Abstract
Enterprise Wi-Fi networks have been increasingly considering energy efficiency. In this paper, we present the Wi-Green project wherein we are investigating new techniques and innovative solutions that will allow the minimization of the energy consumption in Wi-Fi networks. In Wi-Green we will consider an enterprise network, in which there is equipment from different vendors, with different ages and different consumption profiles.

Abstract
The paper analises the use of recommender systems in academic libraries, examining the use of the " Related books in Aleph OPAC" recommendation system for academic libraries' online catalogues. A quantitative approach and descriptive methodology is used to collect, process and analyse the data from a usage log provided by the University of Dundee. The analysis of 13,654 posts and 6,347 sessions provided the following observations: the recommendation was used in 11% of the sessions, and 43.9% of the recorded document views on those sessions where generated by recommendation. 9.6% of the records of document views, were derived from recommendation. Sessions using recommendations were on average 1 minute 18 seconds shorter than the sessions without recommendations. In sessions with recommendation 4.30 records were viewed on average while in sessions without recommendation the average is 1.88. Using more than one type of recommendation is not common, as 82% of the sessions with recommendation have recorded the use of only one kind of recommendation. The analysis of recommendations by kind provided two results: "Related works include" appears in more sessions (348), while " People who borrowed this work also borrowed" has the highest number of posts (584).

Abstract
A system identification approach is used estimate linear time invariant models from the data of physical activity gathered in the Just Walk intervention conducted by the Designing Health Lab and the Control Systems Laboratory at Arizona State University A class of identification algorithms proposed elsewhere by one of the authors, denoted as MoliZoft, was reformulated and adapted to estimate models from data gathered in this experience. In this paper, the identification algorithms are described and the best models estimated for a particular participant are analysed and used to improve the results in future experiments.

Abstract
This paper addresses the issue of learning time series forecasting models in changing environments by leveraging the predictive power of ensemble methods. Concept drift adaptation is performed in an active manner, by dynamically combining base learners according to their recent performance using a non-linear function. Diversity in the ensembles is encouraged with several strategies that include heterogeneity among learners, sampling techniques and computation of summary statistics as extra predictors. Heterogeneity is used with the goal of better coping with different dynamic regimes of the time series. The driving hypotheses of this work are that (i) heterogeneous ensembles should better fit different dynamic regimes and (ii) dynamic aggregation should allow for fast detection and adaptation to regime changes. We extend some strategies typically used in classification tasks to time series forecasting. The proposed methods are validated using Monte Carlo simulations on 16 real-world univariate time series with numerical outcome as well as an artificial series with clear regime shifts. The results provide strong empirical evidence for our hypotheses. To encourage reproducibility the proposed method is publicly available as a software package.

Abstract
The structural patterns in the neighborhood of nodes assign unique roles to the nodes. Mining the set of existing roles in a network provides a descriptive profile of the network and draws its general picture. This paper proposes a new method to determine structural roles in a dynamic network based on the current position of nodes and their historic behavior. We develop a temporal ensemble clustering technique to dynamically find groups of nodes, holding similar tempo-structural roles. We compare two weighting functions, based on age and distribution of data, to incorporate temporal behavior of nodes in the role discovery. To evaluate the performance of the proposed method, we assess the results from two points of view: 1) goodness of fit to current structure of the network; 2) consistency with historic data. We conduct the evaluation using different ensemble clustering techniques. The results on real world networks demonstrate that our method can detect tempo-structural roles that simultaneously depict the topology of a network and reflect its dynamics with high accuracy.

Abstract