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Dissertations / Theses on the topic 'Movie data'
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Müglich, Marcel. "Motion Feature Extraction of Video and Movie Data." Thesis, KTH, Numerisk analys, NA, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214030.
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Since the Video on Demand market grows at a fast rate in terms of available content and user numbers, the task arises to match personal relevant content to each individual user. This problem is tackled by implementing a recommondation system which finds relevant content by automatically detecting patterns in the individual user’s behaviour. To find such patterns, either collaborative filtering, which evaluates patterns of user groups to draw conclusions about a single user’s preferences, or content based strategies can be applied. Those content strategies analyze the watched movies of the individual user and extract quantifiable information from them. This information can be utilized to find relevant movies with similar features. The focus of this thesis lies on the extraction of motion features from movie and video data. Three feature extraction methods are presented and evaluated which classify camera movement, estimate the motion intensity and detect film transitions.VOD-marknaden (Video på begäran) är en växande marknad, dels i mängden tillgängligt innehåll samt till antalet användare. Det skapar en utmaning att matcha personligt relevant innehåll för varje enskild användare. Utmaningen hanteras genom att implementera ett rekommendationssystem som hittar relevant innehåll genom att automatiskt identifiera mönster i varje användaren beteende. För att hitta sådana mönster används i vanliga fall Collaborative filtering; som utvärderar mönster utifrån grupper av flera användare och kors- rekommenderar produkter mellan dem utan att ta nämnvärd hänsyn till produktens innehåll. (De som har köpt X har också köpt Y) Ett alternativ till detta är att tillämpa en innehållsbaserad strategi. Innehållsbaserade strategier analyserar den faktiska video-datan i de produkter som har konsumerats av en enskild användare med syfte att därifrån extrahera kvantifierbar information. Denna information kan användas för att hitta relevanta filmer med liknande videoinnehåll. Inriktningen för denna avhandling berör utvinning av kamerarörelsevektorer från film- och videodata. Tre extraktionsmetoder presenteras och utvärderas för att klassificera kamerans rörelse, kamerarörelsen intensitet och för att detektera scenbyten.
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Almadi, Kanika. "Quantitative study of the movie industry based on IMDb data." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113502.
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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (page 47).
Big Data Analytics is an emerging business capability that is providing far more intelligence to the companies nowadays to make well-informed decisions and better formulate their business strategies. This has been made possible due to easy accessibility of immense volume of data stored in clouds in a secure manner. As a result, online product review platforms have also gained enormous popularity and are successfully providing various services to the consumers primarily via user-generated content. The thesis makes use of raw and unstructured data available on IMDB website, cleans it up and organizes it in a structured format suitable for quick analysis by various analytical softwares. The thesis then examines the available literature on analytics done on IMDB movie dataset and identifies that little work has been carried out in predicting the financial success of the movies. The thesis thus carries out data analytics on the IMDB movie sets and highlights several parameters like movie interconnectedness and director's credentials, which correlates positively with the movie gross revenue. The thesis thereafter loosely defines a movie innovative index encompassing of parameters like number of references, number of follows and number of remake and discusses how the abundance of some of these parameters have a positive impact on box office success of the movie. Contrarily the lack of presence of these parameters thereby characterizing an innovative movie may not be so well received by the audiences thus leading to poor box office performance. The thesis also proposes how the director's credentials in the film industry measured by his/her total number of nominations and awards winning in the Oscar have a positive impact on the financial success of the movie and their own career advancement.
by Kanika Almadi.
S.M. in Engineering and Management
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Wrenn, Alex. "Differences in Seasonality Based on Movie Quality." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2029.
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In recent years, the entertainment industry has begun to announce the release dates of many of their movies years in advance. This leads one to believe that movie studios are not taking into account the quality of a movie when a studio decides its release date. This paper will be an analysis in whether there is a difference in seasonality between different qualities of movies. If a studio announces the release date before filming even begins, it is clear that they do not know, and therefore cannot properly consider, the quality of the movie when they make its release date public. I will use films that make over a million dollars at the box office from 2000-2016 to examine the seasonality of good, average, and bad movies. My models will control for variables that were found to be significant in previous research. These include budget, MPAA rating, genre, and Oscar nominations. I will prove that there is a difference in seasonality between all three of these qualities groups. This will show that the Hollywood is now dismissing a key component in the difficult decision process that is movie release dates.
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Arroniz, Inigo. "EXTRACTING QUANTITATIVE INFORMATIONFROM NONNUMERIC MARKETING DATA: AN AUGMENTEDLATENT SEMANTIC ANALYSIS APPROACH." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3083.
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Despite the widespread availability and importance of nonnumeric data, marketers do not have the tools to extract information from large amounts of nonnumeric data. This dissertation attempts to fill this void: I developed a scalable methodology that is capable of extracting information from extremely large volumes of nonnumeric data. The proposed methodology integrates concepts from information retrieval and content analysis to analyze textual information. This approach avoids a pervasive difficulty of traditional content analysis, namely the classification of terms into predetermined categories, by creating a linear composite of all terms in the document and, then, weighting the terms according to their inferred meaning. In the proposed approach, meaning is inferred by the collocation of the term across all the texts in the corpus. It is assumed that there is a lower dimensional space of concepts that underlies word usage. The semantics of each word are inferred by identifying its various contexts in a document and across documents (i.e., in the corpus). After the semantic similarity space is inferred from the corpus, the words in each document are weighted to obtain their representation on the lower dimensional semantic similarity space, effectively mapping the terms to the concept space and ultimately creating a score that measures the concept of interest. I propose an empirical application of the outlined methodology. For this empirical illustration, I revisit an important marketing problem, the effect of movie critics on the performance of the movies. In the extant literature, researchers have used an overall numerical rating of the review to capture the content of the movie reviews. I contend that valuable information present in the textual materials remains uncovered. I use the proposed methodology to extract this information from the nonnumeric text contained in a movie review. The proposed setting is particularly attractive to validate the methodology because the setting allows for a simple test of the text-derived metrics by comparing them to the numeric ratings provided by the reviewers. I empirically show the application of this methodology and traditional computer-aided content analytic methods to study an important marketing topic, the effect of movie critics on movie performance. In the empirical application of the proposed methodology, I use two datasets that combined contain more than 9,000 movie reviews nested in more than 250 movies. I am restudying this marketing problem in the light of directly obtaining information from the reviews instead of following the usual practice of using an overall rating or a classification of the review as either positive or negative. I find that the addition of direct content and structure of the review adds a significant amount of exploratory power as a determinant of movie performance, even in the presence of actual reviewer overall ratings (stars) and other controls. This effect is robust across distinct opertaionalizations of both the review content and the movie performance metrics. In fact, my findings suggest that as we move from sales to profitability to financial return measures, the role of the content of the review, and therefore the critic's role, becomes increasingly important.Ph.D.
Department of Marketing
Business Administration
Business Administration PhD
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Wu, Yuk Ying. "Movie allocation in parallel video servers /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20WU.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 69-76). Also available in electronic version. Access restricted to campus users.
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Karaman, Hilal. "A Content Based Movie Recommendation System Empowered By Collaborative Missing Data Prediction." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612037/index.pdf.
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The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquoWhich one should I choose?&rdquo
arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.
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Ma, Ke. "Content-based Recommender System for Movie Website." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188494.
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Recommender System is a tool helping users find content and overcome information overload. It predicts interests of users and makes recommendation according to the interest model of users. The original content-based recommender system is the continuation and development of collaborative filtering, which doesn’t need the user’s evaluation for items. Instead, the similarity is calculated based on the information of items that are chose by users, and then make the recommendation accordingly. With the improvement of machine learning, current content-based recommender system can build profile for users and products respectively. Building or updating the profile according to the analysis of items that are bought or visited by users. The system can compare the user and the profile of items and then recommend the most similar products. So this recommender method that compare user and product directly cannot be brought into collaborative filtering model. The foundation of content-based algorithm is acquisition and quantitative analysis of the content. As the research of acquisition and filtering of text information are mature, many current content-based recommender systems make recommendation according to the analysis of text information. This paper introduces content-based recommender system for the movie website of VionLabs. There are a lot of features extracted from the movie, they are diversity and unique, which is also the difference from other recommender systems. We use these features to construct movie model and calculate similarity. We introduce a new approach for setting weight of features, which improves the representative of movies. Finally we evaluate the approach to illustrate the improvement.Recommender System är ett verktyg som hjälper användarna att hitta innehåll och övervinna informationsöverflöd. Det förutspår användarnas intressen och gör rekommendation enligt räntemodellen användare. Den ursprungliga innehållsbaserade recommender är en fortsättning och utveckling av samarbete filtrering, som inte behöver användarens utvärdering artiklar. Istället är likheten beräknas baserat på informationen objekt som har varit valde av användare, och sedan göra rekommendationen därefter. Med förbättringen av maskininlärning, kan nuvarande innehållsbaserad recommender systemet bygga profil för användare och produkt respektive. Bygga eller uppdatera profilen enligt analysen av objekt som köps eller besöks av användare. Systemet kan jämföra användaren och profilen av artiklar och rekommendera den mest liknande produkt. Så här recommender metod som jämför användaren och produkten direkt kan inte föras in collaborative filtreringsmodell. Grunden för innehållsbaserad algoritm är förvärv och kvantitativ analys av innehållet. Eftersom forskning förvärv och filtrering av textinformation är mogen, många aktuella innehållsbaserade recommender system gör rekommendation enligt analysen av textinformation. Denna uppsats införa innehållsbaserad recommender system för film webbplats VionLabs. Det finns en mängd funktioner som extraherats från en film, är de mångfald och unik, vilket är också skillnaden med andra recommender system. Vi använder dessa funktioner för att konstruera film vektor och beräkna likheter. Vi introducerar en ny metod för att fastställa vikten av funktioner, vilket förbättrar företrädare för filmer. Slutligen utvärderar vi tillvägagångssättet för att illustrera förbättringen.
