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URI permanente para esta coleçãohttps://locus.ufv.br/handle/123456789/11798
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Item Sowing date reduces the incidence of wheat blast disease(Pesquisa Agropecuária Brasileira, 2016-05) Coelho, Maurício Antônio de Oliveira; Torres, Gisele Abigail Montan; Cecon, Paulo Roberto; Santana, Flávio MartinsThe objective of this work was to assess the effect of sowing date on the intensity of wheat blast disease, as well as the yield losses caused by this disease in different wheat (Triticum aestivum) genotypes. The experiments were conducted in 2013 at the Sertãozinho experimental station of Empresa de Pesquisa Agropecuária de Minas Gerais (Epamig), in the municipality of Patos de Minas, in the state of Minas Gerais, Brazil. Fourteen wheat genotypes and two sowing dates were evaluated. The experimental design was a randomized complete block with three replicates. The evaluated variables were: incidence, severity, thousand grain weight (TGW), grain yield, and yield losses. A disease index (DI) was calculated, based both on the incidence and the severity of the disease, to measure blast intensity in wheat. The sowing date significantly affected DI, TGW, and grain yield. Significant linear correlations were observed between DI and yield losses (0.89), between losses and TGW (-0.85), and between losses and grain yield (-0.93). For wheat blast, DIs greater than or equal to 0.5 indicate potential yield losses equal to or greater than 70%. The EP063030 line and the MGS Brilhante and BRS 264 cultivars are the most tolerant to blast, when exposed to high disease pressure.Item Design of a corporate SDI in power sector using a formal model(Infrastructures, 2017) Oliveira, Italo L.; Câmara, Jean H. S.; Torres, Rubens M.; Lisboa-Filho, JugurtaGeospatial data are essential for the decision-making process. However, obtaining and keeping such data up to date usually require much time and many financial resources. In order to minimize the production costs and incentivize sharing these data, countries are promoting the implementation of Spatial Data Infrastructures (SDI) at the different public administration levels. The International Cartographic Association (ICA) proposes a formal model that describes the main concepts of an SDI based on three of the five viewpoints of the Reference Model for Open Distributed Processing (RM-ODP). Afterwards, researchers extended ICA’s model to describe, more properly, the actors, hierarchical relationship and interactions related to the policies that drive an SDI. However, the proposed extensions are semantically inconsistent with the original proposal. Moreover, the use of ICA’s formal model and its extensions has not been assessed yet to specify a corporate-level SDI. This study describes the merger of actors and policies proposed by the ICA and its extensions in order to eliminate differences in the semantics or terminology among them. This unified model was applied to specify a corporate SDI for a large Brazilian corporation, the Minas Gerais Power Company (Companhia Energética de Minas Gerais (Cemig)), which is comprised of about 200 companies in the power sector. The case study presents part of the specification of the five RM-ODP viewpoints, i.e., the three viewpoints featured in ICA’s formal model (Enterprise, Information, and Computation) and the other two viewpoints that make up the RM-ODP (Engineering and Technology). The adapted ICA’s model proved adequate to describe SDI-Cemig. In addition, the case study may serve as an example of the specification and implementation of new SDIs, not only corporate ones, but also of public agencies at any hierarchical level.Item JetsonLEAP: A framework to measure power on a heterogeneous system-on-a-chip device(Science of Computer Programming, 2019-03-15) Gull, Christopher; Nacif, José; Bessa, Tarsila; Quintão, Pedro; Pereira, Fernando Magno Quintão; Frank, MichaelComputer science marches towards energy-aware practices. This trend impacts not only the design of computer architectures, but also the design of programs. However, developers still lack affordable and accurate technology to measure energy consumption in computing systems. The goal of this paper is to mitigate such problem. To this end, we introduce JetsonLEAP, a framework that supports the implementation of energy-aware programs. JetsonLEAP consists of an embedded hardware, in our case, the NVIDIA Jetson TK1 development board, a circuit to control the flow of energy, of our own design, plus a library to instrument program parts. We discuss two different circuit setups. The most precise setup lets us reliably measure the energy spent by 225,000 instructions, the least precise, although more affordable setup, gives us a window of 975,000 instructions. To probe the precision of our system, we use it in tandem with a high-precision, high-cost acquisition system, and show that results do not differ in any significant way from those that we get using our simpler apparatus. Our entire infrastructure – board, power meter and both circuits – can be reproduced with about $500.00. To demonstrate the efficacy of our framework, we have used it to measure the energy consumed by programs running on ARM cores, on the GPU, and on a remote server. Furthermore, we have studied the impact of OpenACC directives on the energy efficiency of high-performance applications.Item Visualization in Big Data: a tool for pattern recognition in data stream(Revista de Sistemas de Informação da FSMA, 2018-01) Soares, Victor Hugo Andrade; Santos, Joelson Antônio dos; Naldi, Murilo CoelhoThe development of new technologies is responsible for the generation and storage of continuous and massive amounts of data. Such type of data is known as data stream. The analysis of data streams may be advantageous in many fields, like bioinformatics, medicine, companies and others, as it may result in important information about the data. In this work, we propose a new software tool for Data Visualization that permits the analysis of the evolution of clusters in real time during the data streaming. The proposed visualization tool is add-on for SAMOA, a new variant of MOA (Massive Online Analysis) for massive data streams mining and processing distribution.Item Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction(BMC Bioinformatics, 2017) Marques, Yuri Bento; Oliveira, Alcione de Paiva; Vasconcelos, Ana Tereza Ribeiro; Cerqueira, Fabio RibeiroMicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.Item An iterated greedy algorithm for total flow time minimization in unrelated parallel batch machines with unequal job release times(Engineering Applications of Artificial Intelligence, 2019-01) Arroyo, José Elias C.; Leung, Joseph Y.