Viewpoint Discovery and Understanding in Social Networks

The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a - usually controversial - topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints

A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION

VIDEO segmentation facilitates e±cient video indexing and navigation in large digital video archives. It is an important process in a content-based video indexing and retrieval (CBVIR) system. Many automated solutions performed seg- mentation by utilizing information about the \facts" of the video. These \facts" come in the form of labels that describe the objects which are captured by the cam- era. This type of solutions was able to achieve good and consistent results for some video genres such as news programs and informational presentations. The content format of this type of videos is generally quite standard, and automated solutions were designed to follow these format rules. For example in [1], the presence of news anchor persons was used as a cue to determine the start and end of a meaningful news segment. The same cannot be said for video genres such as movies and feature films. This is because makers of this type of videos utilized different filming techniques to design their videos in order to elicit certain affective response from their targeted audience. Humans usually perform manual video segmentation by trying to relate changes in time and locale to discontinuities in meaning [2]. As a result, viewers usually have doubts about the boundary locations of a meaningful video segment due to their different affective responses. This thesis presents an entirely new view to the problem of high level video segmentation. We developed a novel probabilistic method for affective level video content analysis and segmentation. Our method had two stages. In the first stage, a®ective content labels were assigned to video shots by means of a dynamic bayesian 0. Abstract 3 network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN) topology was proposed for this stage. The topology was based on the pleasure- arousal-dominance (P-A-D) model of a®ect representation [3]. In principle, this model can represent a large number of emotions. In the second stage, the visual, audio and a®ective information of the video was used to compute a statistical feature vector to represent the content of each shot. Affective level video segmentation was achieved by applying spectral clustering to the feature vectors. We evaluated the first stage of our proposal by comparing its emotion detec- tion ability with all the existing works which are related to the field of a®ective video content analysis. To evaluate the second stage, we used the time adaptive clustering (TAC) algorithm as our performance benchmark. The TAC algorithm was the best high level video segmentation method [2]. However, it is a very computationally intensive algorithm. To accelerate its computation speed, we developed a modified TAC (modTAC) algorithm which was designed to be mapped easily onto a field programmable gate array (FPGA) device. Both the TAC and modTAC algorithms were used as performance benchmarks for our proposed method. Since affective video content is a perceptual concept, the segmentation per- formance and human agreement rates were used as our evaluation criteria. To obtain our ground truth data and viewer agreement rates, a pilot panel study which was based on the work of Gross et al. [4] was conducted. Experiment results will show the feasibility of our proposed method. For the first stage of our proposal, our experiment results will show that an average improvement of as high as 38% was achieved over previous works. As for the second stage, an improvement of as high as 37% was achieved over the TAC algorithm

HIERARCHICAL LEARNING OF DISCRIMINATIVE FEATURES AND CLASSIFIERS FOR LARGE-SCALE VISUAL RECOGNITION

Enabling computers to recognize objects present in images has been a long standing but tremendously challenging problem in the field of computer vision for decades. Beyond the difficulties resulting from huge appearance variations, large-scale visual recognition poses unprecedented challenges when the number of visual categories being considered becomes thousands, and the amount of images increases to millions. This dissertation contributes to addressing a number of the challenging issues in large-scale visual recognition. First, we develop an automatic image-text alignment method to collect massive amounts of labeled images from the Web for training visual concept classifiers. Specif- ically, we first crawl a large number of cross-media Web pages containing Web images and their auxiliary texts, and then segment them into a collection of image-text pairs. We then show that near-duplicate image clustering according to visual similarity can significantly reduce the uncertainty on the relatedness of Web images’ semantics to their auxiliary text terms or phrases. Finally, we empirically demonstrate that ran- dom walk over a newly proposed phrase correlation network can help to achieve more precise image-text alignment by refining the relevance scores between Web images and their auxiliary text terms. Second, we propose a visual tree model to reduce the computational complexity of a large-scale visual recognition system by hierarchically organizing and learning the classifiers for a large number of visual categories in a tree structure. Compared to previous tree models, such as the label tree, our visual tree model does not require training a huge amount of classifiers in advance which is computationally expensive. However, we experimentally show that the proposed visual tree achieves results that are comparable or even better to other tree models in terms of recognition accuracy and efficiency. Third, we present a joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries for image content representation. Given a group of visually correlated categories, JDL simul- taneously learns one common dictionary and multiple category-specific dictionaries to explicitly separate the shared visual atoms from the category-specific ones. We accordingly develop three classification schemes to make full use of the dictionaries learned by JDL for visual content representation in the task of image categoriza- tion. Experiments on two image data sets which respectively contain 17 and 1,000 categories demonstrate the effectiveness of the proposed algorithm. In the last part of the dissertation, we develop a novel data-driven algorithm to quantitatively characterize the semantic gaps of different visual concepts for learning complexity estimation and inference model selection. The semantic gaps are estimated directly in the visual feature space since the visual feature space is the common space for concept classifier training and automatic concept detection. We show that the quantitative characterization of the semantic gaps helps to automatically select more effective inference models for classifier training, which further improves the recognition accuracy rates

