Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Assessing the feasibility of impregnating phase change materials in lightweight aggregate for development of thermal energy storage systems

This paper assesses the feasibility of impregnation/encasement of phase change materials (PCMs) in lightweight aggregates (LWAs). An impregnation process was adopted to carry out the encasement study of two different PCMs in four different LWAs. The leakage of the impregnated/encased PCMs was studied when they were submitted to freeze/thawing and oven drying tests, separately. The results confirmed that, the impregnation/encasement method is effective with respect to the large thermal energy storage density, and can be suitable for applications were PCMs cannot be incorporated directly such as asphalt road pavements.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Secure multiParty computation protocols

Dissertação de Mestrado em Engenharia Informática

Using glass-fibre reinforced polymer composites in the production of sustainable water storage vessels

The present work focuses on the use of the life cycle assessment (LCA) and life cycle costing (LCC)methodologies to evaluate environmental and economic impacts of polymers and polymer composites materials and products. Initially a literature review is performed in order to assess the scope and limitations of existing LCA and LCC studies on these topics. Then, a case study, based on the production of a water storage glass-fibre reinforced polymer (GFRP) composite storage tank, is presented. The storage tank was evaluated via a LCA/LCC integrated model, a novel way of analysing the life cycle (LC) environmental and economic performances of structural products. The overarching conclusion of the review is that the environmental and economic performances of polymers composites in non-mobile applications are seldom assessed and never in a combined integrated way.

Dependable decentralized storage management for cloud computing

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

ADSNARK: Nearly practical and privacy-preserving proofs on authenticated data

We study the problem of privacy-preserving proofs on authenticated data, where a party receives data from a trusted source and is requested to prove computations over the data to third parties in a correct and private way, i.e., the third party learns no information on the data but is still assured that the claimed proof is valid. Our work particularly focuses on the challenging requirement that the third party should be able to verify the validity with respect to the specific data authenticated by the source — even without having access to that source. This problem is motivated by various scenarios emerging from several application areas such as wearable computing, smart metering, or general business-to-business interactions. Furthermore, these applications also demand any meaningful solution to satisfy additional properties related to usability and scalability. In this paper, we formalize the above three-party model, discuss concrete application scenarios, and then we design, build, and evaluate ADSNARK, a nearly practical system for proving arbitrary computations over authenticated data in a privacy-preserving manner. ADSNARK improves significantly over state-of-the-art solutions for this model. For instance, compared to corresponding solutions based on Pinocchio (Oakland’13), ADSNARK achieves up to 25× improvement in proof-computation time and a 20× reduction in prover storage space.

Filamentous fungi occurrence in free water and biofilms from drinking water storage tanks

In some regions of Brazil, especially where the water is scarce, drinking water is stored in water storage tanks. This practice gives the consumer the guarantee of available water. The water storage conditions such as the exposure to hot weather when the tanks are on rooftops allow the development of microorganisms and microbial biofilms which can deteriorate the water quality and increase the risk to human health [1,2]. This study describes the filamentous fungi (FF) detected in free water and biofilms in drinking water storage tanks in Recife - Pernambuco, Brazil. Five sampling times in triplicate were performed at two distinct points. Colony-forming units (CFU) of FF fungi were determined with 0.45 μm filtration membranes using peptone glucose rose Bengal agar (PGRBA). From the 30 samples analysed a total of 1136 CFU were obtained. The water biofilms were collected from samplers consisting of polyethylene coupons, previously installed in the reservoirs. These coupons were transferred to PGRBA plates and incubated using with the same conditions described for free FF. For the in situ detection of FF in biofilms the Calcofluor White staining technique was used. This procedure demonstrated FF forming biofilms on the surfaces of the coupons. Brazilian legislation does not define limits for FF in drinking water. However considering the potential risk of fungal contamination, the data obtained in this study will contribute to developing future quantitative and qualitative parameters for the presence of fungi in drinking water distribution systems in Brazil.