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Publications by CRACS

2019

Iris: Secure reliable live-streaming with opportunistic mobile edge cloud offloading

Authors
Martins, R; Correia, ME; Antunes, L; Silva, F;

Publication
Future Generation Computer Systems

Abstract
The ever-increasing demand for higher quality live streams is driving the need for better networking infrastructures, specially when disseminating content over highly congested areas, such as stadiums, concerts and museums. Traditional approaches to handle this type of scenario relies on a combination of cellular data, through 4G distributed antenna arrays (DAS), with a high count of WiFi (802.11) access points. This obvious requires a substantial upfront cost for equipment, planning and deployment. Recently, new efforts have been introduced to securely leverage the capabilities of wireless multipath, including WiFi multicast, 4G, and device-to-device communications. In order to solve these issues, we propose an approach that lessens the requirements imposed on the wireless infrastructures while potentially expanding wireless coverage through the crowd-sourcing of mobile devices. In order to achieve this, we propose a novel pervasive approach that combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading that makes use of hyperlocal mobile edge clouds. We empirically show that our solution is able to offer a 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication. © 2019 Elsevier B.V.

2019

Feature-enriched author ranking in incomplete networks

Authors
Silva, J; Aparício, D; Silva, F;

Publication
Applied Network Science

Abstract
Evaluating scientists based on their scientific production is a controversial topic. Nevertheless, bibliometrics and algorithmic approaches can assist traditional peer review in numerous tasks, such as attributing research grants, deciding scientific committees, or choosing faculty promotions. Traditional bibliometrics rank individual entities (e.g., researchers, journals, faculties) without looking at the whole data (i.e., the whole network). Network algorithms, such as PageRank, have been used to measure node importance in a network, and have been applied to author ranking. However, traditional PageRank only uses network topology and ignores relevant features of scientific collaborations. Multiple extensions of PageRank have been proposed, more suited for author ranking. These methods enrich the network with information about the author’s productivity or the venue and year of the publication/citation. Most state-of-the-art (STOA) feature-enriched methods either ignore or do not combine effectively this information. Furthermore, STOA algorithms typically disregard that the full network is not known for most real-world cases.Here we describe OTARIOS, an author ranking method recently developed by us, which combines multiple publication/citation criteria (i.e., features) to evaluate authors. OTARIOS divides the original network into two subnetworks, insiders and outsiders, which is an adequate representation of citation networks with missing information. We evaluate OTARIOS on a set of five real networks, each with publications in distinct areas of Computer Science, and compare it against STOA methods. When matching OTARIOS’ produced ranking with a ground-truth ranking (comprised of best paper award nominations), we observe that OTARIOS is >30% more accurate than traditional PageRank (i.e., topology based method) and >20% more accurate than STOA (i.e., competing feature enriched methods). We obtain the best results when OTARIOS considers (i) the author’s publication volume and publication recency, (ii) how recently the author’s work is being cited by outsiders, and (iii) how recently the author’s work is being cited by insiders and how individual he is. Our results showcase (a) the importance of efficiently combining relevant features and (b) how to adequately perform author ranking in incomplete networks. © 2019, The Author(s).

2019

Finding Dominant Nodes Using Graphlets

Authors
Aparício, D; Ribeiro, P; Silva, F; Silva, JMB;

Publication
Studies in Computational Intelligence

Abstract
Finding important nodes is a classic task in network science. Nodes are important depending on the context; e.g., they can be (i) nodes that, when removed, cause the network to collapse or (ii) influential spreaders (e.g., of information, or of diseases). Typically, central nodes are assumed to be important, and numerous network centrality measures have been proposed such as the degree centrality, the betweenness centrality, and the subgraph centrality. However, centrality measures are not tailored to capture one particular kind of important nodes: dominant nodes. We define dominant nodes as nodes that dominate many others and are not dominated by many others. We then propose a general graphlet-based measure of node dominance called graphlet-dominance (GD). We analyze how GD differs from traditional network centrality measures. We also study how certain parameters (namely the importance of dominating versus not being dominated and indirect versus direct dominances) influence GD. Finally, we apply GD to author ranking and verify that GD is superior to PageRank in four of the five citation networks tested. © 2020, Springer Nature Switzerland AG.

2019

Estimating time and score uncertainty in generating successful learning paths under time constraints

Authors
Nabizadeh, AH; Jorge, AM; Leal, JP;

Publication
Expert Systems

Abstract

2019

Quarmic: A data-driven web development framework

Authors
Pereira Cunha, PM; Leal, JP;

Publication
OpenAccess Series in Informatics

Abstract
Quarmic is a web framework for rapid prototyping of web applications. Its main goal is to facilitate the development of web applications by providing a high level of abstraction that hides Web communication complexities. This framework allows developers to build scalable applications capable of handling data communication in different models, data persistence and authentication, requiring them just to use simple annotations. Quarmic’s approach consists of the replication of the shared object among clients and server in order to communicate through its methods execution. Where the annotations, namely decorators, are used to indicate the concern (model or view) that each method addresses and to implement the framework’s inversion of control. By indicating the method concern, it enables the separation of its execution across the clients (responsible for the view) and the server (responsible for the model) which facilitates the state management and code maintenance. © Pedro M. P. Cunha and José P. Leal.

2019

Multi-dimensional lock-free arrays for multithreaded mode-directed tabling in Prolog

Authors
Areias, M; Rocha, R;

Publication
Concurrency and Computation: Practice and Experience

Abstract

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