Abstract
Sentence Reduction has recently received a great attention from the research community of Automatic Text Summarization. Sentence Reduction consists in the elimination of sentence components such as words, part-of-speech tags sequences or chunks without highly deteriorating the information contained in the sentence and its grammatical correctness. In this paper, we present an unsupervised scalable methodology for learning sentence reduction rules. Paraphrases are first discovered within a collection of automatically crawled Web News Stories and then textually aligned in order to extract interchangeable text fragment candidates, in particular reduction cases. As only positive examples exist, Inductive Logic Programming (ILP) provides an interesting learning paradigm for the extraction of sentence reduction rules. As a consequence, reduction cases are transformed into first order logic clauses to supply a massive set of suitable learning instances and an ILP learning environment is defined within the context of the Aleph framework. Experiments evidence good results in terms of irrelevancy elimination, syntactical correctness and reduction rate in a real-world environment as opposed to other methodologies proposed so far.

Abstract
In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A-peer-to-peer complementariness, B-concentration process and C-virtual cooperation networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks.

Abstract
The present-day revival of Augmented reality (AR) technology has led to its vast expansion in various applications. In marketing, the hunt for more inventive and intriguing approaches for immersive consumer experiences has endorsed the implementation of AR in multiple brand advertising campaigns, specifically for improved product display. The engaging potential of this technology is established in the fusion between computer-generated data and the physical world as seen by the user, where 3D registration and real time interaction are inseparable parts of this system. Alternatively, impressions from user experiences serve as a principal instrument in the evaluation process of the effectiveness of interactive systems. In order to get deeper insight into consumers' reflections from a real-time AR shopping experience, we present a demo platform for the purchase of sneakers, focusing on users' behavior and more precisely on their perceptions, emotions, personal preferences before, during and after use of the platform. To fully evaluate and compare consumer experiences with the main AR platform, two other shopping systems were designed: a marker-based and a static one. Consecutively, we aim at defining a system of metrics for measuring shopping experiences with AR, as well as at establishing a ground base for subsequent marketing research in the field. Motivated by the large application of the technology and aiming at understanding the impact of AR on consumer psychology, the application will assist in exploring the antecedents of consumer purchase intentions.

Abstract
Augmented Reality (AR) is a technology which produces a synthesis between a computer-generated data and the physical world of a viewer while establishing 3D registration and real time interaction. Among the wide range of application of AR, its use in advertising shopping experiences has recently been embraced by advertisers due to its novelty and engaging potential. Part of a wider research aiming at understanding the impact of AR on consumer psychology, this paper presents a demo platform application developed for a real-time shopping experience for shoes and attempts to define a ground base for posterior marketing research in the field. In order to fully evaluate consumer experiences and compare with the main AR platform two other shopping applications were designed: a marker-based and a static one. The platform will assist in exploring the antecedents of consumer purchase intention and in defining metrics for measuring shopping experiences with AR.

Abstract
In this work we aim at increasing the utility of a preelection poll, by improving the quality of the vote share estimates, both at macro and micro level. Three different methodologies are applied with that purpose: (1) polls aggregation, using existing auxiliary polling; (2) application of multilevel regression methods, using the multilevel structure of the data; and (3) methods of small area estimation, making use of auxiliary information through the application of the Empirical Best Linear Unbiased Prediction (EBLUP). These methods are applied to real data collected from a survey with the aim of estimating the vote share in the Portuguese legislative elections. When auxiliary information is required, we concluded that polls aggregations and EBLUP have to be applied with caution, since this information is extremely important for a good application of these models to the data set and to obtain good reliable forecasts. On the other hand, if auxiliary information is not available or if it is not of good quality, then multilevel regression can and should be seen as a safe alternative to obtain more precise estimates, either at the micro or macro level. Besides, this is the method which further improves the precision of the estimates. In the presence of good auxiliary information, EBLUP proved to be the method with greater proximity with real values. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
This paper presents an approach to infer UI patterns existent in a web application. This reverse engineering process is performed in two steps. First, execution traces are collected from user interactions using the Selenium software. Second, the existing UI patterns within those traces are identified using Machine Learning inference with the Aleph ILP system. The paper describes and illustrates the proposed methodology on a case study over the Amazon web site.