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Peng, Fengjiao S. M. Massachusetts Institute of Technology. "My Personalized Movies : novel system for automatically animating a movie based on personal data and evaluation of its impact on affective and cognitive experience." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120674.
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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-77).
Storytelling is a fundamental way in which human beings make sense of the world. Animated movies tell stories that engage audience across culture and age groups. I designed and built My Personalized Movies (MPM), a novel system where animated stories are automatically created based on data provided by individuals. The data include self-tracked mood and behavior captured in quantitative measures and descriptive text. MPM is designed to engage viewers through an emotive narrative, induce self-reflection about their mood and behavior patterns, and to improve self-compassion and self-esteem, which mediates behavior change. I demonstrate with a few stages of studies, involving in total 107 participants, that viewers show strong emotional engagement with MPM and can explicitly connect animated characters' stories to one's past experiences. An analysis of 22 participants' facial expression data during MPM reveals that participants' change in implicit self-esteem is positively correlated with the happiness of their facial expression. Participants with higher depression severity, as measured by PHQ9, showed less positive facial expression at the happy moments in the animation.
by Fengjiao Peng.
S.M.
9
El, Aouad Sara. "Personalized, Aspect-based Summarization of Movie Reviews." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS019.pdf.
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Les sites web de critiques en ligne aident les utilisateurs à décider quoi acheter ou quels hôtels choisir. Ces plateformes permettent aux utilisateurs d’exprimer leurs opinions à l’aide d’évaluations numériques et de commentaires textuels. Les notes numériques donnent une idée approximative du service. D'autre part, les commentaires textuels donnent des détails complets, ce qui est fastidieux à lire. Dans cette thèse, nous développons de nouvelles méthodes et algorithmes pour générer des résumés personnalisés de critiques de films, basés sur les aspects, pour un utilisateur donné. Le premier problème que nous abordons consiste à extraire un ensemble de mots liés à un aspect des critiques de films. Notre évaluation montre que notre méthode est capable d'extraire même des termes impopulaires qui représentent un aspect, tels que des termes composés ou des abréviations. Nous étudions ensuite le problème de l'annotation des phrases avec des aspects et proposons une nouvelle méthode qui annote les phrases en se basant sur une similitude entre la signature d'aspect et les termes de la phrase. Le troisième problème que nous abordons est la génération de résumés personnalisés, basés sur les aspects. Nous proposons un algorithme d'optimisation pour maximiser la couverture des aspects qui intéressent l'utilisateur et la représentativité des phrases dans le résumé sous réserve de contraintes de longueur et de similarité. Enfin, nous réalisons trois études d’utilisateur qui montrent que l’approche que nous proposons est plus performante que la méthode de pointe en matière de génération de résumésOnline reviewing websites help users decide what to buy or places to go. These platforms allow users to express their opinions using numerical ratings as well as textual comments. The numerical ratings give a coarse idea of the service. On the other hand, textual comments give full details which is tedious for users to read. In this dissertation, we develop novel methods and algorithms to generate personalized, aspect-based summaries of movie reviews for a given user. The first problem we tackle is extracting a set of related words to an aspect from movie reviews. Our evaluation shows that our method is able to extract even unpopular terms that represent an aspect, such as compound terms or abbreviations, as opposed to the methods from the related work. We then study the problem of annotating sentences with aspects, and propose a new method that annotates sentences based on a similarity between the aspect signature and the terms in the sentence. The third problem we tackle is the generation of personalized, aspect-based summaries. We propose an optimization algorithm to maximize the coverage of the aspects the user is interested in and the representativeness of sentences in the summary subject to a length and similarity constraints. Finally, we perform three user studies that show that the approach we propose outperforms the state of art method for generating summaries
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Persson, Karl. "Predicting movie ratings : A comparative study on random forests and support vector machines." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11119.
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The aim of this work is to evaluate the prediction performance of random forests in comparison to support vector machines, for predicting the numerical user ratings of a movie using pre-release attributes such as its cast, directors, budget and movie genres. In order to answer this question an experiment was conducted on predicting the overall user rating of 3376 hollywood movies, using data from the well established movie database IMDb. The prediction performance of the two algorithms was assessed and compared over three commonly used performance and error metrics, as well as evaluated by the means of significance testing in order to further investigate whether or not any significant differences could be identified. The results indicate some differences between the two algorithms, with consistently better performance from random forests in comparison to support vector machines over all of the performance metrics, as well as significantly better results for two out of three metrics. Although a slight difference has been indicated by the results one should also note that both algorithms show great similarities in terms of their prediction performance, making it hard to draw any general conclusions on which algorithm yield the most accurate movie predictions.
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Bhatnagar, Ashish. "Accelerating a Movie Recommender System Using VirtualCL on a Heterogeneous GPU Cluster : Big Data Analysis Using Distributed Accelerators." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175775.
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Present day market offers a large number of movies which overwhelm people with choices. In order to quickly navigate through all the possible movies and find the interesting ones, the user can take advantage of recommender systems for movies. This thesis studies a movie recommender system which uses image processing and computer vision algorithms. The amount of time taken to analyze movies using these computation intensive algorithms is in the order of years. However, exploiting parallel nature of these algorithms using GPUs (Graphics Processing Unit) can help reduce the time many-folds. The primary goal of the thesis is to build a heterogeneous GPU cluster and use it to accelerate the algorithms of the recommender system. The guidelines and steps to build a heterogeneous GPU cluster given in the thesis can be used by other organizations and researchers. Results indicate that the heterogeneous GPU cluster platform can accelerate algorithms of a movie recommender system up to 5 times. The secondary goal of this thesis is to investigate the benefits of using VirtualCL framework which enables remote access to the GPUs of the cluster. Remote access to the GPUs provides energy efficiency and ease of cluster management. Results show that VirtualCL framework provides remote GPU capability at the cost of degradation in performance. Therefore, VCL framework should be used just for application areas where performance can be traded off for physical portability and ease of management.
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Asquith, Phoebe. "Using independent components analysis to identify visually driven regions and networks in the human brain, using data collected during movie watching." Thesis, Cardiff University, 2018. http://orca.cf.ac.uk/120197/.
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Traditionally, regions involved in visual processing are mapped in the brain using simple localisers and/or anatomical techniques. As a more efficient (and interesting) alternative, Bartels & Zeki (2004) suggested that independent components analysis (ICA) could be used to segment the brain into functional regions, using data collected during movie watching. The first aim of this thesis was to explore the potential of this technique for reliable identification of visually driven regions and networks. In Chapter 2 I thoroughly and systematically explore the sensitivity of tensor ICA (TICA) to common pre-processing parameters and identify an optimal analysis pipeline. Despite some sensitivity of TICA to the parameters tested, robust components in visually responsive regions could be identified across outputs. Using an optimized pipeline, in Chapter 3 I demonstrate that visually driven components (in particular, peak voxels) are consistent across different samples and movie clips, supporting the use of this technique. In Chapter 4 I show that established resting state networks can be identified in an ICA analysis using movies, and that by increasing dimensionality sub-regions of these networks can be identified. Chapter 5 shows how these reliable components represented visual regions in the motion processing pathway. Based on the success of the technique at the group level, in Chapter 6 I apply the technique to individual observer data. Results show that functional networks and visual regions of interest can be reliably identified, supporting its use in future neuroscientific research. To address the short-comings of BOLD, the second aim of this thesis was to investigate whether MEG frequency data and fMRI bold data could be combined for analysis in a novel technique using TICA. First in Chapter 7 I address some prerequisites for a combined MEG frequency analysis using the technique. On the back of these results, I use the technique to generate interesting cross-frequency components (Chapter 8) and cross modality components using combined MEG and fMRI data (Chapter 9). These results show exciting promise for potential use in future neuroscientific work. In the final chapter, I investigate the potential use of ICA and changing dimensionality for mapping the functional hierarchy of the visual system. With development this could be a useful tool for understanding connectivity between sub-regions of functional networks. These results have important implications for the identification of visually responsive regions and for understanding neural activity during natural viewing.
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Deirmenci, Hazim. "Enabling Content Discovery in an IPTV System : Using Data from Online Social Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200922.
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Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages.Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.
