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An ounce of prevention: using conversational interviewing and avoiding agreement response scales to prevent acquiescence

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Quality and Quantity 2024年第1期58卷 471-495页

作者： Davis, Rachel E. Conrad, Frederick G. Dong, Shaohua Mesa, Anna Lee, Sunghee Johnson, Timothy P. Arnold School of Public Health Department of Health Promotion Education and Behavior University of South Carolina Columbia SC United States Institute for Social Research Michigan Program in Survey and Data Science University of Michigan Ann Arbor MI United States College of Urban Planning and Public Affairs Department of Public Administration University of Illinois at Chicago Chicago IL United States

Acquiescent response style (ARS), the tendency for survey respondents to agree with survey items, is of particular concern for increasing measurement error in surveys with populations who are more likely to acquiesce, such as Latino respondents in the U.S. In order to develop methods for reducing ARS, this study addressed two questions: (1) Does administering a questionnaire using conversational interviewing (CI) yield less ARS than standardized interviewing (SI)? (2) Do bipolar disagree/agree (DA) response scales lead to higher ARS than unipolar response scales that do not assess agreement (non-AG)? A total of 891 Latino telephone survey respondents were screened for ARS and randomly assigned to four experimental groups determined by crossing interviewing technique (CI or SI) and response format (non-AG or DA): (1) SI/non-AG (n = 301);(2) SI/DA (n = 295);(3) CI/non-AG (n = 149);and (4) CI/DA (n = 146). CI yielded lower ARS than SI (p < 0.001), but there was no difference in ARS between DA and non-AG response scales. A subset of coded interview recordings indicated that the CI interviewers reduced ARS by clarifying questions even in the absence of evidence of respondent confusion and helping with response mapping. These results suggest that difficulty answering questions associated with cognitive decline and cultural norms may have prompted higher use of ARS, but that conversational interviewers were able to mitigate these difficulties and cultural tendencies. Findings from this study suggest that using CI to administer survey questions may decrease ARS and improve data quality among survey respondents who are more likely to engage in ARS. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Acquiescence Conversational interviewing Interviewing Latino Response styles Survey error Surveys

Characterization and Classification of Purity of Limestone in Madura Island for Industrial Application 1

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Characterization and Classification of Purity of Limestone i...

1st International Symposium on Physics and Applications, ISPA 2020

作者： Munawaroh, F. Muharrami, L.K. Triwikantoro Arifin, Z. Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The characterization and classification of purity of limestone at Madura Island was investigated. Sampling was taken from nine quarries from different areas. The chemical analysis was carried out by X-ray fluorescence (XRF);the crystalline phase was characterized by X-ray diffraction (XRD). Analysis of purity classification based on XRF and XRD results according to British Geological Survey. Limestone has different purity, there are very high, medium, low and impure purity. The sample has a very high purity from Pamekasan (P.1) with CaO content of 99.06 wt% and 100% calcite. The samples are medium purity from Bangkalan (B.1, B.3), Pamekasan (P.2), Sumenep (S.1, and S.3) with CaO content of 91.61- 93.67 wt%. The samples are low purity from S.2 with CaO content 85.79 wt% and 100% dolomite. And the sample has impurities from P.3 and B.2 with CaO content 82.3 wt% and 84.7 wt%. Limestone with very high and medium purity which is processed into PCC can be applied in many industries as filler in rubber, paper making, plastic, paint, food, pharmaceutical, ceramic, adhesives, sealants, agriculture, and animal feed. © Published under licence by IOP Publishing Ltd.

关键词： Limestone

Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends

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arXiv 2024年

作者： Paruchuri, Aanish Wang, Yunfei Gu, Xiaodan Jayaraman, Arthi Master of Science in Data Science Program University of Delaware NewarkDE19713 United States School of Polymer Science and Engineering University of Southern Mississippi 118 College Drive #5050 HattiesburgMS39406 United States Department of Chemical and Biomolecular Engineering University of Delaware 150 Academy St NewarkDE19713 United States Department of Materials Science and Engineering University of Delaware NewarkDE19713 United States Data Science Institute University of Delaware NewarkDE19713 United States

In this paper we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films. The goal of the workflow is to identify the spatial location of the two types of polymer domains with little to no manual intervention (Task 1) and calculate the domain size distributions which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered/disordered domains (Task 2). We briefly review existing approaches used in other fields - computer vision and signal processing - that can be applicable for the above tasks that happen frequently in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform (DFT) or discrete cosine transform (DCT) with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of 144 input AFM images, we then used an existing porespy python package to calculate the domain size distribution from the output of that image from DFT-based workflow. The information and open-source codes we share in this paper can serve as a guide for researchers in the polymer and soft materials fields who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline/amorphous domains, sharp/rough interfaces between domains, or micro or macro- phase separated domains. © 2024, CC BY.

