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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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Artificial Neural Network Model for Steel Strip Tandem Cold Mill Power Prediction 3rd

Artificial Neural Network Model for Steel Strip Tandem Cold ...

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3rd International Conference on applied Informatics, ICAI 2020

作者： de Oliveira, Danilo G. da Silva, Eliton M. Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. Graduate Program in Applied Computing Santa Catarina State University – UDESC Joinville Brazil Department of Computer Science Santa Catarina State University – UDESC Joinville Brazil ArcelorMittal – Global Research and Development Group São Francisco do Sul Brazil

ISBN: (纸本)9783030617011

In the cold rolling of flat steel strips, electric energy consumption is one of the highest expenses. Predicting the power requirements according to the line and product conditions can significantly impact the energy cost and, thus, on the business’s profitability. This paper proposes predicting the power requirements of a tandem cold rolling mill of steel strips on a coil-to-coil base applying Artificial Neural Networks (ANN) as a uni-variate regression problem. The tests yielded an MSE of 300.39 kW or 2.4% and are better than the acceptable 5% error margin for this project, indicating that the ANN presented satisfactory results. The application of six full-month worth of data in the trained ANN model showed the excellent correlation of the ANN predictions with the measured data, leading to the conclusion that the system is ready for the deployment for daily use for line engineers. Overall, the results obtained show that the steel industry can highly benefit from Industry 4.0 and Artificial Intelligence technologies. © 2020, Springer Nature Switzerland AG.

关键词： Automotive industry

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Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Aggregated Problem Information 21

Protein Structure Prediction Using Dynamic Speciation Evolut...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Parpinelli, Rafael Stubs Wojeicchowski, Nicholas Will, Nilcimar Neitzel State University of Santa Catarina Graduate Program in Applied Computing Joinville Brazil State University of Santa Catarina Computer Science Department Joinville Brazil

ISBN: (纸本)9798350356632

Protein structure prediction in three dimensions represents a fundamental challenge in Structural Bioinformatics. Leveraging problem-specific information such as fragment insertion, secondary structure, and contact maps can significantly enhance the exploration of the search space. In this study, an evolutionary algorithm is introduced, which incorporates such problem information for protein structure prediction. The proposed method employs a dynamic speciation technique alongside fragment insertion to foster population diversity. To ensure a rich variety of fragments, a fragment library is constructed using the Rosetta Quota protocol. Additionally, information from contact maps and secondary structure is integrated into two selection strategies to facilitate a more thorough exploration of the conformational search space. The results of an experimental evaluation involving 9 proteins are presented, demonstrating competitive performance compared to existing literature. Evaluation metrics include RMSD, GDT, and processing time. © 2024 IEEE.

关键词： Prediction models

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Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Towards providing middleware-level proactive resource reorga...

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作者： Da Rosa Righi, Rodrigo Rodrigues, Vinicius Facco De Nardin, Igor Fontana Da Costa, Cristiano André Alves, Marco Antonio Zanata Pillon, Maurício Aronne Applied Computing Graduate Program Unisinos University São Leopoldo Rio Grande do Sul Brazil Federal University of Paraná – UFPR Curitiba Paraná Brazil Santa Catarina State University – UDESC Joinville Santa Catarina Brazil

Elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Particularly for High Performance computing (HPC), elasticity can provide a better use of resources and also a reduction in the execution time of applications. Today, we observe the emergence of proactive initiatives to handle the elasticity and HPC duet, but they present at least one problem related to the need of a previous user experience, large processing time or completion of parameters. Concerning the aforesaid context, this paper presents ProElastic – a lightweight model that uses proactive elasticity to drive resource reorganisation decisions for HPC applications. Our idea is to explore both performance and adaptivity at middleware level in an effortless way at user perspective. The results showed performance gains and a competitive cost (application time consumed resources). Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Elasticity

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Joint Cache Placement and Request Routing Optimization in Heterogeneous Cellular Networks

Joint Cache Placement and Request Routing Optimization in He...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Marisangila Alves Guilherme Piĉgas Koslovski Graduate Program in Applied Computing - Santa Catarina State University - UDESC

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.

关键词： Cellular networks Measurement 5G mobile communication Quality of service Routing Linear programming Low latency communication

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Modelling IF Steels Using Artificial Neural Networks and Automated Machine Learning 20th

Modelling IF Steels Using Artificial Neural Networks and Aut...

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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： Sgrott, Douglas Macedo Cerqueira, Fabricio Moreira Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. UDESC Graduate Program in Applied Computing Santa Catarina State University JoinvilleSC Brazil Global Research and Development Group ArcelorMittal São Francisco do SulSC Brazil

ISBN: (纸本)9783030730499

The development of steels requires multiples manufacturing steps and involves a high number of variables and non-linear processes. Although there are data-driven models used to predict such steels’ mechanical properties, their design often requires specialized Machine Learning knowledge, hampering the application of such models. In this paper, an Automated Machine Learning (AutoML) framework, namely Auto-Keras, was employed to generate and train a data-driven model on a steel data-set through Neural Architecture Search. This paper proposes predicting the mechanical properties of steels applying Artificial Neural Networks (ANN) as a multivariate regression problem using AutoML. The results show that even with a vast parameter search space, it is possible to generate ANNs with different architectures that provide high performance with free and open-source AutoML. Three ANNs with different activation functions were optimized using Auto-Keras and compared. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A massively parallel speciation-based differential evolution algorithm applied to the 3D-AB protein structure prediction

A massively parallel speciation-based differential evolution...

