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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Wu, Tao Fan, Xiaochen Qu, Yuben Yang, Panlong School of Electronic Engineering National University of Defense Technology China Centre for Assessment and Demonstration Research Academy of Military Science China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781665408783

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge Computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by 'frame' is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by 'time slot' is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity. © 2021 IEEE.

关键词： Mobile edge computing

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Assessing Driving Risk Indicators from Large Driving Data Sets

Assessing Driving Risk Indicators from Large Driving Data Se...

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Bruce Wallace Philippe Masson Jonathan Ojangole Kathleen Van Benthem Chris M. Herdman Jocelyn Keillor Rafik Goubran Frank Knoefel Shawn Marshall Bruyère Research Institute SAM 3 Innovation Hub Systems and Computer Engineering Carleton University Ottawa Canada SAM Innovation Hub Computer Science Carleton University Department of Cognitive Science Carleton University Ottawa Canada Aerospace Research Centre National Research Council Canada Flight Research Laboratory Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM Innovation Hub Ottawa Canada Elisabeth Bruyère Hospital Systems and Computer Engineering Carleton University SAM Innovation Hub Ottawa Hospital Carleton University Bruyère Research Institute Elisabeth-Bruyère Hospital

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Understanding the driving behaviours that lead to increased risk of collision for older drivers is a serious public safety challenge. Driving is important for older drivers to travel to activities that maintain an active independent lifestyle. Research has shown that varying levels of night driving, velocity and driving exposure may be risk factors. However, there is limited knowledge of what levels are indicative of higher risk for older drivers, especially as compared to a group of similar age peers. Drivers with behaviours significantly above or below the typical norm age group may indicate higher risk. For example, while lower levels of night driving could indicate lowered night-time crash risk due to self-regulation associated with reduced confidence or visual issues, greater amounts of nighttime driving may show increased risk related to over confidence or a need to drive at night. The paper analyzes a large dataset of naturalistic driving data that includes 1 year of driving data for 19,800 drivers and proposes normative models for night driving, driving speed and driving exposure for 5-year age groups from the youngest drivers (age 16) through to drivers over the age of 95. The models provide measures of the average and variance for drivers in each age group. This addresses a gap in the literature due to previous limitations of the available data or study population. The proposed measures allow for driver risk to be identified that is associated with atypical behaviour that is higher or lower than the age group norm. Models are proposed for night driving, velocity and driving exposure that allow the driving by other drivers to now be analyzed to identify with increased risk such as those that are outliers (+/- >2 SD from mean) in comparison to their age group peers.

关键词： Analytical models Visualization Sociology Public security Data models Statistics Accidents

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Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

arXiv

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arXiv 2023年

作者： Maama, Mohamed Jasra, Ajay Kamatani, Kengo Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [15], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models applied to examples of Bayesian inference problems using both simulated and real observed data from neuroscience. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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An empirical model for branding oriented by the environmental sustainability of the Amazon Rainforest: a hybrid structural equation modeling with fsQCA approach

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Discover Analytics 2024年第1期2卷 1-21页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues Pedrotti, Alceu de Medeiros, Raphael Mario Bastos de Santana Oliveira, Catuxe Varjão Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS São Cristóvão Brasil Agronomy Engineering Department Universidade Federal de Sergipe-UFS São Cristóvão Brasil Administration Department Universidade da Amazônia Manaus Brasil Computer Science Department Instituto Federal de Sergipe-UFS Lagarto Brasil

Utilizing the Theory of Planned Behavior, a meaningful connection between an organization and its consumers can be established, transcending mere appearances and delving into emotions, relationships, and brand experiences. In line with these principles, this study aims to present an empirical model grounded in factual evidence derived from the perspectives of entrepreneurs and consumers of environmentally sustainable brands in the Brazilian Amazon. The proposed model adopts a mixed-modeling approach, utilizing data analysis techniques such as fuzzy-set qualitative comparative analysis (fsQCA). Two independent data sets were collected: one from 8 companies operating in the Amazon rainforest and another from 535 potential consumers. By analyzing these datasets, consumer motivations for the consumption of sustainable brands, entrepreneurial practices, and the role of traditional communities and the forest environment in shaping brand perceptions were identified. The results reveal that, from the consumer’s perspective, the key factors influencing a positive inclination to purchase sustainable brands include the brand’s engagement with traditional com munities, perceived product quality, brand innovation, and a transparently communicated positioning strategy. These constructs form the core components of the main solution. This article provides valuable information for entrepreneurs operating sustainable brands in tropical forests as well as for policymakers in community-based entrepreneurship. It is also of interest to researchers and individuals involved in brand management as a strategy for empowering small businesses engaged in sustainable practices and community collaboration. The study’s findings and recommendations have the potential to foster more sustainable and socially responsible business practices, thereby enhancing local economies and contributing to environmental preservation.

