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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Anandhasilambarasan, D. Seth, Kanika Aravind, Ar Goyal, Prakhar Kantharaj, I.K. Ingle, Yashwant S. Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350370249

Visibility-based shading techniques for laptop snapshots and Visualization can provide realistic and perceptual shading consequences with the aid of utilizing data from the scene's visibility shape. Those methods comprise visibility statistics to compute the approximate illumination of an object or scene, thinking about occlusion consequences from other objects. Visibility-based totally shading algorithms permit more practical actual-time renderings that extra accurately fit the way people perceive the illumination of scenes. Moreover, these techniques offer performance blessings over global illumination-based total methods, such as improved memory burden and faster computation instances, making them appropriate for plenty of applications. Popular visibility-based strategies, including ray casting and direct illumination, are based totally on the idea that each surface is ideally diffuse and homogeneous, which means their absorption and reflection coefficients are constant. To overcome this issue, a range of new techniques integrate visibility and physics-based total fashions, accounting for fabric homes (such as specular highlights) in the illumination computation. This combination of visibility and physics-based fashions can be, in addition, improved via incorporating mild shipping simulation strategies, inclusive of bidirectional reflectance distribution features and course tracing, to capture better and predict the rendering of actual international materials and surfaces. © 2024 IEEE.

关键词： Consequences Perceptual Rendering Shading Utilizing Visualization

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Automated Image Captioning and Speech Synthesis

Automated Image Captioning and Speech Synthesis

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International Conference on Futuristic Technologies (INCOFT)

作者： Amruta Mankawade Pavitha N Amruta VPatil Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Department of Computer Engineering Vishwakarma University SJJTU Rajasthan

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated Image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology.

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Optimizing Shielding Techniques for High-Frequency EMC Applications in Aerospace Systems 15

Optimizing Shielding Techniques for High-Frequency EMC Appli...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahalle, Parikshit N. Kalanandhini, G. Hemelatha, S. Samrat, Bhavuk Mishra, Mukul Ramachandran, T. Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Karnataka Bangalore India

ISBN: (纸本)9798350370249

The defense of high-frequency electromagnetic compatibility (EMC) for aerospace systems is important to ensure the functionality and reliability of aerospace cars. Moreover, the performance of the system has to follow each plane's requirements and electromagnetic regulations, such as Federal Aviation Administration (FAA) regulations, to hold protection and trustworthiness. While protecting excessive-frequency EMC application is only sometimes taken into consideration, the electrical gadgets, wiring, and numerous components may be electrically laid low with electrical fields, resulting in malfunction of the machine, mistakes in calculations, and faulty indicators. This paper evaluates the optimization of protective techniques for high-frequency EMC packages in aerospace systems through the usage of a mixture of simulation and size strategies. The objectives of the look are to expand an efficient shielding structure and optimize the design to reduce the reflection of electromagnetic wave radiation in the system. A two-layered shielding method with an internal shield of aluminum foil is used to reduce the reflection of electromagnetic wave radiation. Furthermore, the field studies and the simulations executed permit the optimization of the measurements to be greater than it should be predicted. The obtained results imply that the optimized defensive approach makes a good sized enhancement in the defensive overall performance of the device, thereby disposing of errors due to outside EMC resources. ©2024 IEEE.

关键词： Electromagnetic wave reflection

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Empirical Evaluation of Internet of Things Assisted Fire Fighting Robotic Design Based on Intelligent Sensors Association

Empirical Evaluation of Internet of Things Assisted Fire Fig...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： T Mahitha Sajja Suneel M. Amina Begum Er. S. John Pimo B. Swapna Revathi. R Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Oxford College of Engineering Venmani Tamilnadu India Computer Science and Engineering St. Xavier's Catholic College of Engineering Nagercoil ECE Dr MGR Educational and Research Institute Chennai Department of Computer Science Karpagam Academy Of Higher Education Coimbatore

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research work presents the design and implementation of an Internet of Things (IoT) assisted fire fighting robotic system based on intelligent sensors association. The system integrates various components including intelligent sensors for detecting fire, smoke, temperature changes, and gas emissions, Arduino microcontrollers for data processing and control logic execution, Wi-Fi communication modules for remote monitoring and data exchange, and actuators for physical motion and manipulation tasks. Through the integration and coordination of these components, the firefighting robot achieves autonomous capabilities in detecting, localizing, and responding to fire incidents effectively. The intelligent sensors, including the MQ-2 Gas Sensor, TMP36 Temperature Sensor, MQ-7 Carbon Monoxide Sensor, and MLX90614 Non-Contact Infrared Temperature Sensor, provide real-time environmental data inputs to the Arduino Uno microcontroller, which interprets sensor readings, executes decision-making algorithms, and orchestrates responses. Leveraging bidirectional Wi-Fi connectivity facilitated by the ESP8266 Wi-Fi Module, the system enables remote monitoring and data exchange, empowering operators with situational awareness for informed decision-making. Actuators such as DC Gear Motors and SG90 Micro Servo Motors translate digital commands into mechanical motion and manipulation, enabling the robot to navigate through fire-affected environments, manipulate fire fighting tools, and execute precision fire fighting maneuvers. This abstract presents a comprehensive overview of the IoT-assisted fire fighting robotic system, highlighting its autonomous capabilities, integration of components, and potential for enhancing fire fighting efficiency and safety.

