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检索条件"任意字段=International Symposium on Computer Networks and Distributed Systems"

共 64275 条记录，以下是1201-1210 订阅

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Design and Security Analysis of a distributed OS with Adaptive IP Management

Design and Security Analysis of a Distributed OS with Adapti...

引用

2024 international Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Prajapat, Pramod Chadha, Raman Sharma, Aryan Awasthi, Kartikay Chandigarh University Computer Science and Engineering Punjab India

ISBN: (纸本)9798331517953

As we can see, in the era of high-speed internet and the increasing use of technologies, the proper and safe management of IP Addresses is of paramount importance. De- centralized networks' older methods of distributing IP addresses can reveal weaknesses and produce inefficiencies. In the following paper, a new approach towards dynamic IP management which is developed to overcome these difficulties is presented. Our APK Dynamic IP Manager is equipped with a dedicated tier that controls a range of IP addresses and distributes them to nodes focusing on traffic rates as well as security precautions. This makes it more secure and ensure the network is well managed despite the increase in the size of the network. The experimental analysis and theoretical assessment of the model has been performed and the results show that the proposed model presents significant improvement over existing systems. It offers more protection against computer criminal activities and also enhances resource utilizationa. This work offers a potential implementation to Network Management and its applicability spans from complex enterprise networks to cloud computing as well as IoT devices. © 2024 IEEE.

关键词： Efficiency

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Energy-aware neural architecture selection and hyperparameter optimization 36

Energy-aware neural architecture selection and hyperparamete...

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36th IEEE international Parallel and distributed Processing symposium (IEEE IPDPS)

作者： Frey, Nathan C. Zhao, Dan Axelrod, Simon Jones, Michael Bestor, David Gadepally, Vijay Gomez-Bombarelli, Rafael Samsi, Siddharth MIT 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781665497473

Artificial Intelligence (AI) and Deep Learning in particular have increasing computational requirements, with a corresponding increase in energy consumption. There is a tremendous opportunity to reduce the computational cost and environmental impact of deep learning by accelerating neural network architecture search and hyperparameter optimization, as well as explicitly designing neural architectures that optimize for both energy efficiency and performance. Here, we introduce a framework called training performance estimation (TPE), which builds upon existing techniques for training speed estimation in order to monitor energy consumption and rank model performance-without training models to convergence-saving up to 90% of time and energy of the full training budget. We benchmark TPE in the computationally intensive, well-studied domain of computer vision and in the emerging field of graph neural networks for machine-learned inter-atomic potentials, an important domain for scientific discovery with heavy computational demands. We propose variants of early stopping that generalize this common regularization technique to account for energy costs and study the energy costs of deploying increasingly complex, knowledge-informed architectures for AI-accelerated molecular dynamics and image classification. Our work enables immediate, significant energy savings across the entire pipeline of model development and deployment and suggests new research directions for energy-aware, knowledge-informed model architecture development.

关键词： deep learning green AI sustainable AI energy efficiency hyperparameter optimization early stopping graph neural networks computer vision

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Exploring the Effects of Silent Data Corruption in distributed Deep Learning Training 34

Exploring the Effects of Silent Data Corruption in Distribut...

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34th IEEE international symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Rojas, Elvis Perez, Diego Meneses, Esteban Costa Rica Inst Technol Cartago Costa Rica Natl Univ Costa Rica Heredia Costa Rica Costa Rica Natl High Technol Ctr San Jose Costa Rica

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

The profound impact of recent developments in artificial intelligence is unquestionable. The applications of deep learning models are everywhere, from advanced natural language processing to highly accurate prediction of extreme weather. Those models have been continuously increasing in complexity, becoming much more powerful than their original versions. In addition, data to train the models is becoming more available as technological infrastructures sense and collect more readings. Consequently, distributed deep learning training is often times necessary to handle intricate models and massive datasets. Running a distributed training strategy on a supercomputer exposes the models to all the considerations of a large-scale machine;reliability is one of them. As supercomputers integrate a colossal number of components, each fabricated on an ever decreasing feature-size, faults are common during execution of programs. A particular type of fault, silent data corruption, is troublesome because the system does not crash and does not immediately give an evident sign of an error. We set out to explore the effects of that type of faults by inspecting how distributed deep learning training strategies cope with bit-flips that affect their internal data structures. We used checkpoint alteration, a technique that permits the study of this phenomenon on different distributed training platforms and with different deep learning frameworks. We evaluated two distributed learning libraries (distributed Data Parallel and Horovod) and found out Horovod is slightly more resilient to SDCs. However, fault propagation is similar in both cases, and the model is more sensitive to SDCs than the optimizer.

