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Empowering Secure Data-Driven Services for CPS: An Attribute-Based Access Control Model with Experimental Validation

Empowering Secure Data-Driven Services for CPS: An Attribute...

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international conference on computing, Networking and Communications (ICNC)

作者： Htet Htet Hlaing Yuuichi Teranishi Hitoshi Asaeda National Institute of Information and Communications Technology (NICT) Japan

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

The convergence of physical and computational components in cyber-physical systems (CPS) is driving innovations that revolutionize diverse sectors, from self-driving cars and smart industry to healthcare. However, this interconnection expands the risk of cyberattacks and data breaches as the constant exchange of large amounts of sensitive data creates more opportunities for data exploitation. Addressing this problem through effective data access control in dynamic and complex CPS remains challenging due to the trade-off between security and efficiency. Moreover, the lack of large-scale real-world implementations of access control mechanisms in CPS restricts the evaluation of existing solutions, highlighting the need for further research and development. To fulfill these requirements, we propose a secure data sharing framework that leverages ciphertext-policy attribute-based proxy re-encryption for fine-grained access control with flexible policy updates and secure delegation of decryption rights within CPS, named "SDS-CPS." We further enhance our proposal by employing an information-centric networking approach, which decouples data from its source and enables in-network caching for efficient data delivery in heterogeneous CPS environments. For validation, we implemented and evaluated SDS-CPS on an actual testbed. The experimental results demonstrate significant improvements in data access efficiency while securely handling dynamic access policies with minimal computational overhead.

关键词： Access control Industries Technological innovation Scalability Information-centric networking Ecosystems Medical services Proposals Resource management Research and development

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Risk Analysis and Classification of Maternal Data to Enhance Women’s Health

Risk Analysis and Classification of Maternal Data to Enhance...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Siyona Sebastian Harishma R Asha K Department of Advanced Computing School of Information Technology St.Joseph’s University Bengaluru Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Reducing the number of redundant Caesarean sections in tertiary care centers is essential for enhancing the health outcomes of both mothers and newborns. These institutions need to weigh the advantages of surgical procedures against the encouragement of natural childbirth when it is medically suitable. Even though a C-section is a valuable intervention to save both the mother and the child from certain life-threatening conditions during pregnancy, excessive sections pose serious health hazards and lead to huge financial losses. It is a mixed-approach that involves the analysis of quantitative data along with evidence-based interventions. The convergence of machine learning based technologies in gynecology has resulted in the development of innovative digital health that allow easy and accurate patient risk stratification in early pregnancy, labor and delivery and care of newborn. The results reflect that maternal weight and age are amongst the two most influential factors in C-section delivery with increased risks for heavier and older mothers. The results of empirical study recommended targeted weight management interventions, personalized care of older mothers, and better labor practices as strong ways to reduce unnecessary C-sections, improving maternal and neonatal health outcomes

关键词： Pregnancy Pediatrics Technological innovation Prevention and mitigation Surgery Gynecology Machine learning Production Public healthcare Prognostics and health management

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Constrained Global Optimization by Smoothing 4th

Constrained Global Optimization by Smoothing

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Norkin, Vladimir Pichler, Alois Kozyriev, Anton V. M. Glushkov Institute of Cybernetics National Academy of Sciences of Ukraine Kyiv03178 Ukraine Department of Applied Mathematics National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine Department of Mathematics Chemnitz University of Technology Chemnitz09126 Germany

ISBN: (纸本)9783031812408

This paper proposes a novel technique called "successive stochastic smoothing" that optimizes nonsmooth and discontinuous functions while considering various constraints. Our methodology enables local and global optimization, making it a powerful tool for many applications. First, a constrained problem is reduced to an unconstrained one by the exact nonsmooth penalty function method, which does not require the selection of the penalty coefficient. This reduction is exact in the case of minimization of a lower semicontinuous function under convex constraints. Then the resulting objective penalty function is sequentially smoothed by the kernel method starting from relatively strong smoothing and with gradually vanishing degree of smoothing. The finite difference stochastic gradient descent with trajectory averaging minimizes each smoothed function locally. Finite differences over stochastic directions sampled from the kernel estimate the stochastic gradients of the smoothed functions. We investigate convergence rate of such stochastic finite-difference method on convex optimization problems. The "successive smoothing" algorithm uses the results of previous optimization runs to select the starting point for optimizing a consecutive, less smoothed function. Smoothing provides the "successive smoothing" method with some global properties. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constrained optimization

