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8th Brazilian Conference on Intelligent Systems, BRACIS 2019

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

ISBN: (纸本)9781728142531

Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures are required. This paper presents a GPU-based hybrid algorithm, named cuHjDE, to handle the 2D-AB off-lattice PSP problem. The cuHjDE is composed of the jDE algorithm and the Hooke-Jeeves local search algorithm. An important feature present in the cuHjDE algorithm is the use of a crowding mechanism to avoid premature convergence promoting diversification in the search space. The proposed algorithm is compared with four state-of-the-art algorithms in both artificial and real proteins. Also, the impact of using GPU is analyzed. Experimental results point out that the proposed approach is competitive in artificial sequences and achieved new best results for all real sequence proteins. © 2019 IEEE.

关键词： Proteins

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The effects of information search and evaluation of alternatives through social media on the purchase decision: Japanese and Indonesian consumers case

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AIP Conference Proceedings 2023年第1期2594卷

作者： V. Franklin A. S. Ramadhan A. K. Anom Communication Department Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia 11480 International Business Program Management Department BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480

The purpose of this research paper is to identify whether there is an influence of finding information and evaluating alternatives through social media on the purchase decision of Japanese and Indonesian people. In the past, companies conveyed advertising messages through traditional channels, such as radio, television, newspapers, magazines, and direct mail. Nonetheless, this communication method covers a limited targeted market by not being able to transmit individualized messages. However, the development of social media has greatly increased, providing new means of communication through digital marketing. Researchers acquired information from Japanese and Indonesian respondents through designing a questionnaire and semi-structured interview. The questionnaire resulted in 200 respondents: 100 Japanese respondents and 100 Indonesian respondents, and semi-structured interviews occurred with 8 respondents: 4 Japanese respondents and 4 Indonesian respondents. The results showed that information search through social media influenced Japanese and Indonesian consumers' purchase decision but had an insignificant effect. The study also proved that the evaluation of alternatives through social media influenced the purchase decision of Japanese and Indonesian consumers and had a significant effect. The majority of information found in social media is in the form of posts, pictures, descriptive, evaluative comments, discussions, and testimonies from other consumers/people they know. These processes lead to a higher impact on the Indonesian respondents' purchase decisions compared to the Japanese respondents.

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Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis

JMIR Aging

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JMIR Aging 2022年第3期5卷 e33460页

作者： Soroski, Thomas da Cunha Vasco, Thiago Newton-Mason, Sally Granby, Saffrin Lewis, Caitlin Harisinghani, Anuj Rizzo, Matteo Conati, Cristina Murray, Gabriel Carenini, Giuseppe Field, Thalia S. Jang, Hyeju Vancouver Stroke Program Division of Neurology Faculty of Medicine University of British Columbia Vancouver BC Canada Department of Computer Science Faculty of Science University of British Columbia Vancouver BC Canada School of Computing University of the Fraser Valley Abbotsford BC Canada

Background: Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening. Objective: To better understand the use of automatic transcription for classification of neurodegenerative disease, namely, Alzheimer disease (AD), mild cognitive impairment (MCI), or subjective memory complaints (SMC) versus healthy controls, we compared automatically generated transcripts against transcripts that went through manual correction. Methods: We recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-to-moderate AD, (n=44, 30%), MCI (n=20, 13%), SMC (n=8, 5%), as well as healthy controls (n=77, 52%) living in the community. Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. Results: The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantl

关键词： Alzheimer disease machine learning memory mild cognitive impairment natural language processing neurodegenerative disease speech speech recognition software transcription software

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Scaling laws and spatial effects of Brazilian health regions: a research protocol

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Procedia Computer Science 2023年 219卷 1325-1332页

作者： Giovane Thomazini Soares Diego Bettiol Yamada Filipe Andrade Bernardi Mariane Barros Neiva Luis Pedro Lombardi Junior André Luiz Teixeira Vinci Ana Clara de Andrade Mioto Domingos Alves Ribeirão Preto Medical School University of Sao Paulo Av Bandeirantes 3900 14049900 Ribeirao Preto SP Brazil Bioengineering Postgraduate Program Av Trabalhador Sao-Carlense 400 13566590 Sao Carlos SP Brazil Institute of Mathematics and Computer Sciences Av Trabalhador Sao-Carlense 400 13566590 Sao Carlos SP Brazil Institute of Mathematics Statistics and Scientific Computing State University of Campinas Rua Sérgio Buarque de Holanda 651 13083859 Campinas SP Brazil

The literature has already consolidated the importance of health regions for Brazilian public health. Complexity properties strongly mark such regions. In this context, there are abundant indications that health regions should be analyzed with approaches linked to the sciences of complexity. One of these approaches, the estimation of scaling laws, can describe important properties of socio-spatial elements. However, no studies estimate the scaling laws of Brazilian health regions. This research protocol can remedy this limitation, proposing the estimation of scaling laws of the previously mentioned regions, mainly considering variables relevant to Brazilian public health. Still, this paper can substantially mitigate other relevant limitations of usual research that estimate scaling laws of socio-spatial elements. These mitigations, which provide advances in the literature on estimating scaling laws, are given by the proposal of modeling (if necessary) spatial effects and estimating scaling laws for the entire population of the socio-spatial elements. According to the theory, the expected results are non-linear scaling laws, which will likely vary with space and time and coexist with relevant spatial effects. From such laws and effects, it will be possible to accurately characterize the performance of each health region through Spatial and Scale Adjusted Metropolitan Indicators and unravel spatio-temporal properties, stabilities, and instabilities of sets composed of health regions. The expected findings of this paper can help rearrange health regions and improve the quality of information used in Brazilian public health planning.

