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2019 IEEE Global Conference on Internet of Things, GCIoT 2019

作者： Hilal, Amr Khalil, Marwan Salman, Ahmad El-Tawab, Samy Informatics Lab Department of Electrical and Computer Engineering United States Virginia Tech Department of Electrical and Computer Engineering United States College of Integrated Science and Engineering James Madison University United States Department of Computer and Systems Engineering Alexandria University Egypt

ISBN: (数字)9781728148731

ISBN: (纸本)9781728148731

Indoor localization has been gradually improving over the past decade, utilizing emerging and proliferating wireless technologies. Since its inception, WiFi technology has been increasingly penetrating indoor environments, residential, and nonresidential. Becoming ubiquitous, WiFi was considered a fertile subject for use in indoor localization utilizing the wireless access points that predominantly provide network coverage in indoor environments. As the Internet of Things (IoT) technology is speeding towards pervasiveness, IoT devices can offer substantial aid to improve accuracy, which is at the heart of the indoor localization process. In this paper, we provide a preliminary study of the feasibility of using WiFi-enabled devices, including IoT devices, to improve location signature in indoor localization besides the traditionally used wireless access points. Through field experiments, we provide insights into the coverage and penetration of such devices in different domains characterized by the activities exercised in them. Our conclusions highlight the challenges of incorporating such devices into the fingerprint-based indoor localization. Additionally, we focus on the use of machine learning approaches that recently gained momentum in indoor localization research. © 2019 IEEE.

关键词： Internet of things

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Preface

Synthesis Lectures on Computer Vision

引用

Synthesis Lectures on computer Vision 2023年 Part F825卷 v-vi页

作者： Wan, Jun Escalera, Sergio Li, Stan Z. Guo, Guodong Escalante, Hugo Jair State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Mathematics and Informatics University of Barcelona and Computer Vision Center Barcelona Spain AI Lab Westlake University Hangzhou China Department of Computer Science and Electrical Engineering West Virginia University Morgantown China Department of Computer Science Instituto Nacional de Astrofísica Optica y Electrónica Puebla Mexico

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Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses

arXiv

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arXiv 2025年

作者： Kang, Yingkai Kang, Jiawen Wen, Jinbo Zhang, Tao Yang, Zhaohui Niyato, Dusit Zhang, Yan School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University Hangzhou310007 China College of Computing and Data Science Nanyang Technological University Singapore Department of Informatics University of Oslo the Simula Research Laboratory Norway

Vehicular metaverses are an emerging paradigm that merges intelligent transportation systems with virtual spaces, leveraging advanced digital twin and Artificial Intelligence (AI) technologies to seamlessly integrate vehicles, users, and digital environments. In this paradigm, vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities, enabling real-time processing and analysis of multi-modal data to provide users with customized interactive services. Since vehicular AI agents require substantial resources for real-time decision-making, given vehicle mobility and network dynamics conditions, the AI agents are deployed in RoadSide Units (RSUs) with sufficient resources and dynamically migrated among them. However, AI agent migration requires frequent data exchanges, which may expose vehicular metaverses to potential cyber attacks. To this end, we propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling through cooperation between vehicles and RSUs. Additionally, we design a trust evaluation model based on the theory of planned behavior to dynamically quantify the reputation of RSUs, thereby better accommodating the personalized trust preferences of users. We then model the vehicular AI agent migration process as a partially observable markov decision process and develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions. Numerical results demonstrate that the CGDM algorithm significantly outperforms baseline methods in reducing system latency and enhancing robustness against cyber attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Natural-language scene descriptions for accessible non-visual museum exhibit exploration and engagement 2

Natural-language scene descriptions for accessible non-visua...

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2nd Workshop on Speaking of Location: Communicating about Space, SoL 2019

作者： Doore, Stacy A. Sarrazin, Anais C. Giudice, Nicholas A. Department of Computer Science Bowdoin College BrunswickME United States Virtual Environment and Multimodal Interaction Lab Department of Spatial Informatics University of Maine OronoME United States

Indoor navigation and exploration of museum environments present unique challenges for visitors who are blind or have significant vision impairments (BVI). Like other indoor spaces, museums represent dynamic indoor environments that requires the need for both guided and self-tour experiences to allow for BVI visitor independence. In order to fully engage with a museum and its exhibits, BVI visitors need assistive technologies that support natural-language (NL) spatial descriptions that provide flexibility in the way system users receive descriptive information about gallery scenes and exhibit objects. In addition, the user interface must be connected to a robust database of spatial information to interact with mobile device tracking data and user queries. This paper describes the results of an early-stage demonstration project that utilizes an existing graph database model to support a NL information access and art gallery exploration system. Specifically, we investigated using a commercially available voice assistant interface to support NL descriptions of a gallery space and the art objects within it. Future work involves refining the language structures for scene and object descriptions, the integration of the voice assistant interface with tracking and navigation technologies, and additional user testing with sighted and BVI museum visitors. Copyright © 2019 for this paper by its authors.

