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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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A Spark-Based Open Source Framework for Large-Scale Parallel Processing of Rich Text Documents 8

A Spark-Based Open Source Framework for Large-Scale Parallel...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Chen, Qiang Chen, Yinong Wu, Sheng Zhang, Zili College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781665425742

The large amount of unstructured educational data is an important data source of educational big data analysis. However, there is a lack of efficient and simple distributed solutions to process these unstructured documents. To address this problem, this study proposes a Spark-based parallel processing framework of large-scale rich text documents (mainly as MS Word documents), which can seamlessly to extend the serial open-source algorithm to distributed computing algorithm to accomplish parallel processing of large-scale rich text documents. We focus on how to achieve non-intrusive deployment of clusters and how Python codes access to HDFS files. The experimental results show that by using 8 physical nodes (with 128 cores all together), the speedup reaches 48 times compared to the standalone Python-docx in the experiments of extracting 232 GB docx files. The experiments also analyze the Spark parameters and explore how to optimize the parameter settings. © 2021 IEEE.

关键词： Big data

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Real-Time Tracking of Diagnostic Discrepancies in Electronic Health Records for Improved Predictive Modeling

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IEEE Access 2025年 13卷 101841-101851页

作者： Trader, Elizabeth A. Gurupur, Varadraj P. University of Central Florida Department of Electrical and Computer Engineering FL United States University of Central Florida Center for Decision Support Systems and Informatics FL United States University of Central Florida School of Global Health Management and Informatics FL United States

Diagnostic discrepancies, a persistent threat in healthcare, compromise patient outcomes and hinder effective treatment. Despite their prevalence, addressing diagnostic discrepancies remains a complex challenge due to the limitations of traditional methods and fragmented and inaccessible data. The emergence of machine learning (ML) offers a promising solution by analyzing vast medical datasets to detect subtle disease signatures and improve treatment personalization with unparalleled precision. However, data quality and availability remain significant barriers, as incompleteness, inconsistencies, and biases can distort ML models, leading to potentially harmful misinterpretations. This paper highlights the critical role of diagnostic discrepancy statistics in overcoming these challenges and proposes a standardized data schema that ensures interoperable, real-time, and historical data without compromising patient privacy. By addressing the current lack of accessible diagnostic discrepancy data, the schema lays a foundation for more effective analysis, better patient outcomes, and improved use of machine learning in healthcare. The key contribution of this paper is the framework that allows for increased data transparency and improved data quality. © 2013 IEEE.

关键词： Electronic health record

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of Computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Neural embedding of beliefs reveals the role of relative dissonance in human decision-making

arXiv

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arXiv 2024年

作者： Lee, Byunghwee Aiyappa, Rachith Ahn, Yong-Yeol Kwak, Haewoon An, Jisun Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Beliefs serve as the foundation for human cognition and decision-making. They guide individuals in deriving meaning from their lives, shaping their behaviors, and forming social connections. Therefore, a model that encapsulates beliefs and their interrelationships is crucial for quantitatively studying the influence of beliefs on our actions. Despite its importance, research on the interplay between human beliefs has often been limited to a small set of beliefs pertaining to specific issues, with a heavy reliance on surveys or experiments. Here, we propose a method for extracting nuanced relations between thousands of beliefs by leveraging large-scale user participation data from an online debate platform and mapping these beliefs to an embedding space using a fine-tuned large language model (LLM). This belief embedding space effectively encapsulates the interconnectedness of diverse beliefs as well as polarization across various social issues. We discover that the positions within this belief space predict new beliefs of individuals. Furthermore, we find that the relative distance between one's existing beliefs and new beliefs can serve as a quantitative estimate of cognitive dissonance, allowing us to predict new beliefs. Our study highlights how modern LLMs, when combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation and decision-making processes. © 2024, CC BY.

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Shortest-path percolation on random networks

arXiv

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arXiv 2024年

作者： Kim, Minsuk Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

We propose a bond-percolation model intended to describe the consumption, and eventual exhaustion, of resources in transport networks. Edges forming minimum-length paths connecting demanded origin-destination nodes are removed if below a certain budget. As pairs of nodes are demanded and edges are removed, the macroscopic connected component of the graph disappears, i.e., the graph undergoes a percolation transition. Here, we study such a shortest-path-percolation transition in homogeneous random graphs where pairs of demanded origin-destination nodes are randomly generated, and fully characterize it by means of finite-size scaling analysis. If budget is finite, the transition is identical to the one of ordinary percolation, where a single giant cluster shrinks as edges are removed from the graph;for infinite budget, the transition becomes more abrupt than the one of ordinary percolation, being characterized by the sudden fragmentation of the giant connected component into a multitude of clusters of similar size. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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ScaDLES: Scalable Deep Learning over Streaming data at the Edge

arXiv

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arXiv 2023年

作者： Tyagi, Sahil Swany, Martin Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. computing on the edge suffers from both systems and statistical heterogeneity. systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD. © 2023, CC BY.

关键词： Stochastic systems

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Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous decisions

Partitioning and Gaussian Processes for Accelerating Samplin...

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2021 Winter Simulation Conference, WSC 2021

作者： Liu, Menghan Pedrielli, Giulia Cao, Yumeng Arizona State University School of Computing Informatics Decision Systems Engineering Tempe85281 United States Arizona State University School of Mathematical and Statistical Sciences Tempe85281 United States

ISBN: (纸本)9781665433112

We propose Part-MCTS for sampling continuous decisions at each stage of a Monte Carlo Tree Search algorithm. At each MCTS stage, Part-MCTS sequentially partitions the decision space and keeps a collection of Gaussian processes to describe the landscape of the objective function. A classification criteria based on the estimation of the minimum allows us to focus the attention on regions with better predicted behavior, reducing the evaluation effort elsewhere. Within each subregion, we can use any sampling distribution, and we propose to sample using Bayesian optimization. We compare our approach to KR-UCT (Yee et al. 2016) as state of the art competitor. Part-MCTS achieves better accuracy over a set of nonlinear test functions, and it has the ability to identify multiple promising solutions in a single run. This can be important when multiple solutions from a stage can be preserved and expanded at subsequent stages. © 2021 IEEE.

关键词： Gaussian noise (electronic)

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Can Large Language Models Effectively Mitigate Polarization in Social Media Text? 25

Can Large Language Models Effectively Mitigate Polarization ...

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Proceedings of the 17th ACM Web Science Conference 2025

作者： Lucas Raniére Juvino Santos Leandro Balby Marinho Claudio Elizio Calazans Campelo Filippo Menczer Alessandro Flammini Systems and Computing Department Federal University of Campina Grande Campina Grande Paraíba Brazil Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA

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Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness 46

Federated Learning for Enhanced ECG Signal Classification wi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Phan, Quoc Bao Nguyen, Linh Bui, Ngoc Thang Nguyen, Dinh C. Zhang, Lan Nguyen, Tuy Tan Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States Mayo Clinic Department of Radiology RochesterMN55905 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350371499

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring. © 2024 IEEE.

关键词： Federated learning

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