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Proceedings of the 32nd Australian Conference on Human-computer Interaction

作者： Deepti Aggarwal Thuong Hoang Bernd Ploderer Frank Vetere Rohit Ashok Khot Mark Bradford HAFP Research Lab RMIT University Australia School of Information Technology Deakin University Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia Interaction Design Lab School of Computing and Information Systems The University of Melbourne Australia Dept. of Anaesthesia and Pain Management Royal Children's Hospital Australia

ISBN: (纸本)9781450389754

In this paper, we describe the design journey of a smart clothing system, SoPhy from the research laboratory to finally being evaluated in the hospital setting. SoPhy is a smart socks-based system, designed to make physiotherapy video consultations effective for assessing lower limb issues. SoPhy is the result of a 3-year journey of development, study and refinement of a research prototype done in collaboration with a physiotherapist. Drawing on this journey, we present seven lessons that emphasize on the importance of fostering strong collaborations with clinicians to move beyond laboratory studies and reach the target health setting. We provide contextual narratives on how we designed a comfortable smart clothing for patients; how we created an intuitive mapping of sensor data for clinicians; how we integrated hospital practice in the system design, and how we managed the ethics clearance for the field evaluation. We hope that these lessons are useful for HCI and health researchers who aim to innovate the field of health but feel restricted with the little knowledge of design and electronics.

关键词： Telehealth Physiotherapy Video Consultations Smart Clothing Digital health

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A temporal logic-based hierarchical network connectivity controller

arXiv

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arXiv 2020年

作者： Riess, Hans Kantaros, Yiannis Pappas, George Ghrist, Robert University of Pennsylvania Dept. of Electrical/Systems Engineering United States U. Penn. Dept. of Electrical/Systems Eng. Dept. of Computer and Information Science United States U. Penn. Dept. of Electrical/Systems Engineering United States U. Penn. Dept. of Electrical/Systems Eng. Dept. of Mathematics United States

In this paper, we consider networks of static sensors with integrated sensing and communication capabilities. The goal of the sensors is to propagate their collected information to every other agent in the network and possibly a human operator. Such a task requires constant communication among all agents which may result in collisions and congestion in wireless communication. To mitigate this issue, we impose locally non-interfering connectivity constraints that must be respected by every agent. We show that these constraints along with the requirement of propagating information in the network can be captured by a Linear Temporal Logic (LTL) framework. Existing temporal logic control synthesis algorithms can be used to design correct-by-construction communication schedules that satisfy the considered LTL formula. Nevertheless, such approaches are centralized and scale poorly with the size of the network. We propose a hierarchical LTL-based algorithm that designs communication schedules that determine which agents should communicate while maximizing network usage. We show that the proposed algorithm is complete and demonstrate its efficiency and scalability through analysis and numerical *** Codes 03B44, 93B70, 68M18, 68W15, 68Q85 Copyright © 2020, The Authors. All rights reserved.

关键词： Temporal logic

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A Collaborative Multi-Metric Interface Ranking Scheme for Named Data Networks 16

A Collaborative Multi-Metric Interface Ranking Scheme for Na...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Nour, Boubakr Khelifi, Hakima Hussain, Rasheed Moungla, Hassine Bouk, Safdar H. School of Computer Science Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China Institute of Information Security and Cyber-Physical Systems Innopolis University Russia Umr 5157 Cnrs Institute Mines Telecom Telecom SudParis Nano-Innov Cea Saclay France Dept. of Info. and Comm. Engineering Korea Republic of

ISBN: (纸本)9781728131290

Named Data Networking (NDN) uses the content name to enable content sharing in a network using Interest and Data messages. In essence, NDN supports communication through multiple interfaces, therefore, it is imperative to think of the interface that better meets the communication requirements of the application. The current interface ranking is based on single static metric such as minimum number of hops, maximum satisfaction rate, or minimum network delay. However, this ranking may adversely affect the network performance. To fill the gap, in this paper, we propose a new multi-metric robust interface ranking scheme that combines multiple metrics with different objective functions. Furthermore, we also introduce different forwarding modes to handle the forwarding decision according to the available ranked interfaces. Extensive simulation experiments demonstrate that the proposed scheme selects the best and suitable forwarding interface to deliver content. © 2020 IEEE.

关键词： Content-Centric Networking (CCN) Data Forwarding Interface ranking Named-Data Networking (NDN)

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Modified indexing algorithm based on priority queue in metric space for MVP tree 4

Modified indexing algorithm based on priority queue in metri...

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4th International Conference on Future Networks and Distributed systems. ICFNDS 2020

作者： Fomin, Vladimir Aleksandrov, Igor Gallyamov, Denis Kirichek, Ruslan Dept. of Information Systems Herzen State Pedagogical University of Russia Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781450388863

The Internet of Things (IoT) algorithms process huge amounts of heterogeneous data in real-time. One of the most computationally intensive tasks using cloud technologies is the task of clustering and classifying data. The authors propose to develop an approach to data classification within the "Query by Similarity"paradigm, which uses the technology of data indexing based on Metric Access Methods (MAM). To improve the performance of data indexing, this paper proposes a similar nearest neighbor search method combining a multiple vantage point tree (MVP) and improved algorithms for processing the priority queue of nodes. The following two algorithms for processing the priority queue of nodes were developed: 1) algorithm for all kinds of points-queries, which makes it possible to take into account parent nodes of all higher levels;2) algorithm for grouped based on clustering of points-queries by reusing previously obtained search results. Experimental results confirm the effectiveness of the proposed approaches and algorithms. © 2020 ACM.