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Experimental Observations and Analysis of BGP Route Flapping Dynamics

Experimental Observations and Analysis of BGP Route Flapping...

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international conference on Information Networking

作者： Karina Cereceda Jason But Philip Branch School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The Border Gateway Protocol (BGP) is responsible for updating inter-domain routing information. One of the poorly understood aspects of BGP behaviour that has a direct impact on Internet stability is route flapping, where a route is repeatedly advertised but then withdrawn. This behaviour leads to slow convergence times resulting in Autonomous Systems (ASes) having inconsistent views of the Internet. Route flapping is an important matter that needs to be addressed to ensure the stability of the Internet as it continues to grow. However, there is currently no satisfactory solution to prevent or mitigate the effects of route flapping. Before a satisfactory solution can be developed, a better understanding of how route flapping originates and propagates is needed. In this paper, we used our large network testbed to construct a network with twenty ASes. We were able to generate route flapping and demonstrated that it can be caused by a congested link between ASes. We demonstrated that the frequency of route flapping is proportional to the level of congestion. We also showed that route flaps propagate across the entire network. As part of this research, we developed an automation tool to construct medium-to-large BGP testbeds where all interconnected ASes are placed under a common out-of-band management domain, providing a single vantage point to observe the behaviour and propagation of routing flaps across affected BGP peers. As far as we are aware this is the first time route flapping has been generated and analysed using a large scale testbed of commercial equipment. However, others have carried out research using simulation and modelling. We found our results consistent with theirs.

关键词： Automation Autonomous systems Routing Stability analysis Routing protocols Border Gateway Protocol Internet Oscillators convergence

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Enhancing Cybersecurity in Rural Areas - A Multilayered AI Approach to Combat Phishing Threats using Quantum computing

Enhancing Cybersecurity in Rural Areas - A Multilayered AI A...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： A. Jaya Mabel Rani V Manoj Sethi Sashi Kiran Vuppala K. Thilagam Manimegalai L T. C. Manjunath Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Applications Analyst Avaya LLC Irving TX United States Java Technical Architect Hyderabad Telangana India Department of ECE Velammal Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Professor in Computer Science & Engineering Department Dean Research (R & D) Rajarajeswari College of Engineering Bangalore Karnataka India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Rapid advances in quantum computing have raised worries about the security of classical encryption systems, as quantum computers can break many cryptographic techniques. This research strengthens encryption algorithms against quantum-based cryptographic assaults at the convergence of cybersecurity and quantum computing. Quantum computers, using quantum parallelism and quantum algorithms like Shor's and Grover's, threaten the security of widely used cryptographic systems like RSA and ECC, which use integer factorization and discrete logarithms. Post-quantum cryptography (PQC) is needed because quantum assaults can break classical encryption systems. This work emphasizes the need for quantum-resistant encryption techniques by examining the theoretical and practical effects of quantum computing on cryptography. It discusses quantum-safe algorithms like lattice-based, hash-based, and code-based cryptography that could replace or improve encryption standards. The research also examines quantum key distribution (QKD), a new secure communication method that uses quantum physics to share keys. The computational expense, implementation difficulty, and cybersecurity infrastructure integration difficulties of switching to quantum-resistant encryption methods are also examined. Quantum computing is evolving, hence safe encryption algorithms that can withstand quantum-based cryptographic assaults are needed. This paper concludes by emphasizing the need to standardize quantum-resistant algorithms, accelerate quantum cryptography research, and promote international collaboration to protect sensitive data from quantum threats. Future communication systems will depend on post-quantum cryptography to protect digital data.

