Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
Interest
Topics
Details

Details

  • Name

    Fernando Silva
  • Cluster

    Computer Science
  • Role

    Centre Coordinator
  • Since

    01st January 2009
003
Publications

2017

Evolutionary role mining in complex networks by ensemble clustering

Authors
Choobdar, Sarvenaz; Ribeiro, PedroManuelPinto; Silva, FernandoM.A.;

Publication
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Abstract

2017

Feature extraction for the author name disambiguation problem in a bibliographic database

Authors
Silva, JorgeM.B.; Silva, FernandoM.A.;

Publication
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

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.

2017

P3-Mobile: Parallel Computing for Mobile Edge-Clouds

Authors
Silva, J; Silva, D; Marques, ERB; Lopes, LMB; Silva, FMA;

Publication
Proceedings of the 4th Workshop on CrossCloud Infrastructures & Platforms, CrossCloud@EuroSys 2017, Belgrade, Serbia, April 23 - 26, 2017

Abstract
We address the problem of whether networks of mobile devices such as smart-phones or tablets can be used to perform opportunistic, best-effort, parallel computations. We designed and implemented P3-Mobile, a parallel programming system for edge-clouds of Android devices to test the feasibility of this idea. P3-Mobile comes with a programming model that supports parallel computations over peer-to-peer overlays mapped onto mobile networks. The system performs automatic load-balancing by using the overlay to discover work. We present preliminary performance results for a parallel benchmark, using up to 16 devices, and discuss their implications towards future work. Copyright © 2017 ACM.

2017

Extending the Applicability of Graphlets to Directed Networks

Authors
Aparicio, D; Ribeiro, P; Silva, F;

Publication
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

Abstract
With recent advances in high-throughput cell biology, the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights intomolecule-level organization. A possible way to understand their structure is by analyzing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity due to the rich topological information that they offer but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such asmetabolic, cell signaling, or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. Our main purpose in this work is to extend the applicability of graphlets to directed networks by considering their edge direction, thus providing a powerful basis for the analysis of directed biological networks. We tested our approach on two network sets, one composed of synthetic graphs and another of real directed biological networks, and verified that they were more accurately grouped using directed graphlets than undirected graphlets. It is also evident that directed graphlets offer substantially more topological information than simple graph metrics such as degree distribution or reciprocity. However, enumerating graphlets in large networks is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.

2017

Using Edge-Clouds to Reduce Load on Traditional WiFi Infrastructures and Improve Quality of Experience

Authors
Pinto Silva, PM; Rodrigues, J; Silva, J; Martins, R; Lopes, LMB; Silva, FMA;

Publication
1st IEEE International Conference on Fog and Edge Computing, ICFEC 2017, Madrid, Spain, May 14-15, 2017

Abstract
Crowd-sourcing the resources of mobile devices is a hot topic of research given the game-changing applications it may enable. In this paper we study the feasibility of using edge-clouds of mobile devices to reduce the load in traditional WiFi infrastructures for video dissemination applications. For this purpose, we designed and implemented a mobile application for video dissemination in sport venues that retrieves replays from a central server, through the access points in the WiFi infrastructure, into a smartphone. The fan's smartphones organize themselves into WiFi-Direct groups and exchange video replays whenever possible, bypassing the central server and access points. We performed a real-world experiment using the live TV feed for the Champions League game Benfica-Besiktas with the help of a group of volunteers using the application at the student's union lounge. The analysis of the logs strongly suggests that edge-clouds can significantly reduce the load in the access points at such large venues and improve quality of experience. Indeed, the edge-clouds formed were able to serve up to 80% of connected users and provide 56% of all downloads requested from within. © 2017 IEEE.

Supervised
thesis

2016

Pattern Discovery in Complex Networks

Author
David Oliveira Aparício

Institution
UP-FCUP

2016

P3-Mobile Parallel Peer-to-Peer computing on mobile devices

Author
Daniel Filipe Pereira Moreira da Silva

Institution
UP-FCUP

2016

Scheduling computations over high-churn networks of mobile devices

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP

2016

Towards a Middleware for Mobile-Edge-Cloud Applications

Author
João Filipe Rodrigues

Institution
UP-FCUP

2015

On the Characterization and Comparison of Complex Networks

Author
Sarvenaz Choobdar

Institution
UP-FCUP