关键词： Deep learning resilience checkpoint neural networks HDF5 fault injection high performance computing

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distributed and Adaptive Message Dissemination for Vehicle Platooning in Hybrid Traffic

Distributed and Adaptive Message Dissemination for Vehicle P...

引用

2024 IEEE international Conference on systems, Man, and Cybernetics, SMC 2024

作者： Li, Xiang Zhang, Fuxin College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665410205

In vehicular networks, vehicle in the platooning relies on dissemination of beacons to perceive the status of neighbor vehicles and then take control low to maintain a constant inter-vehicle distance. Vehicle platooning communication has stringent high-reliability and low-latency requirements. In this paper, we focus on resource allocation strategies for vehicle platoons in hybrid scenarios. First, we propose a two-dimensional Markov model to describe the channel contention (back-off) processes for platoons and individual vehicles. Based on the model, we then can derive the transmission probabilities of beacons and event messages for platoons and individual vehicles. Finally, we design a distributed and adaptive beacon control scheme to determine the optimal beacon rates for vehicles in the platoons. The simulation results demonstrate that our efforts substantially enhance platooning communication reliability and ensure optimal performance for vehicle safety applications. © 2024 IEEE.

关键词： Markov processes

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Modular Multiport Converter Based Offshore Grid Architecture for Integrating Renewables and HVDC Grid 14

Modular Multiport Converter Based Offshore Grid Architecture...

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14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： Karbozov, Arnur Krishnamoorthy, Harish S. Rajashekara, Kaushik University of Houston Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350328233

This paper proposes a novel offshore grid architecture that interconnects wind turbines, hybrid storage systems consisting of fuel cells, lithium-ion batteries, and an HVDC onshore grid to power subsea electrical loads. The proposed system utilizes Energy Router, which is based on modular structure of Multiport Active Bridge converters, eliminating the need for line frequency transformers and a common DC bus for energy sources. The paper highlights the advantages of the proposed architecture and presents power management control strategy to ensure reliable operation of the grid. © 2023 IEEE.

关键词： Electric loads

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Comparison of the Effectiveness of Face Recognition Algorithms in Terms of Photo Distortion Level 11th

Comparison of the Effectiveness of Face Recognition Algorith...

引用

11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Grabowski, Daniel Saeed, Khalid Faculty of Computer Science Bialystok University of Technology Bialystok Poland Department of Computer Science and Electronics Universidad de la Costa—CUC Barranquilla Colombia

ISBN: (纸本)9789819797615

This research explores the impact of photo distortion on face recognition algorithms performance, focusing on convolutional neural networks (CNNs) due to their superior efficiency. Through a comprehensive review of current solutions and empirical experimentation with the LFW image database, this study investigates how compression, blurring, and noise affect algorithmic accuracy. Experiments utilized the Python-based DeepFace library for comparative analysis of photo similarity, optimizing algorithm parameters through density analysis and decision trees. The study confirms that while compression minimally affects performance, noise—especially at higher levels—significantly degrades accuracy. However, applying median filters to noisy images largely restores effectiveness. Blurring’s impact varies with direction, with vertical blurring severely impairing recognition capabilities. Among the algorithms evaluated, FaceNet512 emerged as the most resilient in handling various types of distortions. The findings underscore the importance of distortion type and parameter optimization in maintaining recognition accuracy, offering valuable insights for enhancing algorithmic resilience against photo quality degradation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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A Study on the Application of Protein Language Models in the Analysis of Membrane Proteins 19th

A Study on the Application of Protein Language Models in...

引用

19th international symposium on distributed Computing and Artificial Intelligence, DCAI 2022

作者： Ghazikhani, Hamed Butler, Gregory Department of Computer Science and Software Engineering Concordia University Montreal Canada Centre for Structural and Functional Genomics Concordia University Montreal Canada

ISBN: (纸本)9783031232091

Membrane proteins make up around 30% of all proteins in a cell. These proteins are difficult to evaluate due to their hydrophobic surface and dependence on their original in vivo environment. There is a tremendous demand for computational approaches for predicting membrane proteins’ function and structure. Protein sequences, the language of life, can benefit from using natural language algorithms. In this study, we propose to use the BERT language model to represent membrane proteins. Our preliminary results, which utilized a Logistic Regression classifier, reveal that BERT can comprehend the context of membrane proteins and enable us to investigate better and distinguish these proteins. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Proteins

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Research on Large-scale Data Anomaly Detection based on Deep Learning 5

Research on Large-scale Data Anomaly Detection based on Deep...

