Abstract
This paper introduces a novel ranking system for competitive sports based around the notion of subgraphs. Although the system is targeted specifically to professional tennis it could be applied to any dominance network due to its generality. The results of about 140,000 tennis matches played between Top-100 players are used to create a colored directed network where colors represent different surfaces and edge direction depends on head-to-read results between players. The main contribution of this work is a ranking system which relies on the occurrences of 4-node directed subgraphs and the positions (or orbits) where the players appear on them. Since the concept of orbit is intrinsically connected with node dominance, appearing frequently in dominant orbits indicates that the player himself is dominant. Even in a very sparse network and without any background knowledge on the tournaments or stages of the matches, our proposal is able to extract meaningful rankings which capture the intricate competitive relationships between players from different eras.

Abstract
Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publicationauthor associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250, 000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37% accuracy in predicting matches and 91.92% in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86% of the cases where authors had no previous publication history. Copyright 2017 ACM.

Abstract
Reducing the dimensionality of datasets is a fundamental step in the task of building a classification model. Feature selection is the process of selecting a smaller subset of features from the original one in order to enhance the performance of the classification model. The problem is known to be NP-hard, and despite the existence of several algorithms there is not one that outperforms the others in all scenarios. Due to the complexity of the problem usually feature selection algorithms have to compromise the quality of their solutions in order to execute in a practicable amount of time. Parallel computing techniques emerge as a potential solution to tackle this problem. There are several approaches that already execute feature selection in parallel resorting to synchronous models. These are preferred due to their simplicity and capability to use with any feature selection algorithm. However, synchronous models implement pausing points during the execution flow, which decrease the parallel performance. In this paper, we discuss the challenges of executing feature selection algorithms in parallel using asynchronous models, and present a feature selection algorithm that favours these models. Furthermore, we present two strategies for an asynchronous parallel execution not only of our algorithm but of any other feature selection approach. The first strategy solves the problem using the distributed memory paradigm, while the second exploits the use of shared memory. We evaluate the parallel performance of our strategies using up to 32 cores. The results show near linear speedups for both strategies, with the shared memory strategy outperforming the distributed one. Additionally, we provide an example of adapting our strategies to execute the Sequential forward Search asynchronously. We further test this version versus a synchronous one. Our results revealed that, by using an asynchronous strategy, we are able to save an average of 7.5% of the execution time.

Abstract

Abstract

Abstract
We describe a case study application for untethered video dissemination using a hybrid edge-cloud architecture featuring Android devices, possibly organised in WiFi-Direct groups, and Raspberry Pi-based cloudlets, structured in a mesh and also working as access points. The application was tested in the real-world scenario of a Portuguese volleyball league game. During the game, users of the application recorded videos and injected them in the edge-cloud. The cloudlet servers continuously synchronised their cached video contents over the mesh network, allowing users on different locations to share their videos, without resorting to any other network infrastructure. An analysis of the logs gathered during the experiment shows that such portable setups can easily disseminate videos to tens of users through the edge-cloud with low latencies. We observe that the edge-cloud may be naturally resilient to faulty cloudlets or devices, taking advantage of video caching within devices and WiFi-Direct groups, and of device churn to opportunistically disseminate videos.