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Feng, Ping Feng. "Examination of the Hollywood Movie Trailers Editing Pattern Evolution over Time by Using the Quantitative Approach of Statistical Stylistic Analysis." Master's thesis, Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/395476.
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Media Studies & ProductionM.A.
In this study, I took the quantitative research approach of film statistical stylistic analysis to examine the editing pattern evolution of 130 Hollywood movie trailers over the past 60 years from 1951 to 2015; the prior studies on the overall evolution of the Hollywood movies’ editing pattern are compared and discussed. The results suggest that although the movie trailers are much shorter than the whole movies, the average shot lengths of the trailers still display a declining trend over the past 60 years, and the variations in the shot lengths are also decreasing. Second, the motions within each framedo not change significantly over the years, while the correlation coefficients between the shot lengths and the motions within the shots are moving toward a more negative correlation relationship over time, suggesting that the trailers are subject to an editing evolution trend that the shorter the shot is, the more motions there are within it, and this also aligns with the overall movies’ editing pattern evolution trend. Last, the luminance of the trailers remains almost the same over time, which does not align with the overall movies’ editing pattern evolution of becoming darker and darker over decades. Together these findings suggest that the movie trailers’ editing rhythm evolution in general aligns with that of overall movies over time while the visual editing pattern evolution of color luminance does not. The study results will improve our understanding on how the Hollywood movie trailers’ editing pattern and style have evolved over time and pave the way for future advertising studies and cognitive psychology studies on the audience’s attention, immersion and emotional response to various editing patterns of movie trailers.
Temple University--Theses
15
Schmidt, Adler Hoff. "Characterizing dissemination of illegal copies of content through BitTorrent networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/87584.
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Redes BitTorrent (BT) atualmente representam o método Par-a-Par (P2P) de compartilhamento de arquivos pela Internet mais utilizado. Relatórios de monitoramento recentes revelam que as cópias de conteúdo sendo compartilhadas são, em grande maioria, ilegais e que filmes são os tipos de mídia mais populares. Iniciativas de pesquisa que tentaram entender a dinâmica da produção e do compartilhamento de conteúdo em redes BT não conseguiram prover informações precisas acerca da disseminação de cópias ilegais. No presente trabalho realizamos um extenso estudo experimental para caracterizar o comportamento de produtores, publicadores, provedores e consumidores de arquivos violando direitos autorais. O estudo conduzido é baseado em dados coletados durante sete meses de monitoração de enxames compartilhando filmes por meio de uma das comunidades públicas mais populares de BT. Os dados foram obtidos via emprego de uma arquitetura de monitoração do \universo" BitTorrent, o que permitiu popular uma base com informações acerca de mais de 55.000 torrents, 1.000 rastreadores e 1,9 milhões de IPs. Nossa análise não somente mostra que um pequeno grupo de usuários ativos _e responsável pela maior parte do compartilhamento de cópias ilegais, como desvenda relacionamentos existentes entre esses atores e caracteriza os padrões de consumo respeitados pelos usuários interessados nesse tipo de conteúdo.BitTorrent (BT) networks are nowadays the most employed method of Peerto- Peer (P2P) le sharing in the Internet. Recent monitoring reports reveal that content copies being shared are mostly illegal and movies are the most popular media type. Research e orts carried out to understand the dynamics of content production and sharing in BT networks have been unable to provide precise information regarding the dissemination of illegal copies. In this work we perform an extensive experimental study in order to characterize the behavior of producers, publishers, providers and consumers of copyright-infringing les. This study is based on seven months of traces obtained by monitoring swarms sharing movies via one of the most popular BT public communities. Traces were obtained with an extension of a BitTorrent \universe" observation architecture, which allowed the collection of a database with information about more than 55,000 torrents, 1,000 trackers and 1.9 million IPs. Our analysis not only shows that a small group of active users is responsible for the majority of disseminated illegal copies, as it unravels existing relationships among these actors and characterizes consuming patterns respected by users interested in this particular set of contents.
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Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.
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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field.
Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand.
The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market.
During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics.
To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered.
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Zabaleta, de Larrañaga Iñaki. "Using objective data from movies to predict other movies’ approval rating through Machine Learning." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-22111.
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Machine Learning is improving at being able to analyze data and find patterns in it, but does machine learning have the capabilities to predict something subjective like a movie’s rating using exclusively objective data such as actors, directors, genres, and their runtime? Previous research has shown the profit and performance of actors on certain genres are somewhat predictable. Other studies have had reasonable results using subjective data such as how many likes the actors and directors have on Facebook or what people say about the movie on Twitter and YouTube. This study presents several machine learning algorithms using data provided by IMDb in order to predict the ratings also provided by IMDb and which features of a movie have the biggest impact on its performance. This study found that almost all conducted algorithms are on average 0.7 stars away from the real rating which might seem quite accurate, but at the same time, 85% of movies have ratings between 5 and 8, which means the importance of the data used seems less relevant.
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Svensson, Peter, and Youssef Taoudi. "Labeling Moods of Movies by Processing Subtitles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254547.
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Labeling movies by moods is a feature that is useful for recommendation engines in modern movie streaming applications. Movie recommendation based on moods is a feature that could improve user experience for movie streaming platforms by recommending more relevant movies to users. This thesis describes the development of a mood labeling feature that labels movies by processing movie subtitles through Natural Language Processing. Movies are processed by analysing subtitles to predict the mood of a movie through computational methods. The prototype utilizes movies pre-labeled with moods to construct a lexicon that contains information of the defining attributes for moods in movie subtitles. Using the constructed lexicon, the similarities between a movie subtitle and a lexicon can be compared to calculate the probability that a movie belongs to a specific mood. Four moods were chosen for analysis in the prototype: fear, sadness, joy, and surprise.The Naive Bayes method was chosen as the classifier for the prototype. A Naive Bayes classifier observes each occurring word in a movie without consideration to the context of the word in a text or sentence. The results showed that the classifier had trouble distinguishing between the moods. However, for all configurations of the prototype, the classifier showed higher precision for the mood fear compared to the other moods. Overall the classifier performed poorly and did not produce a reliable result.Klassificering av filmer via stämning är en funktion som är användbar för rekommendationsmotorer i moderna filmströmmingsprogram. Filmrekommendation baserad på stämning är en funktion som kan förbättra användarupplevelsen på filmströmmande plattformar genom att rekommendera mer relevanta filmer till användarna. Denna uppsats beskriver utvecklingen av en prototyp för att klassificera filmer efter deras stämning genom att bearbeta filmens undertexter med hjälp av metoder inom språkteknologi. Filmer bearbetas genom att analysera undertexter för att avgöra stämningen hos en film. Prototypen använder filmer som är fördefinierade med stämning för att konstruera ett lexikon som innehåller information om de definierande egenskaperna för en stämning i filmtexter. Med hjälp av ett konstruerat lexikon kan likheterna mellan en filmtextning och ett lexikon jämföras för att beräkna sannolikheten för att en film tillhör en viss stämning. Fyra stämningar valdes för analys i prototypen: rädsla, sorg, glädje och överraskning.Navie Bayes-metoden valdes som klassificeringsmedel för prototypen. En Naive Bayes-klassificerare observerar varje förekommande ord utan hänsyn till ordets sammanhang i en mening eller text. Resultaten visade att klassificeringen hade problem att skilja mellan stämningarna. För samtliga konfigurationer av prototypen visade klassificeringsenheten dock högre precision för rädsla jämfört med de andra stämningarna. Sammantaget presterade klassificeraren dåligt och gav inte ett tillförlitligt resultat.
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Tsang, Kwong-ping Loretta. "Offshore office : a strategic move : a post-implementation review of Cathay Pacific Airways Sydney Data Centre move /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18836732.
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Zhang, Linquan, and 张琳泉. "Move my data to the cloud: an online cost-minimizing approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48330140.
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Cloud computing has rapidly emerged as a new computation paradigm, providing agile and scalable resource access in a utility-like fashion. Processing of massive amounts of data has been a primary usage of the clouds in practice. While many efforts have been devoted to designing the computation models (e.g., MapReduce), one important issue has been largely neglected in this respect: how do we efficiently move the data, practically generated from different geographical locations over time, into a cloud for effective processing? The usual approach of shipping data using hard disks lacks flexibility and security. As the first dedicated effort, this paper tackles this massive, dynamic data migration issue. Targeting a cloud encompassing disparate data centers of different resource charges, we model the cost-minimizing data migration problem, and propose efficient offline and online algorithms, which optimize the routes of data into the cloud and the choice of the data center to aggregate the data for processing, at any give time. Three online algorithms are proposed to practically guide data migration over time. With no need of any future information on the data generation pattern, an online lazy migration (OLM) algorithm achieves a competitive ratio as low as 2:55 under typical system settings, and a work function algorithm (WFA) has a linear 2K-1 (K is the number of data centers) competitive ratio. The rest one randomized fixed horizon control algorithm (RFHC) achieves 1+ 1/(l+1 ) κ/λ competitive ratio in theory with a lookahead window of l into the future, where κ and λ are protocol parameters. We conduct extensive experiments to evaluate our online algorithms, using real-world meteorological data generation traces, under realistic cloud settings. Comparisons among online and offline algorithms show a close-to-offline-optimum performance and demonstrate the effectiveness of our online algorithms in practice.published_or_final_version
Computer Science
Master
Master of Philosophy
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Chowdhury, H. (Helal). "Data download on the move in visible light communications:design and analysis." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526213620.
