Comparing clustering solutions: the use of adjusted paired indices

In the present paper we compare clustering solutions using indices of paired agreement. We propose a new method - IADJUST - to correct indices of paired agreement, excluding agreement by chance. This new method overcomes previous limitations known in the literature as it permits the correction of any index. We illustrate its use in external clustering validation, to measure the accordance between clusters and an a priori known structure. The adjusted indices are intended to provide a realistic measure of clustering performance that excludes agreement by chance with ground truth. We use simulated data sets, under a range of scenarios - considering diverse numbers of clusters, clusters overlaps and balances - to discuss the pertinence and the precision of our proposal. Precision is established based on comparisons with the analytical approach for correction specific indices that can be corrected in this way are used for this purpose. The pertinence of the proposed correction is discussed when making a detailed comparison between the performance of two classical clustering approaches, namely Expectation-Maximization (EM) and K-Means (KM) algorithms. Eight indices of paired agreement are studied and new corrected indices are obtained.

Reducing the number of membership functions in linguistic variables

Dissertation presented at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia in fulfilment of the requirements for the Masters degree in Mathematics and Applications, specialization in Actuarial Sciences, Statistics and Operations Research

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

Outbreaks of human-herpes virus 6 (HHV-6) infection in day-care centers in Belém, Pará, Brazil

A total of 730 children aged less than 7 years, attending 8 day-care centers (DCCs) in Belém, Brazil were followed-up from January to December 1997 to investigate the occurrence of human-herpes virus 6 (HHV-6) infection in these institutional settings. Between October and December 1997 there have been outbreaks of a febrile- and -exanthematous disease, affecting at least 15-20% of children in each of the DCCs. Both serum- and- plasma samples were obtained from 401 (55%) of the 730 participating children for the detection of HHV-6 antibodies by enzyme-linked immunosorbent assay (ELISA), and viral DNA amplification through the nested-PCR. Recent HHV-6 infection was diagnosed in 63.8% (256/401) of them, as defined by the presence of both IgM and IgG-specific antibodies (IgM+/IgG+); of these, 114 (44.5%) were symptomatic and 142 (55.5%) had no symptoms (p = 0.03). A subgroup of 123 (30.7%) children were found to be IgM-/IgG+, whereas the remaining 22 (5.5%) children had neither IgM nor IgG HHV-6- antibodies (IgM-/IgG-). Of the 118 children reacting strongly IgM-positive ( > or = 30 PANBIO units), 26 (22.0%) were found to harbour the HHV-6 DNA, as demonstrated by nested-PCR. Taken the ELISA-IgM- and- nested PCR-positive results together, HHV-6 infection was shown to have occurred in 5 of the 8 DCCs under follow-up. Serological evidence of recent infections by Epstein-Barr virus (EBV) and parvovirus B19 were identified in 2.0% (8/401) and 1.5% (6/401) of the children, respectively. Our data provide strong evidence that HHV-6 is a common cause of outbreaks of febrile/exanthematous diseases among children attending DCCs in the Belém area.

Deteção e extinção de incêndios em Data Centers

São muitas as organizações que por todo o mundo possuem instalações deste tipo, em Portugal temos o exemplo da Portugal Telecom que recentemente inaugurou o seu Data Center na Covilhã. O desenvolvimento de um Data Center exige assim um projeto muito cuidado, o qual entre outros aspetos deverá garantir a segurança da informação e das próprias instalações, nomeadamente no que se refere à segurança contra incêndio.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Incorporation of either molybdenum or tungsten into formate dehydrogenase from Desulfovibrio alaskensis NCIMB 13491; EPR assignment of the proximal iron-sulfur cluster to the pterin cofactor in formate dehydrogenases from sulfate-reducing bacteria

J Biol Inorg Chem (2004) 9: 145–151 DOI 10.1007/s00775-003-0506-z

Environment and Health in Children Day Care Centres (ENVIRH) - Study Rationale and Protocol

Background: Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects. Methods --- study design: This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children’s respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected. Results: In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children). Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children’s health. A building ventilation model will also be developed. Discussion: This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

Food Allergy and Anaphylaxis in Infants and Preschool-Age Children

Food allergy (FA) prevalence data in infants and preschool-age children are sparse, and proposed risk factors lack confirmation. In this study, 19 children’s day care centers (DCC) from 2 main Portuguese cities were selected after stratification and cluster analysis. An ISAAC’s (International Study of Asthma and Allergies in Childhood) derived health questionnaire was applied to a sample of children attending DCCs. Outcomes were FA parental report and anaphylaxis. Logistic regression was used to explore potential risk factors for reported FA. From the 2228 distributed questionnaires, 1217 were included in the analysis (54.6%). Children’s median age was 3.5 years, and 10.8% were described as ever having had FA. Current FA was reported in 5.7%. Three (0.2%) reports compatible with anaphylaxis were identified. Reported parental history of FA, personal history of atopic dermatitis, and preterm birth increased the odds for reported current FA. A high prevalence of parental-perceived FA in preschool-age children was identified. Risk factor identification may enhance better prevention.

Building clusters for CRM strategies by mining airlines customer data

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Development of a fuzzy qualitative risk assessment model applied to construction industry

Dissertação para obtenção do Grau de Doutor em Engenharia Industrial

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.