关键词： Resistance Computers Quantum computing Quantum algorithm Switches Elliptic curve cryptography Encryption Quantum key distribution Cryptography Computer security

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Neural Network Algorithm Based on Deterministic Annealing for Vendor Resource Allocation Problem 8th

Neural Network Algorithm Based on Deterministic Annealing f...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Yu, Yaolong Wu, Zhengtian Xie, Yuan Le, Shuting Zhang, Xin School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Department of Statistics University of California DavisCA95616 United States

ISBN: (纸本)9789819602933

The vendor resource allocation problem is a typical NP-hard problem, and finding efficient solutions has always been a challenge in both academia and industry. This paper proposes a neural network-based deterministic annealing algorithm to address this problem. The algorithm consists of two main parts: a globally convergent iterative process and a convergence path composed of minima of barrier functions. The paper proves that, as the barrier factor decreases from a sufficiently large real number to 0, a high-quality approximate solution to the problem can be obtained along a series of convergence paths composed of minima of barrier functions. Simulation results demonstrate the superiority of the proposed algorithm compared to existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： NP-hard

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A New Maintenance Framework of WSN for Consumer and e-Commerce: A convergence of AI, ML, and IoT

A New Maintenance Framework of WSN for Consumer and e-Commer...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Kamurthi Ravi Teja Shakti Raj Chopra Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The aim of this study is to integrate energy-efficient wireless sensor networks (WSNs) into consumer electronics manufacturing and e-commerce systems. The study focuses on two main objectives: developing predictive maintenance machine learning (ML) models for consumer electronics production, enhancing personalization and contextualization in e-commerce platforms using energy-efficient WSN architecture. By analyzing data from sensors embedded in manufacturing processes, the predictive maintenance models predict maintenance needs, which can optimize production efficiency, reduce unscheduled downtime, and improve resource allocation. This study also investigates the financial benefits of implementing predictive maintenance strategies, showing significant cost savings and improved resource allocation. The incorporation of WSN into e-commerce systems encompasses the entire consumer electronics lifecycle. By employing an energy-efficient WSN architecture, reliable data collection for predictive maintenance is achieved while minimizing environmental impact. Within the e-commerce domain, this integration enhances customer experiences by ensuring products are delivered in optimal condition, thereby fostering customer satisfaction and loyalty. Additionally, the architecture facilitates personalization and contextualization, providing consumers with customized recommendations based on their preferences and real-time contextual information.

关键词： Industries Wireless sensor networks Costs Production Energy efficiency Maintenance Manufacturing Electronic commerce Consumer electronics Predictive maintenance

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A Hybrid Swarm Intelligence Model for Efficient Query Optimization in Distributed Databases Using Genetic and Ant Colony Algorithms

A Hybrid Swarm Intelligence Model for Efficient Query Optimi...

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Nemala Jayasri Santosh Kumar B Navdeep Singh Ajeet Singh Taqi Mohammed Khattab Al-Rubaye Mallikarjuna Rao Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Department of CSE GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Optimisation of queries in distributed databases is more imperative to enhance the processing rate and use of resources with limited computational abilities. This work presents a novel GA-ACO hybrid swarm intelligence model to address the query optimization problem. The optimization effort of the proposed model starts with GA for an initial population of probable paths for query execution which uses the concept of evolution to select diverse solutions. After that, ACO strengthens this set by the application of pheromone-based reinforcement that allows adjusting of a query paths toward the best solution or its approximation with the following number values. In this way the enhancement of the convergence rates and the scalability of the proposed solution when distributed databases are used can be explained. The model was tested with artificial query logs with DEAP being used for the implementation of the genetic algorithm while ant colony operations were implemented using Python built ACO libraries. Performance analysis shows the success of the developed model in decreasing query response time as follows: This essentially complex hybrid mechanism shows potential for future use in other areas with large data sets and efficient query handling.