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Abstract In visible light communication (VLC), light emitting diodes (LEDs) are used as transmitters; the air is the transmission medium and the photodiodes are used for receivers. This is often referred to as light fidelity (Li-Fi). In this thesis, we provide the methodology to evaluate the performance of VLC hotspot networks in the context of data downloading on the move scenarios by using throughput-distance relationship models. In this context, first we study the different properties of optical transceiver elements, noise sources, characterization and modelling of artificial light interference, different link topologies and then we introduce the throughput-distance relationship model. Secondly, the analytically based throughput-distance relationship has been developed for evaluating the performance of VLC hotspot networks in indoor environment in both day and night conditions. Simulation results reveal that background noise has a significant impact on the performance of VLC hotspots. As expected, in both indoor and outdoor environments the VLC hotspot performs better at night than during day. The performance of VLC hotspot networks is also quantified in terms of received file size at different bit error rate requirements and velocities of the mobile user. Thirdly, we study the performance of hybrid (Radio-Optical) WLAN-VLC hotspot and compare its performance with stand-alone VLC-only or WLAN-only hotspot cases. In this case, we also consider the data download on the move scenarios in an indoor environment for a single-user as well as for multi-user cases. In this hybrid WLAN-VLC hotspot, both the WLAN and the VLC are characterized by their throughput and communication range. Simulations have been performed to evaluate the performance of such network for data downloading on the move scenario by taking into account performance metrics such as filesize, average connectivity and system throughput. Simulation results reveal that the considered hybrid WLAN-VLC performs always better than stand-alone VLC-only or WLAN-only hotspot both for a single and multi-user cases. Finally, this thesis analyses the feasibility and potential benefits of using hybrid radio-optical wireless systems. In this respect, cooperative communication using optical relays are also introduced in order to increase the coverage and energy efficiency of the battery operated device. Potential benefits are identified as service connectivity and energy efficiency of battery operated device in an indoor environment. Simulation results reveal that user connectivity and energy efficiency depend on user density, coverage range ratio between single-hop and multi-hop, relay probabilities and mobility of the userTiivistelmä Näkyvään valoon pohjautuvassa tiedonsiirrossa (VLC) valodiodeja (LED) käytetään lähettiminä, ilma on siirtokanava ja valoilmaisimia käytetään vastaanottimina. Tätä kutsutaan usein nimellä light fidelity (Li-Fi). Tässä työssä tarjoamme menetelmiä VLC ”hotspot” verkkojen suorituskyvyn arviointiin tiedonsiirtonopeus-etäisyysmalleilla skenaarioissa, jossa tietoa ladataan liikkeessä. Tässä kontekstissa tutkimme ensin optisen lähettimen komponenttien eri ominaisuuksia, kohinan lähteitä, keinovalon häiriömalleja ja tiedonsiirtolinkkien topologioita, jonka jälkeen esittelemme tiedonsiirtonopeuden ja etäisyyden välisen mallin. Toiseksi kehitetyn analyyttisen tiedonsiirto-etäisyys mallia käytetään arvioitaessa VLC hotspot verkkojen suorituskykyä sisäympäristössä sekä päivä että yö olosuhteissa. Simulointien tulokset osoittavat, että taustakohinalla on suuri vaikutus VLC verkkojen suorituskykyyn. Kuten odotettua, sisä- ja ulkotiloissa VLC hotspot toimii paremmin yöllä kuin päivällä. VLC hotspot verkkojen suorituskyky arvioidaan myös vastaanotetun tiedoston koon, eri bittivirhesuhteen vaatimuksilla ja liikkuvan käyttäjän nopeuden suhteen. Kolmanneksi tutkimme hybridi WLAN-VLC hotspot verkon suorituskykyä ja vertaamme sen suorituskykyä pelkän VLC- tai WLAN hotspot tapauksessa. Käsittelemme myös skenaarioita jossa tiedoston lataus tapahtuu liikkeessä sisätilassa yhden käyttäjän sekä monen käyttäjän tapauksissa. Tässä hybridi WLAN-VLC hotspot, sekä erilliset WLAN- ja VLC verkot ovat määritelty niiden tiedonsiirtonopeuden ja kantaman perusteella. Näiden verkkojen suorituskykyä arvioitaessa on tehty joukko tietokonesimulointeja verkossa tapahtuvasta tietojen lataamisesta liikkeessä ottamalla huomioon suorituskyvyn mittarit kuten tiedoston koko, keskimääräinen yhteyden kesto ja saavutettu läpäisy. Simuloinnin tulokset paljastavat, että hybridi WLAN-VLC toimii aina paremmin kuin pelkkä VLC tai WLAN hotspot sekä yhden että monen käyttäjän tapauksessa. Lopuksi työssä analysoidaan ehdotetun järjestelmän toteutettavuus ja mahdolliset edut käytettäessä hybridejä radio-optisia langattomia järjestelmiä. Tältä osin esitellään myös kooperatiiviseen viestintään perustuvat optiset releet parantamaan verkon kattavuutta ja energiatehokkuutta akkukäyttöisissä laitteissa. Mahdolliset hyödyt tunnistetaan palvelun konnektiivisuudessa ja energiatehokkuudessa akkukäyttöisissä laitteissa sisätiloissa. Simulointien tulokset osoittavat, että käyttäjien konnektiivisuus ja energiatehokkuus riippuvat käyttäjätiheydestä, kantaman ja etäisyyden välisestä suhteesta yhden hypyn ja monen hypyn välillä, releointi todennäköisyydestä ja käyttäjien mobiliteetista
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Larsén, Simon. "Spork : Move-enabled structured merge for Java with GumTree and 3DM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281960.
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With software development becoming an increasingly parallel process in which branching lines of development is common, the shortcomings of unstructured merge techniques are becoming more tangible. In particular, merge conflicts occur frequently and often unnecessarily. Structured merge techniques typically operate on abstract syntax trees instead of raw text, and present major improvements over unstructured merge in terms of avoiding unnecessary conflicts. Structured merge can however be prone to slow execution, has problems dealing with refactorings such as renamings and moves of code blocks, and is often poor at preserving source code formatting. In this thesis, we implement and evaluate spork, a structured merge tool for the java programming language. spork is built using move-enabled diff and merge algorithms that can resolve refactoring-related conflicts automatically. The algorithms used are highly efficient, making spork scale well with larger merges. spork also has features for reusing the original source code of the input files in a merge, leading to improved preservation of source code formatting. We compare spork to the state of the art structured merge tool jdime in an empirical evaluation on 890 merge scenarios from 119 different open source java projects, comprising a total of 1740 file merges. spork and jdime tend to be equally fast in the median case, with median running times of 1.45 and 1.48 seconds per file merge, respectively. jdime is the faster tool below the median running time, but spork is faster above the median, indicating that spork scales better with larger merges. Additionally, spork produces fewer but slightly larger conflicts, mostly due to known issues. We also find no statistically significant difference between spork’s and jdime’s abilities to produce correct merges. In some cases, spork’s move-enabled algorithms allows it to automatically resolve merges with renamings that cause conflicts for jdime. In conjunction, these results constitute strong evidence that move-enabled merge is feasible for use in a structured merge tool for the java programming language. spork is also more than 4 times better than jdime at preserving formatting in the median case. Although the results are promising, there are unresolved issues with conflicts related to moves and deletes that reduce spork’s performance and reliability, necessitating further work.I samband med att mjukvaruutveckling blir en alltmer parallelliserad process så blir tillkortakommanden hos ostrukturerad sammanfogning (eng: merge) mer påtagliga. I synnerhet är sammanfogningskonflikter frekvent förekommande, och många av dessa sker i onödan. Strukturerad sammanfogning arbetar vanligtvis på abstrakta syntaxträd istället för på text, och kan till stor del avhjälpa problem med onödiga konflikter. Det finns dock aspekter av strukturerad sammanfogning som är i behov av förbättring, bland annat lång körtid, problem att hantera omstruktureringar såsom namnändringar och förflyttningar av kodblock, samt undermålig förmåga att bevara formatering. I detta examensarbete implementerar och utvärderar vi spork, ett strukturerat sammanfogningsverktyg för program skrivna i java. spork bygger på förflyttningsmedvetna (eng: move-enabled) skillnads- och sammanfogningsalgoritmer som ofta automatiskt kan sammanfoga filer trots omstruktureringar. Dessa algoritmer är även effektiva, vilket gör att spork skalar väl med större sammanfogningar. Utöver detta har spork funktionalitet för att direkt återanvända källkod från filversionerna som sammanfogas, vilket bättre återskapar den ursprungliga formateringen. Genom en experimentell utvärdering jämförs spork med det toppmoderna strukturerade sammanfogningsverktyget jdime på 890 sammanfogningsscenarion från 119 olika java-projekt med öppen källkod, som totalt uppgår till 1740 olika filsammanfogningar. Viktiga resultat inkluderar att spork och jdime är lika snabba i medianfallet, med körtider på 1.45 respektive 1.48 sekunder per filsammanfogning. Under medianen är spork marginellt långsammare än jdime, men över medianen är spork betydligt snabbare, vilket tyder på att spork skalar bättre med större sammanfogningar. spork producerar även färre men något större konflikter än jdime, vilket till stor del beror på kända problem. Ingen statistiskt signifikant skillnad mellan verktygens förmågor att sammanfoga korrekt kan påvisas, men det finns bevis för att sporks förflyttningsmedvetna algoritmer låter det automatiskt lösa sammanfogningar med namnändringar som orsakar konflikter för jdime. Sammantaget utgör dessa resultat starkt bevis för att förflyttningsmedvetna algoritmer är användbara i ett praktiskt sammanfogningsverktyg för java-kod. Vidare finner vi även att sporks återanvändning av källkod leder till en mer än 4 gånger bättre bibehållning av formatering i medianfallet gentemot jdime. Trots dessa lovande resultat finns det fortfarande olösta problem med konflikter relaterade till förflyttningar och borttagningar av kod som sänker sporks prestanda och pålitlighet, och ytterligare arbete krävs för att avhjälpa detta.