关键词： Nearest neighbor search

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Spatio-temporal Parking behaviour forecasting and analysis before and during COVID-19

arXiv

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arXiv 2021年

作者： Gong, Shuhui Mo, Xiaopeng Cao, Rui Liu, Yu Tu, Wei Bai, Ruibin School of Information Engineering China University of Geosciences Beijing Beijing China School of Computer Science University of Nottingham Ningbo China Ningbo China Dept. of LSGI & SCRI The Hong Kong Polytechnic University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China GD Key Lab. of Urban Informatics Dept. of Urban Informatics Shenzhen University Shenzhen China

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. © 2021, CC BY-NC-ND.

关键词： Forecasting

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IoT Security Based on Iris Verification Using Multi-Algorithm Feature Level Fusion Scheme 2

IoT Security Based on Iris Verification Using Multi-Algorith...

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2nd International Conference on computer Applications and information Security, ICCAIS 2019

作者： Gad, Ramadan Abd El-Latif, Ahmed A. Elseuofi, Sherif Ibrahim, Hany M Elmezain, Mahmoud Said, Wael Faculty of Electronic Engineering Computer Science and Engineering Dept. Menoufia University Egypt Faculty of Science Department of Mathematics and Computer Science Menoufia University 32511 Egypt Computer Science Department Higher Institute for Computer Science and Information Systems Culture Science City 6 October Egypt Faculty of Science and Computer Engineering Taibah University Saudi Arabia Computer Science Division Faculty of Science Tanta University Tanta Egypt Computer Science Department Faculty of Computers and Information Zagazig University Egypt

ISBN: (纸本)9781728101088

Internet of Things (IoT) have very big opportunities in businesses and life, especially Internet Banking, Healthcare and Transportation. But developers face challenges in security especially for sensitive data to use IoT safely with Internet Banking. In this paper, a secure IoT connection between clients and broker server based on iris recognition system (Authentication server) will be proposed. The proposed iris recognition system will communicate with MQTT broker server to increase authentication instead of using normal text method (username/password). The proposed authentication server in this paper is to use iris as uni-modal biometric trait, by applying different scenarios of multi-biometrics other than the multimodal, to develop and implement iris-based recognition system. In feature extraction phase, the fusion between vectors of Delta-Mean (DM) and Multi-Algorithm-Mean (MAM) executed in a parallel mode. Then, followed by the proposed reduction method, reduced the feature vector size keeping higher performance. Finally, classification is adopted by using Euclidian Distance (ED) classifier. It is suitable for secure MQTT connection message to authenticate right client in sensitive application like bank transfer operations. © 2019 IEEE.

关键词： Internet of things

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Evaluation and Analysis of Service Broker Algorithms in Cloud-Analyst

Evaluation and Analysis of Service Broker Algorithms in Clou...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Almothana Khodar Vladimir E. Mager Iyad Alkhayat Faten Ahmed Jebur Al-Soudani Elena N. Desyatirikova Dept. of Computer Science Voronezh State University Voronezh Russia Highest School of Cyberphysic Control Systems Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Dept. of Information Systems and Networks Damascus University Damascus Syria Dept. of Computer Systems and Technologies Middle Technical University Baghdad Iraq Dept. of Control Systems and IT Voronezh State Technical University Voronezh Russia

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

Cloud computing is a reasonably distributed computing with heterogeneous computing resources. Resource sharing, convenience of resources that correspond to the needs and a lot of box-management issues facing the cloud. Here, during this paper, analysis and comparison of varied existing algorithms of service brokers and load equalization algorithms in cloud computing . To check the implementation of existing algorithms, varied modeling tools were developed, for instance, a Cloud Analyst. The performance of the present policies of the service broker is compared by considering total response time and execution time in data center.

关键词： Data centers Cloud computing Time factors Virtual machining Heuristic algorithms Tools

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Detecting personal protective equipment (PPE) utilising YOLOv8 in a federated learning environment

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AIP Conference Proceedings 2024年第1期3220卷

作者： Ioannis Makris Anastasios Lytos Konstantinos Kyranou Vasileios Argyriou Thomas Lagkas Konstantinos-Filippos Kollias George F. Fragoulis Panagiotis Sarigianndis METAMIND INNOVATIONS IKE KILA - Administration Building of Univ. of Western Macedonia Kozani Greece SIDROCO Holdings LTD Karyatis 8 Leoforo Kyriakou Matsi 23 4th Floor Office 401 Nicosia Cyprus Dept. of Computing & Information Systems Penrhyn Road Kingston Univ. London London United Kingdom Dept. of Computer Science School of Science International Hellenic University Kavala Campus Kavala Greece Dept. of Electrical And Computer Engineering ZEP Campus University of Western Macedonia Kozani Greece

In the construction industry, where worker safety is a paramount concern, the effective use of PPE is crucial. This study delves into an innovative approach by exploring the integration of Federated Learning (FL) with the YOLOv8 architecture for enhanced PPE object detection. A specialized construction safety dataset was utilized to conduct the corresponding experiments, comparing FL against conventional local training methodologies. The results have clearly shown the superiority of FL in terms of accuracy improvement of PPE object detection which opens a lot of paths for creative solutions leading to the progress of safety performance at construction sites, at the same time, while observing the strictest privacy and security issues for personal data that is a primary consideration in our data-driven era. The proposed solution achieved an average mAP of 79.52%, and an average Recall of 78.49%, proving its effectiveness over the centralized training.

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