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arXiv 2023年

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Zhang, Huayi Feng, Hao Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute Worcester United States Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose United States College of Ag&Environ Sciences North Carolina A&T State University Greensboro United States Computer Science/Data Science Program Worcester Polytechnic Institute Worcester United States

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a class-imbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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Empowering Collective Impact: Introducing SWAP for Resource Sharing

SSRN

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SSRN 2023年

作者： Huang, Weixiao Deshusses, Elise J. Pazour, Jennifer Telliel, Yunus Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute 100 Institute Road WorcesterMA United States Computer Science Program Worcester Polytechnic Institute 100 Institute Road WorcesterMA United States School of Arts and Sciences Worcester Polytechnic Institute 100 Institute Road WorcesterMA United States School of Business Worcester Polytechnic Institute 100 Institute Road WorcesterMA United States Industrial and Systems Engineering Rensselaer Polytechnic Institute 110 Eighth Street TroyNY United States

Nonprofit organizations (NPOs) lack resources, hindering the quality and quantity of service they can deliver. Meanwhile, NPOs at times have underutilized or even spare resources due to the inability to scale expertise in staffing and tangible resources to meet temporally shifting service demands. These observations motivate us to propose a novel resource sharing system, SWAP, which to the best of our knowledge, is the first resource sharing system that facilitates resource exchanges where NPOs can obtain resources by offering their own. SWAP consists of four elements: a collaborative auction-based sharing process, complete with an offering mechanism, a bidding mechanism, and the virtual currency, SWAPcredit, to facilitate liquidity in exchange;a central technology that represents the award determination problem with a multilateral exchange optimization model, generating optimal resource exchange outcomes;an online platform, the SWAP Hub, where NPOs can offer and bid on available resources, and receive exchange results;and human-centric co-design, shaping the understanding and design decisions of a research collective, that includes the authors and NPO professionals. We conduct a series of experiments using both empirical and simulated data to illustrate the benefits and potential of SWAP. Our results demonstrate that SWAP can address temporal resource needs in practice;show that optimal exchange outcomes can be generated even for large-scale SWAP markets;and provide strong evidence in support of guidance to inform the progression for future versions of SWAP. The SWAP system is presently implemented in Howard County, Maryland, USA, with ongoing enhancements and strong potential for future expansion. © 2023, The Authors. All rights reserved.

关键词： Integer programming

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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A flexible ultrasonic velocity profiler development environment

A flexible ultrasonic velocity profiler development environm...

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IEEE Instrumentation and Measurement Technology Conference

作者： César Yutaka Ofuchi Fabio Rizental Coutinho Raquel dos Santos Rasia Lucia Valeria Ramos de Arruda Flávio Neves Rigoberto Eleazar Melgarejo Morales Graduate School of Electrical Engineering and Computer Science (CPGEI) Federal University of Technology-Paraná (UTFPR) Curitiba Brazil Mechanical & Materials Engineering Postgraduate Program (PPGEM) Federal University of Technology-Paraná (UTFPR) Curitiba Brazil

ISBN: (纸本)9781467363846

Ultrasonic velocity profiler method is now an accepted tool in modern experimental fluid mechanics and fluid engineering. As a relative new technology, different applications and improvements of the technique are being researched over the last years. This paper presents an environment to develop new algorithms to improve the ultrasonic velocity estimation technique. The system uses the LabVIEW modular platform integrated with mathematical script language used in many signal-processing software. The phase-shift algorithm was implemented and tested using the proposed system. The results are validated with standard velocity measurements generated by a rotating cylinder.

关键词： Acoustics Velocity measurement Software Signal processing algorithms Ultrasonic variables measurement Fluids Doppler effect

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Implementation and analysis of environment behavior models as a tool for testing real-time, reactive systems

Implementation and analysis of environment behavior models a...

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IEEE International Conference on System of Systems Engineering (SoSE)

作者： H. Tummala M. Auguston J.B. Michael M.-T. Shing D. Little Z. Pace Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA Department of Computer Science Naval Postgraduate School Monterrey CA USA Department of Mathematics California Polytechnic State University San Louis Obispo CA USA Department of Electrical and Computer Engineering Duke University Durham NC USA

We explored the effectiveness of using environment behavior models as a method for testing and analyzing real-time, reactive software systems. Through the automatic generation of test case scenarios based on attributed event grammar, environment behavior models subject the system under test (SUT) to a range of potentially hazardous states. We explored the extent to which experiments with a SUT embedded in an environment behavior model serve as a constructive method for quantitative and qualitative assessment of software system safety. This was done by implementing simulations in which an environment behavior model interacted with the safety-critical computer-assisted resuscitation algorithm (CARA), which is used with a casualty intravenous fluid infusion pump. It was found that with regard to real-time reactive systems, environment behavior models can be used as an effective tool for qualitative and quantitative safety assessment, as well as for improvement of the SUT

