1,720,988 research outputs found
EXPERIMEDIA: Technology enablers for a future media Internet testing facility
No full textCreating innovative Future Media Internet (FMI) products and services is a complex endeavour requiring consideration of socio-technical factors and an increasingly diverse technology landscape. Accelerating time to market requires the availability of technology enablers adapted to local contexts and integrated together to create added value patterns of use. In this paper we present a set of such technology enablers used within a FMI testing facility. We describe how each enabler has been constructed to support the lifecycle of different classes of content and integrated to provide coherent and representative aggregations of content expected in FMI applications and services. A set of lessons learnt are derived from experiments conducted using the enablers at venues of the facility
EXPERIMEDIA: Innovate in New Media - a multi-venue experimentation service supporting technology innovation through new forms of social interaction and user experience
No full textNew media applications and services are revolutionising social interaction and user experience in both society and in wide ranging industry sectors. The rapid emergence of pervasive human and environment sensing technologies, novel immersive presentation devices and high performance, globally connected network and cloud infrastructures is generating huge opportunities for application providers, service provider and content providers. These new applications are driving convergence across devices, clouds, networks and services, and the merging of industries, technology and society. Yet the developers of such systems face many challenges in understanding how to optimise their solutions (Quality of Service - QoS) to enhance user experience (Quality of Experience - QoE) and how their disruptive innovations can be introduced into the market with appropriate business models. In this report, we present the results of a new multi-disciplinary collaborative approach to product and service innovation that brings together users, technology and live events in a series of experiments conducted in real world settings. Through experimentation we have explored a broad range of technical, societal and economic challenges faced by technology providers each aiming to create and exploit new multimedia value chains in markets such as leisure and tourism, cultural and heritage, and sports science and training. The experiments highlight the features of multimedia systems and the future opportunities for companies, as the Internet continues to transition towards the increasingly connected world of Internet of Things and Big Data. We know that putting user values at the heart of design decisions and evaluation is the key to success, and that long term benefits to providers of technology, services and content must derive from enhanced user experience. Engaging users in real-world settings to co-design and assess how technology can be used is now more important than testing how technology will be operated. We have only scratched the surface of possibility in novel networked multimedia systems yet we believe that the individual and collective results in the report are significant as they are grounded in real-world evidence. A new way of conducting research and innovation has been created that maximises the potential for commercial exploitation and societal impact. We think this is extremely important and when adopted will lead to greater benefits for all
EXPERIMEDIA – a multi-venue experimentation service supporting technology innovation through new forms of social interaction and user experience
No full textNew media applications and services are revolutionising social interaction and user experience in both society and in wide ranging industry sectors. The rapid emergence of pervasive human and environment sensing technologies, novel immersive presentation devices and high performance, globally connected network and cloud infrastructures is generating huge opportunities for application providers, service provider and content providers.These new applications are driving convergence across devices, clouds, networks and services, and the merging of industries, technology and society. Yet the developers of such systems face many challenges in understanding how to optimise their solutions (Quality of Service – QoS) to enhance user experience (Quality of Experience – QoE) and how their disruptive innovations can be introduced into the market with appropriate business models
Online classification of visual tasks for industrial workflow monitoring
No full textModelling and classification of time series stemming from visual workflows is a very challenging problem due to the inherent complexity of the activity patterns involved and the difficulty in tracking moving targets. In this paper, we propose a framework for classification of visual tasks in industrial environments. We propose a novel method to automatically segment the input stream and to classify the resulting segments using prior knowledge and hidden Markov models (HMMs), combined through a genetic algorithm. We compare this method to an echo state network (ESN) approach, which is appropriate for general-purpose time-series classification. In addition, we explore the applicability of several fusion schemes for multicamera configuration in order to mitigate the problem of limited visibility and occlusions. The performance of the suggested approaches is evaluated on real-world visual behaviour scenarios
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Méthodes d'apprentissage automatique multimodales pour l'analyse de modèles dans les villes intelligentes et les transports