关键词： Scaling laws Spatial effects Research protocol

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Evaluation of multiple SAR speckling filter techniques performance in irrigated rice areas

Evaluation of multiple SAR speckling filter techniques perfo...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Garcia, Andre Dalla Bernardina Celeste, Jimuel Cheng, Irene Prudente, Victor Hugo Rohden Del’Arco Sanches, Ieda INPE National Institute for Space Research PGSER Remote Sensing Graduate Program Coordination of Teaching COEPE São José dos Campos São Paulo12227-010 Brazil UoA University of Alberta Dept. Of Computing Science EdmontonABT6G 2S4 Canada UofM University of Michigan School for Environment and Sustainability Ann ArborMI48109 United States Multimedia Research Centre University of Alberta EdmontonABT6G 2E8 Canada DIOTG Earth Observation and Geoinformatics Division CG-TG General Coodination of Earth Science INPE National Institute for Space Research Teaching COEPE São Paulo São José dos Campos12227-010 Brazil

Monitoring irrigated rice crops is essential for efficient agricultural management, and Synthetic Aperture Radar (SAR) images are beneficial due to their capability to function in all weather conditions. However, speckle noise in SAR images complicates the classification of land cover. This study evaluates speckle filtering techniques to enhance the image quality of SAR for rice field monitoring. We extend previous work by comparing Bayesian filters in the spatial domain with advanced transform domain methods, including block matching 3D (BM3D) and Discrete Fourier Transform-extracted Edge (DFT Edge) techniques, across different rice growth stages. Twenty-two Sentinel-1B SAR images from the municipality of Turvo, Santa Catarina, Brazil were analyzed. Filters were assessed using metrics for speckle suppression and edge preservation. Our experiments reveal that BM3D with a sigma parameter of 10 (BM3Dsigma10) provided superior results, balancing effective speckle suppression and edge preservation. Specifically, BM3Dsigma10 was superior in preserving edge details compared to other techniques. However, as the σ value increased, a loss in resolution was observed, even for the DFT Edge method, which, while efficient in edge preservation, often resulted in reduced detail resolution. These findings highlight the importance of selecting appropriate filtering techniques for accurate agricultural monitoring using SAR data. © Author(s) 2024.

关键词： Discrete Fourier transforms

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Design and evaluation of mobile mental health resources for first responders 14

Design and evaluation of mobile mental health resources for ...

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14th EAI International Conference on Pervasive computing Technologies for Healthcare, PervasiveHealth 2020

作者： Crooks, Courtney Feinberg, Rachel Ayala-Feliciangeli, Talia Dhodapkar, Kunal Singh, Sonam Arriaga, Rosa Advanced Concepts Laboratory Georgia Tech Research Institute Atlanta United States MS-HCI Program Georgia Institute of Technology Atlanta United States School of Interactive Computing Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781450375320

This project was completed in collaboration with a non- profit community mental health organization and first responders, to develop an app to better facilitate the process of navigating resources and finding them at any time and location. We completed preliminary digital resource repository evaluations, and two rounds of stakeholder focus groups with heuristic evaluations. This helped us identify four main domains to focus on: Resources, Around You, Information, and programs. Focus group discussions were conducted to understand how stakeholders characterized mental health resources and evaluated the initial design. The design components have been explained in detail and future directions have been outlined in this paper. © 2020 ACM.

关键词： mHealth

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Higher-order rich-club phenomenon in collaborative research grant networks

arXiv

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National Science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

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Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software Effort Estimation with Use Case Points Using Ensemb...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Bhaskar Marapelli Anil Carie Sardar M. N. Islam College of Computing and Informatics Wolkite university Wolkite Ethiopia School of Computer Science and Engineering VIT-AP University Amaravathi India ISILC and Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781665442329

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.

关键词： Support vector machines Statistical analysis Computational modeling Estimation Predictive models Boosting Software

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Approximate Autonomous Quantum Error Correction with Reinforcement Learning

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Physical Review Letters 2023年第5期131卷 050601-050601页

作者： Yexiong Zeng Zheng-Yang Zhou Enrico Rinaldi Clemens Gneiting Franco Nori Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wakoshi Saitama 351-0198 Japan Quantum Computing Center RIKEN Wakoshi Saitama 351-0198 Japan Quantinuum K.K. Otemachi Financial City Grand Cube 3F 1-9-2 Otemachi Chiyoda-ku Tokyo Japan Department of Physics University of Michigan Ann Arbor Michigan 48109-1040 USA Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN Wakoshi Saitama 351-0198 Japan

Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances d≥2. Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning (RL), we identify the optimal bosonic set of code words (denoted here by RL code), which, surprisingly, is composed of the Fock states |2⟩ and |4⟩. As we show, the RL code, despite its approximate nature, successfully suppresses single-photon loss, reducing it to an effective dephasing process that well surpasses the break-even threshold. It may thus provide a valuable building block toward full error protection. The error-correcting Hamiltonian, which includes ancilla systems that emulate the engineered dissipation, is entirely based on the Hamiltonian distance d=1, significantly reducing model complexity. Single-qubit gates are implemented in the RL code with a maximum distance dg=2.

关键词： Machine learning Open quantum systems & decoherence Quantum computation Quantum engineering Quantum error correction Quantum cavities Semiconductor microcavities Deep learning Rabi model

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The Second Competition on Spatial Statistics for Large Datasets

arXiv

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arXiv 2022年

作者： Abdulah, Sameh Alamri, Faten Nag, Pratik Sun, Ying Ltaief, Hatem Keyes, David E. Genton, Marc G. Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Mathematical Science Department Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our ExaGeoStat software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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