Abstract
Event labeling is the process of marking events in unlabeled data. Traditionally, this is done by involving one or more human experts through an expensive and timeconsuming task. In this article we propose an event labeling system relying on an ensemble of detectors and background knowledge. The target data are the usage log of a real bike sharing system. We first label events in the data and then evaluate the performance of the ensemble and individual detectors on the labeled data set using ROC analysis and static evaluation metrics in the absence and presence of background knowledge. Our results show that when there is no access to human experts, the proposed approach can be an effective alternative for labeling events. In addition to the main proposal, we conduct a comparative study regarding the various predictive models performance, semi-supervised and unsupervised approaches, train data scale, time series filtering methods, online and offline predictive models, and distance functions in measuring time series similarity. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains. However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods. Although tensor-based anomaly detection (TAD) has been applied within a variety of disciplines over the last twenty years, it is not yet recognized as a formal category in anomaly detection. This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures. We survey the interdisciplinary works in which TAD is reported and characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art.

Abstract
Nowadays, every public transportation company uses Automatic Vehicle Location (AVL) systems to track the services provided by each vehicle. Such information can be used to improve operational planning. This paper describes an AVL-based evaluation framework to test whether the actual Schedule Plan fits, in terms of days covered by each schedule, the network's operational conditions. Firstly, clustering is employed to group days with similar profiles in terms of travel times (this is done for each different route). Secondly, consensus clustering is used to obtain a unique set of clusters for all routes. Finally, a set of rules about the groups content is drawn based on appropriate decision variables. Each group will correspond to a different schedule and the rules identify the days covered by each schedule. This methodology is simultaneously an evaluator of the schedules that are offered by the company (regarding its coverage) and an advisor on possible changes to such offer. It was tested by using data collected for one year in a company running in Porto, Portugal. The results are sound. The main contribution of this paper is that it proposes a way to combine Machine Learning techniques to add a novel dimension to the Schedule Plan evaluation methods: the day coverage. Such approach meets no parallel in the current literature.

Abstract
Large scale social networks streaming and visualization has been a hot topic in recent research. Researchers strive to achieve efficient streaming methods and to be able to gather knowledge from the results. Moreover treating the data as a continuous real time flow is a demand for immediate response to events in daily life. Our contribution is to treat the data as a continuous stream and represent it by streaming the egocentric networks (Ego-Networks) for particular nodes. We propose a non-standard node forgetting factor in the representation of the network data stream. Thus, this representation is sensible to recent events in users networks and less sensible for the past node events. The aim of these techniques is the visualization of large scale Ego-Networks from telecommunications social networks with power law distributions.

Abstract
Visualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node-link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node-link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node-link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node-level and the community-level, based on the concept of temporal trajectory. We resort to three-order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self-reported friendship networks.

Abstract
In this paper, we present a WiSARD-based system facing the problem of Indoor Positioning (IP) by taking advantage of pervasively available infrastructures (WiFi Access Points -AP). The goal is to develop a system to be used to position users in indoor environments, such as: museums, malls, factories, offshore platforms etc. Based on the fingerprint approach, we show how the proposed weightless neural system provides very good results in terms of performance and positioning resolution. Both the approach to the problem and the system will be presented through two correlated experiments.