关键词： Temperature sensors Actuators Microcontrollers Robot kinematics Decision making Robot sensing systems Internet of Things Intelligent sensors Wireless fidelity Remote monitoring

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FedVC: Virtual Clients for Federated Autonomous Driving With Imbalanced Label Distribution

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Yongquan Chen, Hongda Wang, Zhihui Kumar, Rahul Wang, Changmiao Zhejiang University of Finance and Economics School of Data Science Hangzhou310018 China Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266510 China Ssv Pg College Department Of Mathematics Hapur245101 India Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning in autonomous driving safeguards the privacy of individual vehicles during collaborative training by avoiding the exchange of raw data. These vehicles often suffer from imbalanced label distribution, making the federated learning model developed from them critical for safety. Most current solutions addressing label imbalance in federated learning are oriented towards classification tasks. However, there is limited research focused on prediction tasks involving imbalanced datasets relevant to autonomous driving. To address the identified gap, we introduce an innovative peer-to-peer federated learning framework called FedVC. This framework includes the use of virtual clients to enhance the ability of the collaborative training process to recognize the global data distribution effectively. It strategically samples relevant segments of local data from various clients for each training round and controls the execution time for backpropagation associated with each virtual client. Importantly, FedVC constructs a global perspective by utilizing metadata from each client, thereby maintaining data privacy while overcoming the challenge of label imbalance in autonomous driving prediction tasks, all without the need to share any raw datasets. Experimental results demonstrate that FedVC outperforms classical FedAvg and the most recent methods at three steering angle prediction datasets with different levels of imbalanced label distribution. The source code is publicly available at https://***/aikedaerC/FedVC. © 1975-2011 IEEE.

关键词： Autonomous Driving data Imbalance Federated Learning Virtual Clients

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Time-Series Cross-Validation Parallel Programming using MPI

Time-Series Cross-Validation Parallel Programming using MPI

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2021 International Conference on data Analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Khedr, Ahmed Alhaj, Duaa Al Khalifa, Reem Alqaddoumi, Abdulla University of Bahrain Big Data Science and Analytics College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Time-series data has a natural chronological arrangement with modeling and cross-validation techniques, highly dependent on sequential processing of data, which challenges its parallelization. Since the running time of the modeling algorithm largely consists of a main operation of solving the modeling algorithm several times with various training and testing dataset sizes, it is the prime target for optimizing the running time. This research presents a parallel implementation of time-series cross-validation on a rolling basis that involves using a subset of the dataset for training purposes, expanding each dataset with each run to obtain test accuracy. The evaluation is conducted using parallel speedup, parallel efficiency, and computational time. The proposed algorithm distributes the task of modeling among several processors to run independently and obtain results. The computational time recorded ranged from 15.47 seconds at best with five processors to 28.33 seconds at worst when done with a single processor. The parallel system speedup was found to be sub-linear with an efficiency decreasing with the increase in the number of processors. © 2021 IEEE.

关键词： Time series

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

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19th IEEE International Conference on e-science, e-science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

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Service-Based Event Penetration from IoT Sensors to Businesses: a Case Study

Service-Based Event Penetration from IoT Sensors to Business...

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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Wang, Guiling Fang, Jun Wang, Jing Yu, Jian Zhang, Liang Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

By leveraging IoT Big data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach. © 2022 IEEE.

关键词： Internet of things

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Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge Computing 25th

Computation Offloading Algorithm Based on Deep Reinforcemen...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Tengxiang Lin, Cheng-Kuan Chen, Zhen Cheng, Hongju College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9789811995811

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q Network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR data

arXiv

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arXiv 2023年

作者： Meng, Xuran Zou, Difan Cao, Yuan Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Department of Computer Science Institute of Data Science The University of Hong Kong Hong Kong Department of Statistics and Actuarial Science Department of Mathematics The University of Hong Kong Hong Kong

Modern deep learning models are usually highly over-parameterized so that they can overfit the training data. Surprisingly, such overfitting neural networks can usually still achieve high prediction accuracy. To study this "benign overfitting" phenomenon, a line of recent works has theoretically studied the learning of linear models and two-layer neural networks. However, most of these analyses are still limited to the very simple learning problems where the Bayes-optimal classifier is linear. In this work, we investigate a class of XOR-type classification tasks with label-flipping noises. We show that, under a certain condition on the sample complexity and signal-to-noise ratio, an over-parameterized ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy. Moreover, we also establish a matching lower bound result showing that when the previous condition is not satisfied, the prediction accuracy of the obtained CNN is an absolute constant away from the Bayes-optimal rate. Our result demonstrates that CNNs have a remarkable capacity to efficiently learn XOR problems, even in the presence of highly correlated features. © 2023, CC BY-SA.

关键词： Gradient methods

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