Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

Educational Technology Topic Guide

This guide aims to contribute to what we know about the relationship between educational technology (edtech) and educational outcomes by addressing the following overarching question: What is the evidence that the use of edtech, by teachers or students, impacts teaching and learning practices, or learning outcomes? It also offers recommendations to support advisors to strengthen the design, implementation and evaluation of programmes that use edtech. We define edtech as the use of digital or electronic technologies and materials to support teaching and learning. Recognising that technology alone does not enhance learning, evaluations must also consider how programmes are designed and implemented, how teachers are supported, how communities are developed and how outcomes are measured (see http://tel.ac.uk/about-3/, 2014). Effective edtech programmes are characterised by: a clear and specific curriculum focus the use of relevant curriculum materials a focus on teacher development and pedagogy evaluation mechanisms that go beyond outputs. These findings come from a wide range of technology use including: interactive radio instruction (IRI) classroom audio or video resources accessed via teachers’ mobile phones student tablets and eReaders computer-assisted learning (CAL) to supplement classroom teaching. However, there are also examples of large-scale investment in edtech – particularly computers for student use – that produce limited educational outcomes. We need to know more about: how to support teachers to develop appropriate, relevant practices using edtech how such practices are enacted in schools, and what factors contribute to or mitigate against successful outcomes. Recommendations: 1. Edtech programmes should focus on enabling educational change, not delivering technology. In doing so, programmes should provide adequate support for teachers and aim to capture changes in teaching practice and learning outcomes in evaluation. 2. Advisors should support proposals that further develop successful practices or that address gaps in evidence and understanding. 3. Advisors should discourage proposals that have an emphasis on technology over education, weak programmatic support or poor evaluation. 4. In design and evaluation, value-for-money metrics and cost-effectiveness analyses should be carried out

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Automatic fine-grained area detection for thin client systems

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Identification of expressive descriptors for style extraction in music analysis using linear and nonlinear models

La formalización de las interpretaciones expresivas aún se considera relevante debido a la complejidad de la música. La interpretación expresiva forma un aspecto importante de la música, teniendo en cuenta diferentes convenciones como géneros o estilos que una interpretación puede desarrollar con el tiempo. Modelar la relación entre las expresiones musicales y los aspectos estructurales de la información acústica requiere una base probabilística y estadística mínima para la robustez, validación y reproducibilidad de aplicaciones computacionales. Por lo tanto, es necesaria una relación cohesiva y una justificación sobre los resultados. Esta tesis se sustenta en la teoría y aplicaciones de modelos discriminativos y generativos en el marco del aprendizaje de maquina y la relación de procedimientos sistemáticos con los conceptos de la musicología utilizando técnicas de procesamiento de señales y minería de datos. Los resultados se validaron mediante pruebas estadísticas y una experimentación no paramétrica con la implementación de un conjunto de métricas para medir aspectos acústicos y temporales de archivos de audio para entrenar un modelo discriminativo y mejorar el proceso de síntesis de un modelo neuronal profundo. Adicionalmente, el modelo implementado presenta la oportunidad para la aplicación de procedimientos sistemáticos, automatización de transcripciones usando notación musical, entrenamiento de habilidades auditivas para estudiantes de música y mejorar la implementación de redes neuronales profundas usando CPU en lugar de GPU debido a las ventajas de las redes convolucionales para el procesamiento de archivos de audio como vectores o matriz con una secuencia de notas.MaestríaMagister en Ingeniería Electrónic

Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment

Large data collections containing millions of math formulae in different formats are available on-line. Retrieving math expressions from these collections is challenging. We propose a framework for retrieval of mathematical notation using symbol pairs extracted from visual and semantic representations of mathematical expressions on the symbolic domain for retrieval of text documents. We further adapt our model for retrieval of mathematical notation on images and lecture videos. Graph-based representations are used on each modality to describe math formulas. For symbolic formula retrieval, where the structure is known, we use symbol layout trees and operator trees. For image-based formula retrieval, since the structure is unknown we use a more general Line of Sight graph representation. Paths of these graphs define symbol pairs tuples that are used as the entries for our inverted index of mathematical notation. Our retrieval framework uses a three-stage approach with a fast selection of candidates as the first layer, a more detailed matching algorithm with similarity metric computation in the second stage, and finally when relevance assessments are available, we use an optional third layer with linear regression for estimation of relevance using multiple similarity scores for final re-ranking. Our model has been evaluated using large collections of documents, and preliminary results are presented for videos and cross-modal search. The proposed framework can be adapted for other domains like chemistry or technical diagrams where two visually similar elements from a collection are usually related to each other

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion