Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper.

Integrated Management of Cloud Computing Resources

Lunacloud is a cloud service provider with offices in Portugal, Spain, France and UK that focus on delivering reliable, elastic and low cost cloud Infrastructure as a Service (IaaS) solutions. The company currently relies on a proprietary IaaS platform - the Parallels Automation for Cloud Infrastructure (PACI) - and wishes to expand and integrate other IaaS solutions seamlessly, namely open source solutions. This is the challenge addressed in this thesis. This proposal, which was fostered by Eurocloud Portugal Association, contributes to the promotion of interoperability and standardisation in Cloud Computing. The goal is to investigate, propose and develop an interoperable open source solution with standard interfaces for the integrated management of IaaS Cloud Computing resources based on new as well as existing abstraction libraries or frameworks. The solution should provide bothWeb and application programming interfaces. The research conducted consisted of two surveys covering existing open source IaaS platforms and PACI (features and API) and open source IaaS abstraction solutions. The first study was focussed on the characteristics of most popular open source IaaS platforms, namely OpenNebula, OpenStack, CloudStack and Eucalyptus, as well as PACI and included a thorough inventory of the provided Application Programming Interfaces (API), i.e., offered operations, followed by a comparison of these platforms in order to establish their similarities and dissimilarities. The second study on existing open source interoperability solutions included the analysis of existing abstraction libraries and frameworks and their comparison. The approach proposed and adopted, which was supported on the conclusions of the carried surveys, reuses an existing open source abstraction solution – the Apache Deltacloud framework. Deltacloud relies on the development of software driver modules to interface with different IaaS platforms, officially provides and supports drivers to sixteen IaaS platform, including OpenNebula and OpenStack, and allows the development of new provider drivers. The latter functionality was used to develop a new Deltacloud driver for PACI. Furthermore, Deltacloud provides a Web dashboard and REpresentational State Transfer (REST) API interfaces. To evaluate the adopted solution, a test bed integrating OpenNebula, Open- Stack and PACI nodes was assembled and deployed. The tests conducted involved time elapsed and data payload measurements via the Deltacloud framework as well as via the pre-existing IaaS platform API. The Deltacloud framework behaved as expected, i.e., introduced additional delays, but no substantial overheads. Both the Web and the REST interfaces were tested and showed identical measurements. The developed interoperable solution for the seamless integration and provision of IaaS resources from PACI, OpenNebula and OpenStack IaaS platforms fulfils the specified requirements, i.e., provides Lunacloud with the ability to expand the range of adopted IaaS platforms and offers a Web dashboard and REST API for the integrated management. The contributions of this work include the surveys and comparisons made, the selection of the abstraction framework and, last, but not the least, the PACI driver developed.

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

Sistema para a monitorização ativa com recurso ao cloud computing

O desenvolvimento de aplicações para dispositivos móveis já não é uma área recente, contudo continua a crescer a um ritmo veloz. É notório o avanço tecnológico dos últimos anos e a crescente popularidade destes dispositivos. Este avanço deve-se não só à grande evolução no que diz respeito às características destes dispositivos, mas também à possibilidade de criar aplicações inovadoras, práticas e passíveis de solucionar os problemas dos utilizadores em geral. Nesse sentido, as necessidades do quotidiano obrigam à implementação de soluções que satisfaçam os utilizadores, e nos dias de hoje, essa satisfação muitas vezes passa pelos dispositivos móveis, que já tem um papel fundamental na vida das pessoas. Atendendo ao aumento do número de raptos de crianças e à insegurança que se verifica nos dias de hoje, as quais dificultam a tarefa de todos os pais/cuidadores que procuraram manter as suas crianças a salvo, é relevante criar uma nova ferramenta capaz de os auxiliar nesta árdua tarefa. A partir desta realidade, e com vista a cumprir os aspetos acima mencionados, surge assim esta dissertação de mestrado. Esta aborda o estudo e implementação efetuados no sentido de desenvolver um sistema de monitorização de crianças. Assim, o objetivo deste projeto passa por desenvolver uma aplicação nativa para Android e um back-end, utilizando um servidor de base de dados NoSQL para o armazenamento da informação, aplicando os conceitos estudados e as tecnologias existentes. A solução tem como principais premissas: ser o mais user-friendly possível, a otimização, a escalabilidade para outras situações (outros tipos de monitorizações) e a aplicação das mais recentes tecnologias. Assim sendo, um dos estudos mais aprofundados nesta dissertação de mestrado está relacionado com as bases de dados NoSQL, dada a sua importância no projeto.

Prédios inteligentes. Green buildings

Este artigo apresenta alguns dos benefícios e a aplicabilidade da automação predial e residencial, cuja denominação atual e Domótica. Esta tecnologia permite a integração dos serviços e tecnologias, aplicados a residências, escritórios, flats, apartamentos e casas com o propósito de automatizá-los buscando sempre como resultado principal um maior conforto, economia de energia eletrica, redução no consumo de recursos hídricos, segurança dos usuários e um gerenciamento mais centralizado e efetivo, integrando todas as soluções de automação. Serão apresentadas as varias classes de aplicação para a criação dos chamados “prédios inteligentes”, assim como as diversas tecnologias desenvolvidas para esta finalidade

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.

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.

Host-Tailored Sensors for Leucomalachite Green Potentiometric Measurements

A new biomimetic sensor for leucomalachite green host-guest interactions and potentiometric transduction is presented. The artificial host was imprinted in methacrylic acid or acrylamido-2-methyl-1-propanesulfonic acid-based polymers. Molecularly imprinted particles were dispersed in 2-nitrophenyloctyl ether and trapped in poly(vinyl chloride). The potentiometric sensors exhibited a near-Nernstian response in steady state evaluations, with slopes and detection limits ranging from 45.8 to 81.2 mV and 0.28 to 1.01 , respectively. They were independent from the pH of test solutions within 3 to 5. Good selectivity was observed towards drugs that may contaminate water near fish cultures, such as oxycycline, doxycycline, enrofloxacin, trimethoprim, creatinine, chloramphenicol, and dopamine. The sensors were successfully applied to field monitoring of leucomalachite green in river samples. The method offered the advantages of simplicity, accuracy, applicability to colored and turbid samples, and automation feasibility.

Impact of boiling on phytochemicals and antioxidant activity of green vegetables consumed in the Mediterranean diet

The effect of boiling (10 minutes) on eleven green vegetables frequently consumed in the Mediterranean diet was evaluated. For that, some physicochemical parameters and the contents of vitamin C, phenolics and carotenoids, as well as the antioxidant activity, were determined in raw and boiled samples. The raw vegetables analysed in this study were good sources of vitamin C, carotenoids and phenolic compounds, with contents ranging from 10.6 to 255.1 mg/100 g, 0.03 to 3.29 mg/100 g and 202.9 to 1010.7 mg/100 g, respectively. Boiling promoted losses in different extensions considering both the different bioactive compounds and the distinct vegetables analysed. Contrary to phenolics (more resistant), vitamin C was the most affected compound. Boiling also originated significant losses in the antioxidant activity of the vegetables. Considering all the parameters analysed, the vegetables most affected by boiling were broccoli and lettuce. The least affected ones were collard and tronchuda cabbage.

“Green Electrodes” Modified with Au Nanoparticles Synthesized in Glycerol, as Electrochemical Nitrite Sensor

A new environmentally friendly Au nanoparticles (Au NPs) synthesis in glycerol by using ultraviolet irradiation and without extra-added stabilizers is described. The synthesis proposed in this work may impact on the non-polluting production of noble nanoparticles with simple chemicals normally found in standard laboratories. These Au NPs were used to modify a carbon paste electrode (CPE) without having to separate them from the reaction medium. This green electrode was used as an electrochemical sensor for the nitrite detection in water. At the optimum conditions the green sensor presented a linear response in the 2.0×10−7–1.5×10−5 M concentration range, a good detection sensitivity (0.268 A L mol−1), and a low detection limit of 2.0×10−7 M of nitrite. The proposed modified green CPE was used to determine nitrite in tap water samples.

Characterization of green zero-valent iron nanoparticles produced with tree leaf extracts

In the last decades nanotechnology has become increasingly important because it offers indisputable advantages to almost every area of expertise, including environmental remediation. In this area the synthesis of highly reactive nanomaterials (e.g. zero-valent iron nanoparticles, nZVI) is gaining the attention of the scientific community, service providers and other stakeholders. The synthesis of nZVI by the recently developed green bottom-up method is extremely promising. However, the lack of information about the characteristics of the synthetized particles hinders a wider and more extensive application. This work aims to evaluate the characteristics of nZVI synthesized through the green method using leaves from different trees. Considering the requirements of a product for environmental remediation the following characteristics were studied: size, shape, reactivity and agglomeration tendency. The mulberry and pomegranate leaf extracts produced the smallest nZVIs (5–10 nm), the peach, pear and vine leaf extracts produced the most reactive nZVIs while the ones produced with passion fruit, medlar and cherry extracts did not settle at high nZVI concentrations (931 and 266 ppm). Considering all tests, the nZVIs obtained from medlar and vine leaf extracts are the ones that could present better performances in the environmental remediation. The information gathered in this paper will be useful to choose the most appropriate leaf extracts and operational conditions for the application of the green nZVIs in environmental remediation.