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The Mobile Office: A Mobile AR Systems for Productivity Applications in Industrial Environments 13th

The Mobile Office: A Mobile AR Systems for Productivity Appl...

13th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2021, Held as Part of the 23rd HCI International Conference, HCII 2021

作者： Linares Garcia, Daniel Antonio Dongre, Poorvesh Roofigari-Esfahan, Nazila Bowman, Doug A. Department of Building Construction Virginia Tech BlacksburgVA24060 United States Department of Computer Science Virginia Tech BlacksburgVA24060 United States Center for Human-Computer Interaction Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9783030775988

This article proposes the Mobile Office, an AR-Based System for productivity applications in industrial environments. It focuses on addressing workers’ needs while mobilizing in complex environments and when workers want to do productive work similar to a workstation setting. For this purpose, the Mobile Office relies on a bi-modal approach (mobile and static modes) to addresses both scenarios while considering critical aspects such as UI, ergonomics, interaction, safety, and cognitive user abilities. Its development relies first on a systematic literature review for establishing guidelines and system aspects. Then AR-UI mockups are presented to incorporate literature and authors’ expertise in this area. Three hardware configurations are also presented for the implementation of the Mobile Office. Validation of the Mobile Office was made through a survey to potential users that provided their feedback about their perceptions of proposed system. The survey results revealed concordance of critical aspects of particular importance for effective productivity support on the Mobile Office’s focus scenarios. We incorporated these critical aspects and suggested future research direction for the Mobile Office’s further development and significance. © 2021, Springer Nature Switzerland AG.

关键词： Augmented reality

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DP-BART for Privatized Text Rewriting under Local Differential Privacy

arXiv

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arXiv 2023年

作者： Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany

Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protection to individuals. However, existing systems face several issues, such as formal mathematical flaws, unrealistic privacy guarantees, privatization of only individual words, as well as a lack of transparency and reproducibility. In this paper, we propose a new system ‘DP-BART’ that largely outperforms existing LDP systems. Our approach uses a novel clipping method, iterative pruning, and further training of internal representations which drastically reduces the amount of noise required for DP guarantees. We run experiments on five textual datasets of varying sizes, rewriting them at different privacy guarantees and evaluating the rewritten texts on downstream text classification tasks. Finally, we thoroughly discuss the privatized text rewriting approach and its limitations, including the problem of the strict text adjacency constraint in the LDP paradigm that leads to the high noise requirement.1 © 2023, CC BY-SA.

关键词： Privatization

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely Distributed Slimmable Neural Networks

Smart Flood Detection and Communication in Hazardous Zones v...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V. Sathya Shuchi Bhadula R. Srinivasan Bhargav H K Bhavani Sankar Panda Amit Barve Department of Computer Science Periyar Maniammai Institute of Science and Technology (Deemed to be University) Vallam Thanjavur Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tiruvallur Tamil Nadu India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) and Associate Professor Shridevi Institute of Engineering and Technology Tumakuru Karnataka India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Limda Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET.

关键词： Temperature sensors Temperature measurement Accuracy Disasters Neural networks Disaster management Prediction algorithms Transformers Mobile ad hoc networks Load modeling

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How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies

arXiv

引用

arXiv 2025年

作者： Ran, Zihe Fan, Xianzhe Li, Xiyu Li, Franklin Mingzhe Lu, Zhicong Xiao, Qing Wang, Yanyun Communication University of China Beijing China Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science George Mason University FairfaxVA United States College of Media Communication and Information University of Colorado Boulder BoulderCO United States

As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific experiences of these players with mobile games remain underexamined. This study addresses this gap by investigating how BLV users experience mobile games with varying accessibility levels. Through interviews with 32 experienced BLV mobile players, we explore their perceptions, challenges, and strategies for engaging with mobile games. Our findings reveal that BLV players turn to mobile games to alleviate boredom, achieve a sense of accomplishment, and build social connections, but face barriers depending on the game’s accessibility level. We also compare mobile games to other forms of gaming, highlighting the relative advantages of mobile games, such as the inherent accessibility of smartphones. This study contributes to understanding BLV mobile gaming experiences and provides insights for enhancing accessible mobile game design. Copyright © 2025, The Authors. All rights reserved.

关键词： Game design

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Shaping Romance: Mediating Intimacy for Co-located Couples 33

Shaping Romance: Mediating Intimacy for Co-located Couples

引用

33rd Australian computer-human interaction Conference, OzCHI 2021

作者： Ellegaard Christensen, Anne Magnussen, Malene H. Seindal, Tobias S. Raptis, Dimitrios Department of Computer Science Human-Centred Computing Group Aalborg University Denmark

ISBN: (纸本)9781450395984

During recent years multiple studies focused on how to mediate the intimacy of couples over distance by researching various intimacy aspects, such as physical contact and disclosure. At the same time, mediating intimacy for co-located couples remains relatively unexplored. Our paper focuses on this and presents an empirical field study involving 13 co-located couples that interacted with a technology probe titled gShaping Romance'. In short, our qualitative findings show that technology can mediate intimacy by allowing partners to look inwards and reflect on their own desires, look outwards and reflect on the desires of their partner, and look at the whole by remembering, acting and validating. Our contributions to HCI are the technological intervention itself, our findings which highlight limitations and opportunities technology has for mediating the intimacy of co-located couples, and a design space full of dilemmas that we present for future researchers and designers. © 2021 ACM.

