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20th International Conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Will, Nilcimar Neitzel Parpinelli, Rafael Stubs UDESC Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil

ISBN: (纸本)9783030730499

The use of fragment insertion in the protein structure prediction problem can be considered one of the most successful strategies to add problem-dependent information. The well known Rosetta suite provides two protocols for generating fragment libraries: Best and Quota. The first aims to maximize a given score function, while the second aims add fragment diversity. This paper presents the results with four proteins to verify the impact of using more secondary structure information through the quota protocol when compared to the best protocol. The tests were performed considering the PSIPRED, SPIDER2, and MUFold predictors. Results obtained are compared in terms of RMSD, processing time, and convergence. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Method for improvement of transparency: Use of text mining techniques for reclassification of governmental expenditures records in Brazil

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International Journal of Business Intelligence and Data Mining 2021年第2期18卷 155-196页

作者： de Oliveira Almeida, Gustavo Revoredo, Kate Cappelli, Claudia Maciel, Cristiano Graduate Program in Business Federal Fluminense University Niteroi/RJ Brazil Graduate Program in Informatics Federal University of Rio de Janeiro State Rio de Janeiro/RJ Brazil Computing Institute Mato Grosso Federal University Cuiabá/MT Brazil

Many countries have transparency laws requiring availability of data. However, often data is available but not transparent. We present the Transparency Portal of Brazilian Federal Government case and discuss limitations of public acquisitions data stored in free text format. We employed text-mining techniques to reclassify descriptive texts of measurement units related to products and services. The solution presented in KNIME and Java aggregated measurements in the original (n = 69,372 with 78% reduction in number of descriptions, 94% items classified) and in cross validation sample (n = 105,266 with 88% reduction, classifying 78% of items). In addition, we tested computational time for processing of texts for a wide range of data input sizes, suggesting the stability and scalability of the solution to process larger datasets. Finally, we produced analysis identifying probable input errors, suppliers and purchasing units with abnormal transactions and factors affecting procurement prices. We present suggestions for future research and improvements. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Transparency

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The Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System 19th

The Influence of Feature Selection on Job Clustering for an ...

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19th International Conference on Artificial Intelligence and Soft computing, ICAISC 2020

作者： Junior, Joel J. S. Vilasbôas, Fabricio G. de Castro, Leandro N. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University São Paulo Brazil

ISBN: (纸本)9783030615338

Recommender systems aim to effectively recommend items to the user based on their profile. An online recruitment system recommends jobs for a candidate according to his profile and can also act in reverse, recommending more qualified candidates for a particular job. Defining which variables will be used impacts directly the recommendation quality so that, when using the most important variables, we have a better assertiveness in the process. The goal of this work is to select the most important features of an online recruitment database using feature selection techniques. More specifically, we used the algorithms of Mitra, SUD and ACA to perform feature selection. The datasets used were derived from the original dataset assuming three distinct scenarios: the dataset containing the attributes related with the jobs' features;the dataset containing the bag of words of the description feature of the jobs;and the dataset resulting from the union of the two previous ones. The features' subsets selected in each of the above scenarios had their performance evaluated in a clustering task. The results obtained in each scenario show a performance gain of the clustering process when feature selection is made over the original data. Also, it was observed that the jobs' features result in better performance than the other two cases. © 2020, Springer Nature Switzerland AG.

关键词： Recommender systems

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Internet of things scalability: Analyzing the bottlenecks and proposing alternatives 6

Internet of things scalability: Analyzing the bottlenecks an...

