434 research outputs found

    Video advertisement mining for predicting revenue using random forest

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    Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use

    Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

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    Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

    Persönliche Wege der Interaktion mit multimedialen Inhalten

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    Today the world of multimedia is almost completely device- and content-centered. It focuses it’s energy nearly exclusively on technical issues such as computing power, network specifics or content and device characteristics and capabilities. In most multimedia systems, the presentation of multimedia content and the basic controls for playback are main issues. Because of this, a very passive user experience, comparable to that of traditional TV, is most often provided. In the face of recent developments and changes in the realm of multimedia and mass media, this ”traditional” focus seems outdated. The increasing use of multimedia content on mobile devices, along with the continuous growth in the amount and variety of content available, make necessary an urgent re-orientation of this domain. In order to highlight the depth of the increasingly difficult situation faced by users of such systems, it is only logical that these individuals be brought to the center of attention. In this thesis we consider these trends and developments by applying concepts and mechanisms to multimedia systems that were first introduced in the domain of usercentrism. Central to the concept of user-centrism is that devices should provide users with an easy way to access services and applications. Thus, the current challenge is to combine mobility, additional services and easy access in a single and user-centric approach. This thesis presents a framework for introducing and supporting several of the key concepts of user-centrism in multimedia systems. Additionally, a new definition of a user-centric multimedia framework has been developed and implemented. To satisfy the user’s need for mobility and flexibility, our framework makes possible seamless media and service consumption. The main aim of session mobility is to help people cope with the increasing number of different devices in use. Using a mobile agent system, multimedia sessions can be transferred between different devices in a context-sensitive way. The use of the international standard MPEG-21 guarantees extensibility and the integration of content adaptation mechanisms. Furthermore, a concept is presented that will allow for individualized and personalized selection and face the need for finding appropriate content. All of which can be done, using this approach, in an easy and intuitive way. Especially in the realm of television, the demand that such systems cater to the need of the audience is constantly growing. Our approach combines content-filtering methods, state-of-the-art classification techniques and mechanisms well known from the area of information retrieval and text mining. These are all utilized for the generation of recommendations in a promising new way. Additionally, concepts from the area of collaborative tagging systems are also used. An extensive experimental evaluation resulted in several interesting findings and proves the applicability of our approach. In contrast to the ”lean-back” experience of traditional media consumption, interactive media services offer a solution to make possible the active participation of the audience. Thus, we present a concept which enables the use of interactive media services on mobile devices in a personalized way. Finally, a use case for enriching TV with additional content and services demonstrates the feasibility of this concept.Die heutige Welt der Medien und der multimedialen Inhalte ist nahezu ausschließlich inhalts- und geräteorientiert. Im Fokus verschiedener Systeme und Entwicklungen stehen oft primär die Art und Weise der Inhaltspräsentation und technische Spezifika, die meist geräteabhängig sind. Die zunehmende Menge und Vielfalt an multimedialen Inhalten und der verstärkte Einsatz von mobilen Geräten machen ein Umdenken bei der Konzeption von Multimedia Systemen und Frameworks dringend notwendig. Statt an eher starren und passiven Konzepten, wie sie aus dem TV Umfeld bekannt sind, festzuhalten, sollte der Nutzer in den Fokus der multimedialen Konzepte rücken. Um dem Nutzer im Umgang mit dieser immer komplexeren und schwierigen Situation zu helfen, ist ein Umdenken im grundlegenden Paradigma des Medienkonsums notwendig. Durch eine Fokussierung auf den Nutzer kann der beschriebenen Situation entgegengewirkt werden. In der folgenden Arbeit wird auf Konzepte aus dem Bereich Nutzerzentrierung zurückgegriffen, um diese auf den Medienbereich zu übertragen und sie im Sinne einer stärker nutzerspezifischen und nutzerorientierten Ausrichtung einzusetzen. Im Fokus steht hierbei der TV-Bereich, wobei die meisten Konzepte auch auf die allgemeine Mediennutzung übertragbar sind. Im Folgenden wird ein Framework für die Unterstützung der wichtigsten Konzepte der Nutzerzentrierung im Multimedia Bereich vorgestellt. Um dem Trend zur mobilen Mediennutzung Sorge zu tragen, ermöglicht das vorgestellte Framework die Nutzung von multimedialen Diensten und Inhalten auf und über die Grenzen verschiedener Geräte und Netzwerke hinweg (Session mobility). Durch die Nutzung einer mobilen Agentenplattform in Kombination mit dem MPEG-21 Standard konnte ein neuer und flexibel erweiterbarer Ansatz zur Mobilität von Benutzungssitzungen realisiert werden. Im Zusammenhang mit der stetig wachsenden Menge an Inhalten und Diensten stellt diese Arbeit ein Konzept zur einfachen und individualisierten Selektion und dem Auffinden von interessanten Inhalten und Diensten in einer kontextspezifischen Weise vor. Hierbei werden Konzepte und Methoden des inhaltsbasierten Filterns, aktuelle Klassifikationsmechanismen und Methoden aus dem Bereich des ”Textminings” in neuer Art und Weise in einem Multimedia Empfehlungssystem eingesetzt. Zusätzlich sind Methoden des Web 2.0 in eine als Tag-basierte kollaborative Komponente integriert. In einer umfassenden Evaluation wurde sowohl die Umsetzbarkeit als auch der Mehrwert dieser Komponente demonstriert. Eine aktivere Beteiligung im Medienkonsum ermöglicht unsere iTV Komponente. Sie unterstützt das Anbieten und die Nutzung von interaktiven Diensten, begleitend zum Medienkonsum, auf mobilen Geräten. Basierend auf einem Szenario zur Anreicherung von TV Sendungen um interaktive Dienste konnte die Umsetzbarkeit dieses Konzepts demonstriert werden

