检索结果-内蒙古大学图书馆

Proceedings of the CHI Conference on Human Factors in computing Systems

作者： Zaidat Ibrahim Pallavi Panchpor Novia Nurain James Clawson Luddy School of Informatics Computing & Engineering Indiana University United States Indiana University United States The Luddy School of Informatics Computing and Engineering Indiana University United States

ISBN: (纸本)9798400703300

The widespread adoption of menstrual tracking applications has garnered much attention with recent research focusing on inclusive design. However, existing literature has yet to explore the impact of religious practices on menstrual tracking behavior. We investigate the menstrual tracking practices of Muslim women of faith in the United States, a population whose personal reproductive health behaviors are deeply influenced by their faith, values, and religious laws We conducted a three-phase study consisting of preliminary surveys (N=133), semi-structured interviews (N=20), and a post-Roe v. Wade survey (N=77). We highlight motivations for tracking and uncover this overlooked population’s challenges as they engage with menstrual tracking technologies. We reveal an intimate connection between menstrual tracking and religious practices. We uncover challenges from engaging with existing menstrual tracking applications and contribute design recommendations for accommodating faith in the design of health tracking technologies. We amplify a call to action for the HCI community to reduce the "othering" of under-represented populations and to better support the inclusive design of technologies that center religious identities and values for individuals of faith.

关键词： Menstruation Muslim Women Personal informatics Women’s Health

来源：评论

学校读者我要写书评

暂无评论

User Experience (UX) meets Artificial Intelligence (AI) - Designing Engaging User Experiences Through 'Automation of Interaction'

User Experience (UX) meets Artificial Intelligence (AI) - De...

引用

2022 Workshop on Engaging with Automation - Understanding and Designing for Operation, Appropriation, and Behaviour Change, AutomationXP 2022

作者： Wiberg, Mikael Bergqvist, Erik Stolterman Umeå University Department of Informatics Umeå Sweden Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

Interaction design/HCI seems to be at a crossroad. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human-machine interaction through AI. In this paper, we explore this seeming gap to move forward. First, we discuss the fundamental design rationality underpinning UX/engaging interaction and AI/automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we rely on two classic examples, i.e., an autonomous car and a social media platform. Through an interaction analysis of these two examples, we show 1) how interaction and automation are combined in the design, 2) how engaging interaction is dependent on a certain level of automation, and 3) how each example illustrates a different balance between interaction and automation. Based on this analysis, we propose the Engaging Interaction through Automation (EIA) - scale as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed EIA-scale, discuss its theoretical implications, and suggest it as a useful tool - when designing for engaging user experiences (UX) through automation, with AI as an interaction design material. Copyright © 2022 for this paper by its authors.

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

ScaDLES: Scalable Deep Learning over Streaming data at the Edge

ScaDLES: Scalable Deep Learning over Streaming data at the E...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

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

ISBN: (纸本)9781665480451

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, a s w ell a s 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. © 2022 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Social informatics Perspectives on Emerging Technologies: The Way Forward

引用

Proceedings of the Association for Information Science and Technology 2023年第1期60卷 785-788页

作者： Hara, Noriko Fichman, Pnina Chae, Seung Woo Meyer, Eric Rosenbaum, Howard Sawyer, Steve Yang, Shengnan Zhu, Xiaohua Awa Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information University of Texas at Austin United States School of Information Studies Syracuse University United States University of Western Ontario Canada School of Information Sciences University of Tennessee Knoxville United States

Early social informatics research focused primarily on ethnographic, site-specific observations within organizations and was based on smaller case studies. The rising of social media and big data availability have made large-scale data analysis accessible and easier. This has informed social informatics perspectives by examining the roles and impacts of social media in our work and social lives. The panel aims to utilize principles of social informatics approach to understand emerging issues related to social media, which are pervasive in almost every aspect of our daily lives, and to Information and Communication Technologies (ICTs) more broadly. To push social informatics research forward, the panelists will address the questions regarding the future of social informatics. 86 Annual Meeting of the Association for Information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing 2

Exploring Quantum Processors in Decentralized Edge Computing...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Pitkar, Harshad Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798331505745

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems. © 2025 IEEE.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Advancing Breast Cancer Diagnosis:The Development and Validation of the HERA-Net Model for Thermographic Analysis

引用

Computers, Materials & Continua 2024年第12期81卷 3731-3760页

作者： S.Ramacharan Martin Margala Amjan Shaik Prasun Chakrabarti Tulika Chakrabarti School of Computing and Informatics University of LouisianaLafayetteLA 70504USA Computer Science and Engineering St.Peter’s Engineering CollegeHyderabad500100India Director Research and Dean International Affairs Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia Department of Basic Sciences Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia

Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal *** thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 *** dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and *** prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for *** performance assessme

关键词： Cancer breast cancer detection sensitivity deep learning accuracy

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligent Player for Bullet Hell Games Based on Deep Q-Networks 23

Artificial Intelligent Player for Bullet Hell Games Based on...