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Ventura, Marcelo dos Santos. "Monte Carlo simulation studies in log-symmetric regressions." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8278.
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Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
This work deals with two Monte Carlo simulation studies in log-symmetric regression models, which are particularly useful for the cases when the response variable is continuous, strictly positive and asymmetric, with the possibility of the existence of atypical observations. In log- symmetric regression models, the distribution of the random errors multiplicative belongs to the log-symmetric class, which encompasses log-normal, log- Student-t, log-power- exponential, log-slash, log-hyperbolic distributions, among others. The first simulation study has as objective to examine the performance for the maximum-likelihood estimators of the model parameters, where various scenarios are considered. The objective of the second simulation study is to investigate the accuracy of popular information criteria as AIC, BIC, HQIC and their respective corrected versions. As illustration, a movie data set obtained and assembled for this dissertation is analyzed to compare log-symmetric models with the normal linear model and to obtain the best model by using the mentioned information criteria.
Este trabalho aborda dois estudos de simulação de Monte Carlo em modelos de regressão log- simétricos, os quais são particularmente úteis para os casos em que a variável resposta é contínua, estritamente positiva e assimétrica, com possibilidade da existência de observações atípicas. Nos modelos de regressão log-simétricos, a distribuição dos erros aleatórios multiplicativos pertence à classe log-simétrica, a qual engloba as distribuições log-normal, log-Student- t, log-exponencial- potência, log-slash, log-hyperbólica, entre outras. O primeiro estudo de simulação tem como objetivo examinar o desempenho dos estimadores de máxima verossimilhança desses modelos, onde vários cenários são considerados. No segundo estudo de simulação o objetivo é investigar a eficácia critérios de informação populares como AIC, BIC, HQIC e suas respectivas versões corrigidas. Como ilustração, um conjunto de dados de filmes obtido e montado para essa dissertação é analisado para comparar os modelos de regressão log-simétricos com o modelo linear normal e para obter o melhor modelo utilizando os critérios de informação mencionados.
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Kirkpatrick, Charles R., and A. Ayban Tuncay. "On The Move, Interactive Telemetry Data Acquisition System for the Multiple Launch Rocket System (MLRS)." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/611851.
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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, NevadaThis paper describes the Launcher Instrumentation and Data Acquisition System (LIDAS) that has been developed for real-time monitoring and simultaneous recording of a diverse set of data buses on a moving MLRS launcher. The launcher onboard instrumentation consists of a central Bus Data Conversion Module (BDCM) and several specialized, intelligent "break-in" boxes. The break-in boxes collect and tag the data by using the IRIG-B standard time-code, and transfer them to the BDCM using a unique asynchronous scheme. The BDCM is built around an Intel 80960CA processor board in a VME bus environment. It coordinates all the data traffic and also stores selected data to an onboard Flash ROM data storage unit. The data from different MLRS buses are combined into a 1 megabits per second RS4-22 serial stream and telemetered to a ground station, where the user interface is provided through an IBM PC/AT type computer with touch-screen controls. The developed PC software offers several data monitoring options with engineering-unit conversions and allows simultaneous recording on a hard-disk. Because of its interactive capabilities, the system is also well suited for personnel training.
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Amez, Lucy. "Tag based Bayesian latent class models for movies : economic theory reaches out to big data science." Thesis, London Metropolitan University, 2017. http://repository.londonmet.ac.uk/1263/.
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For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data.
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Tsang, Kwong-ping Loretta, and 曾廣萍. "Offshore office: a strategic move : a post-implementation review of Cathay Pacific Airways Sydney Data Centremove." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B3126833X.
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Kurthakoti, Raghu. "THREE ESSAYS ON MEASURING PRODUCT PLACEMENT EFFECTIVENESS IN MOVIES: ECONOMIC WORTH, FORGETTING AND ATTITUDE TOWARD NEGATIVE PLACEMENTS." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/415.
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Product placements are gaining more importance in corporate marketing communication budgets and marketers need to understand the effectiveness of these placements to justify investments into them. Three studies were conducted to study the effectiveness of product placements in movies. Essay one studied the economic worth of product placements on the long term profitability of the firm through an event study. Analysis of 467 placements of movies released during 1968-2007 shows that product placements generate a mean cumulative abnormal return of 0.21% during the (-1, +2) event window. Hierarchical linear modeling of the abnormal returns in cross-sectional analysis indicates that placement duration positively impacts the abnormal returns. Placement blatancy was found to negatively affect placements' worth. We did not find any support for the effect of critical reviews or presence of a star director on the worth of product placements. Crime and comedy genres were found to positively affect abnormal returns of placements. Additional MANCOVA analysis, using different event windows as the dependent variable, suggests that a period of two weeks might be required after a movie's release for the information about placement execution factors to be incorporated by the market in its evaluation of the firm. In essay two we study the effectiveness of product placement from a memory perspective by means of a longitudinal study, using a student subject panel. Subjects were exposed to a full- length movie and recognition was tracked at weekly intervals for a period of four weeks. Results of a dynamic panel analysis using generalized estimating equations indicate that audience recognition for a movie placement significantly diminishes one to two weeks after exposure to the movie. In addition, recognition of placements is enhanced by audiences' attitude toward product placements. Recognition is further affected by placement execution factors. Specifically, we found that audio placements and placements of longer duration positively affect placement recognition. Plot connectivity and character association did not significantly impact recognition over time. Essay three examines the impact of brand-character association on consumer attitude toward the placed brand. A 2x2 within subjects experiment, using a full-length movie as a stimulus, was conducted on a panel of student subjects to assess the interaction effect of character-brand valence on consumer attitude. Results support a significant interaction between character and brand valence. Analyses also indicate that congruency between character and brand valence enhances affective measures toward the placed brand, supporting the congruency theory and Meaning Transfer Model. Additionally, we found that brand familiarity fully mediates the character-valence interaction. Limitations and Implications of the studies were also discussed.
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Ehsan, Jamali Hondori. "Full waveform inversion of supershot-gathered data for optimization of turnaround time in seismic reflection survey." Kyoto University, 2016. http://hdl.handle.net/2433/217744.
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Mallqui, Morales Nayda Isabel. "Diseño de migración de nodos B aplicado para una RNC caida de una red movil." Bachelor's thesis, Universidad Ricardo Palma, 2015. http://cybertesis.urp.edu.pe/handle/urp/1276.
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La presente tesina consiste en el diseño de migración de nodos B aplicado para una RNC caída de una red móvil, con la finalidad de solucionar los problemas que se presenten ante un incidente que afecte los servicios de voz y datos de los usurarios de una red móvil.
En el desarrollo de la tesina, se describe el planteamiento del problema, el marco teórico de la tecnología UMTS y posteriormente nos centramos en los elementos principales de esta tecnología. También describimos los equipos importantes a utilizar en desarrollo del proyecto, en este caso nos enfocamos en la descripción de la RNC. Y finalmente describimos el desarrollo del proyecto, el diseño de la solución e implementación de la misma, y en donde se presentan los resultados del diseño.
This thesis is the design of migration of nodes B and its a applied when RNC fall for a mobile network, in order to solve the problems that arise before an incident affecting voice and data services from a mobile network.
In developing the thesis, we describe the theoretical framework of UMTS technology and then we focus on the main elements of this technology. We also describe the important equipment used in project development; in this case we focus on the description of the RNC. And finally we describe the solution of design and implementation .Also, the results of this project.
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Pouilly, Elisabeth. "L'"état d'esprit performatif" dans le théâtre et le cinéma d'Alejandro Jodorowsky." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCA144.
