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Serverless Computation and Related Analysis Based on Matrix Multiplication 1

2nd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2022

作者： Gong, Jiajun Sun, Haotian Wang, Yilin College of Architecture and Urban Planning Shenyang Jianzhu University Shenyang110000 China College of Stomatology ChongQing Medical University Chengdu610066 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819945542

ISBN: (纸本)9789819945535

This paper investigates the effect of the change in matrix dimensionality and the relationship between function communication time and execution time, as well as the relationship between memory parameters and execution time, on the efficiency and economic cost of computation when using a serverless platform for high-dimensional matrix data. AWS Lambda was used to create the functions, AWS S3 to store the data, perform the high-dimensional matrix fork multiplication and matrix convolution operations, respectively, and S3 to design the monitoring and data analysis interface, and finally, analyze the statistical data. According to the analysis, the average communication time consumption ratio decreases gradually as the number of matrix dimensions increases. The computation time decreases by increasing the number of nodes as far as concurrency allows. As the memory parameter increases, the computation time decreases, and the total cost decreases and then increases. The experimental results show that the impact of communication costs on serverless computing decreases as the size of the data increases. When the concurrency limit is not reached, there is a significant increase in computational efficiency, reducing the time consumed by individual nodes and the overall computing process. Increased memory improves computational efficiency but does not necessarily result in total cost savings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Integrating artificial neural networks and neighborhood analysis for enhanced urban growth prediction depending on planned future development areas: A case study of Ar-Ramtha, Jordan

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Journal of Urban Management 2025年

作者： Gharaibeh, Anne A. Al-Rabee, Mohammad S. Obeidat, Rasha Department of City Planning and Design College of Architecture and Design Jordan University of Science and Technology Irbid Jordan Department of Computer Science Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Artificial intelligence (AI) is revolutionizing urban planning by enhancing the simulation of urban growth and land-use changes. Most AI models often fail to predict how proposed development areas will shape future expansion, leading to misaligned planning and urban sprawl. This study addresses these gaps by integrating master plans and spatial policies into an AI-driven urban growth model for Ar-Ramtha. A hybrid model combining artificial neural networks (ANN) and neighborhood analysis was developed to assess the influence of proposed development areas. Historical data (1990–2005) was used for training and validation, while 2005–2020 data was used for testing. Under-sampling techniques addressed data imbalance, and the PLADJ matrix measured urban growth patterns. Results indicate that incorporating neighborhood analysis and development areas improves model accuracy, achieving a validation accuracy of 92.9 %, an F1-micro of 91.0 %, and a Kappa of 82.1 %. Testing performance remained strong, with an F1-micro of 84.0 % and an accuracy of 91.2 %. Simulations suggest Ar-Ramtha is becoming more compact, with PLADJ values rising from 80.67 % in 2020 to 92.7 % by 2035. However, potential sprawl risks remain due to increased road paving and rising land prices. The study recommends defining development areas based on AI model predictions beyond current planning boundaries and adopting a ring road strategy to promote compact urban growth. These findings contribute to bridging gaps in AI-based urban modeling by improving the integration of planning policies, offering a robust tool for sustainable urban development. © 2025 The Authors

关键词： Artificial neural networks (ANN) Neighborhood analysis PLADJ Urban growth Urban sprawl

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MadDroid: malicious adware detection in Android using deep learning

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Journal of Cyber Security technology 2024年第3期8卷 163-190页

作者： Seraj, Saeed Pavlidis, Michalis Trovati, Marcello Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom Department of Computer Science Edge Hill University Omrskirk United Kingdom

The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Android (operating system)

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

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China Communications 2022年第2期19卷 47-61页

作者： Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie Department of Mechanical Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China College of Civil Engineering and Architecture Anhui Polytechnic UniversityAnhuiWuhu 241000PR China Department of Computer Science and Technology Anhui University of TechnologyAnhuiMa’anshan 243002PR China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterAnhuiHefei230000PR China Department of Management science and Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** proposed method improves the SNR of the dataset from-2.03 to 2.30.

关键词： ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sorting

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Integrating virtual reality technology into architecture education: the case of architectural history courses

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Open House International 2021年第4期46卷 498-509页

作者： Ibrahim, Anwar Al-Rababah, Amneh Ibrahim Bani Baker, Qanita Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Jordan University of Science and Technology Irbid Jordan

Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern architecture course. The participants who were students of Modern architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot

关键词： Architectural education Bloom’s taxonomy Built environment tectonics and technologies History of architecture VR technology

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Distributed Learning by Local Training ADMM 63

Distributed Learning by Local Training ADMM

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 IEEE.

关键词： Peer to peer networks

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A Trusted and Decentralized Federated Learning Framework for IoT devices in Smart City 17

A Trusted and Decentralized Federated Learning Framework for...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Wang, Sheng Chen, Chun Han, Bing Zhu, Jun Nanjing Construction Market Supervision Station Nanjing China Jiangsu Public Works Co.Ltd Nanjing China Nanjing University of Aeronautics and Astronautics Department of College of Computer Science and Technology Nanjing China City University of Hong Kong Department of Architecture and Civil Engineering Hong Kong

ISBN: (纸本)9798350351637

Smart cities, through investment in human and social capital as well as traditional and modern communications infrastructure, drive sustainable economic growth and high quality of life. IoT devices, as crucial components of smart cities, leverage the Federated Learning (FL) paradigm to contribute significantly to the advancement of smart cities by providing the value of data while safeguarding local data. However, existing federated learning frameworks for smart cities face several issues, including excessive centralization of decision-making and data, unbalanced resource allocation, and security and privacy concerns. To address these challenges, This paper aims to propose a blockchain-based federated learning framework for IoT devices in smart cities. We combine multiple security and privacy-preserving techniques such as differential privacy and Trusted Execution Environments (TEE) with federated learning to establish a framework that facilitates secure and trustworthy data exchange and FL requirements for IoT devices in smart cities. Moreover, we offload the local training computation from the limited IoT devices to the edge server and execute trust aggregation on the blockchain smart contracts. We build a prototype of our designed system and conduct experiments on diverse federated learning datasets. Experimental results demonstrate that our scheme achieves high efficiency while ensuring security and privacy. Through this work, we provide a viable solution for federated learning in smart cities, thereby advancing the sustainable development and intelligence of smart cities. © 2024 IEEE.

关键词： Federated learning

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