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作者： Stubs Parpinelli, Rafael Boiani, Mateus Dias, André E. P. Graduate Program in Applied Computing State University of Santa Catarina Santa Catarina Joinville Brazil Rio Grande do Sul Porto Alegre Brazil Computer Science Department State University of Santa Catarina Santa Catarina Joinville Brazil

One of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off-lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D-AB off-lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation-based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state-of-the-art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78 (Figure presented.). © 2021 John Wiley & Sons, Ltd.

关键词： Evolutionary algorithms

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Experimental analysis of microservices architectures for hosting cloud-based Massive Multiplayer Online Role-Playing Game (MMORPG)

Experimental analysis of microservices architectures for hos...

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Marlon H. Schweigert Diego E.G.C. de Oliveira Guilherme P. Koslovski Maurício A. Pillon Charles C. Miers Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil

The popularization increase of MMORPGs demands new technological approaches to supply users’ requirements with a lower cost of computational resources. Designing these architectures, from the network point of view, is relevant and impacts these games’ success. We analyze and identify the computational resources consumed by the architectures Rudy, Salz, and Willson, which are microservices architectures elaborated for MMORPGs. These architectures were analyzed and tested using automated clients on the architectures deployed in our private computational cloud to identify resource bottlenecks. We conclude the performance, from the point of view of response time, is related to better use of CPU, either by data storage microservices or by data processing microservices. In addition, the application of queuing systems or barriers to manage access or minimize the consumption of an internal service proved feasible, directly impacting the flow of data through the architecture.

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Virtual Reality Evolution in Brazil: A Survey over the Papers in the "Symposium on Virtual and Augmented Reality"

Virtual Reality Evolution in Brazil: A Survey over the Paper...

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Symposium on Virtual Reality (SVR)

作者： Juliana Patrícia Detroz Marcio Geovani Jasinski Rafaela Bosse Thiago Luiz Berlim Marcelo Da Silva Hounsell PPGCA-Graduate Program in Applied Computing UDESC-Santa Catarina State University Joinville Brazil UDESC - Santa Catarina State Univ. Brazil

Virtual Reality is becoming a mature technology field. To understand its origins and foresee strategies, a study on the last decade of papers published in the Brazilians' most prominent symposium (the SVR - Symposium on Virtual and Augmented Reality) has been carried out. Papers were classified according to language, international participants, areas and sub-areas of application and related technologies. Although oversees participants has proven not constant, papers published in English has proven stable to SVR editions. The study also shows that health related applications have received most of the attention although techniques and tools proposal have raised the most recently which could be related to the low-level programming languages and frameworks preferences. The number of Augmented Reality papers has grown steadily and a great variety of underlying knowledge fields (such as 3D interaction and real-time simulation) are persistent topics of interest. An expected shift from VRML to other 3D Web technologies have already happened but there are just a few research papers devoted to formulative, evaluative or descriptive approaches. Data show that by improving the research budget to the area could impact productivity, a centralized database of publications would facilitate recovering and analyzing past contributions and, that enforcing more scientifically rigorous and English-written papers could raise the quality and visibility of Brazilians' research in VR.

关键词： Augmented reality Three-dimensional displays Education Communities Avatars Real-time systems

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Understanding bark thickness variations for Araucaria angustifolia in southern Brazil

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Journal of Forestry Research 2021年第3期32卷 1077-1087页

作者： Emanuel Arnoni Costa Veraldo Liesenberg César Augusto Guimaraes Finger AndréFelipe Hess Cristine Tagliapietra Schons Graduate Program in Forest Engineering Santa Catarina State University(UDESC)LagesSC 88520-000Brazil Department of Forest Engineering Federal University of Uberlandia(UFU)Monte CarmeloMG 38500-000Brazil Graduate Program in Forest Engineering Federal University of Santa Maria(UFSM)Santa MariaRS 97105-900Brazil Graduate Program in Agroecosystems Federal Technological University of Parana(UFTPR)Dois VizinhosPR 85660-000Brazil

This study aimed to understand bark thickness variations of Araucaria angustifolia(Bertol.)Kuntze trees growing in natural forest remnants in southern Brazil,and their relationship with quantitative and qualitative *** thickness must be accurately estimated in order to determine timber volume *** is an important variable for the sustainable management and conservation of araucaria *** spite of its importance and visibility,bark thickness variations have not been evaluated for this key species in southern Brazil.A total of 104 trees were selected,and their qualitative and quantitative attributes such as diameter at breast height(D_(BH)),height(H),crown base height(C_(BH)),crown length(C_(L)),social position(S_(P)),stoniness(S_(T)),position on the relief(P_(R)),vitality(V_T)and branch arrangement(B_(A))were *** trees were categorized into two groups:red bark or gray *** analysis and artificial neural networks(ANN)were used for modelling bark *** results indicate that:(1)bark thickness showed good correlation to D_(BH),with 0.76 as coefficient of determination(RS_P),0.540 as Mean Absolute Error(M_(AE))and 22.4 root-meansquare error in percentage(R_(MSE%));(2)the trend changed according to bark colour,with significant differences for the intersection(_0–Pr>F:p=0.0124)and slope(β_(1)–Pr>F:p=0.0126)of bark thickness curves between groups;(3)the highest correlation of bark thickness was found with:D_(BH)(ρ=0.88),H(ρ=0.58),C_(BH)(ρ=0.46),S_(P)(ρ=-0.52),and B_(A)(ρ=-0.32);(4)modelling with ANN confirmed high adjustment(R^(2)=0.99)and accuracy(R_(MSE%)=3.0)of the *** is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest *** effective use of ANN to estimate araucaria bark in natural forests reinforces its potential,besides the possibility of application for other forest species.

关键词： Dendrometry attributes Crown characteristics Prediction models Bark factor Parana-pine

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