We propose the maximin support method, a novel extension of the D'Hondt apportionment method to approval-based multiwinner elections. The maximin support method is a sequential procedure that aims to maximize the support of the least supported elected candidate. It can be computed efficiently and satisfies (adjusted versions of) the main properties of the original D'Hondt method: house monotonicity, population monotonicity, and proportional representation. We also establish a close relationship between the maximin support method and alternative D'Hondt extensions due to Phragmén.

Time management strategies and self-regulated learning have received much attention. Nevertheless, there is a lack of research in terms of student self-awareness related to their own self-regulation and autonomy. This study aims to validate whether students are self-conscious about their working patterns and their time management. In order to do so, several parameters like work sessions regularity or invested time have been computed and analyzed. This invested time is measured thanks to a data-gathering tool that collects events generated by students. Results show that students are, in general, self-aware of their own working patterns and that the regularity of their weekly work time is correlated with their final marks.

The goal of multi-winner elections is to choose a fixed-size committee based on voters’ preferences. An important concern in this setting is representation: large groups of voters with cohesive preferences should be adequately represented by the election winners. Recently, Aziz et al. proposed two axioms that aim to capture this idea: justified representation (JR) and its strengthening extended justified representation (EJR). In this paper, we extend the work of Aziz et al. in several directions. First, we answer an open question of Aziz et al., by showing that Reweighted Approval Voting satisfies JR for k = 3; 4; 5, but fails it for k >= 6. Second, we observe that EJR is incompatible with the Perfect Representation criterion, which is important for many applications of multi-winner voting, and propose a relaxation of EJR, which we call Proportional Justified Representation (PJR). PJR is more demanding than JR, but, unlike EJR, it is compatible with perfect representation, and a committee that provides PJR can be computed in polynomial time if the committee size divides the number of voters. Moreover, just like EJR, PJR can be used to characterize the classic PAV rule in the class of weighted PAV rules. On the other hand, we show that EJR provides stronger guarantees with respect to average voter satisfaction than PJR does.

In recent years, big data systems have become an active area of research and development. Stream processing is one of the potential application scenarios of big data systems where the goal is to process a continuous, high velocity flow of information items. High frequency trading (HFT) in stock markets or trending topic detection in Twitter are some examples of stream processing applications. In some cases (like, for instance, in HFT), these applications have end-to-end quality-of-service requirements and may benefit from the usage of real-time techniques. Taking this into account, the present article analyzes, from the point of view of real-time systems, a set of patterns that can be used when implementing a stream processing application. For each pattern, we discuss its advantages and disadvantages, as well as its impact in application performance, measured as response time, maximum input frequency and changes in utilization demands due to the pattern.

In recent years, the task of automatically linking pieces of text (anchors) mentioned in a document to Wikipedia articles that represent the meaning of these anchors has received extensive research attention. Typically, link-to-Wikipedia systems try to find a set of Wikipedia articles that are candidates to represent the meaning of the anchor and, later, rank these candidates to select the most appropriate one. In this ranking process the systems rely on context information obtained from the document where the anchor is mentioned and/or from Wikipedia. In this paper we center our attention in the use of Wikipedia links as context information. In particular, we offer a review of several candidate ranking approaches in the state-of-the-art that rely on Wikipedia link information. In addition, we provide a comparative empirical evaluation of the different approaches on five different corpora: the TAC 2010 corpus and four corpora built from actual Wikipedia articles and news items.

This paper presents a model for quality-of-service (QoS)-aware service composition in distributed systems with real-time and fault-tolerance requirements. This model can be applied in application domains like, for example, remote monitoring, control and surveillance. Classic approaches to real-time systems do not provide the flexibility and fault-tolerance required in new emerging environments that need to combine a high degree of dynamism with temporal predictability. Our approach addresses these new challenges by combining concepts from the service oriented paradigm and distributed real-time systems. We propose a concrete system model based on a holistic time-triggered-based approach for design and configuration. Based on this model, we propose two algorithms for the composition of QoS-aware service-based applications with temporal requirements: an exhaustive algorithm that computes the optimal service combination in terms of a figure of merit, suitable for offline composition; and an improved algorithm based on heuristics and partial figures of merit, suitable for online composition. Experimental results show that the latter reduces dramatically the number of combinations explored with a minimal degradation in the quality of the solution, making it feasible for online execution in dynamic environments.