    Selected Topics in Audio-based Recommendation of TV Content

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    End to end Multi-Objective Optimisation of H.264 and HEVC Codecs

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    All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC) otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, large number of coding parameters can be used to fine tune their operation, within known constraints of for e.g., available computational power, bandwidth, consumer QoS requirements, etc. With large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. Further how to select the values of the significant parameters so that the CODEC performs optimally under the given constraints is a further important question to be answered. This thesis proposes a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. Means of modelling both the Encoder and Decoder performance is proposed. We define objective functions that can be used to model the performance related properties of a CODEC, i.e., video quality, bit-rate and CPU time. We show that these objective functions can be practically utilised in video Encoder/Decoder designs, in particular in their performance optimisation within given operational and practical constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the CPU Time, Bit-rate and to maximize the quality of the compressed video stream. The thesis presents the use of this framework in the performance modelling and multi-objective optimisation of the most widely used video coding standard in practice at present, H.264 and the latest video coding standard, H.265/HEVC. When a communication network is used to transmit video, performance related parameters of the communication channel will impact the end-to-end performance of the video CODEC. Network delays and packet loss will impact the quality of the video that is received at the decoder via the communication channel, i.e., even if a video CODEC is optimally configured network conditions will make the experience sub-optimal. Given the above the thesis proposes a design, integration and testing of a novel approach to simulating a wired network and the use of UDP protocol for the transmission of video data. This network is subsequently used to simulate the impact of packet loss and network delays on optimally coded video based on the framework previously proposed for the modelling and optimisation of video CODECs. The quality of received video under different levels of packet loss and network delay is simulated, concluding the impact on transmitted video based on their content and features

    Adaptation des images et des vidéos pour des utilisateurs multiples dans des environnements hétérogènes

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    La dernière décennie a connu l'émergence de l'utilisation des équipements mobiles comme les assistants personnels et les téléphones, ainsi que la prolifération des réseaux personnels favorisée par le développement considérable dans les technologies de communications. D'autre part, l'information véhiculée a travers le World Wide Web devient de plus en plus visuelle (images et videos) grâce à la numérisation. Afin de permettre à tous les usagers un accès universel à cette information visuelle dans un environnement caractérisé par la diversité des équipements et l'hétérogénéité des réseaux, il devient nécessaire d'adapter les documents multimédia. L'adaptation consiste à appliquer une ou plusieurs transformations sur un document multimédia. Dans ce cadre, plusieurs travaux ont été élaborés en partant de différentes formulations. Nous pensons qu'un système d'adaptation efficace doit choisir les traitements nécessaires à appliquer sur un document visuel afin de maximiser la satisfaction de l'usager. Il doit considérer conjointement les caractéristiques de cet usager ainsi que les performances de son équipement, la qualité de sa connexion et les conditions de son environnement. La majorité des travaux réalisés dans ce domaine n'ont traité que des cas limités, par exemple ajuster une vidéo pour la capacité d'un réseau donné. Dans la présente recherche, nous proposons une solution globale obtenue à l'aide d'un modèle probabiliste qui utilise les traitements des images et des vidéos et l'extraction des caractéristiques des contenus

    Multimedia Retrieval

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    Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

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    In emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users’ location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the “earthquake” keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naïve Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques. © 2020 Elsevier Lt

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Jointly integrating current context and social influence for improving recommendation

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    La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de données.Due to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity
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