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IEEE International Conference on Control science and Systems Engineering (CCSSE)

作者： Huijie Wang Yuanyuan Cai Fabu Yue Hao Luo Sina Abdipoor Say Leng Goh School of Big Data BaoShan University BaoShan Yunnan China Department of Computer Science Universiti Putra Malaysia Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan Malaysia

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff.

关键词： Uniform resource locators Three-dimensional displays databases Virtual reality Real-time systems Digital twins Maintenance Servers Monitoring Thermal stability

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Towards Resource-Efficient Reactive and Proactive Auto-Scaling for Microservice Architectures

SSRN

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SSRN 2024年

作者： Ahmad, Hussain Treude, Christoph Wagner, Markus Szabo, Claudia School of Computer and Mathematical Sciences University of Adelaide Australia School of Computing and Information Systems Singapore Management University Singapore Department of Data Science and AI Faculty of IT The Monash University Australia

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust microservice resources to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, further exacerbating these issues. In addition, the architectures of existing HPAs exhibit several challenges, including single point of failure and the absence of coordinated scaling operations. To address these concerns, we propose Smart HPA and ProSmart HPA, reactive and proactive horizontal pod auto-scalers. These auto-scalers integrate centralized and decentralized styles. Smart HPA optimizes resource exchange in constrained environments, while ProSmart HPA uses machine learning to manage resource demands proactively, reducing delays and enabling preemptive resource sharing. Our experiments show Smart HPA outperforms Kubernetes HPA, and ProSmart HPA surpasses both by reducing resource overutilization, overprovisioning, underprovisioning, and increasing resource allocation to microservice applications. © 2024, The Authors. All rights reserved.

关键词： Losses

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Development of a Sensor-Based Wearable Fetal and Maternal Health Monitoring System 1

Development of a Sensor-Based Wearable Fetal and Maternal He...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Jayalath, Yugashi Maduranga, M.W.P. Tilwari, Valmik Wijayarathna, Wmsrb Wanniarachchi, Waam Sharma, Debashree School Of Pharmacy & Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Sri Lanka Technology Campus Faculty of Computing and IT Department of Data Science Padukka Sri Lanka Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India General Sir Jhon Kotelawala Defence University Department of Information Technology Colombo Sri Lanka

ISBN: (纸本)9798350364590

According to the World Health Organization, approximately 360,000 pregnant women are registered annually in Sri Lanka. Among these, about 328,000 babies are born healthy, while 5,800 are born with birth defects, some of which are severe and impact daily life. To address the Sri Lankan stillbirth rate of 6.01%, continuous monitoring of fetal and maternal health is essential. Although commercial devices exist to fulfill this requirement, their high costs and safety issues often preclude regular use. To overcome these challenges, there is a need for a passive, wearable, and portable monitoring system for fetal and maternal health. This system should track key parameters such as fetal heart rate, fetal movements, and maternal heart rate to mitigate health risks for both the fetus and the mother. In this study, we developed a device using the Arduino, appropriate sensors, and a microcontroller to monitor these vital parameters. The device is user-friendly and suitable for regular use. MATLAB was utilized for the signal processing requirements of the proposed device. Test results demonstrated that fetal movement detection was accurate, although potential errors may arise during real-time testing. For pulse sensor readings, an average percentage difference of 3.025% was observed compared to pulse oximeter readings, likely due to placement errors and sensor instability in the proposed device. © 2024 IEEE.

关键词： Electronic health record

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Lane Detection Using Deep Learning for Rainy Conditions

Lane Detection Using Deep Learning for Rainy Conditions

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International Conference on Computer and Communication Engineering, ICCCE

作者： Hadhrami Ab Ghani Atiqullah Mohamed Daud Rosli Besar Zamani Md Sani Mohd Nazeri Kamaruddin Syabeela Syahali Faculty of Data Science and Computing Universiti Malaysia Kelantan Kelantan Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Mechatronic Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

Prior research has shown that various road marker classification mechanisms in clear or dry weather conditions have high accuracy performance. However, the performance tends to be lower under rainy driving conditions due to the reduced quality of the road image when detecting the five classes of road markers which are Single, Single-Single, Dashed, Solid-Dashed, and Dashed-Solid. To address this challenging condition, lane marker detection based on deep learning approach is proposed in this paper. The target weather condition is rainy, which is very challenging as it causes the surface of the roads, especially the area which includes the lane marker to become blurry and unclear due to the rainwater. In order to carefully select the right features of the road such that the lane marker can be classified and detected successfully. The lane marker object is captured from the frames of the video clips taken from established published video datasets. With this fast and better lane marker detection, the achievable classification precision is satisfactory although the weather is rainy.

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Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiaoyu Kasprzak, Mikolaj J. Negrea, Jeffrey Bourguin, Solesne Huggins, Jonathan H. Department of Mathematics & Statistics Boston University United States Department of Information Systems Data Analytics & Operations ESSEC Business School France Department of Statistics & Actuarial Science University of Waterloo Canada Faculty of Computing & Data Sciences Boston University United States

Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in large-scale and high-dimensional problems in machine learning, statistics, and engineering. Numerous works have bounded the parameter error in, and characterized the uncertainty of, these approximations. One common approach has been to use scaling limit analyses to relate the distribution of algorithm sample paths to a continuous-time stochastic process approximation, particularly in asymptotic setups. Focusing on the univariate setting, in this paper, we build on previous work to derive non-asymptotic functional approximation error bounds between the algorithm sample paths and the Ornstein-Uhlenbeck approximation using an infinite-dimensional version of Stein's method of exchangeable pairs. We show that this bound implies weak convergence under modest additional assumptions and leads to a bound on the error of the variance of the iterate averages of the algorithm. Furthermore, we use our main result to construct error bounds in terms of two common metrics: the Lévy-Prokhorov and bounded Wasserstein distances. Our results provide a foundation for developing similar error bounds for the multivariate setting and for more sophisticated stochastic approximation *** Codes 60F17 (Primary) 60J60, 62-08, 68T05, 62E17 (Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Enhancing Blockchain Storage Efficiency in Mobile IoT through Multidimensional Structures and Collective Signing

Enhancing Blockchain Storage Efficiency in Mobile IoT throug...

