We consider the problem of selecting a fixed-size committee based on approval ballots. It is desirable to have a committee in which all voters are fairly represented. Aziz et al. (2015a; 2017) proposed an axiom called extended justified representation (EJR), which aims to capture this intuition; subsequently, Sanchez-Fernandez et al. (2017) proposed a weaker variant of this axiom called proportional justified representation (PJR). It was shown that it is coNP-complete to check whether a given committee provides EJR, and it was conjectured that it is hard to find a committee that provides EJR. In contrast, there are polynomial-time computable voting rules that output committees providing PJR, but the complexity of checking whether a given committee provides PJR was an open problem. In this paper, we answer open questions from prior work by showing that EJR and PJR have the same worst-case complexity: we provide two polynomial-time algorithms that output committees providing EJR, yet we show that it is coNP-complete to decide whether a given committee provides PJR. We complement the latter result by fixed-parameter tractability results.
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.
With the eruption of online social networks, like Twitter and Facebook, a series of new APIs have appeared to allow access to the data that these new sources of information accumulate. One of most popular online social networks is the micro blogging site Twitter. Its APIs allow many machines to access the torrent simultaneously to Twitter data, listening to tweets and accessing other useful information such as user profiles. A number of tools have appeared for processing Twitter data with different algorithms and for different purposes. In this paper T Hoarder is described: a framework that enables tweet crawling, data filtering, and which is also able to display summarized and analytical information about the Twitter activity with respect to a certain topic or event in a web page. This information is updated on a daily basis. The tool has been validated with real use cases that allow making a series of analysis on the performance one may expect from this type of infrastructure.
Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving.
Abstract-In recent yeais, 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 aconlinoous, high velochyflow of information items. High frequencytradirg (HFT) in stock markets ortrendirgtopicdetection in Twitter are som e examples of stream processing applications. In some cases (like, for instance, in HFT), these applications have end-t�nd qualhy-of-service reqlirements and may benefh from the usage� real-time techniques. Taking this into account, the present articl e analyzes, from the point� 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 dsadvanlages, as well as its impact in application performance, measured as response time, maximum ill)Ut frequency and changes in utilization demands due to the pattern.
We identify a whole family of approval-based multi-winner voting rules that satisfy PJR. Moreover, we identify a subfamily of voting rules within this family that satisfy EJR. All these voting rules can be computed in polynomial time as long as the subalgorithms that characterize each rule within the family are polynomial. One of the voting rules that satisfy EJR can be computed in O(nmk).
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 voter 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.
The proliferation of new data sources, stemmed from the adoption of open-data schemes, in combination with an increasing computing capacity causes the inception of new type of analytics that process Internet of things with low-cost engines to speed up data processing using parallel computing. In this context, the article presents an initiative, called BIG-Boletín Oficial del Estado (BOE), designed to process the Spanish official government gazette (BOE) with state-of-the-art processing engines, to reduce computation time and to offer additional speed up for big data analysts. The goal of including a big data infrastructure is to be able to process different BOE documents in parallel with specific analytics, to search for several issues in different documents. The application infrastructure processing engine is described from an architectural perspective and from performance, showing evidence on how this type of infrastructure improves the performance of different types of simple analytics as several machines cooperate.
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