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A Novel Architecture for Efficient Mobile communication based on Scattered and Diverse Combination using Prioritize and Postoperative Soft Modulation

A Novel Architecture for Efficient Mobile Communication base...

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International Conference on ent Computing and Control systems (ICICCS)

作者： J. Sumithra M Mercy Theresa S. Geetha Priya Beulah Jackson V. Samuthira Pandi A. Balamurugan Department of Electronics and Communication Engineering R. M. D Engineering College Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering R. M. D Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu India

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate Radio Frequency (RF) interfaces (like cellular) and short-range high-rate RF interfaces (such as IEEE802.11) that communicate over urban Rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as limited short-range RF bandwidth. These strategies are contained within the confines of this paradigm, which serves as the framework for proposed work. Post Soft-Demodulation Combining, often referred to as PSDC, and Priority Maximum Ratio Combining are the two approaches that come highly recommended by our team (PMRC). This work provides evidence that using the offered methods increases throughput, reliability, and signal-to-noise ratio. Moreover, this study found that PMRC and PSDC have the ability to improve throughput performance by more than an order of magnitude across a wide range of communication circumstances.

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Modeling Digital Twins of Kubernetes-Based Applications

Modeling Digital Twins of Kubernetes-Based Applications

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IEEE Symposium on computers and communications (ISCC)

作者： Davide Borsatti Walter Cerroni Luca Foschini Genady Ya. Grabarnik Filippo Poltronieri Domenico Scotece Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Mattia Zaccarini University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Distributed Systems Research Group University of Ferrara Ferrara Italy IBM TJ W2atson Research Center Operational Innovation NY USA

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

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An HCI Experiment to Explore Interactive Artificial Life Art 24th

An HCI Experiment to Explore Interactive Artificial Life Ar...

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24th International Conference on Human-computer Interaction, HCI International, HCII 2022

作者： Dumo, Glare Lind, Pedro G. Nichele, Stefano Department of Computer Science OsloMet – Oslo Metropolitan University P.O. Box 4 St. Olavs plass Oslo0130 Norway AI Lab – OsloMet Artificial Intelligence Lab Pilestredet 52 Oslo0166 Norway NordSTAR – Nordic Center for Sustainable and Trustworthy AI Research Pilestredet 52 Oslo0166 Norway Department of Holistic Systems Simula Metropolitan Center for Digital Engineering Pilestredet 52 Oslo0166 Norway Department of Computer Science and Communication Østfold University College B R A Veien 4 Halden1757 Norway

ISBN: (纸本)9783031063909

We present an interface for creating art using tools from artificial intelligence and artificial life. The interface is tested and validated, through an online experiment with the aim of investigating how AI algorithms can be used for enabling humans - able or disabled - to express their artistic creativity. The interface uses evolutionary algorithms to generate images and videos of life-like patterns of pixels, based on rules describing local updates for cellular automata. Participants were asked to select a sequence of rules, either randomly generated (control group) or assisted by an interactive evolutionary algorithm which takes into account the participant’s previous choices (test group). Our results show a significantly higher satisfaction of the test group with a significance level of 95 % and a power of 82 %. Finally, we also discuss the usefulness of such an interface for people with disabilities and limitations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cellular automata

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Design of a hybrid genetic algorithm for time-sensitive networking 1

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20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing systems, MMB 2020

作者： Arestova, Anna Hielscher, Kai-Steffen Jens German, Reinhard Department of Computer Science 7 Computer Networks and Communication Systems University of Erlangen-Nürnberg Martensstraße 3 Erlangen91058 Germany

ISBN: (数字)9783030430245

ISBN: (纸本)9783030430238

With Time-Sensitive Networking (TSN), the IEEE 802.1 Task Group is extending the Ethernet standard by time-sensitive capabilities to establish a common ground for real-time communication systems via Ethernet. The Time-Sensitive Networking Task Group introduces a time-triggered transmission approach in IEEE 802.1Qbv to enable a deterministic transmission of time-critical network traffic, which requires scheduling strategies. Genetic algorithms are qualified to solve these scheduling problems in Time-Sensitive Networks. The difficulty is to design the genetic algorithm to find an optimal or a near-optimal solution for different complex problems taking performance and quality of the schedule into account. The complexity of schedules for TSN depends on the decision space of a network designer comprising the possibility to combine a variable number of network participants, a variable number of TSN flows, as well as assuming fixed or flexible routes for the flows. In this paper, we discuss a design approach for a hybrid genetic algorithm including chromosome representation for the routing and scheduling problems in TSN, the choice of genetic operators, and a neighborhood search to find a near-optimal solution. Additionally, we introduce an approach to compress the resulting schedules. Our evaluations show that the proposed hybrid genetic algorithm is able to compete with the well-adapted NEH algorithm in terms of schedule quality, and it outperforms the NEH algorithm regarding the computing time. © Springer Nature Switzerland AG 2020.