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2014 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2014

作者： Gomes, Márcio Da Rosa Righi, Rodrigo Da Costa, Cristiano André Applied Computing Graduate Program Universidade Do Vale Do Rio Dos Sinos Brazil

ISBN: (纸本)9781479952915

Internet of Things, or IoT, is a new revolution of the Internet, supporting a connection between objects and humans yet more strong. This area is known by the huge quantity of data that can be generated from the RFID sensors at realtime. On managing this data, the IoT middleware introduces some time constrains regarding both accessing applications and filtering and storing data actions. Thus, middleware scalability is a key requirement for supporting the current growth of IoT. In this context, this article firstly presents μIB - Micro Internet of Things Benchmark - that aims at evaluating the scalability of EPC-compliant middlewares. μIB combines data from tags, RFID readers, sequential requests and number of threads to compose an evaluation setup to assess EPCIS and ALE components. We used this benchmark to evaluate Fosstrak, which is one of the most used IoT middlewares, in a single server. The tests emphasized that the exhaustion in one component influences the performance of the others directly. Following this research line, this article also describes the first ideas on redesigning the IoT middleware using techniques such as cloud virtualization and elasticity, service decomposition and multithreading programming. © 2014 IEEE.

关键词： Middleware

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Optimal resource allocation for survivable virtual infrastructures

Optimal resource allocation for survivable virtual infrastru...

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10th International Conference on Design of Reliable Communication Networks, DRCN 2014

作者： De S. Cavalcanti, Gustavo A. Obelheiro, Rafael R. Koslovski, Guilherme Graduate Program in Applied Computing Department of Computer Science Santa Catarina State University Brazil

ISBN: (纸本)9781479940097

Virtual infrastructures (VIs) consolidated the dynamic provisioning of computing and communication resources. A VI is a set of virtual machines interconnected by virtual links and switches/routers. Infrastructure providers (InPs) manage the physical substrates in which virtual resources requested by VIs (such as CPU, disk, memory, bandwidth) are reserved and allocated. Resource allocation is a complex problem that needs to satisfy different goals: users expect to run their applications on survivable VIs, while InPs aim to maximize profits, minimize costs and reduce substrate fragmentation. However, there is a dichotomy between minimizing substrate fragmentation, by co-locating VIs, and maximizing VI survivability, by sparsely allocating resources in order to decrease the impact of substrate failures. In this context, this paper discusses VI survivability and its impact on substrate fragmentation. We propose a mixed integer linear programming model to allocate resources considering the joint coordination of fragmentation and survivability. Experimental results suggest that it is possible to enhance VI survivability without significantly impacting substrate fragmentation. © 2014 IEEE.

关键词： Substrates

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Identifying Network Congestion on SDN-Based Data Centers with Supervised Classification 37th

Identifying Network Congestion on SDN-Based Data Centers wi...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： da Silva de Oliveira, Filipe Pillon, Maurício Aronne Miers, Charles Christian Koslovski, Guilherme Piêgas Graduate Program in Applied Computing Santa Catarina State University Santa Catarina Joinville Brazil

ISBN: (纸本)9783031284502

Data centers have networks that provide, for a large number of users, various services such as video streaming, financial services, file storage, among others. Like any network-based system, a Data Center (DC) is subject to congestion, a fact that can cause slowness and instability, affecting the performance of applications for end users. Among the technologies for data center management, Software Defined Networking (SDN) separate the control from the data plane, allowing applications to access statistical information and other details about current state of the network through a controller. In this sense, SDN brings a new opportunity to detect congestion on DC networks in a centralized manner. However, identifying the occurrence of congestion in networks is an arduous task due to the high number of related data. With these data, this work seeks to apply supervised classification to identify congestion events. The experimental campaign demonstrated that the SDN-based supervised classification mechanisms find more congestion events then the native congestion control algorithms from Transmission Control Protocol (TCP). Moreover, among diverse supervised algorithms, we demonstrate which one has better accuracy and efficient performance when analyzing Cubic and DCTCP variants. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transmission control protocol

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Differential evolution with cluster-based external archive and local search for multimodal optimization 8

Differential evolution with cluster-based external archive a...

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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

作者： Dominico, Gabriel Boiani, Mateus Stubs Parpinelli, Rafael Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

ISBN: (纸本)9781728142531

One of the main objectives in multimodal optimization is to find multiple optima solutions in a search space. Hence, population-based metaheuristics are suitable for this class of problems but their loss of diversity while converging may become a problem when tracking multiple optima. In this paper, we propose the use of a cluster-based external archive maintenance strategy along with the jDE algorithm, namely NCjDE-HJar. The DBSCAN algorithm is employed to group candidate solutions in an external archive representing multiple peaks that will feed the Hooke-Jeeves local search algorithm. Also, the Michalewicz mutation strategy is applied to refine the solutions found by the jDE algorithm. The proposed approach is compared with five state-of-the-art algorithms in terms of peak ratio and the results obtained show that the proposed modifications favor the finding more multimodal peaks. © 2019 IEEE.

