Maria Teresa Andrade

Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation

Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system.

A Platform for Context-Aware and Digital Rights Management-Enabled Content Adaptation

This article presents a scalable and modular platform for contextaware adaptation of multimedia content that is governed by digital rights management (DRM). This platform adopts several new approaches, such as (1) combining the use of ontologies and lowlevel context to drive the adaptation decision process, (2) verifying and enforcing usage rights within the adaptation operations, and (3) incorporating multifaceted adaptation tools to provide a wide range of on-the-fly and on-demand adaptation operations to suit various dynamic requirements.

Using context to assist the adaptation of protected multimedia content in virtual collaboration applications

This paper proposes a framework for a virtual classroom application based on a virtual collaboration system (VCS), which is being developed under the VISNET II network of excellence (NoE), and discusses adaptation technologies that enable seamless access to classroom sessions while intellectual property and digital rights are managed. The proposed virtual classroom framework enables academic institutions to conduct their collaborative lecture series, to which registered students will be able to attend remotely and interactively over the Internet. Furthermore, the general public may also follow the classroom sessions under certain restrictions imposed by the participating institutions. In order to facilitate seamless access to a heterogeneous audience that is composed of users with various preferences and privileges accessing the classroom sessions over different network infrastructures and using terminal devices with diverse capabilities, context-aware content adaptation is required to meet constraints imposed by the usage context and enhance the quality of the user experience. Thus, this paper describes the concepts and functionalities of a context-aware content adaptation platform that suits the requirements of such multimedia application scenarios. This platform is able to consume low-level contextual information to infer higher-level contexts, and thus decide the need for and type of adaptation operations to be performed upon the content. In this way, it is aimed to meet usage constraints while also satisfying restrictions imposed by the digital rights management (DRM) to govern the use of protected content.

Supporting Context-Aware Cloud-Based Media Recommendations for Smartphones

The unprecedented advancements in broadband and mobile networks, the proliferation and the incredible appeal of smart devices such as smartphones, and the recent emergence of cloud computing are poised to drive the next generation of ubiquitous media delivery and consumption. As more media services become available, mobile users will waste invaluable time, seeking relevant media items. Therefore, to deliver relevant media services, with rich experience to mobile users, media service providers must consider the services that match users contextual consumption choices. This paper proposes context-aware recommendation techniques to support the delivery of contextually relevant cloud-based media items to mobile users. The recommendation service works with a contextual user profile service, which relates user preferences to contexts in which such preferences are expressed, relying on a context recognition service, which identifies the users dynamic contextual situation from smartphone built-in sensors. Experimental evaluations, using real world user and online movie data, established that the context-aware recommendation techniques are promising.

Novel Hybrid Approach to Content Recommendation Based on Predicted Profiles

The present phenomenon of technology convergence is blurring away the frontiers between the Internet and the TV, operating a shift on the way TV is consumed. TV viewers have now access to a huge selection of TV programming as well as online contents, either previously broadcasted or natively produced for the Internet. This reality creates new necessities whilst opening new opportunities for the creation of services capable of filtering this information and presenting the user with the most relevant content. This article describes an innovative hybrid strategy for delivering recommendations of TV content to individual users. It was developed specifically for the TV entertainment services of hotels, but it can be applied to any multimedia consumption service. Without requiring users to explicitly rate the programs they have watched, it is still able to recommend similar programs to similar users. It adopts an improved Pearson correlation method to establish similarities between different users, comparing profiles that have been automatically generated based on the user viewing history. It builds a predicted user profile, which is then used within a content-based approach to generate recommendations.

A Structured and Flexible Language for Physical Activity Assessment and Characterization.

Developing more accurate assessments of physical activity (PA) and sedentary behavior (SB) is an important public health research priority. Assessing PA and SB is challenging in all segments of the population, but it is especially difficult in children due to cognitive limitations and more sporadic and intermittent activity patterns. Moreover, they are influenced by several factors including temporal-spatial constraints and social conditions. To accurately assess PA and SB, it is essential to clearly define methods for describing all these factors. The goal of this paper is to potentiate advances in the field by proposing a base ontology for characterizing physical activity, sedentary behavior, and the context in which it occurs. The ontology would establish a flexible base language to facilitate standardized descriptions of these behaviors for researchers and public health professionals.

Advanced delivery of sensitive multimedia content for better serving user expectations in Virtual Collaboration applications

A major challenge when accessing protected multimedia content in heterogeneous usage environments is the ability to provide acceptable levels of quality of experience to all involving users. Additionally, different levels of protection should be possible to be addressed when manipulating the content towards the quality of experience maximization. This paper describes the use of a context-aware and Digital Rights Management (DRM)-enabled content adaptation platform towards meeting these challenges. The platform was conceived to deliver advanced content adaptation within different application scenarios, among which Virtual Collaboration (VC) was central. Descriptions of use cases implemented by the platform in heterogeneous VC environments are provided. Conducted experiments have highlighted the benefits to users when compared to an operation without the platform. Results of different adaptations suitable to sensed context conditions are also provided and analyzed. A brief description of the platform functionality is included together with pointers to additional information.

Context Representation for Context-Aware Mobile Multimedia Content Recommendation

Very few of the current solutions for content recommendation take into consideration the context of usage when analyzing the preferences of the user and issuing recommendations. Nonetheless, context can be extremely useful to help identify appropriate content for the specific situation or activity the user is in, while consuming the content. In this paper, we present a solution to allow content-based recommendation systems to take full potential of contextual data, by defining a standards-based representation model which accounts for possible relationships among low-level contexts. The MPEG-7 and MPEG-21 standards are used for content description and low-level context representation. OWL/RDF ontologies are used to capture contextual concepts and, together with SWRL to establish relationships and perform reasoning to derive high-level concepts the way humans do. This knowledge is then used to drive the recommendation and content adaptation processes. As a side achievement, an extension to the MPEG-21 specification was developed to accommodate the description of user activities, which we believe have a great impact on the type of content to be recommended.

An Integrated Management Supervisor for End-to-End Management of Heterogeneous Contents, Networks, and Terminals enabling Quality of Service

End-to-end support for Quality of Service (QoS) has been broadly discussed in the literature. Many technologies have been proposed, each focusing on specific aspects for providing QoS guarantees to the end user. However, the integrated management of the end-to-end chain preserving QoS in heterogeneous environments is still a significant issue and insufficiently addressed to date. In this paper we propose an integrated management supervisor that takes into account the requirements from all stakeholders along the multimedia content delivery chain and provides an end-to-end management solution enabling QoS to the end user. This architecture and the subsystems that can be distributed along the end-to-end chain are detailed in this paper.

The semantics of MPEG-21 digital items revisited

The MPEG-21 standard forms a comprehensive multimedia framework covering the entire multimedia distribution chain. In particular, it provides a flexible approach to represent, process, and transact complex multimedia objects which are referred to as Digital Items (DIs). DIs can be quite generic, independent of the application domain, and can encompass a diversity of media resources and metadata. This flexibility has an impact on the level of interoperability between systems and applications, since not all the functionality needs to be implemented. Furthermore, additional semantic rules may be implemented through the processing of the Digital Item which is possibly driven by proprietary metadata. This jeopardizes interoperability and consequently raises barriers to the successful achievement of augmented and transparent use of multimedia resources. In this context, we have investigated and evaluated the interoperability at the semantic level of Digital Items throughout the automated production, delivery and consumption of complex multimedia resources in heterogeneous environments. This paper describes the studies conducted, the experiments performed, and the conclusions reached towards that goal.