-T.; Tavares, Ricardo GonçalvesThis paper investigates the problem of scheduling a set of jobs with arbitrary sizes and non-zero release times on a set of unrelated parallel batch machines with different capacities so as to minimize the total flow time of the jobs. The total flow time, defined as the total amount of time that the jobs spend in the system (i.e. the period between the job release dates and its completion times), is one of the most important objectives in scheduling problems, since it can lead to stable utilization of resources and reduction of working-in-process inventory. Motivated by the computational complexity of the problem, a simple and effective iterated greedy (IG) algorithm is proposed to solve it. The IG algorithm uses an efficient greedy heuristic to reconstruct solutions and a local search procedure to further enhance the solution quality. In attempting to obtain optimal solutions for small-medium size instances, a mixed integer programming model for the problem is also presented. The performance of the proposed algorithm is tested on a comprehensive set of small, medium and large benchmark of randomly generated instances, and is compared to three benchmark meta-heuristic algorithms (Discrete Differential Evolution, Ant Colony Optimization and Simulated Annealing) recently proposed for similar parallel batch machine scheduling problems. Experimental results and statistical tests show that the proposed algorithm is significantly superior in performance than the other algorithmsItem Minimum tiling of a rectangle by squares(Annals of Operations Research, 2018-12) Santos, André Gustavo dos; Monaci, MicheleWe consider a two-dimensional problem in which one is required to split a given rectangular bin into the smallest number of items. The resulting items must be squares to be packed, without overlapping, into the bin so as to cover all the given rectangle. We present a mathematical model and a heuristic algorithm that is proved to find the optimal solution in some special cases. Then, we introduce a relaxation of the problem and present different exact approaches based on this relaxation. Finally, we report computational experiments on the performances of the algorithms on a large set of randomly generated instances.Item Scheduling unrelated parallel batch processing machines with non-identical job sizes and unequal ready times(Computers & Operations Research, 2017-02) Arroyo, José Elías Cláudio; Leung, Joseph Y. T.This research analyzes the problem of scheduling a set of n jobs with arbitrary job sizes and non-zero ready times on a set of m unrelated parallel batch processing machines so as to minimize the makespan. Unrelated parallel machine is a generalization of the identical parallel processing machines and is closer to real-world production systems. Each machine can accommodate and process several jobs simultaneously as a batch as long as the machine capacity is not exceeded. The batch processing time and the batch ready time are respectively equal to the largest processing time and the largest ready time among all the jobs in the batch. Motivated by the computational complexity and the practical relevance of the problem, we present several heuristics based on first-fit and best-fit earliest job ready time rules. We also present a mixed integer programming model for the problem and a lower bound to evaluate the quality of the heuristics. The small computational effort of deterministic heuristics, which is valuable in some practical applications, is also one of the reasons that motivates this study. The results show that the heuristic proposed in this paper has a superior performance compared to the heuristics based on ideas proposed in the literature.Item Predicting optimal solution costs with bidirectional stratified sampling in regular search spaces(Artificial Intelligence, 2016-01) Lelis, Levi H. S.; Stern, Roni; Arfaee, Shahab Jabbari; Zilles, Sandra; Felner, Ariel; Holte, Robert C.Optimal planning and heuristic search systems solve state-space search problems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., without actually finding a solution path of that cost. We present an algorithm, BiSS, which is a hybrid of bidirectional search and stratified sampling that produces accurate estimates of the optimal solution cost. BiSS is guaranteed to return the optimal solution cost in the limit as the sample size goes to infinity. We show empirically that BiSS produces accurate predictions in several domains. In addition, we show that BiSS scales to state spaces much larger than can be solved optimally. In particular, we estimate the average solution cost for the 6×6, 7×7, and 8×8 Sliding-Tile puzzle and provide indirect evidence that these estimates are accurate. As a practical application of BiSS, we show how to use its predictions to reduce the time required by another system to learn strong heuristic functions from days to minutes in the domains tested.Item Exploring potential implementations of PCE in IoT world(Optical Switching and Networking, 2017-11) Souza, Vitor Barbosa C.; Ramirez, Wilson; Marin-Tordera, Eva; Sanchez, SergioThe recently coined Internet of Things (IoT) paradigm leverages a large volume of heterogeneous Network Elements (NEs) demanding broad connectivity anywhere, anytime and anyhow, fueling the deployment of innovative Internet services, such as Cloud or Fog Computing, Data Center Networks (DCNs), Smart Cities or Smart Transportation. The proper deployment of these novel Internet services is imposing hard connectivity constraints, such as high transmission capacity, reliable communications, as well as an efficient control scheme capable of enabling an agile coordination of actions in large heterogeneous scenarios. In recent years, novel control schemes, such as the so-called Path Computation Element (PCE) has gained momentum in the network research community turning into real PCE implementations. Indeed, there is a wealth of studies assessing the PCE performance, clearly showing the potential benefits of decoupling routing control tasks from the forwarding nodes. Nevertheless, recognized the need for a control solution in IoT scenarios, there is not much published information analyzing PCE benefits in these IoT scenarios. In this paper, we distill how the PCE may gracefully provide for service composition in an agile manner, handling the specific constraints and requirements found in IoT scenarios. To this end, we propose a novel PCE strategy referred to as Service-Oriented PCE (SPCE), which enables network-aware service composition.