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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Statistical physics of deep neural networks: Initialization toward optimal channels

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Physical Review Research 2023年第2期5卷 023023-023023页

作者： Kangyu Weng Aohua Cheng Ziyang Zhang Pei Sun Yang Tian Tsien Excellence in Engineering Program Tsinghua University Beijing 100084 China Laboratory of Advanced Computing and Storage Central Research Institute 2012 Laboratories Huawei Technologies Co. Ltd. Beijing 100084 China Department of Psychology & Tsinghua Laboratory of Brain and Intelligence Tsinghua University Beijing 100084 China

In deep learning, neural networks serve as noisy channels between input data and its latent representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission and representation. While considerable efforts are concentrated on realizing optimal channel properties during network optimization, we study a frequently overlooked possibility that neural networks can be initialized toward optimal channels. Our theory, consistent with experimental validation, identifies primary mechanics underlying this unknown possibility and suggests intrinsic connections between statistical physics and deep learning. Unlike the conventional theories that characterize neural networks applying the classic mean-field approximation, we offer analytic proof that this extensively applied simplification scheme is not appropriate in studying neural networks as information channels. To fill this gap, we develop a restricted mean-field framework applicable for characterizing the limiting behaviors of information propagation in neural networks without strong assumptions on inputs. Based on it, we propose an analytic theory to prove that mutual information maximization is realized between inputs and propagated signals when neural networks are initialized at dynamic isometry, a case where information transmits via norm-preserving mappings. These theoretical predictions are validated by experiments on real neural networks, suggesting the robustness of our theory against finite-size effects. Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning. Our work may lay a cornerstone for promoting deep learning in terms of network initialization and suggest general statistical physics mechanisms underlying diverse deep learning techniques.

关键词： Neuronal dynamics Artificial neural networks Deep learning Information theory Random matrix theory

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Instance Segmentation of Damaged Insulators for Power System Inspection and Maintenance

Instance Segmentation of Damaged Insulators for Power System...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Jovencio Merin Alvin Sarraga Alon Joel Pinkihan Ricky C. Sandil Michael Angelo Delos Santos Ligayo Ryan Flores Arago Rovenson Sevilla Christopher Cunanan Dept. of Electrical Engineering Technological University of the Philippines Manila Philippines Department of Trade and Industry Center for AI Research Makati City Philippines College of Computing Sciences Ifugao State University Ifugao Philippines College Preparatory Program Kingdom of Saudi Arabia Saudi Arabia College of Engineering Quezon City University Quezon City Philippines Department of Electrical Engineering Technological University of the Philippines Manila Philippines Christopher Franco Cunanan Computer Engineering Department Bulacan State University Bulacan Philippines

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper introduces a novel approach for power system inspection using Mask-RCNN, an advanced model for instance segmentation. The accuracy of our model in identifying and diagnosing damaged low- to medium-voltage insulators is exceptional, as evidenced by its Mean Average Precision (mAP) score of 98.7 % • Throughout the training process, the model exhibited minimal loss metrics, indicating its proficiency in predicting bounding boxes precisely, segmenting, classifying, and aligning masks. The model exhibited consistent reliability in distinguishing between undamaged and damaged components during the inference process, as seen by its consistently high detection rates for both “normal intact” and “damaged broken” insulators. The results demonstrate the practical usefulness of the model and offer promising prospects for enhancing power system maintenance and inspection protocols. The model's consistent and accurate identification of damaged insulators has the potential to significantly enhance the safety and efficiency of power systems, thereby revolutionizing the industry.

关键词： Measurement Training Accuracy Medium voltage Inspection Predictive models Insulators Maintenance Power system reliability Safety

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Ultra-Reliable Device-Centric Uplink Communications in Airborne Networks: A Spatiotemporal Analysis

arXiv

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arXiv 2023年

作者： Nabil, Yasser ElSawy, Hesham Al-Dharrab, Suhail Attia, Hussein Mostafa, Hassan Department of Electronics and Communication Engineering Cairo University Giza12613 Egypt School of Computing Queen’s University KingstonON Canada Dhahran31261 Saudi Arabia University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza12578 Egypt

This paper proposes an ultra-reliable device-centric uplink (URDC-UL) communication scheme for airborne networks. In particular, base stations (BSs) are mounted on unmanned aerial vehicles (UAVs) that travel to schedule UL transmissions and collect data from devices. To attain an ultra-reliable unified device-centric performance, the UL connection is established when the UAV-BS is hovering at the nearest possible distance from the scheduled device. The performance of the proposed URDC-UL scheme is benchmarked against a stationary UAV-centric uplink (SUC-UL) scheme where the devices are scheduled to communicate to UAV-BSs that are continuously hovering at static locations. Utilizing stochastic geometry and queueing theory, novel spatiotemporal mathematical models are developed, which account for the UAV-BS spatial densities, mobility, altitude, antenna directivity, ground-to-air channel, and temporal traffic, among other factors. The results demonstrate the sensitivity of the URDC-UL scheme to the ratio between hovering and traveling time. In particular, the hovering to traveling time ratio should be carefully adjusted to maximize the harvested performance gains for the URDC-UL scheme in terms of link reliability, transmission rate, energy efficiency, and delay. Exploiting the URDC-UL scheme allows IoT devices to minimize transmission power while maintaining unified reliable transmission. This preserves the device’s battery and addresses a critical IoT design challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Agile Assessment Methods: Current State of the Art

arXiv

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arXiv 2022年

作者： Telemaco, Ulisses Alencar, Paulo Cowan, Donald Oliveira, Toacy David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

Agility Assessment (AA) comprises tools, assessment techniques, and frameworks that focus on indicating how a company or a team is applying agile techniques and eventually pointing out problems in adopting agile practices at a project-level, organization-level or individual-level. There are many approaches for AA such as agility assessment models, agility checklists, agility surveys, and agility assessment tools. This report presents the state of the art approaches that support agility assessment. © 2022, CC BY.