关键词： Probes

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Decoding Imagined Speech of Daily Use Words from EEG Signals Using Binary Classification 45th

Decoding Imagined Speech of Daily Use Words from EEG Signa...

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45th Mexican Conference on Biomedical Engineering, CNIB 2022

作者： Gutiérrez-Zermeño, Marianna Aguilera-Rodríguez, Edgar Barajas-González, Emilio Román-Godínez, Israel Torres-Ramos, Sulema Salido-Ruiz, Ricardo A. Translational Bioengineering Department Division of Cyber-Human Integration Technologies University of Guadalajara Jalisco Guadalajara44430 Mexico Department of Innovation Based on Information and Knowledge Division of Cyber-Human Integration Technologies University of Guadalajara Jalisco Guadalajara44430 Mexico Computer Science Department Division of Cyber-Human Integration Technologies University of Guadalajara Jalisco Guadalajara44430 Mexico

ISBN: (纸本)9783031182556

The simplest form of communication between people is done through speech. However, there are situations in which this communication is not possible, hence, there is great interest in decoding imagined speech. To address this problem, this work proposes a method for recognizing imagined speech from electroencephalographic signals applying artificial intelligence models, focusing on covering daily use words. We used the OpenBCI system with a reduced number of channels using dry electrodes and machine learning methods. Mean, variance, skewness, RMS and kurtosis were extracted as characteristics for each channel of the EEG. A Decision Tree and a Support Vector Machine were tested to classify the words via a one-vs-rest approach. The SVM gave the best results for the task of imagined speech classification, with an accuracy of 92.5%, 76.3%, 70% and 70% for "Descansar", "Baño", "Comida" and "Okay" respectively. The Decision Tree presents lower accuracy results, but allows us to identify the decision criteria used for each classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

arXiv

引用

arXiv 2025年

作者： Hejrati, Behzad Banerjee, Soumyanil Glide-Hurst, Carri Dong, Ming Department of Computer Science Wayne State University MI United States Department of Human Oncology University of Wisconsin-Madison WI United States

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://***/Hejrati/cDAL/. © 2025, CC BY.

关键词： Image segmentation

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ADVANCEMENTS IN CROWD-MONITORING SYSTEM: A COMPREHENSIVE ANALYSIS OF SYSTEMATIC APPROACHES AND AUTOMATION ALGORITHMS: STATE-OF-THE-ART

arXiv

引用

arXiv 2023年

作者： Ameen, Mohammed Stone, Richard Department of Human-Computer Interaction Iowa State University Ames United States Industrial and Manufacturing Systems Engineering Department Iowa State University Ames United States

Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts. © 2023, CC0.

关键词： computer vision

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Private Synthetic Text Generation with Diffusion Models

arXiv

引用

arXiv 2024年

作者： Ochs, Sebastian Habernal, Ivan Trustworthy Human Language Technologies 1 Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

How capable are diffusion models of generating synthetics texts? Recent research shows their strengths, with performance reaching that of auto-regressive LLMs. But are they also good in generating synthetic data if the training was under differential privacy? Here the evidence is missing, yet the promises from private image generation look strong. In this paper we address this open question by extensive experiments. At the same time, we critically assess (and reimplement) previous works on synthetic private text generation with LLMs and reveal some unmet assumptions that might have led to violating the differential privacy guarantees. Our results partly contradict previous non-private findings and show that fully open-source LLMs outperform diffusion models in the privacy regime. Our complete source codes, datasets, and experimental setup are publicly available to foster future research1 © 2024, CC BY-SA.

关键词： Differential privacy

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Role of Machine Learning in Managing Cloud Computing Security 2

Role of Machine Learning in Managing Cloud Computing Securit...

引用

2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Kumar, Santosh Nithya, S. Prethi, K.N Apinaya Singh, Suraj Lourens, Melanie Patil, Nisha Computer Science and Engineering Dr. Apj Abdul Kalam Women's Institute of Technology Darbhanga India Coimbatore Institute of Technology Department of Computer Science and Engineering India Cit Department of CSE India Mullana Electrical Engineering Department Ambala India Durban University of Technology Department of Human Resources Management South Africa School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9781665437899

Cloud computing is one of the most trending technology through which digitalized data management and storing becomes easier and more effective. However, besides the advancement of technology, data protection is another top priority concerning factor as millions of sensitive data are gets stored and transferred through the cloud computing system. As per the current days of cyber-activities, data security threat in cloud computing have increased, which posses threat in the development of the business. In that field, machine learning based advanced algorithm develop the virtual encrypted environment to protect the data from unauthorized access and hacking. © 2022 IEEE.

关键词： Cryptography

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