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distributed Search Engine Query Optimization Using Artificial neural network

Distributed Search Engine Query Optimization Using Artificia...

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2022 International conference on Cyber Resilience, ICCR 2022

作者： Ali, Liaqat Al-Sakhnini, Mahmoud M. Kalra, Deepak Afzaal, Farheen Pervaiz, Madiha Khan, Muhammad Farrukh College of Engineering and Technology University of Science and Technology of Fujairah Fujairah United Arab Emirates School of Business Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Faculty of Computer and Information Technology Al-Madinah International University Kuala Lumpur57100 Malaysia School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates National College of Business Administration and Economics Lahore Pakistan University of South Asia Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665461221

In this research, we propose a distributed Search Engine Query Optimization (DSEQO) based sensor network concept for instantaneous forest fire exposure. The sensor network may identify and predict forest fire more sharp than the outdated satellite-based prediction method. The research mainly defines the information gathering and managing in sensor networks for real-time forest fire detection. To predict the real-time fire identification, an ANN technique is utilized to in-network information processing. After simulation it was seen that the suggested approach gives better results with LM approach in terms of Accuracy and Miss Rate. © 2022 IEEE.

关键词： Sensor networks

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DynaGraph: Dynamic Graph neural networks at Sc 5

DynaGraph: Dynamic Graph Neural Networks at Sc

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5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences and Systems and network Data Analytics, GRADES-NDA 2022, co-located with ACM SIGMOD conference

作者： Guan, Mingyu Iyer, Anand Padmanabha Kim, Taesoo Georgia Institute of Technology Atlanta United States Microsoft Research Redmond United States

ISBN: (纸本)9781450393843

In this paper, we present DynaGraph, a system that supports dynamic Graph neural networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic GNN architectures combine techniques for structural and temporal information encoding independently, DynaGraph proposes novel techniques that enable cross optimizations across these tasks. It uses cached message passing and timestep fusion to significantly reduce the overhead associated with dynamic GNN processing. It further proposes a simple distributed data-parallel dynamic graph processing strategy that enables scalable dynamic GNN computation. Our evaluation of DynaGraph on a variety of dynamic GNN architectures and use cases shows a speedup of up to 2.7X compared to existing state-of-the-art frameworks. © 2022 Owner/Author.

关键词： Graph neural networks

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A Noise-Based Approach Augmented with neural Cleanse and JPEG Compression to Counter Adversarial Attacks on Image Classification Systems

A Noise-Based Approach Augmented with Neural Cleanse and JPE...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Igor Kotenko Igor Saenko Oleg Lauta Nikita Vasiliev Vladimir Sadovnikov Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia Department of Telecommunications and Information Security Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Adversarial attacks are now becoming quite a dangerous means of disrupting image processing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against adversarial attacks is becoming quite an important area of cybersecurity. The paper proposes a noise-based approach to countering adversarial attacks that is augmented with neural-cleanse and jpeg-compression technologies. The idea of the proposed approach is that adding noise distorts the effect of an adversarial attack, and neural cleaning and jpeg compression eliminate the consequences of such an effect. The paper examines the three most well-known types of adversarial attacks: Fast Gradient Sign Method, Zeroth Order Optimization and One Pixel Attack. These attacks manipulate input data, resulting in misclassification or incorrect predictions by exploiting high-frequency components that are undetectable to humans. The research was carried out on two datasets: MNIST-JPG and PC Parts Images. Two types of noise were used: Gaussian and Poisson. During the experiments, optimal parameters for these types of noise were found, ensuring maximum accuracy of image recognition after exposure to adversarial attacks.

关键词： Resistance Image recognition Image coding Image resolution Noise Transform coding Machine learning Distortion Optimization Image classification

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Production Security Evaluation Strategy of Power Data Center Based on Data Warehouse 3

Production Security Evaluation Strategy of Power Data Center...