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IEEE International Conference on Blockchain (Blockchain)

作者： Hussein Zangoti Alex Pissinou Makki Wazir Zada Khan Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia Frost Institute of Data Science and Computing University of Miami Miami FL USA University of Wah Wah Cantt Pakistan

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

The rapidly evolving mobile IoT landscape, driven by mobile financial applications, self-driving cars, wearables, and health monitoring devices, confronts substantial blockchain storage challenges due to decentralized networks, limited band-width, and high storage overhead. To tackle these issues, we propose the Collective Signing-Based Blockchain Storage Optimization (CSBSO) algorithm. CSBSO mitigates storage over-head by leveraging the Collective Signing (CoSi) protocol and employing a multidimensional blockchain structure for efficient block management and retrieval. It focuses on identifying and pruning irrelevant blocks and implementing streamlined data management. Evaluations with Ethereum Classic Blockchain and Facebook Users datasets show CSBSO achieves up to 92% storage optimization, surpassing current models. These results underscore the effectiveness of CoSi-based strategies in reducing blockchain storage overhead in resource-constrained environments.

关键词： Social networking (online) Scalability Smart contracts Blockchains Cryptocurrency Autonomous automobiles Wearable devices

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Modulated Spike-Time Dependent Plasticity (Stdp)-Based Learning for Spiking Neural Network (Snn): A Review

SSRN

引用

SSRN 2024年

作者： Rahman, Nazeerah Abdul Yusoff, Nooraini Khamis, Nurulaqilla Faculty of Data Science & Computing Universiti Malaysia Kelantan UMK City Campus Pengkalan Chepa Malaysia Institute for Artificial Intelligence & Big Data Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kelantan Kota Bharu16100 Malaysia

Computational models called Spiking Neural Networks (SNNs) are modelled after the intricate information processing discovered in the brain. A key learning principle, spike-time dependent plasticity (STDP), controls how the temporal connection between pre- and post-synaptic spikes affects synaptic weight changes. STDP is a Hebbian learning rule used in training algorithms for SNNs. SNNs encode information in the precise timing of spikes. Modified variants of STDP have recently evolved to improve the learning and adaptation of SNNs. These versions incorporate extra elements like neuromodulators and dendritic processing. This review focuses on the underlying principles, experimental results, and computational models to provide an in-depth overview of the developments in modulated STDP-based learning for SNNs. It also deals with the difficulties of modified STDP, such as computational complexity, parameter optimisation, scalability, and the quest for biological plausibility. This review is invaluable for researchers and practitioners interested in creating practical and biologically plausible learning algorithms for modulated STDP. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Safe and Reliable Diffusion Models via Subspace Projection

arXiv

引用

arXiv 2025年

作者： Chen, Huiqiang Zhu, Tianqing Wang, Linlin Yu, Xin Gao, Longxiang Zhou, Wanlei Faculty of Data Science City University of Macau China School of Computer Science University of Queensland QLD Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these models may inadvertently generate inappropriate content, such as copyrighted works or offensive images. While existing methods attempt to eliminate specific unwanted concepts, they often fail to ensure complete removal—allowing the concept to reappear in subtle forms. For instance, a model may successfully avoid generating images in Van Gogh’s style when explicitly prompted with "Van Gogh", yet still reproduce his signature artwork when given the prompt "Starry Night". In this paper, we propose SAFER, a novel and efficient approach for thoroughly removing target concepts from diffusion models. At a high level, SAFER is inspired by the observed low-dimensional structure of the text embedding space. The method first identifies a concept-specific subspace Sc associated with the target concept c. It then projects the prompt embeddings onto the complementary subspace of Sc, effectively erasing the concept from the generated images. Since concepts can be abstract and difficult to fully capture using natural language alone, we employ textual inversion to learn an optimized embedding of the target concept from a reference image. This enables more precise subspace estimation and enhances removal performance. Furthermore, we introduce a subspace expansion strategy to ensure comprehensive and robust concept erasure. Extensive experiments demonstrate that SAFER consistently and effectively erases unwanted concepts from diffusion models while preserving generation quality. © 2025, CC BY.

关键词： Embeddings

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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International Conference on Computer and Applications (ICCA)

作者： Salah A. Aly Ali Bakhiet Mazen Balat Faculty of Computing & Data Science Badya University Giza Egypt Computer Science Dept. Faculty of Science Fayoum University Fayoum Egypt Computer Science Dept. Culture & Science October 6th City Giza Egypt CS & IT Dept. E-Japanese University of Science & Tech. Alexandria Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology.

关键词： Deep learning Accuracy Biological system modeling Neural networks Transfer learning Leukemia Oncology Robustness Optimization Testing

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MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

arXiv

引用

arXiv 2024年

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024, CC BY.

关键词： Hamiltonians

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