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About

Maria Teresa Andrade is an Assistant Professor at FEUP, at DEEC. She obtained a degree in Electrotechnical and Computing Engineering in 1986, the MSc in 1992 and the PhD in 2008, at FEUP. She participates in research activities at INESC TEC, integrated in the research team of the Multimedia Systems Area of the Center for Telecommunications and Multimedia. Main interests include context-awareness, mobile and adaptable multimedia applications in heterogeneous environments; 3D and multiview video streaming; quality of service and of experience in multimedia services; semantic technologies and content recommendation; digital television, digital cinema and new media.

Interest
Topics
Details

Details

007
Publications

2017

2D/3D Video Content Adaptation Decision Engine Based on Content Classification and User Assessment

Authors
Fernandes, R; Andrade, MT;

Publication
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2016 (ICNAAM-2016)

Abstract
Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

2016

User context recognition using smartphone sensors and classification models

Authors
Otebolaku, AM; Andrade, MT;

Publication
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

Abstract
Context recognition is an indispensable functionality of context-aware applications that deals with automatic determination and inference of contextual information from a set of observations captured by sensors. It enables developing applications that can respond and adapt to user's situations. Thus much attention has been paid to developing innovative context recognition capabilities into context-aware systems. However, some existing studies rely on wearable sensors for context recognition and this practice has limited the incorporation of contexts into practical applications. Additionally, contexts are usually provided as low-level data, which are not suitable for more advanced mobile applications. This article explores and evaluates the use of smartphone's built-in sensors and classification algorithms for context recognition. To realize this goal, labeled sensor data were collected as training and test datasets from volunteers' smartphones while performing daily activities. Time series features were then extracted from the collected data, summarizing user's contexts with 50% overlapping slide windows. Context recognition is achieved by inducing a set of classifiers with the extracted features. Using cross validation, experimental results show that instance-based learners and decision trees are best suitable for smart phone -based context recognition, achieving over 90% recognition accuracy. Nevertheless, using leave one -subject-out validation, the performance drops to 79%. The results also show that smartphone's orientation and rotation data can be used to recognize user contexts. Furthermore, using data from multiple sensors, our results indicate improvement in context recognition performance between 1.5% and 5%. To demonstrate its applicability, the context recognition system has been incorporated into a mobile application to support context-aware personalized media recommendations.

2016

Context-Aware Personalization Using Neighborhood-Based Context Similarity

Authors
Otebolaku, AM; Andrade, MT;

Publication
Wireless Personal Communications

Abstract
With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Context-aware personalization, having the capability to predict user’s contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the so-called cold-start problem that occurs as a result of personalization systems’ lack of adequate knowledge of either a new user’s preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem. © 2016 Springer Science+Business Media New York

2015

Semantically connected web resources with MPEG-21

Authors
Castro, H; Andrade, MT; Almeida, F; Tropea, G; Melazzi, NB; Mousas, AS; Kaklamani, DI; Chiariglione, L; Difino, A;

Publication
MULTIMEDIA TOOLS AND APPLICATIONS

Abstract
The Web is rapidly becoming the prime medium for human socialization. The resources that enable that process (social web sites, blogs, media objects, etc.) present growing complexity and, collectively, weave an ever more intricate web of relationships. Current technology for declaring those relationships is predominantly implicit, ambiguous and semantically poor. As a consequence, their automatic assessment is complex and error prone, preventing the satisfaction of users' needs such as effective semantic searches. To address these limitations, whilst enabling the explicit declaration of semantically unambiguous relationships between digital resources, a solution employing structured semantic descriptors and ontologies was conceived, based on MPEG-21. This paper explains the functioning of the devised mechanism, and goes beyond that, into the definition of two novel employment venues for it, at the service of two real-world usage scenarios. These demonstrate the mechanism's added value as a powerful alternative for the semantically aware interconnection of web resources, and highlight the increased QoE that said mechanism enables.

2015

Gaze-Based Personalized Multi-View Experiences

Authors
Andrade, MT; Costa, TSd;

Publication
JMMC - Journal of Media & Mass Communication

Abstract

Supervised
thesis

2017

MATT - Media Asset Tracking Tool

Author
Tomás Fernandes Brandão Tavares

Institution
UP-FEUP

2017

Plataforma de agregação de serviços OTT

Author
Paulo Sérgio Martins da Silva

Institution
UP-FEUP

2017

Design de Usabilidade em Interfaces Conversacionais Híbridas

Author
Ana Sofia Ferreira de Sousa

Institution
UP-FEUP

2017

Social TV: A integração entre a televisão convencional e as redes sociais

Author
Paulo António da Silva Brandão

Institution
UP-FEUP

2017

Interfaces Conversacionais – Chatbot para a Casa da Música

Author
Sara Filipa Gomes Oliveira

Institution
UP-FEUP