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Issu de Dada et du surréalisme, le groupe Panique, constitué de Fernando Arrabal, Roland Topor et Alejandro Jodorowsky, est créé en 1962. L’évolution de la pratique artistique de Jodorowsky de 1962 à 2016 est riche d’enseignements sur la performance théâtrale et l’interaction entre le théâtre et le cinéma. Nous suivons le constat que fait Joseph Danan dans Entre théâtre et performance: la question du texte qu’à un « état d’esprit dramaturgique » évoqué par Bernard Dort a succédé un « état d’esprit performatif » sur la scène contemporaine. L’œuvre de Jodorowsky est traversée par cet « état d’esprit performatif », de ses « éphémères paniques » qu’il réalise entre 1962 et 1967, à son « théâtre panique » puis son « théâtre essentiel », à son cinéma. L’analyse des éphémères, proches temporellement et essentiellement des premières performances historiques, permet de faire ressortir les caractéristiques de la performance qui peuvent passer dans d’autres formes artistiques. Avant la présentation d’une performance se noue implicitement un pacte entre les performeurs et les spectateurs. Ce « pacte performatif », correspondant aux attentes et aux croyances du spectateur de performance, repose sur trois points : l’implication de l’artiste lui-même dans son œuvre, l’unicité de la performance et la réalisation d’actes réels. Notre étude permet de voir comment ces trois points, tout en s’adaptant à l’art dans lequel ils sont apportés, constituent le noyau de l’ « état d’esprit performatif » qui parcourt l’ensemble de l’œuvre de JodorowskyDeriving from Dada and surrealism, the Panic group, made up of Fernando Arrabal, Roland Topor and Alejandro Jodorowsky, was created in 1962. The evolution of Jodorowsky's artistic practice from 1962 to 2016 is rich in teachings on theatrical performance and the interaction between theatre and cinema. We agree with the observation made by Joseph Danan in Entre théâtre et performance: la question du texte that from a "dramaturgical state of mind" evoked by Bernard Dort has succeeded a "performative state of mind" on the contemporary scene. Jodorowsky's work is crossed by this "performative state of mind", from his "panic ephemera" that he realised between 1962 and 1967, to his "panic theatre" and his "essential theatre" and his cinema. The analysis of the ephemera, temporally and essentially close of the first historical performances, makes it possible to highlight the characteristics of the performance which can pass in other artistic forms. Before the presentation of a performance is implicitly a pact between the performers and the spectators. This "performance pact", corresponding to the expectations and beliefs of the performance spectator, is based on three points: the involvement of the artist himself in his work, the uniqueness of the performance and the realization of real acts. With our study we are able to see how these three points, while adapting to the art in which they are brought, constitute the core of the "performative state of mind" that runs through the whole of Jodorowsky's work
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Justa, Ticiana Sá da. "O consumo de filmes em cinemas no Brasil: uma análise de florestas aleatórias." Universidade de São Paulo, 2019. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-31012019-181148/.
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Baseado em dois momentos distintos da realidade brasileira, os anos de 2002/03 e 2008/09, este estudo busca avaliar se houve mudança no perfil dos consumidore de filmes em salas de cinema, por meio de variáveis observáveis de indivíduos consumidores e não consumidores. Para esta finalidade são empregadas técnicas de mineração de dados de florestas aleatórias sobre os microdados das Pesquisas de Orçamentos Familiares (POFs) de 2002/2003, quando o uso de banda larga no Brasil era praticamente nulo, e de 2008/2009, quando esta já estava estabelecida no país. Esta diferença no tempo e de acesso à banda larga proporcionam uma janela de oportunidade para nosso objetivo, dado o elevado grau de mudanças tecnológicas do período investigado. Apesar de metodologicamente não ser possível isolar completamente o efeito do acesso à Internet sobre o consumo de cinema, espera-se que quanto maior a velocidade de acesso e maior a evolução das tecnologias de compactação de arquivos, a distribuição de conteúdo aumente na rede e permita maior consumo de filmes de maneira alternativa ao em salas de cinema. Neste contexto, além de identificar se houve alguma mudança de perfil dos consumidores de filmes em salas de cinema no Brasil após a popularização da banda larga no país, que oficialmente pode ser considerada no ano de 2006, também avaliamos se estes consumidores diferem significativamente dos não consumidores em suas características observáveis. Adicionalmente, num segundo estudo, usando um modelo de intervenção de Diferenças em Diferenças, investigamos o efeito da banda larga sobre o consumo de cinema. Os resultados apontam divergência clara entre os perfis de consumidores e não consumidores de filmes de cinema.I investigate whether film at theaters consumers in two distinct time are different in their observable characteristics. My investigation is based on observable variables of consumers and non-consumers data from the Family Budget Survey (POF) of 2002/2003, when the use of broadband in Brazil was almost nil, and from 2008/2009, when it already was established in the country. This change in addition to the high degree of technological changes over the period provides a window of opportunity for our objective. Although methodologically it is not possible to full isolate the effect of Internet access on film consumption, it is expected that the higher the user access speed and the greater the evolution of file compression technologies, the faster and bigger will be the traffic of films files on the web. Accordingly, the greater distribution of content on the network enables the consumption of films in alternative ways to movie theaters. Thus, in addition to identifying possible changes in the profile of movie consumers at the theaters in Brazil since the popularization of broadband in the country, which can officially be considered in the year 2006, the study assess whether these consumers differ significantly from not consumers in their observable characteristics. The results points to several distinctions between cinema consumers and no cinema consumers
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Štys, Jiří. "Implementace statistických kompresních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-413295.
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This thesis describes Burrow-Wheeler compression algorithm. It focuses on each part of Burrow-Wheeler algorithm, most of all on and entropic coders. In section are described methods like move to front, inverse frequences, interval coding, etc. Among the described entropy coders are Huffman, arithmetic and Rice-Golomg coders. In conclusion there is testing of described methods of global structure transformation and entropic coders. Best combinations are compared with the most common compress algorithm.
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Duan, Cheng-Fu, and 段承甫. "Timing Prediction of Movie Scoring Based on Data Mining Techniques." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7tn3fy.
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碩士國立政治大學
資訊科學系
106
Film score is essential to movies. Composers compose background scores for movies according to movie styles and genres. Much research has been done on video content analysis, but none has been done on timing prediction of movie score. In this thesis, we investigate the timing prediction of film score based on data mining techniques. It is helpful for timing prediction of background music for user generated content. In the proposed approach, the timing prediction problem is transformed as a binary classification problem. We first segment movies into scenes by alignment between scripts and subtitles of movies. After movie segmentation, visual features, text features, movie metadata and sentiment features of each scene are extracted and are used to learn the prediction model. In the experiments, Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and Conditional Random Field algorithms are employed for model training. The result of experiments show that timestamp, proportion of subtitles and word density of scenes are key factors of timing prediction and taking context into consideration can improve prediction performance.
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Liao, Shiue-ru, and 廖雪如. "Determinants of American Movie Box Office-Analysis of Panel data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/59783q.
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碩士世新大學
財務金融學研究所(含碩專班)
100
The film industry is a very important industry worldwide. Many products are created and launched relating to a scuccessful movie. Producing and marketing a movie is notoriously risky so determinants of Box Office are very important. Past studies always use OLS regression model to analysis the cross-section of motion picture box office. This study applies panel data analysis to examine key factors affecting movie box office in American. Our data includes not only film characteristics but also distribution-related variables and daily box office. The results show that previous cumulated box office, runtime, theaters and MAPP rating have siginificant positive effects on box office. The fixed effect panel analysis reveals that the positive relationship between time and daily box office may not always hold. The negative time effect appears in the 27th days after the release day of the movie.
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Tseng, Jen-Tsung, and 曾仁宗. "Using the Technologies of Linked Open Data to Explore a Military Movie Retrieval Service." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/41452219338170633866.
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碩士國防大學
資訊管理學系
102
In recent years, due to the rapid development of digital devices and network/communication technology, the volumes of various video files have dramatic growth; the diversity and complexity of video contents have also continuous increases. Therefore, how to strengthen the accuracy of video resources description and improve the machine readability to facilitate web search is an important issue in the digital video era. This study is considered military movies as research targets. The author uses a video annotation tool (ELAN) to increase movie-related descriptive data in detail, providing more extra metadata for supporting video retrieval. Besides, this study also uses the characteristic of linked open data (LOD) to extract RDF from open datasets, enabling the extensible knowledge exploration for each annotation concept. The purpose of linked data is to build a machine-readable and semantic-richness data web. Through the exploration of linking processes, this service could be constructed to a global data space. This study intends to develop an LOD-based military movie knowledge retrieval service (LODMKRS) to improve document-based movie description. The service not only provides basic metadata and annotated words that include the characteristic of timeline, but also links specified concept with the related and extensive knowledge. That is helpful for enhancing the effectiveness of military movie retrieval service. Moreover, this study designed a survey questionnaire to evaluate the degree of users’ satisfaction. In short, the LODMKRS system was accepted in usefulness, trust, comfort, enjoyment and overall satisfaction by most of the participants.