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5th international Conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Zhuang, Gancheng Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350352719

With the continuous progress of big data, Internet of Things, artificial intelligence and other technologies, we are living in an era of information explosion. A large amount of data is constantly recorded, and this data contains important information at the critical moment. With the rapid increase in data volume, traditional statistical methods are gradually unable to meet the needs due to high computational complexity and poor adaptability. In this work, we utilize autoencoder and generative adversarial network deep learning methods to detect anomalies in large-scale data. Autoencoders learn low-dimensional representations of data through the encoding and decoding process, and are mainly used to reconstruct normal data and identify anomalies by reconstructing errors. The generative adversarial network grasps the distribution characteristics of the data through the adversarial learning of its generative model and discriminant model, and uses this ability to detect abnormal patterns in the data. The effectiveness of the proposed model was verified by conducting experiments on multiple real-world datasets. Experimental results show that compared with the traditional method, the proposed deep learning model has significant improvement in detection accuracy and computing efficiency. © 2024 IEEE.

关键词： Generative adversarial networks

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Generative Adversarial networks for Augmenting EEG Data in P300-based Applications: A Comparative Study 35

Generative Adversarial Networks for Augmenting EEG Data in P...

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35th IEEE international symposium on computer-Based Medical systems (CBMS)

作者： Abdelghaffar, Yasmin Hashem, Ahmed Eldawlatly, Seif German Univ Cairo Comp Sci & Engn Dept Cairo Egypt Ain Shams Univ Comp & Syst Engn Dept Cairo Egypt German Univ Cairo Digital Media Engn & Technol Dept Cairo Egypt

ISBN: (数字)9781665467704

ISBN: (纸本)9781665467704

The performance of P300-based Brain-computer Interface (BCI) applications is highly dependent on both the quality and quantity of the recorded Electroencephalography (EEG) signals. As recording extended datasets from users for calibration is often a difficult and tedious task, data augmentation can be used to help supplement the training data for machine learning classifiers that are typically used in P300-based BCI applications. In this paper, we analyze and compare the performance of three different generative adversarial networks (GANs) as data augmentation techniques; namely, deep convolutional GAN (DCGAN), conditional GAN (cGAN), and the auxiliary classifier GAN (ACGAN). We first investigated the effect of increasing the training data size using each of these GANs on the performance of P300 classification. Our results revealed that the cGAN increased the classification accuracy by up to 18% relative to the baseline data under the best conditions. We also investigated the effect of decreasing the training data size and compensating for the reduced data size using data generated from the GANs. Our analysis indicated that the training data size could be reduced by ~30% while maintaining the accuracy on par with the baseline accuracy. These results demonstrate the utility of GANs in addressing the challenges associated with the limited data typically available for BCI applications.

关键词： P300 data augmentation generative adversarial networks DCGAN cGAN ACGAN

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Verifying Feedforward Neural networks for Classification in Isabelle/HOL 25th

Verifying Feedforward Neural Networks for Classification in ...

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25th international symposium on Formal Methods (FM)

作者： Brucker, Achim D. Stell, Amy Univ Exeter Dept Comp Sci Exeter Devon England

ISBN: (纸本)9783031274800;9783031274817

Neural networks are being used successfully to solve classification problems, e.g., for detecting objects in images. It is well known that neural networks are susceptible if small changes applied to their input result in misclassification. Situations in which such a slight input change, often hardly noticeable by a human expert, results in a misclassification are called adversarial examples. If such inputs are used for adversarial attacks, they can be life-threatening if, for example, they occur in image classification systems used in autonomous cars or medical diagnosis. systems employing neural networks, e.g., for safety or security-critical functionality, are a particular challenge for formal verification, which usually expects a formal specification (e.g., given as source code in a programming language for which a formal semantics exists). Such a formal specification does, per se, not exist for neural networks. In this paper, we address this challenge by presenting a formal embedding of feedforward neural networks into Isabelle/HOL and discussing desirable properties for neural networks in critical applications. Our Isabelle-based prototype can import neural networks trained in TensorFlow, and we demonstrate our approach using a neural network trained for the classification of digits on a dot-matrix display.

关键词： Neural network Deep learning Classification network Feedforward network Verification Isabelle/HOL

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