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arXiv 2022年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Mahmood, Sajjad Rafi, Saima Department of Software Engineering LUT University Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia University of Murcia Department of Informatics and Systems Murcia Spain

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM. © 2022, CC BY.

关键词： Standardization

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Merging synthetic and real embryo data for advanced AI predictions

arXiv

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arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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Lightweight CNN-based head pose estimation using heatmaps and anthropometric facial measures

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ICT Express 2025年

作者： Anam Memon Ali Asghar Manjotho Qasim Ali Arain Adel Sulaiman Nasrullah Pirzada Mana Saleh Al Reshan Mohammad Alsulami Asadullah Shaikh Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro 76060 Pakistan Department of Software Engineering Mehran University of Engineering and Technology Jamshoro 76060 Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran 61441 Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran 61441 Saudi Arabia Department of Telecommunication Engineering Mehran University of Engineering and Technology Jamshoro 76060 Pakistan Department of Information Systems College of Computer Science and Information Systems Najran University Najran 61441 Saudi Arabia

Head pose estimation from a monocular image is crucial for applications in computer vision, AR/VR, and human–computer interaction. However, it remains challenging due to occlusions, lighting variations, and limited data. Landmark-based methods often suffer from localization errors, while landmark-free models tend to be complex and computationally expensive. To address these issues, we propose a lightweight, landmark-free CNN regressor guided by anthropometric facial measures. The model comprises two components: an Anthropometric Facial Measure Regressor (AFMR) that estimates a 4D vector of key facial segment lengths, and a CNN-based module that generates five uncertainty-based facial heatmaps. Evaluations on the BIWI and AFLW datasets show that our method outperforms state-of-the-art approaches, reducing localization error by 0.13° and 0.67°, respectively, while achieving faster convergence, lower parameter count, and real-time suitability.

关键词： Head pose estimation Lightweight Anthropometric constraints CNN-based computer vision

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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Design and demonstration of an operating system for executing applications on quantum network nodes

arXiv

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arXiv 2024年

作者： Donne, Carlo Delle Iuliano, Mariagrazia van der Vecht, Bart Ferreira, Guilherme M. Jirovská, Hana van der Steenhoven, Thom J.W. Dahlberg, Axel Skrzypczyk, Matt Fioretto, Dario Teller, Markus Filippov, Pavel Montblanch, Alejandro R.-P. Fischer, Julius van Ommen, H. Benjamin Demetriou, Nicolas Leichtle, Dominik Music, Luka Ollivier, Harold te Raa, Ingmar Kozlowski, Wojciech Taminiau, Tim H. Pawelczak, Przemyslaw Northup, Tracy E. Hanson, Ronald Wehner, Stephanie QuTech Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Quantum Computer Science Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Embedded Systems Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Institut für Experimentalphysik Universität Innsbruck Technikerstraße 25 Innsbruck6020 Austria QAT DIENS Ecole Normale Supérieure PSL University CNRS INRIA. 45 rue d’Ulm Paris75005 France LIP6 CNRS Sorbonne Université 4 Place Jussieu Paris75005 France

The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication[1, 2, 3]. Up to now, demonstrations of quantum network applications[4, 5, 6] and functionalities[7, 8, 9, 10, 11, 12] on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture’s capability to execute applications in high-level software, by implementing it as a quantum network operating system – QNodeOS – and executing test programs including a delegated computation from a client to a server[13] on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond[14, 15]. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single 40Ca+atom[16]. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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Towards more effective and economic sparsely-activated model

arXiv

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arXiv 2021年

作者： Jiang, Hao Zhan, Ke Qu, Jianwei Wu, Yongkang Fei, Zhaoye Zhang, Xinyu Chen, Lei Dou, Zhicheng Qiu, Xipeng Guo, Zikai Lai, Ruofei Wu, Jiawen Hu, Enrui Zhang, Yinxia Jia, Yantao Yu, Fan Cao, Zhao Distributed and Parallel Software Lab Huawei China School of Computer Science Fudan University Department of Computer Science and Engineering Hong Kong University of Science and Technology Gaoling School of Artificial Intelligence Renmin University of China

The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become a feasible technique for training and implementing extremely large models. Due to the limit of communication cost, activating multiple experts is hardly affordable during training and inference. Therefore, previous work usually activate just one expert at a time to alleviate additional communication cost. Such routing mechanism limits the upper bound of model performance. In this paper, we first investigate a phenomenon that increasing the number of activated experts can boost the model performance with higher sparse ratio. To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU). Our methods shed light on the training of extremely large sparse models and experiments prove that our models can achieve significant performance gain with great efficiency improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains

arXiv

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arXiv 2022年

作者： Montazerin, Mansooreh Rahimian, Elahe Naderkhani, Farnoosh Atashzar, S. Farokh Yanushkevich, Svetlana Mohammadi, Arash Department of Electrical and Computer Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada Departments of Electrical & Computer Engineering and Mechanical & Aerospace Engineering New YorkNY10003 United States New YorkNY10003 United States Department of Electrical and Software Engineering Biometric Technologies Laboratory Schulich School of Engineering University of Calgary CalgaryAB Canada

Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a compact deep learning framework referred to as the CT-HGR, which employs a vision transformer network to conduct hand gesture recognition using high-density sEMG (HD-sEMG) signals. Taking advantage of the attention mechanism, which is incorporated into the transformer architectures, our proposed CT-HGR framework overcomes major constraints associated with most of the existing deep learning models such as model complexity;requiring feature engineering;inability to consider both temporal and spatial information of HD-sEMG signals, and;requiring a large number of training samples. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. CT-HGR can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the CT-HGR is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. The av

关键词： Electrodes

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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How to characterize imprecision in multi-view clustering?

arXiv

引用

arXiv 2024年

作者： Xu, Jinyi Zhang, Zuowei Lin, Ze Chen, Yixiang Liu, Zhe Ding, Weiping Department of Embedded Software and Systems Software Engineering Institute East China Normal University Shanghai200062 China Institute de Recherche en Informatique et Systèmes Aléatoires Rennes35042 France Institute de Recherche en Informatique et Systèmes Aléatoires University of Rennes 1 Lannion22300 France School of Computer Science and Technology Hainan University Haikou570228 China Nantong University Nantong226019 China

It is still challenging to cluster multi-view data since existing methods can only assign an object to a specific (singleton) cluster when combining different view information. As a result, it fails to characterize imprecision of objects in overlapping regions of different clusters, thus leading to a high risk of errors. In this paper, we thereby want to answer the question: how to characterize imprecision in multi-view clustering? Correspondingly, we propose a multi-view low-rank evidential c-means based on entropy constraint (MvLRECM). The proposed MvLRECM can be considered as a multi-view version of evidential c-means based on the theory of belief functions. In MvLRECM, each object is allowed to belong to different clusters with various degrees of support (masses of belief) to characterize uncertainty when decision-making. Moreover, if an object is in the overlapping region of several singleton clusters, it can be assigned to a meta-cluster, defined as the union of these singleton clusters, to characterize the local imprecision in the result. In addition, entropy-weighting and low-rank constraints are employed to reduce imprecision and improve accuracy. Compared to state-of-the-art methods, the effectiveness of MvLRECM is demonstrated based on several toy and UCI real datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Automated Controller Placement for software-Defined Networks to Resist DDoS Attacks

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computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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