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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism 24

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (纸本)9798350355543

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N2) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

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Building a pathway to stability: strategies for overcoming the Architect's registration crisis in Nigeria

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Discover Civil engineering 2024年第1期1卷 1-30页

作者： Okonta, Donatus Ebere Oluigbo, Chinedu Uchechukwu Egwuma, Macaulay Atahchegbe Njoku, Rose Chigozie School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UK Department of Architecture Niger Delta University Wilberforce Island Nigeria Salis Ventilated Homes Abuja Nigeria Department of Architecture Imo State University Owerri Nigeria

The Architect's registration crisis in Nigeria for eight straight years (2015–2023) has significantly impacted the architectural profession, creating a pressing need for effective solutions to restore stability and ensure a sustainable future. This research investigated the challenges and consequences the crisis has caused the architectural profession in Nigeria and potential strategies for resolving the situation. The study relied on thirty-four (34) structured interviews with Provisionally Stage II registered architects and fully registered architects from different parts of Nigeria and a literature review involving a thorough analysis to identify key issues and challenges related to the Architect's registration crisis in Nigeria. Findings reveal the major factors contributing to the registration crisis include institutional issues, legislative and policy issues, power-mongering, and personality battles within professional associations and the regulatory body. Some of the proposed solutions involve strengthening accreditation standards and monitoring, addressing issues about the HND degree holders, advocating for fair registration practices, fostering collaboration and professionalism, overcoming internal issues and power struggles within professional associations and regulatory bodies, and the study proposed a framework for the registration of architects in different cadres. Ultimately, this research provided valuable insights for policymakers, professional bodies, and stakeholders. By identifying viable pathways to resolution, this study paves the way for a sustainable future for the architectural profession in Nigeria, ensuring its continued growth in the built environment.

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos

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Computers, Materials & Continua 2021年第5期67卷 1697-1712页

作者： Muhammad Arslan Usman Muhammad Rehan Usman Gandeva Bayu Satrya Muhammad Ashfaq Khan Christos Politis Nada Philip Soo Young Shin Faculty of Science Engineering and ComputingKingston UniversityLondonKT12EEUK School of Electrical Engineering Superior UniversityLahorePakistan School of Applied Sciences Telkom UniversityBandungIndonesia Department of Network Computing System Architecture LabDongguk UniversitySeoulKorea Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumiKorea

With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network *** standards for provision of such services are application specic as medical imagery is quite different than general purpose images and *** paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy(WCE)videos containing bleeding *** regions in gastrointestinal tract have been focused in this research,as bleeding is one of the major reasons behind several diseases within the *** method jointly estimates the diagnostic as well as perceptual quality of WCE videos,and accurately predicts the quality,which is in high correlation with the subjective differential mean opinion scores(DMOS).The proposed combines motion quality estimates,bleeding regions’quality estimates based on support vector machine(SVM)and perceptual quality estimates using the pristine and impaired WCE *** method Quality Index for Bleeding Regions in Capsule Endoscopy(QI-BRiCE)videos is one of its kind and the results show high correlation in terms of Pearson’s linear correlation coefcient(PLCC)and Spearman’s rank order correlation coefcient(SROCC).An F-test is also provided in the results section to prove the statistical signicance of our proposed method.

关键词： Automated bleeding detection high efciency video coding video quality assessment wireless capsule endoscopy

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MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensors 31

MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensor...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Sichani, Nazila Karimian Alaee-Kerahroodi, Mohammad Raei, Ehsan Bhavani Shankar, M.R. Mehrshahi, Esfandiar Ghorashi, Seyyed Ali Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran*** Iran University of Luxembourg Luxembourg Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom

ISBN: (纸本)9789464593600

The recently developed 4D-Imaging mmWave MIMO radars have significant advantages over conventional radar sensors because they offer a large virtual array in both azimuth and elevation. However, the physical placement of transmit and receive antennas to achieve the desired virtual array, while considering cost and efficiency is not intuitive. In this paper, we propose an optimization framework for optimally placing the transmit and receive array physical elements. The proposed framework obtains a sequence of optimal placements based on the coordinate descent by defining a desired virtual array and number of transmit and receive antenna elements as the objective. The objective function is then monotonically minimized in each iteration of the proposed algorithm, converging to a solution that guarantees the desired number of transmit and receive antenna elements, while the obtained virtual array is as close to the desired virtual array as possible. The simulation results are validated by simulating the virtual array of multiple commercially available 4D-Imaging radar products. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Radar imaging

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Autonomous Navigation for TurtleBot3 Robots in Gazebo Simulation Environment

Autonomous Navigation for TurtleBot3 Robots in Gazebo Simula...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Madhav Theeng Tamang Darshana Maheriya Mhd Saeed Sharif Thamo Sutharssan Intelligent Technologies Research Group School of Architecture Computing and Engineering University of East London London UK Department of Engineering University of East London London UK

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

The fast-paced growth in the field of robotics has driven the creation of autonomous navigation systems that are necessary for robots to work autonomously in diverse environments. This research targets TurtleBot3, an immensely popular robotic platform that is known for its affordability and adaptability. The objective of TurtleBot3 is to improve the capabilities within the Gazebo simulation environment, an open-source robotic simulator that provides a realistic virtual environment for testing and developing navigation algorithms for robotics. The combination of intricate sensor technologies, leading-edge control systems, and innovative artificial intelligence strategies is the core of this research, enabling autonomous navigation in complicated surroundings. The initial focus of the study is the application and utilization of path planning algorithms, such as A * (A Star) and Rapidly exploring Random Tree (RRT) and using Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations demonstrate that the A * algorithm achieved a path accuracy rate of over 95% in static environments, while the RRT algorithm proved effective in dynamic, multi-dimensional spaces. The aim of this analysis is to enhance autonomous navigation algorithms using the Gazebo simulation environment. Conducting tests in a virtual setting minimizes the risks and costs compared to real-time testing. The outcomes of this research are anticipated to advance the field of robotics and hold significant value for applications in autonomous systems, including engineering, urban mobility, and domestic assistance.