引用

Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application

作者： Dongxin Tian Luddy School of Informatics Computing and Engineering Indiana University United States

ISBN: (纸本)9798400709449

This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. The trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. These challenges may provide directions for future research.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SePaint: Semantic Map Inpainting via Multinomial Diffusion

SePaint: Semantic Map Inpainting via Multinomial Diffusion

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zheng Chen Deepak Duggirala David Crandall Lei Jiang Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution. In this work, we consider the inpainting of semantic Bird’s-Eye-View maps. We propose SePaint, an inpainting model for semantic data based on generative multinomial diffusion. To maintain semantic consistency, we need to condition the prediction for the missing regions on the known regions. We propose a novel and efficient condition strategy, Look-Back Condition (LB-Con), which performs one-step look-back operations during the reverse diffusion process. By doing so, we are able to strengthen the harmonization between unknown and known parts, leading to better completion performance. We have conducted extensive experiments on different datasets, showing our proposed model outperforms commonly used interpolation methods in various robotic applications.

关键词： Interpolation Navigation Semantics Diffusion processes Robot sensing systems Data models Sensors Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Utilizing ETL Processes to Enhance Healthcare Education: Migrating MIMIC-IV to OpenEMR

Utilizing ETL Processes to Enhance Healthcare Education: Mig...

引用

Annual IEEE Symposium on Computer-Based Medical Systems

作者： Mohith Surya Kiran Kasula Sri Harsha Sudalagunta Keerthika Sunchu Saptarshi Purkayastha Luddy School of Informatics Computing and Engineering Indiana University Indianapolis Indianapolis IN

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

The integration of real-world clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database into the Open Electronic Medical Records (OpenEMR) system presents a unique opportunity to develop and implement innovative Extract, Transform, and Load (ETL) methodologies. This study focuses on the complex process of migrating 40,000 patient records from 33 tables in MIMIC-IV to the intricate schema of OpenEMR, which comprises 264 tables. The primary objective is to outline the novel approaches employed in the ETL process to ensure data fidelity, integrity, and usability within OpenEMR’s frontend interface. The methodology involves a three-stage process: extraction, transformation, and loading. During the extraction phase, relevant data is carefully selected from MIMIC-IV, taking into account data privacy considerations. The transformation phase involves intricate data mapping and manipulation to align the extracted data with OpenEMR’s schema, addressing challenges such as schema mismatches and data format inconsistencies. Finally, the loading phase ensures the accurate and efficient population of OpenEMR with the transformed data. The ETL process is designed to maintain data quality and integrity throughout the migration, employing robust validation and error-handling mechanisms. This study contributes to the advancement of ETL methodologies in the context of integrating real-world clinical data into electronic medical record systems. The detailed description of the innovative approaches employed in this project might serve as a valuable resource for researchers and practitioners working on similar data migration tasks.

关键词： Data privacy Databases MIMICs Education Loading Medical services Transforms

来源：评论

学校读者我要写书评

暂无评论

Visual-Geometry GP-based Navigable Space for Autonomous Navigation

Visual-Geometry GP-based Navigable Space for Autonomous Navi...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mahmoud Ali Durgkant Pushp Zheng Chen Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous navigation in unknown environments is challenging and requires the consideration of both geometric and semantic information to assess the navigability of the environment. In this work, we propose a novel space modeling framework, Visual-Geometry Sparse Gaussian Process (VG-SGP), that simultaneously considers the semantics and geometry of the scene. Our proposed approach can overcome the limitation of visual planners that fail to recognize geometry associated with the semantic and the geometric planners that completely overlook the semantic information which is very critical in real-world navigation. The proposed method leverages dual Sparse Gaussian Processes in an integrated manner; the first is trained to forecast geometrically navigable spaces while the second predicts the semantically navigable areas. This integrated model is able to pinpoint the overlapping (geometric and semantic) navigable space. The simulation and real-world experiments demonstrate that the proposed VG-SGP model, coupled with our innovative navigation strategy, outperforms models solely reliant on visual or geometric navigation algorithms, highlighting a superior adaptive behavior. We provided a demonstration video 1 and open-sourced our code 2 .

关键词： Geometry Visualization Adaptation models Navigation Semantics Gaussian processes Prediction algorithms Reliability Intelligent robots Autonomous robots

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：