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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

arXiv

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arXiv 2023年

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level *** Codes 68T07 © 2023, CC BY-NC-ND.

关键词： eHealth

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A Flexibility-Oriented Optimization Model for Enhancing Economic and Emission Sustainability in a Local Multi-Carrier Energy Community Considering Hydrogen Fuel Station and Power-to-Gas Unit

SSRN

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SSRN 2023年

作者： Dorahaki, Sobhan MollahassaniPour, Mojgan Rashidi Nejad, Masoud Siano, Pierluigi Shafie-Khah, Miadreza Department of Electrical Engineering Shahid Bahonar University of Kerman Kerman Iran Faculty of Electrical and Computer Engineering University of Sistan and Baluchestan Zahedan Iran Department of Management & Innovation Systems University of Salerno Fisciano Italy National University of Science and Technology Politehnica Bucharest Romania School of Technology and Innovations University of Vaasa Vaasa Finland

Hydrogen vehicles (HVs) represent a promising advancement towards a greener and more sustainable world. In this context, Local Multi-Carrier Energy systems (LMCESs) offer intelligent solutions for addressing this opportunity. The LMCES incorporate Power-to-Gas (P2G), Combined Heat and Power (CHP) units, water desalination, electrical, water, hydrogen, and heat energy storages, and renewable energy resources to facilitate the integration of HVs and enhance overall energy efficiency. This paper proposes a probabilistic approach for operating an LMCES, taking into account various energy carriers such as electricity, heat, gas, and water. To simultaneously address economic and emission-related objectives, the lexicographic method as a Multi-Criteria Decision-Making (MCDM) approach is employed. Furthermore, the integration of flexibility commitments imposed by upstream levels into the LMCES is incorporated as a constraint within the optimization problem. Additionally, the objective function accounts for the incentive to encourage cooperation from the LMCES in the flexibility program. Moreover, a scenario-based method is employed to model the uncertainty. The optimization results underscore that increasing flexibility defaults while keeping flexibility prices constant reduces the operational cost of LMCES, but the impact on cost when increasing flexibility prices depends on whether the flexibility default is low or high. © 2023, The Authors. All rights reserved.

关键词： Costs

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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Hard Region Aware Network for Remote Sensing Change Detection

arXiv

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arXiv 2023年

作者： Li, Zhenglai Tang, Chang Liu, Xinwang Hu, Xingchen Li, Xianju Li, Ning Li, Changdong The faculty of data science City University of Macau 00853 China The school of computer China University of Geosciences Wuhan430074 China The school of computer National University of Defense Technology Changsha410073 China The College of Systems Engineering National University of Defense Technology Changsha410073 China Key Laboratory of Target Cognition and Application Technology TCAT Aerospace Information Research Institute Beijing100190 China The faculty of engineering China University of Geosciences Wuhan430074 China

Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. Numerous CD methods have been proposed, and considerable results have been achieved recently. However, detecting changes in hard regions, i.e., the change boundary and irrelevant pseudo changes caused by background clutters, remains difficult for these methods, since they pose equal attention for all regions in bi-temporal images. This paper proposes a novel change detection network, termed as HRANet, which provides accurate change maps via hard region mining. Specifically, an online hard region estimation branch is constructed to model the pixel-wise hard samples, supervised by the error between predicted change maps and corresponding ground truth during the training process. A cross-layer knowledge review module is introduced to distill temporal change information from low-level to high-level features, thereby enhancing the feature representation capabilities. Finally, the hard region aware features extracted from the online hard region estimation branch and multi-level temporal difference features are aggregated into an unified feature representation to improve the accuracy of CD. Experimental results on two benchmark datasets demonstrate the superior performance of HRANet in the CD task. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical remote sensing

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Resource Usage Prediction Algorithms for Optimal Selection of Multimedia Content Delivery Methods

Resource Usage Prediction Algorithms for Optimal Selection o...

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IEEE International Conference on Communications

作者： Yiannos Kryftis Constandinos X. Mavromoustakis Joel J. P. C. Rodrigues George Mastorakis Evangelos Pallis Ciprian Dobre Jordi Mongay Batalla Georgios Kormentzas Department of Computer Science University of Nicosia Department of Informatics Instituto de Telecomunicacoes University of Beira Interior Portugal and University ITMO Department of Informatics Engineering Technological Educational Institute of Crete Faculty of Autonomic Control and Computers University Politehnica of Bucharest National Institute of Telecommunications Department of Information and Communication Systems Engineering University of the Aegean

ISBN: (纸本)9781467364300

This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource prediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.

关键词： Resource Prediction Engine Resource Usage Prediction Algorithms Content Delivery Networks Multimedia Services systems

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Intelligent Hypermedia for the Adaptive Web: Foreword to the SMAP '08 Special Session

Intelligent Hypermedia for the Adaptive Web: Foreword to the...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Ioannis Anagnostopoulos Maria Bielikova Manolis Wallace John W.T. Lee Department of Information Communication Systems Engineering University of Aegean Samos Greece Institute of Informatics and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovicova Bratislava Slovakia Department of Computer Science and Technology University of Peloponnese Tripoli Greece Department of Computing Hong Kong Polytechnic University Hong Kong China

Researchers need to explore and study the plethora of challenges that emergent intelligent hypermedia technologies bring to the Adaptive Web. This special session aims to increase the awareness of researchers in this area, providing an in-depth investigation on several research and deployment issues, regarding the impact of intelligent methods on this new form of Web. In addition, this special session examines the performance characteristics of various approaches in Web-based systems, while it focuses on the development of intelligent methods among different hypermedia applications.