关键词： Technological innovation Simultaneous localization and mapping Navigation Heuristic algorithms Virtual environments Stars Real-time systems Planning Autonomous robots Testing

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Zero-day Android botnet detection using neural networks

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Neural computing and Applications 2025年第17期37卷 10795-10805页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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Deep Learning-Augmented Hybrid Optimization for Crop Classification using Hyperspectral images

Deep Learning-Augmented Hybrid Optimization for Crop Classif...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mohana, J. Nirmala, G. Damacharla, Praveen Bdair, Mohammad Logarasu, R. Karpagam, S. Saveetha School of Engineering Dept of Electronics and Communication Engineering Chennai India Mahendra Institute of Technology Dept of Electronics & Communication Engineering Namakkal India KineticAI Inc. The WoodlandsTX77380 United States University of East London School of Architecture Computing and Engineering London United Kingdom Department of Ece Namakkal India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Mathematics Chennai India

ISBN: (纸本)9798350375442

Precise and efficient solutions are necessary for the essential job of classifying crops using hyperspectral images in precision agriculture. The paper introduces a framework called Deep Learning-Augmented Hybrid Optimization (DLAHO), which combines a CNN for feature extraction with a hybrid optimization technique that combines Genetic Algorithms (GA) and Gradient-Based Optimization (GBO) to optimize model parameters. The Hyperspectral Image Database (HID) preprocesses hyperspectral images by using normalization and PCA to reduce dimensionality. The CNN utilizes both spectral and spatial data, which are then enhanced by the hybrid GA-GBO method. The improved model accurately identifies crops with a high level of precision. The results indicate that DLAHO obtains an accuracy of 95.4% for wheat, 92.1% for maize, 94.3% for soybean, 93.2% for barley, 96.0% for rice, 91.5% for sugarcane, 93.8% for potato, and 92.7% for cotton. DLAHO outperforms baseline models and state-of-the-art algorithms, achieving an F1-score of 93.1%, recall of 93.5%, precision of 92.8%, and total accuracy of 93.6%. The findings show that the recommended DLAHO architecture significantly increases the precision and dependability of crop categorization, thereby making it a viable tool for precision agriculture. © 2024 IEEE.

关键词： deep learning-augmented hybrid optimization genetic algorithms gradient-based optimization principal component analysis

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Architectural licensing in Nigeria, a pathway to professionalism or an obstacle to practice?

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Journal of Building Pathology and Rehabilitation 2025年第2期10卷 1-18页

作者： Okonta, Ebere Donatus Okeke, Francis Ogochuwku Oluigbo, Chinedu Uchechukwu Mgbemena, Emeka Ebuz School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UK School of Engineering Technology and Design Canterbury Christ Church University Canterbury UK Design Surveying and Planning East Kent College Canterbury UK Department of Architecture Niger Delta University Bayelsa Nigeria Department of Architecture Obafemi Awolowo University Ile-Ife Nigeria

Licensure is the ultimate milestone in the journey towards becoming an architect. However, anecdotal evidence suggests many architecture graduates lack interest in getting a practice license. Numerous investigations have yet to undertake a qualitative assessment to ascertain the advantages arising from the professional licensing of architects in Nigeria. By employing a combination of narrative literature reviews and 37 semi-structured interviews with architects, this study qualitatively investigates how architectural licensing in Nigeria impacts the profession and the broader built environment considering the unique challenges and opportunities in the country's dynamic construction landscape. The data were categorised according to emerging themes and qualitatively analysed using thematic analysis. Findings underscore the critical role of licensing in upholding professionalism, ethical standards, legal accountability in the architectural community and the quality of architectural practice within the built environment. It reveals that the registration trend in the last 47 years has been inconsistent, resulting in a shortage of licensed architects, highlighting barriers to obtaining and maintaining licenses to include regulatory inefficiencies and socio-economic challenges. Recommendations include policy interventions, streamlined licensing processes, and mentorship programs to increase the number of licensed architects. This work contributes to understanding licensing’s role in improving architectural performance and addressing national development needs.

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A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Kumar, Senthil Atyam, Nageswara Rao Khadar Nawas, K. Awasthi, Shashank Kotteeswaran, M. Thirunavukkarasu, K.S. Koneru Lakshmanan Education Foundation Kl School of Architecture Andhra Pradesh Guntur India Cmr Institute of Technology Electrical and Electronics Department Karnataka Bengaluru India Vellore Institute of Technolog School of Computing Science and Engineering Tamil Nadu Chennai India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Greater Noida India School of Management and Commerce Tamil Nadu Pallavaram Chennai India School of Computing Science Tamil Nadu Pallavaram Chennai India

ISBN: (纸本)9798350323887

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards. © 2023 IEEE.

关键词： Foundations

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