引用

2023 IEEE 3rd International conference on Data Science and Computer Application, ICDSCA 2023

作者： Huang, Zhenlin Wen, Xing Zhao, Liuqi China Southern Power Grid Ehv Transmission Company Guangzhou China

ISBN: (纸本)9798350341546

This paper studies the task scheduling strategy of cloud data center based on data warehouse. Combined with the characteristics of power data center, a multi-qos evaluation model for power data center is defined. In this paper, a neural network algorithm is used for task scheduling with QoS overall effect value as the target. Then this paper establishes the overall technical architecture and hardware and software system of the big data center of the electric power company, and studies the key technologies such as data access and preprocessing, data integration and storage, data computing and processing, data mining and analysis, data security and management. Finally, taking the transformer equipment status evaluation experiment center as an example, the tasks of fault diagnosis, status evaluation, overhaul and maintenance are simulated and scheduled, and the Cloud Sim platform is used for simulation test. The results show that this algorithm can meet the multi-qos requirements of power users to the maximum extent and effectively improve the efficiency of power data center operation. © 2023 IEEE.

关键词： data warehouse distributed storage Power data production safety evaluation system task scheduling

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Analysis on Roles of DNNs in End-to-End Acoustic Scene Analysis Framework with distributed Sound-to-Light Conversion Devices

Analysis on Roles of DNNs in End-to-End Acoustic Scene Analy...

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Asia-Pacific-Signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Kinoshita, Yuma Ono, Nobutaka Tokyo Metropolitan Univ Tokyo Japan

ISBN: (纸本)9789881476890

We conduct an analysis on roles of deep neural networks (DNNs) in an end-la-end acoustic scene analysis framework with distributed sound-to-light conversion devices called Blinkies. Blinkies transmit sound information as the intensity of an onboard light-emitting diode (LED). A video camera can Then easily collect acoustic information by capturing the LED intensities from multiple Blinkies distributed over a large area. In the end-to-end framework, both sound-lo-light conversion and scene analysis processes are performed using two types of DNNs: an encoding network and a scene analysis network. These DNNs are optimized in an end-to-end manner for acoustic scene analysis. Although the efficacy of the end-to-end framework is already confirmed, it is unclear what role each network plays in the framework. In this paper, we examine the role of these networks through a simulation experiment using intermediate signal shuffling. Experimental results suggest that an encoding network does not output scene analysis results, but it achieves node-specific sound-to-light conversion that encodes spatial information of sounds. By using the node-dependent features obtained by encoding networks, the scene analysis network classifies scenes.

关键词： Deep neural networks

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Room Transfer Function Reconstruction Using Complex-valued neural networks and Irregularly distributed Microphones

Room Transfer Function Reconstruction Using Complex-valued N...

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European Signal processing conference (EUSIPCO)

作者： Francesca Ronchini Luca Comanducci Mirco Pezzoli Fabio Antonacci Augusto Sarti Dipartimento di Elettronica Informazione e Bioingegneria (DEIB) Milan Italy

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal processing methods, deep learning techniques have been applied to reconstruct the room transfer function starting from a very limited set of measurements at scattered points in the room. In this paper, we employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances, using a few irregularly distributed microphones. To the best of our knowledge, this is the first time that complex-valued neural networks are used to estimate room transfer functions. To analyze the benefits of applying complex-valued optimization to the considered task, we compare the proposed technique with a state-of-the-art kernel-based signal processing approach for sound field reconstruction, showing that the proposed technique exhibits relevant advantages in terms of phase accuracy and overall quality of the reconstructed sound field. For informative purposes, we also compare the model with a similarly-structured data-driven approach that, however, applies a real-valued neural network to reconstruct only the magnitude of the sound field.

关键词： Knowledge engineering Deep learning neural networks Transfer functions Resonant frequency Europe Signal processing Frequency estimation Optimization Microphones

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distributed Accumulation based Energy Efficient STT-MRAM based Digital PIM Architecture 19

Distributed Accumulation based Energy Efficient STT-MRAM bas...

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19th International SoC Design conference (ISOCC) - SoC Technology Towards a New Era of Innovation

作者： Kim, Dongsu Park, Jongsun Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665459716

Spin transfer torque MRAM (STT-MRAM) based digital processing-in-memory (PIM) has been recently proposed for energy-efficient processing of convolutional neural network (CNN) without analog-to-digital converters (ADC). However, since only computations between operands stored in the same row are possible in the digital PIM, it consumes considerable energy for data transfer between memory rows when computations between data stored in different rows are performed. In this paper, we present energyefficient digital PIM architecture that supports the distributed accumulation scheme. In the proposed PIM architecture, the computations for accumulation are distributed to the memory arrays and peripheral circuits, and only a portion of the workload for the accumulations which does not require heavy data transfer cost is performed in the memory array. Then, the remaining of the accumulations that needs complicated data transfer among the memory array are performed using the peripheral circuits. In addition, as the value of the partial sums is read before accumulation, the zero-skipping technique, which cannot be applied when the computations are fully performed in the memory array, can be also applied in the proposed PIM architecture. The simulations with 28nm CMOS process show that the proposed digital PIM architecture with zero prediction achieves the energy savings up to 54.3% over the conventional digital PIMs.