关键词： Intelligent structures Privacy Informatics Protocols System testing Collaboration Service oriented architecture systems engineering and theory Software engineering Information technology

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Semantics, Web and Mining: Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2006年 4289 LNAI卷 v-vi页

作者： Berendt, Bettina Hotho, Andreas Mladenič, Dunja Semeraro, Giovanni Spiliopoulou, Myra Stumme, Gerd Van Someren, Maarten Ackermann, Markus Grobelnik, Marko Svátek, Vojtěch Institute of Information Systems Humboldt University Berlin Germany Knowledge and Data Engineering Group University of Kassel Germany J. Stefan Institute Ljubljana Slovenia Department of Informatics University of Bari Italy Faculty of Computer Science Otto-von-Guericke-Univ. Magdeburg Germany Informatics Institute University of Amsterdam Netherlands Dept. of Natural Language Processing Institute for Computer Science University of Leipzig Germany University of Economics Prague Czech Republic

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

引用

Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： John Raphael G. Cuevas John Kenneth R. Rili Anny Neolicia G. Santillan Vivien A. Agustin Mary Grace N. Gonzales Criselle J. Centeno Richard C. Regala Department of Information Technology College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Associate Dean of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Faculty of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Director of Graduate Program of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Computer Lab Administrator of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design.

关键词： Solid modeling Three-dimensional displays Layout Decision making Clustering algorithms Real-time systems User experience Mobile applications Augmented reality Optimization

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Retransmission-based Successful Delivery Tuning in Damaged Critical Infrastructures for VANETs

Retransmission-based Successful Delivery Tuning in Damaged C...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X. Mavromoustakis George Mastorakis Jordi Mongay Batalla Hoda Maalouf Mithun Mukherjee Evangelos Pallis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Science and Technology Hellenic Mediterranean University Agios Nikolaos Crete Greece National Institute of Telecommunications and Warsaw University of Technology Warsaw Poland Computer Science Department Faculty of Natural and Applied Sciences Notre Dame University Louaize Zouk Mosbeh Lebanon Guangdong University of Petrochemical Technology P.R. China Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Breakdown or interruption of communication infrastructure is one of the most immediate and significant impacts of natural disasters. This paper is devoted to the modeling of the successful uplink in Vehicular ad-hoc networks (VANET) in disaster cases where vehicles encounter disrupted communication. The packets' size to be transmitted, the vehicles' speed, the infrastructure coverage range, the disconnected distance, and the number of copies sent have a strong influence on the probability of success for uploading the packets. A simple connectionless retransmission scheme is proposed where several copies of the same packet are transmitted to make sure that the vehicle will upload successfully at least one copy of the packet. The optimum number of retransmission and the choice of inter-frame gap required between successive copy packets are also found. In this respect, this paper studies the mentioned parameters' effect on the probability of success for the uplink and the throughput. MATLAB was used to run a simulation and validate the theoretical analysis.

关键词： Vehicular ad hoc networks Routing protocols Uplink Throughput Reliability

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Exploring ERP User Behaviour in Developing Countries: Integrating Self-efficacy and UTAUT Perspectives

Exploring ERP User Behaviour in Developing Countries: Integr...

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Advancement in Data science, E-learning and Information systems (ICADEIS), International Conference on

作者： Dedy Syamsuar Muhammad Angga Oktaharisetia Deden Witarsyah Ahmad Luthfi Peter Dell Yudistira Sira Permana Darius Antoni Information Systems Department School of Information Systems Bina Nusantara University Indonesia Program Pasca Sarjana Universitas Bina Darma Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia School of Management and Marketing Faculty of Business and Law Curtin University Australia Teknik Informatika Politeknik Sekayu Sekayu Indonesia Faculty of Computer Science Universitas Indo Global Mandiri Palembang Indonesia

ERP stands for enterprise resource planning. It is an information system that is all rolled into one, is very flexible and adaptable, and optimizes business operations while also centralizing all of the company's data. This study aims to empirically investigate the elements that affect users' intention and usage of enterprise resource planning (ERP). This research incorporates self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A quantitative technique was taken, and as a result, 143 replies that might be used were effectively collected. After that, the information was put through a structural equation modelling partial least square (SEM-PLS) analysis. According to the findings of this research, self-efficacy appears to have a significant influence on all endogenous variables (EE, BI, and UB), as indicated by t-values of 3.951, 7.573, and 5.492, respectively. In addition, the analysis also indicated that PE (4.317), EE (2.397), and HM (3.084) are likewise statistically and substantively significant to BI. The result shows that the R2 for BI and UB indicate a moderate impact of 0.603 and 0.521, respectively. It is suggested that the model has valuable applications in academic and real-world contexts.

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