关键词： Costs Query processing Computational modeling Scalability Distributed databases Time factors Particle swarm optimization Optimization Genetic algorithms Load modeling

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An Implementation of a DSP-Based PAM-4 Digital Equalizer with 32-Way TI-ADC

An Implementation of a DSP-Based PAM-4 Digital Equalizer wit...

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international conference on Electronics, Information and Communications (ICEIC)

作者： Sang-Hyeon Ok Dong-Hoe Heo Jae-Geon Lee Seung-Mo Jin Kwang-Ho Lee Daniel Jeong Min-Seong Choo The School of Electronics Engineering Hanyang University Ansan South Korea Convergence Signal SoC Korea Electronics Technology Institute Seongnam South Korea Ayar Labs CA USA

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper presents an ADC-based digital equalization architecture designed for high-speed wireline communication systems under PAM-4. The system integrates a 32-way time-interleaved 7-bit SAR ADC with following a 14-tap feedforward equalizer (FFE) using sign-sign least mean square (SS-LMS) adaptation and a 1-tap decision feedback equalizer (DFE). It also includes a continuous-time linear equalizer (CTLE) and a bit-error rate tester (BERT) for performance validation. The proposed design addresses the challenges of long-distance transmission over lossy channels by minimizing intersymbol interference (ISI) and improving signal integrity through adaptive equalization.

关键词： Communication systems Simulation High performance computing Intersymbol interference Adaptive filters Computer architecture Filtering algorithms Propagation losses Decision feedback equalizers Signal integrity

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Nature-Inspired Adaptive Evolutionary Swarm Optimization for Energy-Efficient Computational Mathematics in High-Performance computing Systems

Nature-Inspired Adaptive Evolutionary Swarm Optimization for...

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international conference on Inventive Computation Technologies (ICICT)

作者： Nayakallu Somanna V. Rama Krishna P. Hussain Basha Sk. Khaja Shareef Nannaparaju Vasudha M Bhavsingh CAI G. Pullaiah College of Engineering & Technology Kurnool Andhra Pradesh India Department of Computer Sciecne and Engineering CVR College of Engineering Hyderabad India Department of CSE Pace institute of Technology and scienc. ongole Andhra Pradesh India Department of computer science & Information Technology Koneru Lakshmaiah Education Foundation Hyderabad Telangana India Department of Mathematics Vasavi College of Engineering Hyderabad Department of CSE Ashoka Womens Engineering College Kurnool Andhra Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

High-Performance computing (HPC) systems play a critical role in enabling complex simulations, large-scale data analysis, and scientific research. However, the significant energy demands of these systems raise concerns around operational sustainability and long-term cost-efficiency. Existing natureinspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)-have shown promise in task scheduling but often fall short due to issues like high computational overhead, premature convergence, and limited responsiveness to real-time energy dynamics. In response to these limitations, this study presents the Adaptive Evolutionary Swarm Optimization (AESO) algorithm, which integrates adaptive mutation control with swarm-based task clustering to improve energy-aware scheduling in HPC environments. Unlike conventional methods, AESO dynamically adjusts its behavior based on current workload and energy trends, aiming to strike a balance between energy consumption, execution speed, and computational accuracy. Evaluation using the Google Cloud Workload dataset indicates that AESO reduces execution time by 16.9% (118s vs. 142s in GA) and energy consumption by 31.4% (1680 J vs. 2450 J in GA), while achieving a scheduling efficiency of 94% and maintaining a high accuracy of 99.85%. These outcomes suggest that the pursuit of energy efficiency need not come at the cost of performance. Looking ahead, future work will explore integrating AI-driven predictive models and reinforcement learning to further enhance AESO's adaptability within evolving, cloud-based, and distributed HPC systems.

关键词： Energy consumption Processor scheduling Heuristic algorithms High performance computing Dynamic scheduling Scheduling Energy efficiency Computational efficiency Particle swarm optimization Genetic algorithms

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