Patterns in Motion - From the Detection of Primitives to Steering Animations

In recent decades, the world of technology has developed rapidly. Illustrative of this trend is the growing number of affrdable methods for recording new and bigger data sets. The resulting masses of multivariate and high-dimensional data represent a new challenge for research and industry. This thesis is dedicated to the development of novel methods for processing multivariate time series data, thus meeting this Data Science related challenge. This is done by introducing a range of different methods designed to deal with time series data. The variety of methods re ects the different requirements and the typical stage of data processing ranging from pre-processing to post- processing and data recycling. Many of the techniques introduced work in a general setting. However, various types of motion recordings of human and animal subjects were chosen as representatives of multi-variate time series. The different data modalities include Motion Capture data, accelerations, gyroscopes, electromyography, depth data (Kinect) and animated 3D-meshes. It is the goal of this thesis to provide a deeper understanding of working with multi-variate time series by taking the example of multi-variate motion data. However, in order to maintain an overview of the matter, the thesis follows a basic general pipeline. This pipeline was developed as a guideline for time series processing and is the first contribution of this work. Each part of the thesis represents one important stage of this pipeline which can be summarized under the topics segmentation, analysis and synthesis. Specific examples of different data modalities, processing requirements and methods to meet those are discussed in the chapters of the respective parts. One important contribution of this thesis is a novel method for temporal segmentation of motion data. It is based on the idea of self-similarities within motion data and is capable of unsupervised segmentation of range of motion data into distinct activities and motion primitives. The examples concerned with the analysis of multi-variate time series re ect the role of data analysis in different inter-disciplinary contexts and also the variety of requirements that comes with collaboration with other sciences. These requirements are directly connected to current challenges in data science. Finally, the problem of synthesis of multi-variate time series is discussed using a graph-based example and examples related to rigging or steering of meshes. Synthesis is an important stage in data processing because it creates new data from existing ones in a controlled way. This makes exploiting existing data sets and and access of more condensed data possible, thus providing feasible alternatives to otherwise time-consuming manual processing.Muster in Bewegung - Von der Erkennung von Primitiven zur Steuerung von Animationen In den letzten Jahrzehnten hat sich die Welt der Technologie rapide entwickelt. Beispielhaft für diese Entwicklung ist die wachsende Zahl erschwinglicher Methoden zum Aufzeichnen neuer und immer größerer Datenmengen. Die sich daraus ergebenden Massen multivariater und hochdimensionaler Daten stellen Forschung wie Industrie vor neuartige Probleme. Diese Arbeit ist der Entwicklung neuer Verfahren zur Verarbeitung multivariater Zeitreihen gewidmet und stellt sich damit einer großen Herausforderung, welche unmittelbar mit dem neuen Feld der sogenannten Data Science verbunden ist. In ihr werden ein Reihe von verschiedenen Verfahren zur Verarbeitung multivariater Zeitserien eingeführt. Die verschiedenen Verfahren gehen jeweils auf unterschiedliche Anforderungen und typische Stadien der Datenverarbeitung ein und reichen von Vorverarbeitung bis zur Nachverarbeitung und darüber hinaus zur Wiederverwertung. Viele der vorgestellten Techniken eignen sich zur Verarbeitung allgemeiner multivariater Zeitreihen. Allerdings wurden hier eine Anzahl verschiedenartiger Aufnahmen von menschlichen und tierischen Subjekte ausgewählt, welche als Vertreter für allgemeine multivariate Zeitreihen gelten können. Zu den unterschiedlichen Modalitäten der Aufnahmen gehören Motion Capture Daten, Beschleunigungen, Gyroskopdaten, Elektromyographie, Tiefenbilder ( Kinect ) und animierte 3D -Meshes. Es ist das Ziel dieser Arbeit, am Beispiel der multivariaten Bewegungsdaten ein tieferes Verstndnis für den Umgang mit multivariaten Zeitreihen zu vermitteln. Um jedoch einen Überblick ber die Materie zu wahren, folgt sie jedoch einer grundlegenden und allgemeinen Pipeline. Diese Pipeline wurde als Leitfaden für die Verarbeitung von Zeitreihen entwickelt und ist der erste Beitrag dieser Arbeit. Jeder weitere Teil der Arbeit behandelt eine von drei größeren Stationen in der Pipeline, welche sich unter unter die Themen Segmentierung, Analyse und Synthese eingliedern lassen. Beispiele verschiedener Datenmodalitäten und Anforderungen an ihre Verarbeitung erläutern die jeweiligen Verfahren. Ein wichtiger Beitrag dieser Arbeit ist ein neuartiges Verfahren zur zeitlichen Segmentierung von Bewegungsdaten. Dieses basiert auf der Idee der Selbstähnlichkeit von Bewegungsdaten und ist in der Lage, verschiedenste Bewegungsdaten voll-automatisch in unterschiedliche Aktivitäten und Bewegungs-Primitive zu zerlegen. Die Beispiele fr die Analyse multivariater Zeitreihen spiegeln die Rolle der Datenanalyse in verschiedenen interdisziplinären Zusammenhänge besonders wider und illustrieren auch die Vielfalt der Anforderungen, die sich in interdisziplinären Kontexten auftun. Schließlich wird das Problem der Synthese multivariater Zeitreihen unter Verwendung eines graph-basierten und eines Steering Beispiels diskutiert. Synthese ist insofern ein wichtiger Schritt in der Datenverarbeitung, da sie es erlaubt, auf kontrollierte Art neue Daten aus vorhandenen zu erzeugen. Dies macht die Nutzung bestehender Datensätze und den Zugang zu dichteren Datenmodellen möglich, wodurch Alternativen zur ansonsten zeitaufwendigen manuellen Verarbeitung aufgezeigt werden