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Chou, Ting Chih, and 周鼎智. "Application of Matrix Factorization and Random Effect Model to analysis and comparison of movie rating data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/86d87y.
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碩士國立政治大學
統計學系
106
The recommender system (RS) appeared to solve the problem of information overload. The demand of the RS has increased with the advancement of technology and the popularity of the Internet, and related techniques have become more diverse and mature. The statistical models widely used in various fields are also in the list of techniques. The operation of the RS relies on user preference information, and the space of users’ preference to items is often large and unbalanced. Statistically, relatively complex random effects models or mixed effects models are needed to describe such variable structures, and often require a large number of iterations to estimate model parameters. Perry (2014), Gao & Owen (2016) proposed using the moment-based method to deal with hierarchical linear models and two-factor random effects models, respectively, expressing an idea of sacrificing statistical efficiency in exchange for computational efficiency. In this study, we analyze and compare the random effects model, using the maximum likelihood method and the moment-based method to estimate the parameters with the matrix factorization. Through the prediction accuracy and computational efficiency to evaluate the performance of each algorithm on the MoiveLens data. According to the experiment results, the random effects model is not as good as the matrix factorization in terms of the prediction accuracy no matter what kind of parameter estimation method is used; however, the performance of the moment-based parameter estimation is consistent with the matrix factorization in terms of the prediction stability, and much better in terms of the efficiency.
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Huang, Yi-Ting, and 黃怡婷. "Using Social Media Data and the Least Squares Support Vector Regression to Predict Movie Box Office." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8b2m49.
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碩士國立暨南國際大學
資訊管理學系
106
Nowadays increasingly busy lives and the and easy accessibility of Internet, the development of social networking sites has been promoted, and the number of users has increased dramatically year by year. This study used Twitter, one of the top 10 global community websites in 2017, as a source of collection of emotional analysis data, and as the combination of this study. The other data were collected from movie websites of Box Office Mojo and IMDB (Internet Movie Database) This study uses the least square support vector regression (LSSVR) and the following three models Multivariate Linear Regression (MLR), Back Propagation Neural Networks (BPNN), the General Regression Neural Network (GRNN) to analyze the data. The cross validation procedure was performed. The numerical results indicated that the Mean Absolute Percentage Error (MAPE) of the emotional data combined with structured data is lower than that generated by the single data (emotional data or structured data). In addition, the prediction results of LSSVR model are better than that of the other modes.
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Podhůrská, Martina. "Využití data miningu v analýze filmových anotací." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-384348.
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This work will focus on the possibilities of data mining in film analysis and its use in predicting ratings on ČSFD movie diabase, based on film annotations and other attributes of the film. The main goal is to determine whether it is possible to predict user ratings of new movies and identify the attributes that affect the most the ratings on Movie Database, as well as other factors such as. revenues and attendance.
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CHEN, HSIANG, and 陳翔. "The Use of Deep Learning Method for Analyzing EEG Data to Determine the Feasibility Study of the Emotional Movie Watching." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8p9672.
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博士國立臺北大學
企業管理學系
107
Emotion is the most important part of everyday interaction between people. Today, it is important for computers to understand the emotions that users interact with in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. In recent years, emotional recognition based on EEG signals has attracted many researchers to research and develop many methods. Different types of features are extracted from the EEG signal, and then different types of classifiers are applied to these features. In this paper, we first explore the advantages and disadvantages of each study through the discussion and analysis of 80 articles, and use the research results as the basis for this study. Therefore, in the course of conducting the experiment, the brainwave data of the subject was taken, and the subject was asked to watch the film for more than 20 minutes for the subject to watch, and finally the collected data was used for analysis. A deep learning method is proposed to identify the emotional model of the original EEG signal, and the Long and Short Memory Model (LSTM) is used to learn the characteristics of the EEG signal, and then these features are divided into six emotion annotations and brought into the model. Compared with traditional statistical method techniques, the method proposed in this study can increase the accuracy.
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SU, YOU-PEI, and 蘇友培. "Crawling Technology of Big Data in Internet Behavior Applied to Exception Confirmation Theory Retest: Taking Yahoo Movie as an Example." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/263423.
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碩士國立臺北科技大學
工業工程與管理系
107
In the past, consumer satisfaction was learned through the expectation confirmation theory. However, in this well-developed generation of networks, most people use the Internet to express their opinions. For example, after we watch a movie, many people will post their opinions on the Internet, and others can decide to go to watch movie or not. However, the comments are mixed. When the number of comments exceeds hundreds or even thousands, the readers will lose patience; In addiction, not everyone is willing to comment on the text, through star ratings as a measure of satisfaction, the public can be more willing to be investigated. Therefore, this study conducts a retest of the expectation confirmation theory by applying the big data crawling technology to the expectations and satisfaction of Yahoo movies.
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SHUANG, QIU, and 邱爽. "Using Big Data to Analyze the Important Factors of Movie Box Office to Explore the Relationship between Taiwan and Mainland Box Offices." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/stxc9t.
Full textAbstract:
碩士中國文化大學
資訊管理學系
107
With the improvement of people's living standards, more and more people enter the cinema to enjoy movies. Watching movies has become an indispensable part of people's entertainment life. The economic development of China has driven the vigorous development of the film industry. China has already become the second largest film market after the United States. Such a huge film market, if the theater can predict the trend of upcoming movies, will be very beneficial to investors. The Taiwanese film market is similar to Chinese film market. The film released in Taiwan will be 7-25 days earlier than China. If we can find the correlation between the Taiwanese movie box office and Chinese movie box office, then the investor can make corresponding strategies for the upcoming movie in China. This study used data exploration to study and influence the factors affecting the box office of the movie. The method used is to mine association rules. Firstly, find out the influencing factors of Taiwanese box office, and then explore the connection between Taiwanese box office and Chinese movie box office. Finally, through experiments, if a movie gets a high box office in Taiwan, when it released in China, it will have 82% possibility get a high box office.
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Fang, Cheng, and 方正. "Taiwanese' Lifestyle and their Radio, TV, Movie Usage─Using 2005 Shih Hsin University Communication Database and In-depth Interview Data as an Example." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/95551461728673479801.
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碩士世新大學
廣播電視電影學研究所(含碩專班)
95
This research is for the purpose of discussing the appraisal relevance of the life style and the usage of broadcasts, the televisions, and the movies. The research is based upon the raw data of 2005 Shih Hsin Communication Database authorized by Shih Hsin Communication College. 2005 Shih Hsin Communication Database is based on 1,100 interviewees of the populaces which investigates the usage of broadcasts, the televisions, and the movies. Also, in the questionnaire contains life style as a variable. The purpose of this research is to analyze the populace using behaviors and evaluations of broadcasts, the televisions, and the movies. This research also makes deep investigations and interviews with the representatives of different groups to understand the characteristics of types of groups and their activities on expressing opinions. Six factors (including fashion elements, family orientation, outgoing modern, planned consuming, health care, and political issues concerned) are identified by the factor analysis of life style variables are named as Regular Group, Smart Family Group, Traditional Elite Group, Youth Ease Group, Modern Faison Group, and Fogy Conserve Group. In the statistics and analysis of the in-depth investigations and interviews, it can be found that people of Regular Group, who are married and at work, care about their health the most. They prefer listening to the broadcasts during the period before work and love watching foreign movies on TV. Literate Romance is their favorite movie style. People of Smart Family Group do not have regular hours of listening broadcasts or watching televisions. They love watching leisure-time and travel TV programs with their families. People of Traditional Elite Group who usually listen to the radio in car prefer listening to call-in shows and watching television for at least three hours a day, especially fiscal television shows; though, rarely going to movie theaters, sometimes they still go to see the big casting Hollywood movies. People of Youth Ease Group who are mostly single students, usually listen to the radio while working at the part time job, especially Chinese pop music the most; on holidays they love watching pop entertainment shows, do not enjoy watching call-in TV shows, however the idol drama is their favorite type, especially Set TV Taiwan Station is their first option; they also go to the movie theaters the most and like to rent compact disks of Hollywood comedy movies. People of Modern Faison Group who prefer using technology usually listen to the musical radio stations, watch dramas of Chi Dynasty produced in Mainland China, and also use internet to watch televisions and movies. People of Fogy Conserve Group who are retired elders with clear political orientation like news reports the most (either on broadcast or television) and usually listen to the radio stations which sell the herb and medicine; they watch call-in TV shows and traditional Taiwanese drama sequels; they barely go to the movie theaters.
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Xia, Feng-yun, and 夏鳳筠. "Research on the Lifestyle and Movie-viewing Behavior of Different Generations in Taiwan－Using 2008-2009 Shih Hsin University Communication Database and In-depth Interview Data as an Example." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/18808146208715151330.