关键词： Genetic algorithms

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AI-Infused Telemedicine for Rural Wellness: A Comprehensive Approach

AI-Infused Telemedicine for Rural Wellness: A Comprehensive ...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Nidhi B Kurpad S Dhanyashree V Shivank T A S Surya Shobith S Vinila Jinny Computer Science and Engineering Vellore Institute of Technology Vellore India Electronics and Communication Engineering S J B Institute of Technology Bengaluru India Electrical and Electronics Engineering B N M Institute of Technology Bengaluru India Department of Software Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The paper introduces a transformative Telemedicine Kiosk designed to enhance healthcare in remote areas by harnessing the synergy of computational intelligence and telemedicine. This advanced kiosk utilizes real-time video conferencing and comprehensive patient monitoring systems, all underpinned by Responsible AI practices that ensure ethical data management and the protection of patient privacy. Equipped with an ESP32 Wi-Fi module, the kiosk provides seamless communication between patients and medical officers, facilitating immediate care and consultation. The system goes beyond diagnostics: it supports healthcare professionals with an integrated medication dispensing mechanism powered by IoT technology. Our investigation delves into the implications of human factors, economics, and technological advances for the future of telemedicine. We present an innovative solution that not only advances healthcare accessibility but also embodies a commitment to responsible and ethical healthcare service provision in underserved regions.

关键词： Ethics Data privacy Patient monitoring Telemedicine Medical services Real-time systems Protection

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Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning

arXiv

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arXiv 2024年

作者： Zimmerman, Julia Witte Hudon, Denis Cramer, Kathryn Ruiz, Alejandro J. Beauregard, Calla Fehr, Ashley Fudolig, Mikaela Irene Demarest, Bradford Bird, Yoshi Meke Trujillo, Milo Z. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Institute University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Institute University of Vermont BurlingtonVT05405 United States The Luddite *** Department of Computer Science St. Michael’s College ColchesterVT05439 United States Communication Media and Marginalization Lab Northeastern University BostonMA02215 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States

Tokenization is a necessary component within the current architecture of many language models, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model’s cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient for reasonably human-like language performance, and that the emergence of human-meaningful linguistic units among tokens and current structural constraints motivate changes to existing, linguistically-agnostic tokenization techniques, particularly with respect to their roles as (1) semantic primitives and as (2) vehicles for conveying salient distributional patterns from human language to the model. We explore tokenizations from a BPE tokenizer;extant model vocabularies obtained from Hugging Face and tiktoken;and the information in exemplar token vectors as they move through the layers of a RoBERTa (large) model. Besides creating suboptimal semantic building blocks and obscuring the model’s access to the necessary distributional patterns, we describe how tokens and pretraining can act as a backdoor for bias and other unwanted content, which current alignment practices may not remediate. Additionally, we relay evidence that the tokenization algorithm’s objective function impacts the LLM’s cognition, despite being arguably meaningfully insulated from the main system intelligence. [First uploaded to arXiv in December, 2024.] Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Game-Oriented Security Strategy Against Hotspot Attacks for Internet of Vehicles

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computers, Materials & Continua 2021年第8期68卷 2145-2157页

作者： Juan Guo Yanzhu Liu Shan Li Zhi Li Sonia Kherbachi Guilin University of Electronic Technology Beihai536000GuangxiChina Great Wall Computer Software and Systems Inc. Beijing100083China Department of Electronics and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing100070China State Grid Information&Telecommunication Group Co. Ltd.Beijing100031China Department of Management University of BejaiaBejaia06000Algeria

With the rapid development of mobile communication technology,the application of internet of vehicles(IoV)services,such as for information services,driving safety,and traffic efficiency,is growing *** businesses with low transmission delay,high data processing capacity and large storage capacity,by deploying edge computing in the IoV,data processing,encryption and decision-making can be completed at the local end,thus providing real-time and highly reliable communication *** roadside unit(RSU),as an important part of edge computing in the IoV,fulfils an important data forwarding function and provides an interactive communication channel for vehicles and server *** computing resources can be configured to accommodate the computing requirements of *** this study,a virtual traffic defense strategy based on a differential game is proposed to solve the security problem of user-sensitive information leakage when an RSU is *** incentive mechanism encourages service vehicles within the hot range to send virtual traffic to another *** attracting the attention of attackers,it covers the target RSU and protects the system from *** results show that the scheme provides the optimal strategy for intelligent vehicles to transmit virtual data,and ensures the maximization of users’interests.

关键词： Edge computing internet of vehicles differential games security defense

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Process Industry System Modeling Based on Time Series Imputation: A Case Study of Paste Thickener

Process Industry System Modeling Based on Time Series Imputa...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xiaorui Li Xiaojuan Ban Yizhou Shao Haowei Chen Chao Yao Zhaolin Yuan Guangsheng Liu Lijie Guo Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China School of Artificial Intelligence Hebei University of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China BGRIMM Technology Group Beijing China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In process CPS systems, highly accurate modeling of the production system is essential for optimal control, fault diagnosis, and flexible manufacturing. Traditional data-driven modeling of industrial processes focuses on predicting future trends based on variable periodicity and trends, often neglecting the impact of control variables. This paper proposes a new approach to improve CPS accuracy, particularly for systems heavily influenced by controlled variables. Firstly, CPS modeling is redefined as a time series imputation problem, introducing exogenous variables via a sequence modeling algorithm based on a state space model to enhance forecasting accuracy. Secondly, sequence inputs are encoded in patches, improving the model's ability to process local sequence information. The proposed method is evaluated on a real deep cone thickener underflow concentration forecasting task and compared with state-of-the-art time series forecasting methods, demonstrating superior performance. This study not only provides a more accurate model for CPS but also offers a framework for integrating control variables into predictive modeling.

关键词： Accuracy Time series analysis Process control Predictive models systems modeling Prediction algorithms Market research Imputation Manufacturing Forecasting

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