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检索条件"任意字段=3rd International Workshop on Real-Time Computing Systems and Applications"

共 3673 条记录，以下是11-20 订阅

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A Comprehensive Review on Functional Analysis of real-time Operating systems 3

A Comprehensive Review on Functional Analysis of Real-Time O...

引用

3rd international Conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Reddy, B. Sharan Nripesh Venkat, B. Sai Naidu, G. Narasimha Nisha, K.S. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350343632

A real-time operating system (RTOS) is a system made to help us in real-time using real-time applications that provide no delay. A RTOS is time-bound, which means that time is the main part of the system, as the system will fail if it goes beyond the prescribed time. Processing the time requirements in this system are measured in tenths of seconds. The integrity of an RTOS system established not only takes the logical effects of computation into account but also the amount of time required to get such results. It must prioritize vital jobs while keeping context-switching time to a minimum. This research aims to perform a comprehensive functional analysis of real-time operating systems (RTOS), with a particular emphasis on key components such as interrupt handling strategies, synchronization mechanisms, and job scheduling algorithms. The purpose of the study is to shed light on the state of the art at the moment, point out roadblocks, and suggest possible enhancements for raising RTOS capability and performance in significant embedded systems. © 2023 IEEE.

关键词： Hard RTOS real-time Operating systems Soft RTOS

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3rd international workshop on Industrial Recommendation systems (IRS) 24

3rd International Workshop on Industrial Recommendation Syst...

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33rd ACM international Conference on Information and Knowledge Management (CIKM)

作者： Ma, Luyi Li, Xiaohan Ahmadi, Kamilia Xu, Jianpeng Yu, Philip S. Karypis, George Walmart Global Tech Sunnyvale CA 94086 USA Warner Bros Discovery San Francisco CA USA Univ Illinois Chicago IL USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9798400704369

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience [1]. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.

关键词： Industrial Recommendation systems E-commerce Personalization

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OpenDSS-Based real-time RMS Simulator: Design and applications 3

OpenDSS-Based Real-Time RMS Simulator: Design and Applicatio...

引用

3rd international workshop on Open Source Modelling and Simulation of Energy systems (OSMSES)

作者： Espinoza, Renzo Fabian Godoy, Paulo KEMA Labs Arnhem Netherlands Itaipu Technol Pk Brazil Foz Do Iguacu PR Brazil

ISBN: (纸本)9798350384697;9798350384680

This paper introduces DSS-RT, an innovative opensource, real-time root-mean-square (RMS) simulator for power grids. DSS-RT leverages OpenDSS, a widely recognized distribution system simulator. Enabled by the AltDSS/DSS C-API, an unofficial C application programming interface, the integration of OpenDSS functionalities into C-based projects is facilitated. This capability, paired with the inherent power of the C language, has been effectively utilized to extend OpenDSS into the domain of real-time simulation applications. The simulator has been implemented on GNU/Linux platforms equipped with a real-time kernel, showcasing its broad application potential, particularly in hardware-in-the-loop (HiL) testing environments. Key tests performed include a real-time co-simulation that integrates an RMS simulation implemented on a standard personal computer with an Electromagnetic Transient simulation conducted in RTDS, and a HiL test featuring a protective relay, where the proposed simulator was deployed on an NI cRIO controller. These evaluations confirm the robustness and precision of the proposed RMS simulator in practical scenarios, highlighting its adaptability across various platforms.

关键词： Hardware-in-the-loop OpenDSS real-time simulation RMS simulation

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AI-Driven Dynamic Workload Balancing for real-time applications on Cloud Infrastructure 3

AI-Driven Dynamic Workload Balancing for Real-Time Applicati...

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3rd international Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Baskaran, Saravanakumar Sayiram, Sathyanarayanan Vedula, Jagadeesh Muthusamy, Rajkumar Jiwani, Nasmin Kiruthiga, T. Microsoft Corporation Seattle United States IBM San Jose United States Houston United States Ntt Data Irving United States University of The Cumberlands Deptartment of Information Technology Williamsburg United States Vetri Vinayaha College of Engineering and Technology Department of Ece Trichy India

ISBN: (纸本)9798331508456

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven real-time applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods to responsively reallocate resources between virtual machines by the required Quality of Service (QoS) levels and capacity requirements. It efficiently schedules jobs to available resources and delivers results promptly, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing. © 2025 IEEE.

关键词： Computer aided instruction

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NetraAI-The 3rd Eye: AI-Empowered Surveillance 6

NetraAI-The 3rd Eye: AI-Empowered Surveillance

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6th IEEE international Conference on Image Processing, applications and systems, IPAS 2025

作者： Dave, Harsh K. Pathak, Sparsh J. Sutaria, Kamal Parul University CSE PIET Vadodara India Parul University PIET Department of CSE Vadodara India

ISBN: (纸本)9798331506520

This research paper explores the development and implementation of 'NetraAI - The 3rd Eye,' an AI-powered surveillance system aimed at enhancing public safety and security measures. The study investigates the technical architecture, real-time functionalities, and ethical considerations surrounding the deployment of NetraAI. It examines its impact on object detection, people tracking, vehicle recognition, and proactive threat detection in diverse surveillance scenarios. The findings highlight the system's efficacy, ethical deployment, and potential contributions to the field of AI-driven surveillance. © 2025 IEEE.

