Martín López Nores

AVATAR: ENHANCING THE PERSONALIZED TELEVISION BY SEMANTIC INFERENCE ∗

The generalized arrival of Digital TV will lead to a significant increase in the amount of channels and programs available to end users, making it difficult to find interesting programs among a myriad of irrelevant contents. Thus, in this field, automatic content recommenders should receive special attention in the following years to improve assistance to users. Current approaches of content recommenders have significant well-known deficiencies that hamper their wide acceptance. In this paper, a new approach for automatic content recommendation is presented that considerably reduces those deficiencies. This approach, based on the so-called Semantic Web technologies, has been implemented in the AVATAR tool, a hybrid content recommender that makes extensive use of well-known standards, such as TV-Anytime and OWL. Our proposal has been evaluated experimentally with real users, showing significant increases in the recommendation accuracy with respect to other existing approaches.

Adaptive Learning Objects for t-learning

IDTV (Interactive Digital TV) opens new learning opportunities for those social groups that would hardly have access to traditional forms of education. However, viewers are not usually active learners, for this reason, education through IDTV should be offered in an attractive way, so as they get engaged in the learning experience. For this to be possible, we need to introduce personalization in the t-learning field. To achieve this goal, we present, in this paper, a proposal of self-adaptive t-learning objects, which show a different behavior depending on users characteristics. These objects are conformant to the ADL SCORM (Sharable Content Object Reference Model) standard for which we propose an extension in order to permit this type of learning objects. We expose as well an authoring tool to these objects, which hides the implementation details to the content creator.

On the interplay between inconsistency and incompleteness in multi-perspective requirements specifications

A major challenge for dealing with multi-perspective specifications, and more concretely, with merging of several descriptions or views is toleration of incompleteness and inconsistency: views may be inconclusive, and may have conflicts over the concepts being modeled. The desire of being able to tolerate both phenomena introduces the need to evaluate and quantify the significance of a detected inconsistency as well as to measure the degree of conflict and uncertainty of the merged view as the specification process evolves. We show in this paper to what extent disagreement and incompleteness are closely interrelated and play a central role to obtain a measure of the level of inconsistency and to define a merging operator whose aim is getting the model which best reflects the combined knowledge of all stakeholders. We will also propose two kinds of interesting and useful orderings among perspectives which are based on differences of behavior and inconsistency, respectively.

Composing requirements specifications from multiple prioritized sources

The formal methodology MultiSpec supports the evolution of software specifications gathered from multiple perspectives. A viewpoint-based approach is used to explicitly separate the descriptions provided by different stakeholders, and concentrate on identifying and resolving conflicts between them. The challenge addressed in this article consists in taking into account that some views may have greater degrees of relevance and, consequently, their opinion will have more importance when either obtaining the merged model or resolving the contradictions. To this end, we propose a priority-based approach, where such priority value is twofold. On the one hand, it considers external factors to the perspectives such as the importance assigned to each view by the analyst depending on who is specifying the view or the amount of stakeholders involved in that specification. On the other hand, this priority value also considers internal factors related to the quality of the views and, in order to be able to quantify this value, MultiSpec proposes two measures: coverage and density of each perspective which will be combined in a completeness value. The contributions of this approach will be clearly illustrated through a simple example.The formal methodology MultiSpec supports the evolution of software specifications gathered from multiple perspectives. A viewpoint-based approach is used to explicitly separate the descriptions provided by different stakeholders, and concentrate on identifying and resolving conflicts between them. The challenge addressed in this article consists in taking into account that some views may have greater degrees of relevance and, consequently, their opinion will have more importance when either obtaining the merged model or resolving the contradictions. To this end, we propose a priority-based approach, where such priority value is twofold. On the one hand, it considers external factors to the perspectives such as the importance assigned to each view by the analyst depending on who is specifying the view or the amount of stakeholders involved in that specification. On the other hand, this priority value also considers internal factors related to the quality of the views and, in order to be able to quantify this value, MultiSpec proposes two measures: coverage and density of each perspective which will be combined in a completeness value. The contributions of this approach will be clearly illustrated through a simple example.