Full textAbstract:
碩士世新大學
廣播電視電影學研究所(含碩專班)
99
The aim of the research is to analyze the difference and correlation between audiences in different generations in Taiwan in terms of demographic characteristics, lifestyles and consumer movie-going behaviors. Secondary data analysis and in-depth interview were used as the research methods. Firstly, quantitative analysis was applied on the questionnaire data from 2008 and 2009 Shih Hsin Communication Database. The reasons behind the data obtained from the quantitative analysis were explored by in-depth interview. It was found in the research that demographic characteristics and lifestyles were the causes for the significant difference in consumer movie-going behaviors for people in different generations. For “people born in the 1940s,” everything to them needed to be practical and had to meet the basic needs for food, clothing and shelter. They were not willing to spend extra expense for movie consumption. Moreover, as people from this generation were mostly elementary school graduates, it was more difficult for them to accept foreign movies. “People born in the 1950s”were most enthusiastic in participating in political and social affairs and they were most indifferent toward computer and the Internet. In terms of movie appreciation, they preferred watching movies alone and valued the movie content. “People born in the 1960s” played the generation transitioning role for which they inherited the concept of being practical and economical from the previous generation while they were also open to adapt to new things which was just like their next generation. In terms of consumer movie-going behavior, they preferred movies with special effects and audio/visual stimulation. When watching movies at home, they preferred drama as it emphasizes on the content. “People born in the 1970s” were a group of people who obviously had a change in lifestyle and began to show the characteristic of hedonism. They pursued fashion and technology and were less concerned about social and political affairs. In terms of movies, they valued a lot the entertainment stimuli such as audio/visual stimulation. “People born in the 1980s” were a group of people who sought a lot of pleasure and were immersed in debt. It means they had to budget carefully when consuming. As they pursued easy, happy and rapid-changing life and that they advocated individualism and hedonism, they preferred interesting movies and action movies that emphasize on heroism. “People born in the 1990s” were the generation that was used to computers and the generation that liked “virtual reality” the most. Therefore, movies with sci-fi or adventure plot attracted them the most. In terms of the social background each generation had, it was found that the appearances of computer and the Internet affected the generation’s lifestyle and consumer movie-going behavior the most
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Kęstutis, Černiauskas. "The effect of film sharing on P2P networks on box office sales." Thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15295.
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Context. Online piracy is widespread, controversial and poorly understood social phenomena that affects content creators, owners, and consumers. Online piracy, born from recent, rapid ITC changes, raises legal, ethical, and business challenges. Content owners, authors and content consumers should benefit from better understanding of online piracy. Improved, better adapted to marketplace and ITC changes content distribution models should benefit content owners and audiences.Objectives. Investigate online piracy effect on pirated product sales. Improve understanding of online piracy behaviors and process scale.Methods. This observational study investigated movie-sharing effect on U.S. box office. Movie sharing was observed over BitTorrent network, the most popular peer-to-peer file-sharing network. Relationship between piracy and sales was analyzed using linear regression model.Results. File sharing was found to have a slightly positive correlation with U.S. box office sales during first few weeks after film release, and no effect afterwards. Most of newly released movies are shared over BitTorrent network. File sharing is a global, massive phenomenon.Conclusions. I conclude that online movie file sharing has no negative correlation on U.S. box office. Slightly positive movie sharing correlation to box office sales could have occurred because sharing rather informs, than substitutes cinema going.
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"Estimation of 3D wireframe face models from movies." 2003. http://library.cuhk.edu.hk/record=b5891558.
Full textAbstract:
Tang Yuk Ming = 電影中三維人面模型之估計 / 鄧育明.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 107-113).
Text in English; abstracts in English and Chinese.
Tang Yuk Ming = Dian ying zhong san wei ren mian mo xing zhi gu ji / Deng Yuming.
Acknowledgement --- p.i
Abstract --- p.ii
Contents --- p.vi
List of Figures --- p.viii
List of Tables --- p.x
List of Abbreviations and Notations --- p.xi
Chapter 1. --- Introduction --- p.1
Chapter 1.1 --- Introduction --- p.1
Chapter 1.2 --- Recent Research Works --- p.2
Chapter 1.2.1 --- Face modeling from images --- p.2
Chapter 1.2.2 --- Pose estimation --- p.4
Chapter 1.3 --- Objectives and Assumptions --- p.7
Chapter 1.4 --- Our Method --- p.8
Chapter 1.5 --- Thesis Outline --- p.10
Chapter 2. --- Basic Theory on 3D Modeling of a Head --- p.11
Chapter 2.1 --- Introduction --- p.11
Chapter 2.2 --- Perspective Projection --- p.13
Chapter 2.3 --- Initialization --- p.17
Chapter 2.3.1 --- Generic wireframe face model and fiducial points --- p.17
Chapter 2.3.2 --- Deformations --- p.22
Chapter 2.3.3 --- Experimental results --- p.35
Chapter 2.4 --- Summary --- p.39
Chapter 3. --- Pose Estimation --- p.40
Chapter 3.1 --- Introduction --- p.40
Chapter 3.2 --- Problem Description --- p.42
Chapter 3.3 --- Iterative Least-Square Minimization --- p.45
Chapter 3.3.1 --- Depth estimation --- p.45
Chapter 3.3.2 --- Least-square minimization --- p.47
Chapter 3.3.3 --- Iterative process --- p.52
Chapter 3.4 --- Experimental Results --- p.54
Chapter 3.4.1 --- Synthetic data --- p.54
Chapter 3.4.2 --- Real data --- p.65
Chapter 3.5 --- Summary --- p.69
Chapter 4. --- 3D Wireframe Model Estimation --- p.70
Chapter 4.1 --- Introduction --- p.70
Chapter 4.2 --- 3D Wireframe Model Estimation --- p.72
Chapter 4.2.1 --- Least-square minimization --- p.73
Chapter 4.2.2 --- Iterative process --- p.74
Chapter 4.3 --- 3D Wireframe Model Estimation of the Subsequent Frames --- p.77
Chapter 4.4 --- Experimental Results --- p.78
Chapter 4.4.1 --- Synthetic data --- p.78
Chapter 4.4.2 --- Real data --- p.84
Chapter 4.5 --- Summary --- p.98
Chapter 5. --- Contributions and Conclusions --- p.99
Chapter 5.1 --- Contributions and conclusions --- p.99
Chapter 5.2 --- Future Developments --- p.102
Appendix A Triangles and vertices on the IST model --- p.104
Bibliography --- p.107
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(11198013), Kevin Wee. "Creation, deconstruction, and evaluation of a biochemistry animation about the role of the actin cytoskeleton in cell motility." Thesis, 2021.
Find full textAbstract:
External representations (ERs) used in science education are multimodal ensembles consisting of design elements to convey educational meanings to the audience. As an example of a dynamic ER, an animation presenting its content features (i.e., scientific concepts) via varying the feature’s depiction over time. A production team invited the dissertation author to inspect their creation of a biochemistry animation about the role of the actin cytoskeleton in cell motility and the animation’s implication on learning. To address this, the author developed a four-step methodology entitled the Multimodal Variation Analysis of Dynamic External Representations (MVADER) that deconstructs the animation’s content and design to inspect how each content feature is conveyed via the animation’s design elements.
This dissertation research investigated the actin animation’s educational value and the MVADER’s utility in animation evaluation. The research design was guided by descriptive case study methodology and an integrated framework consisting of the variation theory, multimodal analysis, and visual analytics. As stated above, the animation was analyzed using MVADER. The development of the actin animation and the content features the production team members intended to convey via the animation were studied by analyzing the communication records between the members, observing the team meetings, and interviewing the members individually. Furthermore, students’ learning experiences from watching the animation were examined via semi-structured interviews coupled with post- storyboarding. Moreover, the instructions of MVADER and its applications in studying the actin animation were reviewed to determine the MVADER’s usefulness as an animation evaluation tool.
Findings of this research indicate that the three educators in the production team intended the actin animation to convey forty-three content features to the undergraduate biology students. At least 50% of the student who participated in this thesis learned thirty-five of these forty-three (> 80%) features. Evidence suggests that the animation’s effectiveness to convey its features was associated with the features’ depiction time, the number of identified design elements applied to depict the features, and the features’ variation of depiction over time.
Additionally, one-third of the student participants made similar mistakes regarding two content features after watching the actin animation: the F-actin elongation and the F-actin crosslink structure in lamellipodia. The analysis reveals the animation’s potential design flaws that might have contributed to these common misconceptions. Furthermore, two disruptors to the creation process and the educational value of the actin animation were identified: the vagueness of the learning goals and the designer’s placement of the animation’s beauty over its reach to the learning goals. The vagueness of the learning goals hampered the narration scripting process. On the other hand, the designer’s prioritization of the animation’s aesthetic led to the inclusion of a “beauty shot” in the animation that caused students’ confusion.
MVADER was used to examine the content, design, and their relationships in the actin animation at multiple aspects and granularities. The result of MVADER was compared with the students’ learning outcomes from watching the animation to identify the characteristics of content’s depiction that were constructive and disruptive to learning. These findings led to several practical recommendations to teach using the actin animation and create educational ERs.
To conclude, this dissertation discloses the connections between the creation process, the content and design, and the educational implication of a biochemistry animation. It also introduces MVADER as a novel ER analysis tool to the education research and visualization communities. MVADER can be applied in various formats of static and dynamic ERs and beyond the disciplines of biology and chemistry.