l'œil et la durée des fixations. Cependant, nous avons constaté que la couleur augmente le nombre de régions de regard. De plus, cet influence de la couleur s'accroît au cours du temps. En nous appuyant sur ces résultats, nous avons proposé une méthode de calcul des cartes de saillance couleur. Nous avons intégré ces cartes dans un modèle de saillance existant. / The studies conducted in this thesis focus on the role of colour in visual attention. We tried to understand the influence of colour information on the eye movements while observing videos, to incorporate colour information into a model of visual saliency. For this, we analysed different characteristics of eye movements of observers while freely watching videos in two conditions: colour and grayscale videos. We also have compared the main regions of regard of colour videos with those of grayscale. We observed that colour information influences only moderately, the eye movement characteristics such as the position of gaze and duration of fixations. However, we found that colour increases the number of the regions of interest in video stimuli. Moreover, this varies across time. Based on these observations, we proposed a method to compute colour saliency maps for videos. We have incorporated colour saliency maps in an existing model of saliency.

Human vision is remarkable. By limiting the main concentration of high-acuity photoreceptors to the eye's central fovea region, we efficiently view the world by redirecting the fovea between points of interest using eye movements called <em>saccades</em>. <br /><br /> Part I describes a saccadic vision system prototype design. The dual-resolution saccadic camera detects objects of interest in a scene by processing low-resolution image information; it then revisits salient regions in high-resolution. The end product is a dual-resolution image in which background information is displayed in low-resolution, and salient areas are captured in high-acuity. This lends to a resource-efficient active vision system. <br /><br />Part II describes CMOS image sensor designs for active vision. Specifically, this discussion focuses on methods to determine regions of interest and achieve high dynamic range on the sensor.

A atenção seletiva é uma característica central do sistema visual humano, uma vez que todo o cérebro é otimizado de modo a perceber as informações ao seu redor da forma mais rápida possível. Porém, em geral os trabalhos nesta área apenas verificam quais são as regiões de maior freqüência da atenção seletiva, dando pouca importância para a sua mecânica. A presente dissertação propõe um modelo que represente a atenção seletiva como uma rede complexa, combinando naturalmente as áreas de redes complexas, cadeias de Markov, análise de imagens, atenção seletiva e saliência visual num modelo biologicamente plausível para simular a atenção seletiva. O modelo propõe que pontos importantes da imagem, pontos salientes, sejam caracterizados como vértices da rede complexa, e que as arestas sejam distribuídas de acordo com a probabilidade da mudança de atenção entre dois vértices. Desta forma, a mecânica da atenção seletiva seria simulada pela mecânica da rede complexa correspondente. Foram estudadas imagens em níveis de cinza, sendo estas correspondentes à cena observada. A probabilidade de mudança entre duas regiões, as arestas da rede, foram definidas através de diversos métodos de composição da saliência visual, e as redes resultantes comparadas com redes complexas provenientes de um experimento protótipo realizado. A partir deste experimento foram propostos refinamentos no modelo original, tornando assim a mecânica do modelo o mais próximo possível da mecânica humana da atenção seletiva. / Selective attention is a central feature of the human visual system, since the entire brain is optimized in order to understand the information around as quickly as possible. In general works in this area only search which regions has a higher frequency of selective attention, with little consideration for their mechanics. This study proposes a model that represents the selective attention as a complex network, combining naturally areas of complex networks, Markov chains, image analysis, selective attention and visual salience in a biologically plausible model to simulate the selective attention. The model proposes that the important points of the image, salient points, are identified as vertices of the complex network, and the edges are distributed according to the probability of shift of attention between two vertices. Thus, the mechanics of selective attention would be simulated by the mechanics of correspondent complex network. We studied images in gray levels, which are corresponding to the scene observed. The probability of switching between two regions, the edges of the network were identified through various methods of visual saliency composition, and the resulting networks compared with complex networks from a prototype experiment performed. From this experiment were proposed refinements to the original model, thereby making the mechanical design as close as possible to the mechanics of human selective attention.

abstract: The detection and segmentation of objects appearing in a natural scene, often referred to as Object Detection, has gained a lot of interest in the computer vision field. Although most existing object detectors aim to detect all the objects in a given scene, it is important to evaluate whether these methods are capable of detecting the salient objects in the scene when constraining the number of proposals that can be generated due to constraints on timing or computations during execution. Salient objects are objects that tend to be more fixated by human subjects. The detection of salient objects is important in applications such as image collection browsing, image display on small devices, and perceptual compression. This thesis proposes a novel evaluation framework that analyses the performance of popular existing object proposal generators in detecting the most salient objects. This work also shows that, by incorporating saliency constraints, the number of generated object proposals and thus the computational cost can be decreased significantly for a target true positive detection rate (TPR). As part of the proposed framework, salient ground-truth masks are generated from the given original ground-truth masks for a given dataset. Given an object detection dataset, this work constructs salient object location ground-truth data, referred to here as salient ground-truth data for short, that only denotes the locations of salient objects. This is obtained by first computing a saliency map for the input image and then using it to assign a saliency score to each object in the image. Objects whose saliency scores are sufficiently high are referred to as salient objects. The detection rates are analyzed for existing object proposal generators with respect to the original ground-truth masks and the generated salient ground-truth masks. As part of this work, a salient object detection database with salient ground-truth masks was constructed from the PASCAL VOC 2007 dataset. Not only does this dataset aid in analyzing the performance of existing object detectors for salient object detection, but it also helps in the development of new object detection methods and evaluating their performance in terms of successful detection of salient objects. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