关键词： Evolutionary algorithms

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Parameter analysis of a bee-inspired algorithm to solve the traveling salesman problem 15

Parameter analysis of a bee-inspired algorithm to solve the ...

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15th IASTED International Conference on Intelligent Systems and Control, ISC 2016

作者： Masutti, Thiago A.S. De Castro, Leandro Nunes Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University São Paulo Brazil

ISBN: (纸本)9780889869837

The Traveling Salesman Problem (TSP) is extensively used to test new algorithms that tackle combinatorial optimiza-tion problems. Bee inspired algorithms are receiving a con-siderable attention from the Swarm Intelligence field. This paper presents the TSPoptBees, a bee inspired algorithm to solve the TSP. A vast discussion is made in regards to the parameter analysis of the proposed algorithm, showing trends on varying certain parameters and proposing default values for them. The quality is assessed using instances from the TSPLIB and the results show the algorithm is able to provide good quality solutions.

关键词： Traveling salesman problem

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Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part Approximate Parallel Prefix Adder 30

Accuracy-, Delay- and Area-Driven Evaluation of Lower-Part A...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Rosa, Morgana M. A. Costa, Eduardo Soares, Rafael Bampi, Sergio Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil Porto Alegre Brazil

ISBN: (纸本)9798350326499

This work proposes an accuracy-driven evaluation model of an approximate adder (AxA) called an approximate parallel prefix adder (AxPPA). It explores the significance of approximate computing (AxC) in optimizing performance and energy efficiency in computer systems by accepting minor accuracy trade-offs. We examine the AxPPA through a meticulous error analysis model and an automated analysis framework that comprehensively evaluates area and delay based on the fundamental equations of parallel prefix adders (PPAs). Additionally, it emphasizes the importance of lower-part approximation in adders, effectively addressing the elongation of carry chains and enhancing overall efficiency. The choice of approximation method directly impacts computational accuracy, resulting in variations in the number of approximated bits and carry chain length for a given tolerance level. Conducting a thorough comparison of accuracy trade-offs requires a unique design and simulation for each unit and approximation level, which can be time-consuming, particularly in larger circuits. Therefore, the proposed approach facilitates the exploration of larger circuit design space encompassing multiple AxAs, significantly minimizing time investment. In our experiments, the accuracy-driven evaluation of the lower part approximation method in AxA took 71.268 milliseconds for 1 million random inputs, with a K -bits approximation ranging from 1 to 16. The results indicate that AxPPA exhibits a minor error magnitude among the explored literature AxAs, being approximately 50.97% more accurate on average. © 2023 IEEE.

关键词： Adders

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Customer-oriented diagnosis of memory provisioning for IaaS clouds

Customer-oriented diagnosis of memory provisioning for IaaS ...

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Brazilian Symposium on Computer System Engineering, SBESC 2013

作者： Pfitscher, Ricardo J. Pillon, Mauricio A. Obelheiro, Rafael R. Santa Catarina State University Graduate Program in Applied Computing Department of Computer Science Brazil

Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct provisioning of their resources. Many users end up underprovisioning, hurting application performance, or overprovisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a performance-limiting factor in cloud environments. In this work, we propose a model that enables cloud customers to determine whether the memory allocated to their virtual machines is correctly provisioned, underprovisioned, or overprovisioned. The model uses two metrics collected inside a VM, resident and committed memory, and defines thresholds for these metrics that characterize each provisioning level. Experimental results with Linux guests on Xen, running four benchmarks with different workloads and varying memory capacity, show that the model was able to accurately diagnose memory provisioning in 98% of the scenarios evaluated. Copyright 2013 by the Authors.

关键词： Customer satisfaction

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