Abstract
In this paper, we present GTE-Cluster an online temporal search interface which consistently allows searching for topics in a temporal perspective by clustering relevant temporal Web search results. GTE-Cluster is designed to improve user experience by augmenting document relevance with temporal relevance. The rationale is that offering the user a comprehensive temporal perspective of a topic is intuitively more informative than retrieving a result that only contains topical information. Our system does not pose any constraint in terms of language or domain, thus users can issue queries in any language ranging from business, cultural, political to musical perspective, to cite just a few. The ability to exploit this information in a temporal manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or temporal clustering. © 2014 Springer International Publishing Switzerland.

Abstract
In this paper we describe an algorithm for heart sound classification (classes Normal, Murmur and Extrasystole) based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The general strategy is to automatically discover relevant top frequent motifs and relate them with the occurrence of systolic (S1) and diastolic (S2) sounds in the audio signals. The algorithm was tuned using motifs generated from a collection of audio signals obtained from a clinical trial in a hospital. Validation was performed on a separate set of unlabeled audio signals. Results indicate ability to improve the precision of the classification of the classes Normal and Murmur.

Abstract
Recommender systems (RS) are increasingly adopted by e-business, social networks and many other user-centric websites. Based on the user's previous choices or interests, a RS suggests new items in which the user might be interested. With constant changes in user behavior, the quality of a RS may decrease over time. Therefore, we need to monitor the performance of the RS, giving timely information to management, who can than manage the RS to maximize results. Our work consists in creating a monitoring platform - based on Business Intelligence (BI) and On-line Analytical Processing (OLAP) tools - that provides information about the recommender system, in order to assess its quality, the impact it has on users and their adherence to the recommendations. We present a case study with Palco Principal(1), a social network for music.

Abstract
The ability to have an automated real time detection of user interest during a web session is very appealing and can be very useful for a number of web intelligence applications. Low level interaction events associated with user interest manifestations form the basis of user interest models. However such data sets present a number of challenges from a machine learning perspective, including the level of noise in the data and class imbalance (given that the majority of content will not be of interest to a user). In this paper we evaluate a large number of machine learning techniques aimed at learning from class imbalanced data using two data sets collected from a real user study. We use the AUC, recall, precision and model complexity to compare the relative merits of these techniques and conclude that useful models with AUC above 0.8 can be obtained using a mix of sampling and cost based methods. Ensemble models can provide further accuracy but make deployment more complex.

Abstract
In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple mobile devices or digital stethoscopes. In our approach, sounds are first pre-processed using signal processing techniques (decimate, low-pass filter, normalize, Shannon envelope). Then the pre-processed symbols are transformed into sequences of discrete SAX symbols. These sequences are subject to a process of motif discovery. Frequent sequences of symbols (motifs) are adopted as features. Each sound is then characterized by the frequent motifs that occur in it and their respective frequency. This is similar to the term frequency (TF) model used in text mining. In this paper we compare the TF model with the application of the TFIDF (Term frequency - Inverse Document Frequency) and the use of bi-grams (frequent size two sequences of motifs). Results show the ability of the motifs based TF approach to separate classes and the relative value of the TFIDF and the bi-grams variants. The separation of the Extra-systole class is overly difficult and much better results are obtained for separating the Murmur class. Empirical validation is conducted using real data collected in noisy environments. We have also assessed the cost-reduction potential of the proposed methods by considering a fixed cost model and using a cost sensitive meta algorithm.

Abstract
Temporal information retrieval has been a topic of great interest in recent years. Despite the efforts that have been conducted so far, most popular search engines remain underdeveloped when it comes to explicitly considering the use of temporal information in their search process. In this paper we present GTE-Rank, an online searching tool that takes time into account when ranking time-sensitive query web search results. GTE-Rank is defined as a linear combination of topical and temporal scores to reflect the relevance of any web page both in topical and temporal dimensions. The resulting system can be explored graphically through a search interface made available for research purposes.