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Brazilian Symposium on computing System Engineering (SBESC)

作者： Diogo Olsen Carlos Maziero Graduate Program in Computer Science Pontifical Catholic University of Paraná Brazil Graduate Program in Applied Computing Federal University of Technology-Paraná Curitiba Brazil

Context-Aware computing aims at making computing systems able to capture context information and to adapt to it, providing context-related services and information to users. Context is any information used to characterize in some sense a person, object, or place of interest for a given user or application. Initial definitions of context-aware computing date from twenty years ago, but until now it was not yet fully made available to end users. This paper proposes a generic model of a context manager for general-purpose operating systems, alongside with its implementation and first evaluation results. This model includes methods for building, storing, and managing contexts, and an API to make such information available to applications and users.

关键词： Context modeling Context Computational modeling Context-aware services Software Integrated circuit modeling

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Genetic programming for attribute construction in data mining

Genetic programming for attribute construction in data minin...

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6th European Conference on Genetic programming, EuroGP 2003

作者： Otero, Fernando E. B. Silva, Monique M. S. Freitas, Alex A. Nievola, Julio C. Postgraduate Program in Applied Computer Science Rua Imaculada Conceicao 1155 CuritibaPR80215-901 Brazil Computing Laboratory University of Kent Canterbury KentCT2 7NF United Kingdom

ISBN: (纸本)354000971X

For a given data set, its set of attributes defines its data space representation. The quality of a data space representation is one of the most important factors influencing the performance of a data mining algorithm. The attributes defining the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem, this paper proposes a Genetic programming algorithm developed for attribute construction. This algorithm constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm. © 2003, Springer-Verlag Berlin Heidelberg, All rights Reserved.

关键词： Genetic programming

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Generation and reconstruction of time-bin entangled ququarts

Generation and reconstruction of time-bin entangled ququarts

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Frontiers in Optics, FiO 2016

作者： Nowierski, Samantha J. Oza, Neal N. Kumar, Prem Kanter, Gregory S. Northwestern University Applied Physics Program 2220 Campus Drive EvanstonIL60208 United States Northwestern University Center for Photonic Communication and Computing Department of Electrical Engineering and Computer Science 2145 Sheridan Road EvanstonIL60208 United States

We generate and reconstruct a maximally-entangled time-bin ququart using quantum state tomography and polarization-projective measurements. We measure a fidelity of 93.7±0.4% to a maximally-entangled ququart with... 详细信息

ISBN: (纸本)9781943580194

关键词： Fiber optic communications Four wave mixing Frequency combs Photons Single photon detectors Tomography

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MILPdM: A Predictive Maintenance Architecture for the Military Domain 22

MILPdM: A Predictive Maintenance Architecture for the Milita...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Dalzochio, Jovani Kunst, Rafael Barbosa, Jorge Pignaton De Freitas, Edison Binotto, Alecio University of Vale Do Rio Dos Sinos Graduate Program in Applied Computing São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande Do sui Porto Alegre Brazil Ibm Consulting Asset and Edge Optimization Munich Germany

ISBN: (纸本)9798350345346

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness. © 2023 IEEE.

关键词： IoBT machine learning neural networks predictive maintenance random forest

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Evaluation of convolutional neural networks for COVID-19 classification on chest X-rays

arXiv

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arXiv 2021年

作者： Zeiser, Felipe André Da Costa, Cristiano André De Oliveira Ramos, Gabriel Bohn, Henrique Santos, Ismael Da Rosa Righi, Rodrigo Graduate Program in Applied Computing Universidade Do Vale Do Rio Dos Sinos São Leopoldo Brazil

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results demonstrate that the VGG16 architecture obtained superior performance in the classification of XR for the evaluation metrics using the methodology proposed in this article. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758. © 2021, CC BY-NC-SA.

关键词： Convolutional neural networks

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ARTERIAL: A Natural Language Processing Model for Prevention of Information Leakage from Electronic Health Records

ARTERIAL: A Natural Language Processing Model for Prevention...