关键词： Unsupervised learning

Unbiased Parameter Estimation for Bayesian Inverse Problems

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China 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

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

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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

Minimization of High Computational Cost in data Preprocessing and Modeling Using MPI4Py

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SSRN

SSRN 2023年

作者： Ebenezer, Oluwasakin Thomas, Torku Tingting, Sun Yinusa, Ahmeed Hamden, Sammi Poudel, Samir Vargas, Jorge Poudel, Khem Narayan Computation and Data Science Program Middle Tennessee State University TN United States Engineering Technology Middle Tennessee State University TN United States Computer Science Program Middle Tennessee State University TN United States

data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall. In this research, we developed a novel approach that effectively deployed tools such as MPI and MPI4Py from parallel computing to handle data preprocessing and deep learning modeling processes. As a case study, the technique is applied to COVID-19 data from state of Tennessee, USA. Finally, the effectiveness of our approach is demonstrated by comparing it with existing methods without parallel computing concepts like MPI4Py. Our results demonstrate promising outcome for the deployment of parallel computing in modeling to minimize high computational cost. © 2023, The Authors. All rights reserved.

关键词： Large dataset

LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation

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arXiv 2024年

作者： Song, Steven Subramanyam, Anirudh Madejski, Irene Grossman, Robert L. Department of Computer Science University of Chicago United States Medical Scientist Training Program Pritzker School of Medicine United States Center for Translational Data Science University of Chicago United States Department of Medicine University of Chicago United States

In the current paradigm of image captioning, deep learning models are trained to generate text from image embeddings of latent features. We challenge the assumption that these latent features ought to be high-dimensional vectors which require model fine tuning to handle. Here we propose Label Boosted Retrieval Augmented Generation (LaB-RAG), a text-based approach to image captioning that leverages image descriptors in the form of categorical labels to boost standard retrieval augmented generation (RAG) with pretrained large language models (LLMs). We study our method in the context of radiology report generation (RRG), where the task is to generate a clinician's report detailing their observations from a set of radiological images, such as X-rays. We argue that simple linear classifiers over extracted image embeddings can effectively transform X-rays into text-space as radiology-specific labels. In combination with standard RAG, we show that these derived text labels can be used with general-domain LLMs to generate radiology reports. Without ever training our generative language model or image feature encoder models, and without ever directly "showing" the LLM an X-ray, we demonstrate that LaB-RAG achieves better results across natural language and radiology language metrics compared with other retrieval-based RRG methods, while attaining competitive results compared to other fine-tuned vision-language RRG models. We further present results of our experiments with various components of LaB-RAG to better understand our method. Finally, we critique the use of a popular RRG metric, arguing it is possible to artificially inflate its results without true data-leakage. Our code is available at: https://***/uccdis/label-boosted-RAG-for-RRG. © 2024, CC BY.

关键词： Visual languages

HAR-CTGAN: A Mobile Sensor data Generation Tool for Human Activity Recognition

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HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Ac...

IEEE International Conference on Big data

作者： Joshua DeOliveira Walter Gerych Aruzhan Koshkarova Elke Rundensteiner Emmanuel Agu Data Science Program Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665480468

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features — features that are represented by real numbers — recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves.

关键词： Training Medical services Big data Generative adversarial networks data models Stability analysis Human activity recognition

Text Simplification for Children: Evaluating LLMs vis-à-vis Human Experts

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Text Simplification for Children: Evaluating LLMs vis-à-vis...

2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Smirnova, Anastasia Chun, Kyu Beom Rothman, Wil Louis Sarma, Siyona Department of English Language and Literature Linguistics Program San Francisco State University San Francisco CA United States University of California Berkeley Berkeley CA United States College of Computing Data Science and Society University of California Berkeley Berkeley CA United States

ISBN: (纸本)9798400713958

Large Language Models (LLMs) can facilitate teaching and learning by generating educational content for specific grade levels. Generation of age and grade-appropriate materials often involves text simplification. Previous work evaluating LLMs’ text simplification for children in educational context showed that LLMs can reduce lexical complexity of a text and improve its readability, but they often use vocabulary that is still too difficult for the targeted grade. In this study we focus on GPT-4o, the most advanced LLM at the time of writing. We evaluate its ability to simplify text for elementary school children and compare its performance vis-à-vis the human baseline. We show that GPT-4o can successfully reduce text complexity on lexical, syntactic, and discourse levels. However, compared to human experts, it generates syntactically more complex text. This suggests that further fine-tuning with focus on syntax is needed before LLM-generated output reaches the intended user group. © 2025 Copyright held by the owner/author(s).

关键词： Children GPT-4o LLMs Syntactic Complexity Text Simplification