COMPOSING MULTI-PERSPECTIVE SOFTWARE REQUIREMENTS SPECIFICATIONS

One of the main needs when dealing with multi-perspective specifications is to be able to have at our disposal, at intermediate stages of the development process, a merged view which properly reflects the knowledge of each participant in the elicitation tasks (and over which we can reason, even in the presence of disagreement and incompleteness). We show in this paper to what extent there can be many merged models, having all of them useful application. So there is not a unique operator which can be qualified as the best; on the contrary, there will be a suitable merging operator depending on the goal of the merging process. More concretely, we will propose a set of four composition operators: ∐max, ∐min, ∐maj and ∐maj+inc. They will be evaluated making use of a list of desired algebraic properties proposed by researchers on merging and which should be held by an ideal merging operator. This analysis can help us to compare the different operators, revealing the key features of each, and identifying weaknesses that may require further research. The conclusion drawn after this analysis points out that these properties are not useful enough to adequately characterize a merging operator. Therefore, new properties will be provided in order to complete the previous list and help to define better the behavior of the different merging operators.

Supporting Software Variability by Reusing Generic Incomplete Models at the Requirements Specification Stage

Selecting components that satisfy a given set of requirements is a key problem in software reuse, especially in reusing between different domains of functionality. This concern has been treated in the ARIFS methodology, which provides an environment to reuse partial and formal requirements specifications, managing the variability implicit in their incompleteness. In this paper, we define generic incomplete specifications, to introduce an explicit source of variability that allows reusing models across different domains, accommodating them to operate in multiple contexts. An extended formal basis is defined to deal with these tasks, that entails improvements in the reuse environment.

Collaborative t-learning: bringing greater levels of interactivity into the home

T-learning - the provision of educational services over interactive digital TV - is emerging as a important medium to create opportunities for learning at home. In this paper, we elaborate on a framework for the development and deployment of collaborative t-learning services, presenting an architecture that allows distributing the logic of a service, a way to define complex interaction patterns among users and a network infrastructure that provides adequate support for the communication needs.

An improved repository system for effective and efficient reuse of formal verification efforts

This paper presents several enhancements to ARIFS, a reuse environment that sets the foundations for reusing formal verification efforts in an iterative and incremental software process for the design of distributed reactive systems. A criterion based on generic components is added, together with a self-learning mechanism, to reduce the search space and maximize the probability of retrieving useful information. Besides, a formalization is given on how to apply verification tasks on a reduced number of states when the retrieved information is not enough for the users intents. These enhancements are shown to improve both the effectiveness and the efficiency of ARIFS.

exhiSTORY: Smart exhibits that tell their own stories

Abstract Museum exhibitions are designed to tell a story; this story is woven by curators and in its context a particular aspect of each exhibit, fitting to the message that the story is intended to convey, is highlighted. Adding new exhibits to the story requires curators to identify for each exhibit its aspects that fit to the message of the story and position the exhibit at the right place in the story thread. The availability of rich semantic information for exhibits, allows for exploiting the wealth of meanings that museum exhibits express, enabling the automated or semi-automated generation of practically countless stories that can be told. Personalization algorithms can then be employed to choose from these stories the ones most suitable for each individual user, based on the semantics of the stories and information within the user profile. In this work we examine how opportunities arising from technological advances in the fields of IoT and semantics can be used to develop smart, self-organizing exhibits that cooperate with each other and provide visitors with comprehensible, rich, diverse, personalized and highly stimulating experiences. These notions are included in the design of a system named exhiSTORY, which also exploits previously ignored information and identifies previously unseen semantic links. We present the architecture of the system and discuss its application potential.

The Use of Semantics in the CrossCult H2020 Project

CrossCult is a newly started project that aims to make reflective history a reality in the European cultural context. In this paper we examine how the project aims to take advantage of advances in semantic technologies in order to achieve its goals. Specifically, we see what the quest for reflection is and, through practical examples from two of the project’s flagship pilots, explain how semantics can assist in this direction.