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Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology

Towards Automatic Cetacean Photo-Identification: A Framework...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Trotter, Cameron Wright, Nick Stephen McGough, A. Sharpe, Matt Cheney, Barbara Civil, Monica Arso Tyson Moore, Reny Allen, Jason Berggren, Per Newcastle University School of Engineering United Kingdom Newcastle University School of Computing United Kingdom Sciences Newcastle University School of Natural & Environmental United Kingdom University of Aberdeen School of Biological Sciences United Kingdom University of St Andrews School of Biology United Kingdom Chicago Zoological Society's Sarasota Dolphin Research Program SarasotaFL United States

ISBN: (纸本)9781665480451

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA. © 2022 IEEE.

关键词： Mammals

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An integrated model of electric bus energy consumption and optimised depot charging

npj Sustainable Mobility and Transport

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npj Sustainable Mobility and Transport 2024年第1期1卷 1-14页

作者： Johannes N. Hendriks Björn. C. P. Sturmberg Battery Storage and Grid Integration Program School of Engineering College of Engineering Computing and Cybernetics The Australian National University Canberra ACT Australia

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

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MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

arXiv

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arXiv 2023年

作者： Taghibakhshi, Ali Nytko, Nicolas Zaman, Tareq Uz MacLachlan, Scott Olson, Luke N. West, Matthew Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign United States Department of Computer Science University of Illinois Urbana-Champaign United States Scientific Computing Program Memorial University of Newfoundland Canada Department of Mathematics and Statistics Memorial University of Newfoundland Canada

Domain decomposition methods (DDMs) are popular solvers for discretized systems of partial differential equations (PDEs), with one-level and multilevel variants. These solvers rely on several algorithmic and mathematical parameters, prescribing overlap, subdomain boundary conditions, and other properties of the DDM. While some work has been done on optimizing these parameters, it has mostly focused on the one-level setting or special cases such as structured-grid discretizations with regular subdomain construction. In this paper, we propose multigrid graph neural networks (MG-GNN), a novel GNN architecture for learning optimized parameters in two-level DDMs. We train MG-GNN using a new unsupervised loss function, enabling effective training on small problems that yields robust performance on unstructured grids that are orders of magnitude larger than those in the training set. We show that MG-GNN outperforms popular hierarchical graph network architectures for this optimization and that our proposed loss function is critical to achieving this improved performance. © 2023, CC BY.

关键词： Domain decomposition methods

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Multi-Active Multi-Datacenter Distributed Database Architecture Design based-on Secondary Development Zookeeper

Multi-Active Multi-Datacenter Distributed Database Architect...

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2022 International Conference on Emerging Technologies in Electronics, computing and Communication, ICETECC 2022

作者： Yang, Zhen Guang Alejandro, Leonard L. Vergara, Charlene I. Maaliw, Renato R. Alon, Alvin Sarraga Evangelista, Ryan Soriente Rivera, Reden Paul Layug Santos, Ronnel Cabral Delos Adamson University Graduate Program Manila Philippines Adamson University It&is Department Manila Philippines Southern Luzon State University College of Engineering Quezon Lucban Philippines Batangas State University Digital Transformation Center Batangas Philippines Sulu State College School of Graduate Studies Sulu Philippines Don Honorio Ventura State University College of Computing Studies Pampanga Philippines

ISBN: (纸本)9781665490870

Multi-Active Multi-Datacenter zookeeper is a very core basic component of ***. It is equivalent to the database of basic components, including GZS (Global Zone Service, global state coordinator), DAL (Data Access Layer, database middleware software), Huskar (SOA framework that the online publishing system depends on), maxq (*** message queue), Samaritan (load balancer with service discovery), abtest and other basic components. We are based on the apache official zookeeper for the secondary development to enable it to synchronize data in multiple IDCs. Such an important component must improve the stability and efficiency by the way of DevOps. For this reason, we have developed the EZK operation and maintenance management platform. © 2022 IEEE.

关键词： Distributed database systems

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2025

作者： Chen, Kuan-Cheng Li, Yi-Tien Li, Tai-Yu Liu, Chen-Yu Lee, Po-Heng Henry Chen, Cheng-Yu Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Imperial College London Centre for Quantum Engineering London United Kingdom Taipei Medical University Hospital Translational Imaging Research Center Taipei Taiwan Taipei Medical University Research Center for Neuroscience Taipei Taiwan Taipei Medical University Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Taiwan NARlabs National Center for High-Performance Computing Hsinchu Taiwan National Taiwan University Graduate Institute of Applied Physics Taipei Taiwan Taipei Medical University Hospital Department of Medical Imaging Taipei Taiwan National Defense Medical Center Department of Radiology Taipei Taiwan College of Medicine School of Medicine Department of Radiology Taipei Taiwan

ISBN: (纸本)9798331519315

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer's Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline's superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing. © 2025 IEEE.

关键词： Network coding

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