关键词： Security systems

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real time Poisoning Attacks and Privacy Strategies on Machine Learning systems 3

Real Time Poisoning Attacks and Privacy Strategies on Machin...

引用

3rd international Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Archa, A.T. Kartheeban, K. Kalasalingam Academy of Research and Education Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396157

Machine Learning aims to learn computer systems and predict output. Nowadays, Deep learning uses continuous training based on a large network of interconnected neurons to mimic the way humans think, analyze, and make decisions. It plays an important role in real-time applications. There are some security and privacy threats faced by deep learning systems. One of the major threats faced by such learning systems is attacks during training. The manipulation of training data leads to poisoning attacks. The resulting models are severely impacted by poisoning attacks, which may prevent them from converging or alter the outcomes of their predictions. The need to defend against such poisoning attacks is urgent and difficult. This study explains a thorough and detailed analysis of different types of poisoning attacks in real time applications. Its countermeasures to mitigate such attacks and various privacy-enhancing methods are also discussed here. © 2024 IEEE.

关键词： Learning systems

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Technical Report: Consistency Assurance for Edge-Assisted Multi-Party computing and Transport-Intensive applications 3

Technical Report: Consistency Assurance for Edge-Assisted Mu...

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3rd IEEE international Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Mei, Wenming Zhang, Lizhi Song, Wen Sun, Yunqi China State Grid Corporation of China Beijing China State Grid Electric Power Research Institute Nanjing China Nanjing University Nanjing China

ISBN: (纸本)9798350380989

In the rapidly evolving digital era, we have witnessed the rise of computation-intensive and transmission-intensive applications like multi-party real-time video communication, remote medical surgeries, and online education. Cloud-edge collaborative resource scheduling has been pivotal in enhancing computational efficiency and reducing end-to-end latency for such applications. However, providing services while ensuring system consistency presents a new challenge in multiparty settings. This study focuses on efficient resource scheduling within cloud-edge collaborative environments, especially addressing system consistency. We propose an innovative real-time consistency assurance model based on latency differentiation aimed at optimizing real-time consistency issues in cloud-edge settings while ensuring data accuracy and timeliness. This problem falls within integer programming, which can transform into a convex optimization issue, efficiently solvable via convex optimization methods. The solution effectively addresses consistency in resource scheduling, significantly improving overall system performance and reliability. Experimental results demonstrate that our method is suitable for computation-intensive and transmission-intensive tasks, substantially enhancing the system's consistency performance and providing robust technical support for the design and optimization of practical cloud-edge collaborative systems. © 2024 IEEE.

关键词： Cloud-edge collaborative resource scheduling Integer programming real-time consistency

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Prediction of Cyber Attacks Utilizing Deep Learning Model using Network/Web Traffic Data 3

Prediction of Cyber Attacks Utilizing Deep Learning Model us...

引用

3rd international Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kannan, Balaji Sakthivanitha, M. Jayashree, S. Maruthi, R. School of Computing Science Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Nowadays, cyber-attacks are growing predominantly due to the development of technologies. It will lead to financial losses to a company and the other problems related to attacks. It is very important to predict such attacks from outsiders to safeguard our networking systems to provide effective security. The Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques leverage enormous amounts of data to identify the cyber attacks. These learning approaches are used to identify a broad range of cyber-attacks by analyzing the web traffic or network traffic to identify potential threats such as malware, network intrusions and other types of attacks. The study demonstrates the various deep learning methods to predict the anomalies and other potential threats with more accuracy in real time. © 2024 IEEE.

关键词： Forecasting

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FRAME 2023 - Proceedings of the 3rd workshop on Flexible Resource and Application Management on the Edge

FRAME 2023 - Proceedings of the 3rd Workshop on Flexible Res...

引用

3rd workshop on Flexible Resource and Application Management on the Edge, FRAME 2023, held in conjunction with the HPDC 2023 and the FCRC 2023 Conferences

ISBN: (纸本)9798400701641

The proceedings contain 8 papers. The topics discussed include: DATA7: a dataset for assessing resource and application management solutions at the edge;multi-agent deep reinforcement learning for weighted multi-path routing;real-time monitoring and analysis of edge and cloud resources;simulating FaaS orchestrations in the cloud-edge continuum;contention-aware performance modeling for heterogeneous edge and cloud systems;innovation potential of the ACCOrdION Platform;TEACHING: a computing toolkit for building efficient autonomous applications leveraging humanistic intelligence;and EDGELESS Project: on the road to serverless edge AI.

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StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests 3

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

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3rd international Conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ching, Cheng-Wei Guan, Boyuan Xu, Hailu Hu, Liting University of California Santa Cruz Department of Computer Science and Engineering Santa CruzCA United States Center of Florida International University MiamiFL United States California State University Department of Computer Engineering and Computer Science Long BeachCA United States

ISBN: (纸本)9798331530280

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure. © 2024 IEEE.

关键词： Resource allocation

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