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Percorrer ESTG - Capítulos de livros por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Matemáticas"
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- Applying Scatter Search to the Location Areas ProblemPublication . Luz, Sónia Maria Almeida da; Vega-Rodríguez, Miguel A.; Gómez-Pulido, Juan A.; Sánchez-Pérez, Juan M.The Location Areas scheme is one of the most common strategies to solve the location management problem, which corresponds to the management of the mobile network configuration with the objective of minimizing the involved costs. This paper presents a new approach that uses a Scatter Search based algorithm applied to the Location Areas scheme as a cost optimization problem. With this work we pretend to analyze and set the main parameters of scatter search, using four distinct test networks and compare our results with those achieved by other authors. This is a new approach to this problem and the results obtained are very encouraging because they show that the proposed technique outperforms the existing methods in the literature.
- Computational Resource Management for Video Coding in Mobile EnvironmentsPublication . Correa, Guilherme; Assunção, Pedro; Agostini, Luciano; Cruz, Luis A. da SilvaThe increase of computational resources in mobile devices and the availability of reliable communication infrastructures provide support for acquisition, display, coding/decoding and transmission of high-resolution video in a broad set of equipment such as tablets and smartphones. Nevertheless, real-time video encoding and decoding is still a challenge in such computing environments, especially when considering the amount of computational resources required by state-of-the-art video coding standards. Moreover, battery technologies did not evolve as much as desired, which makes power consumption minimization an important issue for the mobile devices industry and users. Therefore, in current mobile systems, the available computational resources along with battery-life are responsible for imposing significant limitations on mobile real-time multimedia communications. This chapter presents an overview of the state-of-the-art research on management of computational resources for video encoding systems in mobile communications equipment. A review on computational complexity analysis of both H.264/AVC and HEVC video coding standards is presented, followed by a description of current methods for modelling, reducing and controlling the expenditure of computational resources on these video codecs. Finally, future trends on computational complexity management for video codecs implemented on power-constrained devices are lined out.
- FlexSPMF: A Framework for Modelling and Learning Flexibility in Software ProcessesPublication . Martinho, Ricardo; Varajão, João; Domingos, DulceSoftware processes are dynamic entities that are often changed and evolved by skillful knowledge workers such as software development team members. Consequently, flexibility is one of the most important features within software process representations and related tools. However, in the everyday practice, team members do not wish for total flexibility. They rather prefer to learn about and follow previously defined advices on which, where and how they can change/adapt process representations. In this paper we present FlexSPMF: a framework for modelling controlled flexibility in software processes. It comprises three main contributions: 1) identifying a core set of flexibility concepts; 2) extending a Process Modelling Language (PML)'s metamodel with these concepts; and 3) providing modelling resources to this extended PML. This enables process engineers to define and publish software process models with additional (textual/graphical) flexibility information. Other team members can then visualise and learn about this information, and change processes accordingly.
- Fraud Prediction in Smart Supply Chains Using Machine Learning TechniquesPublication . Constante-Nicolalde, Fabián-Vinicio; Guerra-Terán, Paulo; Pérez-Medina, Jorge-LuisIn the domain of Big Data, the company’s supply chain has a very high-risk exposure and this must be observed from a preventive perspective, that is, act before such situations occur. As a company grows and diversifies the number of suppliers, customers and therefore increases its number of daily transactions and associated risks. Despite the innovation and improvements that have been incorporated into financial management, credit and debit cards are the main means of exchanging cash online, with the expansion of e-commerce, online shopping has also increased number of extortion cases that have been identified and that continues to expand greatly. It takes a lot of time, effort and investment to restore the impact of these damages. In this paper, we work with machine learning techniques, used in predicting smart supply chain fraud, are valuable for estimating, classifying whether a transaction is normal or fraudulent, and mitigating future dangers.
- A Hybrid Differential Evolution Algorithm for Solving the Terminal Assignment ProblemPublication . Bernardino, Eugénia; Bernardino, Anabela; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelThe field of communication networks has witnessed tremendous growth in recent years resulting in a large variety of combinatorial optimization problems in the design and in the management of communication networks. One of these problems is the terminal assignment problem. The task here is to assign a given set of terminals to a given set of concentrators. In this paper, we propose a Hybrid Differential Evolution Algorithm to solve the terminal assignment problem. We compare our results with the results obtained by the classical Genetic Algorithm and the Tabu Search Algorithm, widely used in literature.
- Improving Text Classification Performance with Incremental Background KnowledgePublication . Silva, Catarina; Ribeiro, BernardeteText classification is generally the process of extracting interesting and non-trivial information and knowledge from text. One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we propose an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to deliver oracle decisions. The defined incremental SVM margin-based method was tested in the Reuters-21578 benchmark showing promising results.
- Improving Visualization, Scalability and Performance of Multiclass Problems with SVM Manifold LearningPublication . Silva, Catarina; Bernardete RibeiroWe propose a learning framework to address multiclass challenges, namely visualization, scalability and performance. We focus on supervised problems by presenting an approach that uses prior information about training labels, manifold learning and support vector machines (SVMs). We employ manifold learning as a feature reduction step, nonlinearly embedding data in a low dimensional space using Isomap (Isometric Mapping), enhancing geometric characteristics and preserving the geodesic distance within the manifold. Structured SVMs are used in a multiclass setting with benefits for final multiclass classification in this reduced space. Results on a text classification toy example and on ISOLET, an isolated letter speech recognition problem, demonstrate the remarkable visualization capabilities of the method for multiclass problems in the severely reduced space, whilst improving SVMs baseline performance.
- Knowledge Extraction with Non-Negative Matrix Factorization for Text ClassificationPublication . Silva, Catarina; Ribeiro, BernardeteText classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness.
- Parameter Analysis for Differential Evolution with Pareto Tournaments in a Multiobjective Frequency Assignment ProblemPublication . Maximiano, Marisa; Vega-Rodríguez, Miguel A.; Gómez-Pulido, Juan A.; Sánchez-Pérez, Juan M.This paper presents a multiobjective approach for the Frequency Assignment Problem (FAP) in a real-world GSM network. Indeed, nowadays in GSM systems, the FAP stills continues to be a critical task for the mobile communication operators. In this work we propose a new method to address the FAP by applying the Differential Evolution (DE) algorithm in its multiobjective optimization, using the concept of Pareto Tournaments (DEPT). We present the results obtained in the tuning process of the DEPT parameters. Two distinct real-world instances of the problem - being currently operating - were tested with DEPT algorithm. Therefore, with this multiobjective approach for the FAP we are contributing to a really important applicability.
- Photocrosslinkable Materials for the Fabrication of Tissue-Engineered Constructs by StereolithographyPublication . Pereira, Rúben F.; Bártolo, Paulo J.Stereolithography is an additive technique that produces three-dimensional (3D) solid objects using a multi-layer procedure through the selective photoinitiated curing reaction of a liquid photosensitive material. Stereolithographic processes have been widely employed in Tissue Engineering for the fabrication of temporary constructs, using natural and synthetic polymers, and polymer-ceramic composites. These processes allow the fabrication of complex structures with a high accuracy and precision at physiological temperatures, incorporating cells and growth factors without significant damage or denaturation. Despite recent advances on the development of novel biomaterials and biocompatible crosslinking agents, the main limitation of these techniques are the lack number of available photocrosslinkable materials, exhibiting appropriate biocompatibility and biodegradability. This chapter gives an overview of the current state-of-art of materials and stereolithographic techniques to produce constructs for tissue regeneration, outlining challenges for future research.