The efficacy of two online instructional methods, guided inquiry and video, were experimentally examined for learning and change of misconceptions regarding learning styles; the relationship of student characteristics to outcomes was also examined. Learners’ mindset interacted with instructional method for learning; additional learner characteristics also indicated some relationship to the efficacy of these instructional methods for learning. Misconceptions, however, were generally resistant to change. Implications for online instruction and future directions for research are explored

In this thesis, we reexamine the classical problem of video super-resolution, with an aim to reproduce fine edge/texture details of acquired digital videos. In general, the video super-resolution reconstruction is an ill-posed inverse problem, because of an insufficient number of observations from registered low-resolution video frames. To stabilize the problem and make its solution more accurate, we develop two video super-resolution techniques: 1) a 2D autoregressive modeling and interpolation technique for video super-resolution reconstruction, with model parameters estimated from multiple registered low-resolution frames; 2) the use of image model as a regularization term to improve the performance of the traditional video super-resolution algorithm. We further investigate the interactions of various unknown variables involved in video super-resolution reconstruction, including motion parameters, high-resolution pixel intensities and the parameters of the image model used for regularization. We succeed in developing a joint estimation technique that infers these unknowns simultaneously to achieve statistical consistency among them. / Thesis / Master of Applied Science (MASc)

[pt] Cada vez mais, os vídeos se tornam uma parte integrante de nossa vida cotidiana. Plataformas como YouTube, Facebook e Instagram recebem uma enorme quantidade de horas de vídeo todos os dias. Quando focamos na categoria de vídeos esportivos, é evidente o crescente interesse em obter dados estatísticos, especialmente no futebol. Isso é valioso tanto para melhorar a performance de atletas e equipes quanto para plataformas que utilizam essas informações, como as de apostas. Consequentemente, o interesse em resolver problemas relacionados à Visão Computacional tem aumentado. No caso do Aprendizado Supervisionado, a qualidade das anotações dos dados é mais um ponto importante para o sucesso das pesquisas. Existem várias ferramentas de anotação disponíveis no mercado, porém poucas com o foco nos quadros relevantes e com suporte a modelos de Inteligência Artificial. Neste sentido, este trabalho envolve a utilização da técnica de Transfer Learning com a extração de features em uma Rede Neural Convolucional (CNN); a investigação de um modelo de classificação baseado na abordagem Few-Shot Learning em conjunto com o algoritmo K-Nearest Neighbors (KNN); a avaliação dos resultados com abordagens diferentes para o balanceamento de classes; o estudo da geração do gráfico 2D com o t-Distributed Stochastic Neighbor Embedding (t-SNE) para análise das anotações e a criação de uma ferramenta para anotação de frames importantes em vídeos, com o intuito de auxiliar as pesquisas e testes. / [en] More and more videos are part of our daily life. Platforms like Youtube, Facebook and Instagram receive a large amount of hours of videos every day. When we focus on the sports videos category, the growing interest in obtaining statistical data is evident, especially in soccer. This is valuable both for improving the performance of athletes and teams and for platforms that use this information, such as betting platforms. Consequently, interest in solving problems related to Computer Vision has increased. In the case of Supervised Learning, the quality of data annotations is another important point for the success of research. There are several annotation tools available on the market, but few focus on relevant frames and support Artificial Intelligence models. In this sense, this work involves the use of the Transfer Learning technique for Feature Extraction in a Convolutional Neural Network (CNN); the investigation of a classification model based on the Few-Shot Learning approach together with the K-Nearest Neighbors (KNN) algorithm; evaluating results with different approaches to class balancing; the study of 2D graph generation with t-Distributed Stochastic Neighbor Embedding (t-SNE) for annotation analysis and the creation of a tool for annotating important frames in videos, with the aim of assisting research and testing.

Five-dimensional (5-D) light field video (LFV) (also known as plenoptic video) is a more powerful form of representing information of dynamic scenes compared to conventional three-dimensional (3-D) video. In this dissertation, the spectra of moving objects in LFVs are analyzed, and it is shown that such moving objects can be enhanced based on their depth and velocity by employing 5-D digital filters, what is defined as depth-velocity filters. In particular, the spectral region of support (ROS) of a Lambertian object moving with constant velocity and at constant depth is shown to be a skewed 3-D hyperfan in the 5-D frequency domain. Furthermore, it is shown that the spectral ROS of a Lambertian object moving at non-constant depth can be approximated as a sequence of ROSs, each of which is a skewed 3-D hyperfan, in the 5-D continuous frequency domain. Based on the spectral analysis, a novel 5-D finite-extent impulse response (FIR) depth-velocity filter and a novel ultra-low complexity 5-D infinite-extent impulse response (IIR) depth-velocity filter are proposed for enhancing objects moving with constant velocity and at constant depth in LFVs. Furthermore, a novel ultra-low complexity 5-D IIR adaptive depth-velocity filter is proposed for enhancing objects moving at non-constant depth in LFVs. Also, an ultra-low complexity 3-D linear-phase IIR velocity filter that can be incorporated to design 5-D IIR depth-velocity filters is proposed. To the best of the author’s knowledge, the proposed 5-D FIR and IIR depth-velocity filters and the proposed 5-D IIR adaptive depth-velocity filter are the first such 5-D filters applied for enhancing moving objects in LFVs based on their depth and velocity. Numerically generated LFVs and LFVs of real scenes, generated by means of a commercially available Lytro light field (LF) camera, are used to test the effectiveness of the proposed 5-D depth-velocity filters. Numerical simulation results indicate that the proposed 5-D depth-velocity filters outperform the 3-D velocity filters and the four-dimensional (4-D) depth filters in enhancing moving objects in LFVs. More importantly, the proposed 5-D depth-velocity filters are capable of exposing heavily occluded parts of a scene and of attenuating noise significantly. Considering the ultra-low complexity, the proposed 5-D IIR depth-velocity filter and the proposed 5-D IIR adaptive depth-velocity filter have significant potentials to be employed in real-time applications. / Graduate / 0544