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Littérature sur l'évaluation sociale a principalement analysé la dyade “audience-candidat”,laissant La recherche sur les évaluations sociales s’est principalement focalisé sur la dyade “audiencecandidat”,sans s’intéresser à la façon dont l’audience principale (par exemple un agent exerçant uncontrôle social) influence l’évaluation d’une autre audience. Cette thèse explore la question desévaluations sociales dans un contexte d’audiences multiples. Elle se focalise sur les comportementsorganisationnels condamnables – une important forme d’évaluation sociale, pourtant en partieignorée par la recherche – et pose la question suivante : “Pourquoi une audience change-t-elle sonévaluation après un comportement organisationnel condamnable?”. Les trois essais s’intéressent àune différente forme d’audience (ou d’évaluation) : les individus (plaintes), les investisseurs (prix del’action) et les médias (évaluation de la presse écrite). Deux contextes novateurs et données uniquesont été utilisés : l’auto régulation du secteur de la publicité en Grande-Bretagne, et Calciopoli, lescandale qui a affecté la Série A en Italie en 2006. Les résultats montrent qu’en cas de comportementorganisationnel condamnable, l’évaluation des agents de contrôle social influence l’évaluation d’autreaudience, mais cet effet n’est pas mécanique. Trois modérateurs sont identifiés : l’ambiguïté de lanorme, la proéminence de l’évènement, et à quel point les transgresseurs sont des acteurs locaux. Enrésumé, cette thèse montre que les normes sociales sont mieux comprises dans un cadre triadique :“candidat – agent de contrôle social – autre audience”. Les normes sociales ne sont pas exogènes,mais sont crées de manière endogène par les actions des candidats et les évaluations de deuxaudiences au moins. / Literature on social evaluations has mainly analyzed the “audience-candidate” dyad,leaving underexplored the way the evaluation of a main audience (e.g. a social-control agent)influences the evaluation of another audience. This dissertation looks at social evaluations in amultiple-audience context. It focuses on organizational social misconduct - an important, yetunderstudied social evaluation - and it investigates “Why does an audience change its evaluationfollowing organizational social misconduct?”. Each of the three essays focuses on a differentaudience (evaluation): people (people’s complaints), investors (share price) and the media(newspapers’ evaluation). Two novel settings and unique databases were used: advertising selfregulationin the UK and Calciopoli, the scandal that affected the Italian Serie A in 2006. Resultsshow that in case of organizational social misconduct, the evaluation of a social control agent doesinfluence the evaluation of another audience, however this effect is not mechanical. Three primarymoderators emerge from the three essays: the ambiguity of the norm, the saliency of the event, andlocalness of the transgressors. In summary, this dissertation shows that social norms are betterunderstood in a triadic framework: “candidate – social-control agent – another audience”. Socialnorms are not set exogenously, but are endogenously created by the actions of the candidates andthe evaluations of (at least) two audiences.

<p>A major feature of the human eye is the compressed sensitiveness of the retina. An image coder, which makes use of this, can heavily encode the parts of the image which is not close to the focus of our eyes. Existing image coding schemes require that the gaze direction of the viewer is measured. However, a great advantage would be if an estimator predicts the focus of attention (FOA) regions in the image. </p><p>This report presents such an implementation, which is based on a model that mimics many of the biological features of the human visual system (HVS). For example, it uses a center-surround mechanism, which is a replica of the receptive fields of the neurons in the HVS. </p><p>An extra feature of the implementation is the extension to handle video sequences, and the expansion of the FOA:s. The test results of the system show good results from a large variety of images.</p>

The objective of this dissertation research is to identify sounds that grab a listener's attention. These sounds that draw a person's attention are sounds that are considered salient. The focus here will be on investigating the role of saliency in the auditory attentional process. In order to identify these salient sounds, we have developed a computational auditory saliency model inspired by our understanding of the human auditory system and auditory perception. By identifying salient sounds we can obtain a better understanding of how sounds are processed by the auditory system, and in particular, the key features contributing to sound salience. Additionally, studying the salience of different auditory stimuli can lead to improvements in the performance of current computational models in several different areas, by making use of the information obtained about what stands out perceptually to observers in a particular scene. Auditory saliency also helps to rapidly sort the information present in a complex auditory scene. Since our resources are finite, not all information can be processed equally. We must, therefore, be able to quickly determine the importance of different objects in a scene. Additionally, an immediate response or decision may be required. In order to respond, the observer needs to know the key elements of the scene. The issue of saliency is closely related to many different areas, including scene analysis. The thesis provides a comprehensive look at auditory saliency. It explores the advantages and limitations of using auditory saliency models through different experiments and presents a general computational auditory saliency model that can be used for various applications.