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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

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

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 accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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A Neuroevolution-Inspired Scheme for Generating Robust Internet of Things 25

A Neuroevolution-Inspired Scheme for Generating Robust Inter...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhang, Lidi Zhang, Songwei Chen, Ning Liu, Wenyuan Zhou, Xiaobo Qiu, Tie Tianjin University College of Intelligence and Computing Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China YanShan University Key Laboratory of Software Engineering of Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9781665405270

Internet of Things (IoT) is growing with various applications linked in, and node failures are becoming more common as a result of malicious strikes and other issues. The cascading collapse induced by local node failures can be mitigated by robust network topology. Existing approaches for fixed topology enhance the robustness of IoT topology by reconstructing device connections. However, using existing techniques necessitates global topology optimization when new nodes are added, which takes time. To tackle this situation, this study introduces an evolutionary algorithm based on neuroevolution that generates robust IoT topology. It provides IoT topology with inherent robustness when adding extra nodes by utilizing unique mutation and crossover operators. What's more, we establish an adaptive edge density management method to reduce the rise in energy consumption caused by redundant connections when nodes join. Experimental results indicate that the proposed scheme can effectively build robust topology than multiple existing topology optimization methods in less time for diverse network sizes. © 2022 IEEE.

关键词： Network topology

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A Framework for Blockchain-Based Secure Management of Mobile Healthcare (mHealth) Systems

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Journal of Web engineering 2025年第3期24卷 317-354页

作者： Adel Alkhalil Abdul Razzaq Aakash Ahmad Magdy Abdelrhman Yaser Altameemi Mohammed Altamimi Zhang Tao Department of Software Engineering College of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia School of Software Northwestern Polytechnical University Xi'an Shaanxi P.R. China School of Computing and Communications Lancaster University Leipzig Germany Department of Foundation of Education College of Education New Valley University Egypt Applied College University of Ha'il Ha'il Saudi Arabia

In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algori

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Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

arXiv

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arXiv 2023年

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computing and Mathematical Sciences University of Leicester United Kingdom

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5 .MSC Codes 68T40 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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Key Features and Formation of Transactions in the Case of Using UTxO, EUTxO and Account Based Data Storage Models 4

Key Features and Formation of Transactions in the Case of Us...

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4th International Science and Technology Conference "Modern Network Technologies", MoNeTec 2022

作者： Pomogalova, Albina V. Martyniuk, Anastasia A. Yesalov, Kirill E. Bonch-Bruevich Saint-Petersburg State University of Telecommunications Infocommunication Systems Department Saint Petersburg Russia Bonch-Bruevich Saint-Petersburg State University of Telecommunications Software Engineering and Computing Department Saint Petersburg Russia

ISBN: (数字)9781665472463

ISBN: (纸本)9781665472463

At the moment there are various logical concepts of constructing data storage and making transactions in blockchain networks. These concepts can be divided into account-based and (Extended) Unspent Transaction Output (E)UTxO. Account-based is a system with multiple accounts that can interact with each other through the creation and signing the transaction by a private key. While (E)UTxO is a model in which the values at the transaction outputs determine the current state of both a single smart contract and the entire blockchain network. To move from one state to another, transactions use the unspent outputs of previous transactions. The unspent outputs then become spent outputs: inputs for the new transaction also forming its unspent transaction outputs. These outputs can store both ADA and native tokens assigned to the public key. They can also store other information depending on the implementation of the script. The paper investigates the architectural features and differences of systems with two concepts of data storage construction, application features, as well as transaction construction models and the impact of models on network performance considered on the example of a practical case - development of an authorization module in the system. © 2022 IEEE.

关键词： Blockchain

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Recommender Systems Based on Tensor Decomposition

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Computers, Materials & Continua 2021年第1期66卷 621-630页

作者： Zhoubao Sun Xiaodong Zhang Haoyuan Li Yan Xiao Haifeng Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Computing National University of Singapore117417Singapore Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Recommender system is an effective tool to solve the problems of information *** traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and *** social networks contain abundant social information,such as tags,places and *** show that the social information has a great impact on recommendation *** not only describe the characteristics of items,but also reflect the interests and characteristics of *** the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender *** original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between *** on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

关键词： Recommender system social information tensor decomposition tag

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Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling

arXiv

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arXiv 2025年

作者： Wu, Bingjie Yu, Zitong Xie, Yiping Liu, Wei Luo, Chaoqi Liu, Yong Goh, Rick Siow Mong Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

Remote Photoplethysmography (rPPG) is a promising technique to monitor physiological signals such as heart rate from facial videos. However, the labeled facial videos in this research are challenging to collect. Current rPPG research is mainly based on several small public datasets collected in simple environments, which limits the generalization and scale of the AI models. Semi-supervised methods that leverage a small amount of labeled data and abundant unlabeled data can fill this gap for rPPG learning. In this study, a novel semi-supervised learning method named Semi-rPPG that combines curriculum pseudo-labeling and consistency regularization is proposed to extract intrinsic physiological features from unlabelled data without impairing the model from noises. Specifically, a curriculum pseudo-labeling strategy with signal-to-noise ratio (SNR) criteria is proposed to annotate the unlabelled data while adaptively filtering out the low-quality unlabelled data. Besides, a novel consistency regularization term for quasi-periodic signals is proposed through weak and strong augmented clips. To benefit the research on semi-supervised rPPG measurement, we establish a novel semi-supervised benchmark for rPPG learning through intra-dataset and cross-dataset evaluation on four public datasets. The proposed Semi-rPPG method achieves the best results compared with three classical semi-supervised methods under different protocols. Ablation studies are conducted to prove the effectiveness of the proposed methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Curricula

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Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

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A Short Review of Truck and Drone Collaborative Delivery Problem

A Short Review of Truck and Drone Collaborative Delivery Pro...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Yena Zhu Min Liu Dongyao Jin School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented.

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