by Ngai Yau Ki. / Appendices in Chinese or English. / Thesis (M.A.Ed.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 79-82). / ACKNOWLEDGEMENTS --- p.ii / ABSTRACT --- p.iii / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.viii / LIST OF FIGURES --- p.x / Chapter CHAPTER 1: --- INTRODUCTION --- p.1 / Background --- p.1 / Purpose of the Study --- p.3 / Statement of the Problem --- p.6 / Significance of the Study --- p.6 / Chapter CHAPTER 2： --- REVIEW OF LITERATURE --- p.8 / Bloom's Learning Theory --- p.8 / Cognitive Entry Behaviour --- p.10 / Affective Entry Behaviour --- p.11 / Quality of Instruction --- p.18 / Application of Microcomputer in Education --- p.18 / Computer-Assisted Instruction --- p.19 / Interactive Video --- p.21 / Feedback --- p.24 / Interactive Videodisc --- p.25 / Interactive Videotape --- p.26 / Hardware of Interactive Videotape --- p.28 / Authoring Language of Interactive Videotape --- p.29 / Interaction Between Cognitive Entry Behaviour and Aptitude-Treatment Interaction --- p.29 / Aptitude-Treatment Interaction --- p.29 / Learner Control and Prior Knowledge --- p.32 / Instructional Design --- p.34 / Macro Variables in Instructional Design --- p.37 / Delivery Media and Learner --- p.38 / Chapter CHAPTER 3： --- METHODOLOGY --- p.41 / Sample of Subjects --- p.41 / Research Design --- p.45 / Procedure --- p.46 / Materials --- p.48 / Variables --- p.49 / Hardware Configuration --- p.51 / Authoring Language and Courseware Design --- p.51 / Null Hypotheses --- p.52 / Data Analysis --- p.52 / Chapter CHAPTER 4： --- RESULTS --- p.54 / Treatment Sessions --- p.54 / Reliability of the Instruments --- p.55 / Attitude --- p.55 / Perceived Self-Efficacy --- p.56 / Perceived Demand Characteristic --- p.57 / Learning Achievement --- p.60 / Immediate Posttest --- p.60 / Retention Posttest --- p.62 / Attitude --- p.64 / Regression --- p.66 / Chapter CHAPTER 5： --- "SUMMARY, DISCUSSION AND RECOMMENDATION" --- p.68 / Summary --- p.68 / Discussion --- p.70 / Development of Low Cost Interactive Videotape System --- p.70 / First and Second Null Hypotheses --- p.71 / Third Null Hypothesis --- p.73 / Bloom's Learning Theory --- p.74 / Limitations --- p.75 / Recommendations --- p.77 / REFERENCES --- p.79 / APPENDICES --- p.83 / Chapter Appendix A: --- Posttest --- p.83 / Chapter Appendix B: --- Remote Control of Domestic Electrical Appliance By Infrared Signal Emitted From Microcomputer --- p.85 / Chapter Appendix C: --- (I) Programme Structure and (II) Programme Listing --- p.87 / Chapter Appendix D: --- Programme Structure of Remediation Strategy --- p.99 / Chapter Appendix E: --- Questionnaire --- p.100 / Chapter Appendix F: --- Attitude Questionnaire --- p.105

