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Projeto de investigação
NOVA Laboratory for Computer Science and Informatics
Financiador
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Publicações
DeMMon Decentralized Management and Monitoring Framework
Publication . Morais, Nuno; Leitão, João
The centralized model proposed by the Cloud computing paradigm mismatches the decentralized
nature of mobile and IoT applications, given the fact that most of the data
production and consumption is performed by end-user devices outside of the Data Center
(DC). As the number of these devices grows, and given the need to transport data to and
from DCs for computation, application providers incur additional infrastructure costs,
and end-users incur delays when performing operations.
These reasons have led us into a post-cloud era, where a new computing paradigm
arose: Edge Computing. Edge Computing takes into account the broad spectrum of
devices residing outside of the DC, closer to the clients, as potential targets for computations,
potentially reducing infrastructure costs, improving the quality of service (QoS)
for end-users and allowing new interaction paradigms between users and applications.
Managing and monitoring the execution of these devices raises new challenges previously
unaddressed by Cloud computing, given the scale of these systems and the devices’
(potentially) unreliable data connections and heterogenous computational power. The
study of the state-of-the-art has revealed that existing resource monitoring and management
solutions require manual configuration and have centralized components, which
we believe do not scale for larger-scale systems.
In this work, we address these limitations by presenting a novel Decentralized Management
and Monitoring (“DeMMon”) system, targeted for edge settings. DeMMon provides
primitives to ease the development of tools that manage computational resources
that support edge-enabled applications, decomposed in components, through decentralized
actions, taking advantage of partial knowledge of the system. Our solution was
evaluated to amount to its benefits regarding information dissemination and monitoring
capabilities across a set of realistic emulated scenarios of up to 750 nodes with variable
failure rates. The results show the validity of our approach and that it can outperform
state-of-the-art solutions regarding scalability and reliability
Forgetting in Answer Set Programming - A Survey
Publication . Gonçalves, Ricardo; Knorr, Matthias; Leite, João; NOVALincs; DI - Departamento de Informática; Cambridge University Press
Forgetting - or variable elimination - is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this article, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.
ROSIE
Publication . Jensch, Antje; Lopes, Marta B.; Vinga, Susana; Radde, Nicole; NOVALincs; CMA - Centro de Matemática e Aplicações; SAGE Publications
The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of (Formula presented.) genes and more than (Formula presented.) samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.
Neural and Symbolic AI - mind the gap! Aligning Artificial Neural Networks and Ontologies
Publication . Ribeiro, Manuel António de Melo Chinopa de Sousa; Leite, João
Artificial neural networks have been the key to solve a variety of different problems.
However, neural network models are still essentially regarded as black boxes, since they
do not provide any human-interpretable evidence as to why they output a certain re sult. In this dissertation, we address this issue by leveraging on ontologies and building
small classifiers that map a neural network’s internal representations to concepts from
an ontology, enabling the generation of symbolic justifications for the output of neural
networks. Using two image classification problems as testing ground, we discuss how to
map the internal representations of a neural network to the concepts of an ontology, exam ine whether the results obtained by the established mappings match our understanding
of the mapped concepts, and analyze the justifications obtained through this method.
FLeeC
Publication . Lourenço, João M.; Preguiça, Nuno; Costa, André João César; DI - Departamento de Informática; NOVALincs
When compared to blocking concurrency, non-blocking concurrency can provide higher performance in parallel shared-memory contexts, especially in high contention scenarios. This paper proposes FLeeC, an application-level cache system based on Memcached, which leverages re-designed data structures and non-blocking (or lock-free) concurrency to improve performance by allowing any number of concurrent writes and reads to its main data structures, even in high-contention scenarios. We discuss and evaluate its new algorithms, which allow a lock-free eviction policy and lock-free fast lookups. FLeeC can be used as a plug-in replacement for the original Memcached, and its new algorithms and concurrency control strategies result in considerable performance improvements (up to 6×).
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Financiadores
Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/04516/2020