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Refined Risk Bounds for Unbounded Losses via Transductive Priors

arXiv

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arXiv 2024年

作者： Qian, Jian Rakhlin, Alexander Zhivotovskiy, Nikita Electrical Engineering and Computer Science Department Massachusetts Institute of Technology CambridgeMA02139 United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Statistics University of California Berkeley BerkeleyCA94720 United States

We revisit the sequential variants of linear regression with the squared loss, classification problems with hinge loss, and logistic regression, all characterized by unbounded losses in the setup where no assumptions are made on the magnitude of design vectors and the norm of the optimal vector of parameters. The key distinction from existing results lies in our assumption that the set of design vectors is known in advance (though their order is not), a setup sometimes referred to as transductive online learning. While this assumption might seem similar to fixed design regression or denoising, we demonstrate that the sequential nature of our algorithms allows us to convert our bounds into statistical ones with random design without making any additional assumptions about the distribution of the design vectors—an impossibility for standard denoising results. Our key tools are based on the exponential weights algorithm with carefully chosen transductive (design-dependent) priors, which exploit the full horizon of the design vectors, as well as additional aggregation tools that address the possibly unbounded norm of the vector of the optimal solution. Our classification regret bounds have a feature that is only attributed to bounded losses in the literature: they depend solely on the dimension of the parameter space and on the number of rounds, independent of the design vectors or the norm of the optimal solution. For linear regression with squared loss, we further extend our analysis to the sparse case, providing sparsity regret bounds that depend additionally only on the magnitude of the response variables. We argue that these improved bounds are specific to the transductive setting and unattainable in the worst-case sequential setup. Our algorithms, in several cases, have polynomial time approximations and reduce to sampling with respect to log-concave measures instead of aggregating over hard-to-construct Ε-covers of classes. Copyright © 2024, The Authors. All righ

关键词： Vectors

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Design of big data technology prototype for classification of village status based on village development index involves k-means algorithm to support village ministry Pdtt work programs 2

Design of big data technology prototype for classification o...

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2nd International Conference on sciences and Technology Applications, ICOSTA 2020

作者： Hasugian, Paska Marto Hutahaean, Harvei Desmon Sinaga, Bosker Sriadhi Silaban, Saronom Informatics Engineering Study Program STMIK Pelita Nusantara Medan Indonesia Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan20221 Indonesia Departement of Chemistry Universitas Negeri Medan Medan20221 Indonesia

Big data technology is the overall technology that can handle the processing associated with analyzing the data to explore the potential that is in it. Some of the uses of big data are based on the biggest data traffic sources, such as social media, financial transactions, public data, censorship data, and company data. The problem found in the accumulation of village status data that has not been utilized in the decision-making process to determine the status of the next village and the absence of an application to process the data stored in the data database so that a tool is developed to analyze the buildup. The results of this study are to produce a prototype design that is Unified Modeling Language (UML) design, Form Design, and Database Design that will be used to develop Big Data Technology in determining the classification of village status based on the Village Build index. © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

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Extrinsic LiDAR-Robot And LiDAR-Camera Calibration For Multi-sensor Fusion*

Extrinsic LiDAR-Robot And LiDAR-Camera Calibration For Multi...

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IEEE Latin American Robotics Symposium, LARS

作者： Gilmar Pereira Da Cruz Hector Azpúrua Fernando Lizarralde Gustavo Medeiros Freitas Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais - Av. Antônio Carlos 6627 Belo Horizonte MG Brazil Department of Computer Science Universidade Federal de Minas Gerais MG Brasil Electrical Engineering Program - COPPE Universidade Federal do Rio de Janeiro RJ Brasil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

In mobile robotics, an essential requirement for the fusion of different sensors is that measurements are expressed with respect to the same reference. In this sense, the transformation between sensors and robot is necessary to ensure better sensor fusion. Therefore, this article proposes an extrinsic sensor calibration based on markers and associated to three orthogonal planes. This technique is applied to two calibration approaches, LiDAR-Robot and LiDAR-Camera. The first one calculates the transformation between a 3D LiDAR sensor and a robot, and the second system calculates the transformation between a 3D LiDAR and an embedded RGB camera. To demonstrate the efficiency of our method, we performed simulations on the CoppeliaSim simulator and experiments in the laboratory. Then, the results show that it is possible to calibrate the sensors with the methodologies.

关键词： Visualization Laser radar Three-dimensional displays Robot vision systems Sensor fusion Cameras Sensor systems Sensors Calibration Registers

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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