by Mary Y.Y. Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 64-66). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Video-On-Demand Environment --- p.1 / Chapter 1.2 --- Problem Definition --- p.3 / Chapter 2 --- Related Work --- p.7 / Chapter 2.1 --- Data Sharing Techniques --- p.7 / Chapter 2.1.1 --- Batching --- p.7 / Chapter 2.1.2 --- Buffering --- p.9 / Chapter 2.1.3 --- Static Partitioning --- p.10 / Chapter 2.1.4 --- Adaptive Piggybacking --- p.10 / Chapter 2.2 --- Providing VCR Functionalities --- p.12 / Chapter 3 --- System Model --- p.15 / Chapter 3.1 --- Operations involved in VCR Control --- p.15 / Chapter 3.2 --- Normal Playback Model --- p.17 / Chapter 3.3 --- VCR Model --- p.18 / Chapter 4 --- Resource Allocation for Normal Playback --- p.21 / Chapter 4.1 --- Mathematical Model --- p.22 / Chapter 4.1.1 --- Hits occurring within the same partition (hit w) --- p.24 / Chapter 4.1.2 --- Jump to other partitions (hito) --- p.27 / Chapter 4.1.3 --- Fast-forwarding to the end of a movie --- p.30 / Chapter 4.1.4 --- The expected hit probability P(hit) --- p.31 / Chapter 4.2 --- Model Verification --- p.32 / Chapter 5 --- Resource Allocation for VCR mode --- p.35 / Chapter 5.1 --- Scheme 1: No merging --- p.36 / Chapter 5.2 --- Scheme 2: Merging by adaptive piggybacking and buffering --- p.36 / Chapter 5.2.1 --- Resuming within the threshold (Δ ≤ k) --- p.38 / Chapter 5.2.2 --- Resuming beyond the threshold (Δ > k) --- p.39 / Chapter 5.3 --- Verification --- p.42 / Chapter 6 --- Applications to System sizing --- p.45 / Chapter 6.1 --- Cost of Resources for Normal Playback --- p.46 / Chapter 6.2 --- Cost of Resources for VCR functions --- p.48 / Chapter 6.3 --- Overall system cost --- p.49 / Chapter 6.4 --- Comparison --- p.50 / Chapter 6.4.1 --- Scheme 1 vs. Scheme 2 --- p.51 / Chapter 6.4.2 --- Scheme 2 vs. pure I/O & pure buffer --- p.54 / Chapter 6.4.3 --- Different values of k --- p.58 / Chapter 6.4.4 --- Different values of ψ --- p.60 / Chapter 7 --- Conclusions --- p.62 / Bibliography --- p.64 / Chapter A --- Appendix --- p.67 / Chapter A.l --- Rewind --- p.67 / Chapter A.1.1 --- Hits occurring within the same partition (hit w) --- p.67 / Chapter A.1.2 --- Jump to other partitions (hit0) --- p.68 / Chapter A.2 --- Pause --- p.70

This thesis analyzes the YouTube service and describes its main deficiencies. Based on theoretical methods and analyses, its main goal is to design a service that will solve the main YouTube problems, build a company around this service and introduce this service to the market. This service will not replace YouTube, but it will supplement it. Further, this work will suggest a possible structure, strategy and information strategy of this new company and its estimated financial results in the first few years.

No description available.

Music video is an intriguing genre of television due to the fact that music drives the images and ideas found in numerous and varied examples of the form. Pre-recorded pieces of pop music are visually written upon in a palimpsest manner, resulting in an immediate and entertaining synchronisation of sound and vision. Ever since the popularity of MTV in the early 1980s, music video has been a persistent fixture in academic discussion, most notably in the work of writers like E. Ann Kaplan, Simon Frith and Andrew Goodwin. What has been of major interest to such cultural scholars is the fact that music video was designed as a promotional tool in their inception, supporting album sales and increasing the stardom of the featured recording artists. Authorship in music video studies has been traditionally kept to the representation of music stars, how they incorporate post-modern references and touch upon wider cultural themes (the Marilyn Monroe pastiche for the Madonna video, Material Girl (1985) for instance). What has not been greatly discussed is the contribution of music video directors, and the reason for that is the target audience for music videos are teenagers, who respond more to the presence of the singer or the band than the unknown figure of the director, a view that is also adhered to by music television channels like MTV.

Providing computer-aided support for human analysis of videos has been a battle of extremes. Powerful solutions exist, but they tend to be domain-specific and complex. The user-friendly, simple systems provide little analysis support beyond basic media player functionality. We propose a human-centered, domain-independent solution between these two points. Our proposed model and system, VAST, is based on our experience in two diverse video analysis domains: science and athletics. Multiple-perspective location metadata is used to group related video clips together. Users interact with these clip groups through a novel interaction paradigm ? views. Each view provides a different context by which users can judge and evaluate the events that are captured by the video. Easy conversion between views allows the user to quickly switch between contexts. The model is designed to support a variety of user goals and expertise with minimal producer overhead. To evaluate our model, we developed a system prototype and conducted several rounds of user testing requiring the analysis of volleyball practice videos. The user tasks included: foreground analysis, ambiguous identification, background analysis, and planning. Both domain novices and experts participated in the study. User feedback, participant performance, and system logs were used to evaluate the system. VAST successfully supported a variety of problem solving strategies employed by participants during the course of the study. Participants had no difficulty handling multiple views (and resulting multiple video clips) simultaneously opened in the workspace. The capability to view multiple related clips at one time was highly regarded. In all tasks, except the open-ended portion of the background analysis, participants performed well. However, performance was not significantly influenced by domain expertise. Participants had a favorable opinion of the system?s intuitiveness, ease of use, enjoyability, and aesthetics. The majority of participants stated a desire to use VAST outside of the study, given the opportunity.