关键词： Convolutional neural network STT-MRAM digital in memory computing

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Building representative and balanced datasets of OpenMP parallel regions 29

Building representative and balanced datasets of OpenMP para...

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29th Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Alcaraz, Jordi Sleder, Steven TehraniJamsaz, Ali Sikora, Anna Jannesari, Ali Sorribes, Joan Cesar, Eduardo Univ Autonoma Barcelona Cerdanyola Del Valles Spain Iowa State Univ Ames IA USA

ISBN: (纸本)9781665414555

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications' executions. This work proposes a methodology for building datasets OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counter;to represent the execution of the region. which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, H . this correlation is helm a given threshold. the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify . different patterns of OpenMP parallel regions.

关键词： hardware performance counters machine learning artificial neural networks parallel applications OpeaMP

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distributed Artificial Intelligent Model Training and Evaluation 8th

Distributed Artificial Intelligent Model Training and Evalua...

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8th Latin American High Performance Computing conference (CARLA)

作者： Monahan, Christina Garcia, Alexander Zhang, Evan Timokhin, Dmitriy Egbert, Hanson Pantoja, Maria Calif Polytech State Univ San Luis Obispo San Luis Obispo CA 95116 USA

ISBN: (纸本)9783031042096;9783031042089

Machine Learning (ML) and in particular neural networks (NN) are currently being used for different image/video processing, speech recognition and other tasks. The goal of supervised NN is to classify raw input data according to the patterns learned from an input training set. Training and validation of NN is very computationally intensive. In this paper we present an NN infrastructure to accelerate model training, specifically tuning of hyper-parameters, and model inference or prediction using distributed systems techniques. By accelerating model training, we give ability to researchers to obtain a large set of potential models to use and compare in a shorter amount of time. Automating this process not only reduces development time but will provide an easy means for comparing results for different classifiers and/or different hyper-parameters. With a single set of training data, our application will run different classifiers on different servers each running models with tweaked hyper-parameters. To give more control over the automation process the degree by which these hyper-parameters will be tweaked can be set by the user prior to running. The prediction step in most ML algorithms can also be very slow, especially on video prediction where current systems calculate their inference predictions on an entire input video, and then evaluate accuracy based on human annotations of objects of interest within the video. To reduce this bottleneck, we also accelerate and distribute this important part of the ML algorithm development. This process involves sending to each server the data;the model weights;and human annotations within the video segmentation. Our efficient distribution of input frames among each node greatly reduced the amount taken for testing and to generate accuracy metrics. To make our implementation robust to common distributed system failures (servers going down, lost of communication among some nodes, and others) we use heartbeat/gossip style protocol f

关键词： Machine learning neural network distributed computing HPC

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distributed-order fractional graph operating network 24

Distributed-order fractional graph operating network

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Proceedings of the 38th International conference on neural Information processing Systems

作者： Kai Zhao Xuhao Li Qiyu Kang Feng Ji Qinxu Ding Yanan Zhao Wenfei Liang Wee Peng Tay Nanyang Technological University Anhui University Singapore University of Social Sciences

ISBN: (纸本)9798331314385

We introduce the distributed-order fRActional Graph Operating network (DRAGON), a novel continuous Graph neural network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a learnable probability distribution over a range of real numbers for the derivative orders. By allowing a flexible and learnable superposition of multiple derivative orders, our framework captures complex graph feature updating dynamics beyond the reach of conventional models. We provide a comprehensive interpretation of our framework's capability to capture intricate dynamics through the lens of a non-Markovian graph random walk with node feature updating driven by an anomalous diffusion process over the graph. Furthermore, to highlight the versatility of the DRAGON framework, we conduct empirical evaluations across a range of graph learning tasks. The results consistently demonstrate superior performance when compared to traditional continuous GNN models. The implementation code is available at https://***/zknus/NeurIPS-2024-DRAGON.

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