No full textDans le contexte des environnements urbains modernes et densément peuplés, la gestion efficace des transports et la structure des Systèmes de Transport Intelligents (STI) sont primordiales. Le secteur des transports publics connaît actuellement une expansion et une transformation significatives dans le but d'améliorer l'accessibilité, d'accommoder des volumes de passagers plus importants sans compromettre la qualité des déplacements, et d'adopter des pratiques respectueuses de l'environnement et durables. Les avancées technologiques, notamment dans l'Intelligence Artificielle (IA), l'Analyse de Données Massives (BDA), et les Capteurs Avancés (CA), ont joué un rôle essentiel dans la réalisation de ces objectifs et ont contribué au développement, à l'amélioration et à l'expansion des Systèmes de Transport Intelligents. Cette thèse aborde deux défis critiques dans le domaine des villes intelligentes, se concentrant spécifiquement sur l'identification des modes de transport utilisés par les citoyens à un moment donné et sur l'estimation et la prédiction du flux de transport au sein de divers systèmes de transport. Dans le contexte du premier défi, deux approches distinctes ont été développées pour la Détection des Modes de Transport. Tout d'abord, une approche d'apprentissage approfondi pour l'identification de huit médias de transport est proposée, utilisant des données de capteurs multimodaux collectées à partir des smartphones des utilisateurs. Cette approche est basée sur un réseau Long Short-Term Memory (LSTM) et une optimisation bayésienne des paramètres du modèle. À travers une évaluation expérimentale approfondie, l'approche proposée démontre des taux de reconnaissance remarquablement élevés par rapport à diverses approches d'apprentissage automatique, y compris des méthodes de pointe. La thèse aborde également des problèmes liés à la corrélation des caractéristiques et à l'impact de la réduction de la dimensionnalité. La deuxième approche implique un modèle basé sur un transformateur pour la détection des modes de transport appelé TMD-BERT. Ce modèle traite l'ensemble de la séquence de données, comprend l'importance de chaque partie de la séquence d'entrée, et attribue des poids en conséquence en utilisant des mécanismes d'attention pour saisir les dépendances globales dans la séquence. Les évaluations expérimentales mettent en évidence les performances exceptionnelles du modèle par rapport aux méthodes de pointe, soulignant sa haute précision de prédiction. Pour relever le défi de l'estimation du flux de transport, un Réseau Convolutif Temporel et Spatial (ST-GCN) est proposé. Ce réseau apprend à la fois des données spatiales du réseau de stations et des séries temporelles des changements de mobilité historiques pour prédire le flux de métro urbain et le partage de vélos à un moment futur. Le modèle combine des Réseaux Convolutifs Graphiques (GCN) et des Réseaux Long Short-Term Memory (LSTM) pour améliorer la précision de l'estimation. Des expériences approfondies menées sur des ensembles de données du monde réel du système de métro de Hangzhou et du système de partage de vélos de la ville de New York valident l'efficacité du modèle proposé, démontrant sa capacité à identifier des corrélations spatiales dynamiques entre les stations et à faire des prévisions précises à long terme.In the context of modern, densely populated urban environments, the effective management of transportation and the structure of Intelligent Transportation Systems (ITSs) are paramount. The public transportation sector is currently undergoing a significant expansion and transformation with the objective of enhancing accessibility, accommodating larger passenger volumes without compromising travel quality, and embracing environmentally conscious and sustainable practices. Technological advancements, particularly in Artificial Intelligence (AI), Big Data Analytics (BDA), and Advanced Sensors (AS), have played a pivotal role in achieving these goals and contributing to the development, enhancement, and expansion of Intelligent Transportation Systems. This thesis addresses two critical challenges within the realm of smart cities, specifically focusing on the identification of transportation modes utilized by citizens at any given moment and the estimation and prediction of transportation flow within diverse transportation systems. In the context of the first challenge, two distinct approaches have been developed for Transportation Mode Detection. Firstly, a deep learning approach for the identification of eight transportation media is proposed, utilizing multimodal sensor data collected from user smartphones. This approach is based on a Long Short-Term Memory (LSTM) network and Bayesian optimization of model’s parameters. Through extensive experimental evaluation, the proposed approach demonstrates remarkably high recognition rates compared to a variety of machine learning approaches, including state-of-the-art methods. The thesis also delves into issues related to feature correlation and the impact of dimensionality reduction. The second approach involves a transformer-based model for transportation mode detection named TMD-BERT. This model processes the entire sequence of data, comprehends the importance of each part of the input sequence, and assigns weights accordingly using attention mechanisms to grasp global dependencies in the sequence. Experimental evaluations showcase the model's exceptional performance compared to state-of-the-art methods, highlighting its high prediction accuracy. In addressing the challenge of transportation flow estimation, a Spatial-Temporal Graph Convolutional Recurrent Network is proposed. This network learns from both the spatial stations network data and time-series of historical mobility changes to predict urban metro and bike sharing flow at a future time. The model combines Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) Networks to enhance estimation accuracy. Extensive experiments conducted on real-world datasets from the Hangzhou metro system and the NY City bike sharing system validate the effectiveness of the proposed model, showcasing its ability to identify dynamic spatial correlations between stations and make accurate long-term forecasts
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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