Algorithms and representations for supporting online music creation with large-scale audio databases

The rapid adoption of Internet and web technologies has created an opportunity for making music collaboratively by sharing information online. However, current applications for online music making do not take advantage of the potential of shared information. The goal of this dissertation is to provide and evaluate algorithms and representations for interacting with large audio databases that facilitate music creation by online communities. This work has been developed in the context of Freesound, a large-scale, community-driven database of audio recordings shared under Creative Commons (CC) licenses. The diversity of sounds available through this kind of platform is unprecedented. At the same time, the unstructured nature of community-driven processes poses new challenges for indexing and retrieving information to support musical creativity. In this dissertation we propose and evaluate algorithms and representations for dealing with the main elements required by online music making applications based on large-scale audio databases: sound files, including time-varying and aggregate representations, taxonomies for retrieving sounds, music representations and community models. As a generic low-level representation for audio signals, we analyze the framework of cepstral coefficients, evaluating their performance with example classification tasks. We found that switching to more recent auditory filter such as gammatone filters improves, at large scales, on traditional representations based on the mel scale. We then consider common types of sounds for obtaining aggregated representations. We show that several time series analysis features computed from the cepstral coefficients complement traditional statistics for improved performance. For interacting with large databases of sounds, we propose a novel unsupervised algorithm that automatically generates taxonomical organizations based on the low-level signal representations. Based on user studies, we show that our approach can be used in place of traditional supervised classification approaches for providing a lexicon of acoustic categories suitable for creative applications. Next, a computational representation is described for music based on audio samples. We demonstrate through a user experiment that it facilitates collaborative creation and supports computational analysis using the lexicons generated by sound taxonomies. Finally, we deal with representation and analysis of user communities. We propose a method for measuring collective creativity in audio sharing. By analyzing the activity of the Freesound community over a period of more than 5 years, we show that the proposed creativity measures can be significantly related to social structure characterized by network analysis.La ràpida adopció dInternet i de les tecnologies web ha creat una oportunitat per fer música col•laborativa mitjançant l'intercanvi d'informació en línia. No obstant això, les aplicacions actuals per fer música en línia no aprofiten el potencial de la informació compartida. L'objectiu d'aquesta tesi és proporcionar i avaluar algorismes i representacions per a interactuar amb grans bases de dades d'àudio que facilitin la creació de música per part de comunitats virtuals. Aquest treball ha estat desenvolupat en el context de Freesound, una base de dades d'enregistraments sonors compartits sota llicència Creative Commons (CC) a gran escala, impulsada per la comunitat d'usuaris. La diversitat de sons disponibles a través d'aquest tipus de plataforma no té precedents. Alhora, la naturalesa desestructurada dels processos impulsats per comunitats planteja nous reptes per a la indexació i recuperació d'informació que dona suport a la creativitat musical. En aquesta tesi proposem i avaluem algorismes i representacions per tractar amb els principals elements requerits per les aplicacions de creació musical en línia basades en bases de dades d'àudio a gran escala: els arxius de so, incloent representacions temporals i agregades, taxonomies per a cercar sons, representacions musicals i models de comunitat. Com a representació de baix nivell genèrica per a senyals d'àudio, s'analitza el marc dels coeficients cepstrum, avaluant el seu rendiment en tasques de classificació d'exemple. Hem trobat que el canvi a un filtre auditiu més recent com els filtres de gammatons millora, a gran escala, respecte de les representacions tradicionals basades en l'escala mel. Després considerem tres tipus comuns de sons per a l'obtenció de representacions agregades. Es demostra que diverses funcions d'anàlisi de sèries temporals calculades a partir dels coeficients cepstrum complementen les estadístiques tradicionals per a un millor rendiment. Per interactuar amb grans bases de dades de sons, es proposa un nou algorisme no supervisat que genera automàticament organitzacions taxonòmiques basades en les representacions de senyal de baix nivell. Em base a estudis amb usuaris, mostrem que el sistema proposat es pot utilitzar en lloc dels sistemes tradicionals de classificació supervisada per proporcionar un lèxic de categories acústiques adequades per a aplicacions creatives. A continuació, es descriu una representació computacional per a música creada a partir de mostres d'àudio. Demostrem a través d'un experiment amb usuaris que facilita la creació col•laborativa i dóna suport l'anàlisi computacional usant els lèxics generats per les taxonomies de so. Finalment, ens centrem en la representació i anàlisi de comunitats d'usuaris. Proposem un mètode per mesurar la creativitat col•lectiva en l'intercanvi d'àudio. Mitjançant l'anàlisi de l'activitat de la comunitat Freesound durant un període de més de 5 anys, es mostra que les mesures proposades de creativitat es poden relacionar significativament amb l'estructura social descrita mitjançant l'anàlisi de xarxes.La rápida adopción de Internet y de las tecnologías web ha creado una oportunidad para hacer música colaborativa mediante el intercambio de información en línea. Sin embargo, las aplicaciones actuales para hacer música en línea no aprovechan el potencial de la información compartida. El objetivo de esta tesis es proporcionar y evaluar algoritmos y representaciones para interactuar con grandes bases de datos de audio que faciliten la creación de música por parte de comunidades virtuales. Este trabajo ha sido desarrollado en el contexto de Freesound, una base de datos de grabaciones sonoras compartidos bajo licencia Creative Commons (CC) a gran escala, impulsada por la comunidad de usuarios. La diversidad de sonidos disponibles a través de este tipo de plataforma no tiene precedentes. Al mismo tiempo, la naturaleza desestructurada de los procesos impulsados por comunidades plantea nuevos retos para la indexación y recuperación de información en apoyo de la creatividad musical. En esta tesis proponemos y evaluamos algoritmos y representaciones para tratar con los principales elementos requeridos por las aplicaciones de creación musical en línea basadas en bases de datos de audio a gran escala: archivos de sonido, incluyendo representaciones temporales y agregadas, taxonomías para buscar sonidos, representaciones musicales y modelos de comunidad. Como representación de bajo nivel genérica para señales de audio, se analiza el marco de los coeficientes cepstrum, evaluando su rendimiento en tareas de clasificación. Encontramos que el cambio a un filtro auditivo más reciente como los filtros de gammatonos mejora, a gran escala, respecto de las representaciones tradicionales basadas en la escala mel. Después consideramos tres tipos comunes de sonidos para la obtención de representaciones agregadas. Se demuestra que varias funciones de análisis de series temporales calculadas a partir de los coeficientes cepstrum complementan las estadísticas tradicionales para un mejor rendimiento. Para interactuar con grandes bases de datos de sonidos, se propone un nuevo algoritmo no supervisado que genera automáticamente organizaciones taxonómicas basadas en las representaciones de señal de bajo nivel. En base a estudios con usuarios, mostramos que nuestro enfoque se puede utilizar en lugar de los sistemas tradicionales de clasificación supervisada para proporcionar un léxico de categorías acústicas adecuadas para aplicaciones creativas. A continuación, se describe una representación computacional para música creada a partir de muestras de audio. Demostramos, a través de un experimento con usuarios, que facilita la creación colaborativa y posibilita el análisis computacional usando los léxicos generados por las taxonomías de sonido. Finalmente, nos centramos en la representación y análisis de comunidades de usuarios. Proponemos un método para medir la creatividad colectiva en el intercambio de audio. Mediante un análisis de la actividad de la comunidad Freesound durante un periodo de más de 5 años, se muestra que las medidas propuestas de creatividad se pueden relacionar significativamente con la estructura social descrita mediante análisis de redes