关键词： System testing Real time systems Automatic testing Mathematical model Software testing Software systems Safety Automation computer science Mathematics

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Fast time-domain edge-diffraction calculations for interactive acoustic simulations

Eurasip Journal on Advances in Signal Processing

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Eurasip Journal on Advances in Signal Processing 2007年 2007卷

作者： Calamia, Paul T. Svensson, U. Peter Program in Architectural Acoustics School of Architecture Rensselaer Polytechnic Institute Troy NY 12180 United States Department of Computer Science Princeton University Princeton NJ 08544 United States Acoustics Research Centre Department of Electronics and Telecommunications Norwegian University of Science and Technology NO-7491 Trondheim Norway

The inclusion of edge diffraction has long been recognized as an improvement to geometrical-acoustics (GA)modeling techniques, particularly for acoustic simulations of complex environments that are represented as collections of finite-sized planar surfaces. One particular benefit of combining edge diffraction with GA components is that the resulting total sound field is continuous when an acoustic source or receiver crosses a specular-zone or shadow-zone boundary, despite the discontinuity experienced by the associated GA *** interactive acoustic simulations which include only GA components, such discontinuities may be heard as clicks or other undesirable audible artifacts, and thus diffraction calculations are important for high perceptual quality as well as physical realism. While exact diffraction calculations are difficult to compute at interactive rates, approximate calculations are possible and sufficient for situations in which the ultimate goal is a perceptually plausible simulation rather than a numerically exact one. In this paper, we describe an edge-subdivision strategy that allows for fast time-domain edge-diffraction calculations with relatively low error when compared with results from a more numerically accurate solution. The tradeoff between computation time and accuracy can be controlled with a number of parameters, allowing the user to choose the speed that is necessary and the error that is tolerable for a specific modeling scenario.

关键词： Edge detection

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EnLoB: Energy and Load Balancing-Driven Container Placement Strategy for Data Centers

EnLoB: Energy and Load Balancing-Driven Container Placement ...

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IEEE Globecom Workshops

作者： Kuljeet Kaur Sahil Garg Georges Kaddoum Francois Gagnon Dushantha Nalin K. Jayakody Electrical Engineering Department École de technologie supérieure Canada Ecole de technologie superieure Montreal QC Canada School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia School of Postgraduate Studies & Research Sri Lanka Technological Campus Padukka Sri Lanka

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Cloud data centers (DCs) can be aptly regarded as the epicenter of today's business and economy; which support seamless data processing, analysis, and storage. However, various studies advocate that the existing DCs are often underutilized. To be precise, almost 30% of the installed DCs in the United States are comatose. In addition to this, the existing DC architecture leads to extensive energy utilization, which severely hampers the environment and places a severe risk on the power sector. Thus, it is highly essential to reduce DC's energy utilization through efficient resource consolidation approaches. In this work, we investigate the joint impact of resource consolidation and load balancing on cutting down the energy utilization indices of the cloud DCs. In this vein, we formulate a multi-objective optimization problem (MOOP) for container placement across heterogeneous infrastructure, primarily with the intent to minimize the overall energy consumption and balance the load amongst the operating hosts. However, due to the hardness of the underlying problem and its infeasibility to furnish optimal solutions in polynomial time, we designed an online solution based on the incremental exploration of the solution space to map containers on the available array of hosts such that the objectives mentioned above can be attained. Finally, we evaluated the performance of the proposed algorithm in contrast to an existing algorithm on real-time workload traces obtained from PlanetLab. The obtained results confirm the superior performance of the proposed algorithm relative state-of-the-art.

关键词： Containers Energy consumption Mathematical model Load management Bandwidth Linear programming Optimization

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Predicting the effect of variations in ambient temperature and operating power on the response of a microwave filter

Predicting the effect of variations in ambient temperature a...

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Antennas and Propagation Conference, LAPC, Loughborough

作者： L. M. de la Torre Rodríguez L. L. Bravo Roger Jose Pissolato Filho Yuso Iano H.E. Hernández Figueroa Miguel A. Sanchez-Soriano A. A. C. Alves School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Fellow of the Students-Agreement Postgraduate Program - PEC-PG CAPES/CNPq Brazil Polytechnic University College University of Alicante Alicante Alicante Spain Institute of Electrical Systems and Energy Federal University of Itajubá Itajubá MG Brazil

ISBN: (纸本)9781509007844

A multiphysics study of the electromagnetic behavior of a filter designed for use in high-power microwave systems is presented. In the past, the methodology used to design filters generally assumed ideal working conditions for these devices. However, when such filters are used in a real environment, factors such as temperature and working power affect their performance. Here, we use modern simulation tools to evaluate the electromagnetic response of a filter at different temperatures and under different operating conditions.

关键词： Electromagnetics Dielectric constant Radio frequency Microwave filters Performance evaluation Substrates Radar

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