关键词： Museums

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Hydrometeorological Network of Namal Valley, Pakistan

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Scientific Data 2025年第1期12卷 1-10页

作者： Muhammad Sheraz Talha Manzoor Malik Jahan Khan Usman Ali Hamza Tariq Shabeh ul Hasson Centre for Water Informatics and Technology Lahore University of Management Sciences (LUMS) Lahore Pakistan Department of Computer Science Lahore University of Management Sciences (LUMS) Lahore Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Faculty of Computing Engineering and Media De Montfort University Leicester LE1 9BH UK HAREME Lab Institute of Geography CEN Universität Hamburg Bundesstrasse 55 Hamburg 20146 Germany

This article describes data collected from a sensor network deployed in the catchment of The Namal Lake, situated in Mianwali district, Pakistan. The lake serves as a reservoir for the Namal Dam. The sensor network consists of 14 unique observation points in a watershed spanning a 425 sq km area. The data includes measurements of precipitation at a resolution of 0.246 mm at all sensor locations and measurements of water level at a 1 mm resolution at 7 locations (including the lake itself). The first sensor installation was made in November 2020 with the last one deployed in July 2022. The associated time series for both variables is generated at a 10-minute sampling frequency. Multiple aggregations of the dataset have been made available at hourly, daily, and monthly timescales. The data can be used for calibrating hydrological models for various applications such as flood prediction, watershed management, and decision-making for reservoir operation. The dataset also contributes to understanding the hydrometeorological processes in similar small catchments at the Monsoon Margin.

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Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions 1

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4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020

作者： Longo, Luca Goebel, Randy Lecue, Freddy Kieseberg, Peter Holzinger, Andreas School of Computer Science Technological University Dublin Dublin Ireland xAI-Lab Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Inria Sophia Antipolis Biot France Thales Montreal Canada JRC Blockchains University of Applied Sciences St. Pölten Sankt Pölten Austria Human-Centered AI Lab Institute for Medical Informatics Statistics and Documentation Medical University Graz Graz Austria

ISBN: (数字)9783030573218

ISBN: (纸本)9783030573201

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward. © 2020, IFIP International Federation for Information Processing.

关键词： Forecasting

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1xN Pattern for Pruning Convolutional Neural Networks

arXiv

引用

arXiv 2021年

作者： Lin, Mingbao Zhang, Yuxin Li, Yuchao Chen, Bohong Chao, Fei Wang, Mengdi Li, Shen Tian, Yonghong Ji, Rongrong The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Tencent Youtu Lab Shanghai200233 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Alibaba Group China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as well as achieve significant speedups on general CPUs. In this paper, we propose a novel 1×N pruning pattern to break this limitation. In particular, consecutive N output kernels with the same input channel index are grouped into one block, which serves as a basic pruning granularity of our pruning pattern. Our 1×N pattern prunes these blocks considered unimportant. We also provide a workflow of filter rearrangement that first rearranges the weight matrix in the output channel dimension to derive more influential blocks for accuracy improvements and then applies similar rearrangement to the next-layer weights in the input channel dimension to ensure correct convolutional operations. Moreover, the output computation after our 1×N pruning can be realized via a parallelized block-wise vectorized operation, leading to significant speedups on general CPUs. The efficacy of our pruning pattern is proved with experiments on ILSVRC-2012. For example, given the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Copyright © 2021, The Authors. All rights reserved.

关键词： Program processors

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Identification of altitude profiles in 3D geovisualizations:the role of interaction and spatial abilities

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International Journal of Digital Earth 2019年第2期12卷 156-172页

作者： Petr Kubíček Čeněk Šašinka Zdeněk Stachoň Lukáš Herman Vojtěch Juřík Tomáš Urbánek Jiří Chmelík Department of Geography Faculty of ScienceMasaryk UniversityBrnoCzech Republic HUME Lab Faculty of ArtsMasaryk UniversityBrnoCzech Republic Department of Psychology Faculty of ArtsMasaryk UniversityBrnoCzech Republic Department of Computer Graphics and Design Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

Three-dimensional geovisualizations are currently pushed both by technological development and by the demands of experts in various applied *** the presented empirical study,we compared the features of real 3D(stereoscopic)versus pseudo 3D(monoscopic)geovisualizations in static and interactive digital elevation *** tested 39 high-school students in their ability to identify the correct terrain profile from digital elevation ***’performance was recorded and further analysed with respect to their spatial abilities,which were measured by a psychological mental rotation test and think aloud *** results of the study indicated that the influence of the type of 3D visualization(monoscopic/stereoscopic)on the performance of the users is not clear,the level of navigational interactivity has significant influence on the usability of a particular 3D visualization,and finally no influences of the spatial abilities on the performance of the user within the 3D environment were identified.

关键词： 3D geovisualizations stereoscopic and monoscopic 3D vision spatial abilities level of navigational interactivity

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

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Healthcare Analytics 2024年 6卷 100362-100362页

作者： Ahmed, Rasel Fahad, Nafiz Miah, Md Saef Ullah Hossen, Md. Jakir Morol, Md. Kishor Mahmud, Mufti Mostafizur Rahman, M. Faculty of Science and Technology American International University-Bangladesh Kuratoli Khilkhet Dhaka 1229 Bangladesh Faculty of Information Science and Technology (FIST) Multimedia University Melaka Malaysia Faculty of Engineering and Technology (FET) Multimedia University Jalan Ayer Keroh Lama 75450 Bukit Beruang Melaka Malaysia Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Nottingham NG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Nottingham NG11 8NS United Kingdom Department of Mathematics American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computing and Information Science Cornell University NY United States EliteLab.Ai NY United States DataTech Research and Innovation Learning Lab Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model's effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model's predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation. © 2024 The Authors

关键词： Dementia prediction Early detection Logistic regression Recursive feature elimination Shapley Additive exPlanations (SHAP)

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