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Brazilian Symposium on computing System Engineering (SBESC)

作者： Guilherme Goldschmidt Felipe André Zeiser Rodrigo Da Rosa Righi Cristiano André Da Costa Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

Over the past decade, there has been a steady increase in health security breaches. Therefore, healthcare organizations must protect their sensitive information such as test results, diagnoses, prescriptions, research, and customer personal information. A leak of sensitive data can result in significant economic loss and damage to the organization’s image. In this sense, Data Leakage Prevention (DLP) systems can help to identify, monitor, protect, and reduce the risks of leaking sensitive data. However, state-of-the-art DLP solutions only use signature comparisons and static comparisons. Therefore, we propose to develop the ARTERIAL model based on Natural Language Processing (NLP), Entity Recognition (NER), and Artificial Neural Networks (ANN) to be more assertive in extracting information and recognizing entities from Electronic Health Records (EHR). Different from the current literature, ARTERIAL considers semantic features present in the EHR. Three approaches were implemented and tested, two based on ANN and the following based on machine learning algorithms. As a result, the approach taken in its implementation using a machine learning algorithm reached 98.0% of Precision, 86.0% of Recall, and 91.0% of F1-Score.

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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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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents 5

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paulus, Gustavo B. Vargas, Daniel P. Assuncao, Joaquim V. C. Silva, Luis A. L. Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Federal University of Santa Maria Graduate Program in Computer Science Santa Maria Brazil Federal University of Santa Maria Applied Computing Department Santa Maria Brazil

ISBN: (纸本)9798350322774

Computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently. © 2023 IEEE.

关键词： Case based reasoning

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Toward compact data from big data

arXiv

引用

arXiv 2020年

作者： Kim, Song-Kyoo Computing Program School of Applied Sciences Macao Polytechnic Institute R. de Luis Gonzaga Gomes China

Bigdata is a dataset of which size is beyond the ability of handling a valuable raw material that can be refined and distilled into valuable specific insights. Compact data is a method that optimizes the big dataset that gives best assets without handling complex bigdata. The compact dataset contains the maximum knowledge patterns at fine grained level for effective and personalized utilization of bigdata systems without bigdata. The compact data method is a tailor-made design which depends on problem situations. Various compact data techniques have been demonstrated into various data-driven research area in the paper. © 2020, CC BY-NC-ND.

关键词： Big data

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Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

arXiv

引用

arXiv 2025年

作者： Murthy, Akshay A. Bal, Mustafa Bedzyk, Michael J. Cansizoglu, Hilal Chan, Randall K. Chandrasekhar, Venkat Crisa, Francesco Datta, Amlan Deng, Yanpei Matute Diaz, Celeo D. Dravid, Vinayak P. Garcia-Wetten, David A. Garattoni, Sabrina Ghimire, Sunil Goronzy, Dominic P. de Graaf, Sebastian E. Haeuser, Sam Hersam, Mark C. Isheim, Dieter Joshi, Kamal Kim, Richard Kolachina, Saagar Kopas, Cameron J. Kramer, Matthew J. Lachman, Ella O. Lee, Jaeyel Lim, Peter G. Lunin, Andrei Mah, William Marshall, Jayss Mutus, Josh Y. Oh, Jin-Su Pappas, David P. Park, Joong-Mok Prozorov, Ruslan dos Reis, Roberto Seidman, David N. Sung, Zuhawn Tanatar, Makariy Walker, Mitchell J. Wang, Jigang Wu, Haotian Zhou, Lin Zhu, Shaojiang Grassellino, Anna Romanenko, Alexander BataviaIL60510 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Rigetti Computing BerkeleyCA94710 United States Ames National Laboratory AmesIA50011 United States Department of Physics & Astronomy Iowa State University AmesIA50011 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States National Physical Laboratory TeddingtonTW11 0LW United Kingdom Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Applied Physics Graduate Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Iowa State University AmesIA50011 United States

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values. © 2025, CC BY.

关键词： Qubits

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