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MDZ: An Efficient Error-bounded Lossy Compressor for Molecular Dynamics 38

MDZ: An Efficient Error-bounded Lossy Compressor for Molecul...

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38th ieee International conference on data Engineering (ICDE)

作者： Zhao, Kai Di, Sheng Perez, Danny Liang, Xin Chen, Zizhong Cappello, Franck Univ Calif Riverside Riverside CA 92521 USA Argonne Natl Lab Lemont IL 60439 USA Los Alamos Natl Lab Los Alamos NM USA Missouri Univ Sci & Technol Rolla MO 65409 USA Univ Illinois Champaign IL USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an indepth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize two commonly-used execution models. (2) We develop an adaptive error-bounded lossy compression framework (called MDZ), which can optimize the compression for both execution models adaptively by taking advantage of their specific characteristics. (3) We compare our solution with six other state-of-the-art related works by using three MD simulation packages each with multiple configurations. Experiments show that our solution has up to 233% higher compression ratios than the second-best lossy compressor in most cases. (4) We demonstrate that MDZ is fully capable of handing particle data beyond MD simulations.

关键词： Adaptation models Materials science and technology Analytical models Biological system modeling conferences data engineering data models

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An Edge Aware Motion Modeling Technique Leveraging on the Discrete Cosine Basis Oriented Motion Model and Frame Super Resolution

An Edge Aware Motion Modeling Technique Leveraging on the Di...

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data compression conference (DCC)

作者： Ahmmed, Ashek Paul, Manoranjan Pickering, Mark Lambert, Andrew Univ New South Wales Sch Engn & Informat Technol Kensington NSW Australia Charles Sturt Univ Sch Comp & Math Bathurst NSW Australia

ISBN: (纸本)9781665478939

To capture motion homogeneity between successive frames, the edge position difference (EPD) measure based motion modeling (EPD-MM) has shown good motion compensation capabilities. The EPD-MM technique is underpinned by the fact that from one frame to next, edges map to edges and such mapping can be captured by an appropriate motion model. An example of such a motion model is the discrete cosine basis oriented (DCO) motion model, which can capture complex motion and has a smooth and sparse representation. However, for higher resolution video sequences, the baseline EPD-MM approach equipped with the DCO motion model, may fail to approximate the underlying motion field accurately. This is due to the difficulty in fitting motion model parameters by incorporating significantly large number of moving edge pixels. Observing the fact that in lower resolution version of the current frame C, the same scene structure is present although scaled down moving objects contain smaller number of edge pixels;in this paper we propose to carry out the EPD-MM technique, augmented by the DCO motion model, over lower resolution form of C. The resultant edge motion compensated prediction is then upsampled back to the original resolution of C, employing single image super resolution (SISR) technique. Experimental results show an improved prediction PSNR of 1.85 dB, on average, from the proposed approach compared to that of the baseline EPD-MM. Moreover, if this predicted frame is employed as an additional reference frame to encode C, bit rate savings of up to 7.90% is achievable over a HEVC reference.

关键词： Video coding Image resolution Image edge detection Bit rate Video sequences Fitting data compression

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Perceptual Deadband for Haptic data compression: Symmetric or Asymmetric? 31

Perceptual Deadband for Haptic Data Compression: Symmetric o...

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31st ieee International conference on Robot and Human Interactive Communication (RO-MAN) - Social, Asocial, and Antisocial Robots

作者： Bhardwaj, Amit IIT Jodhpur Dept Elect Engn Jodhpur Rajasthan India

ISBN: (纸本)9781728188591

In the literature, perceptual deadband approach based on the Weber's law of perception has been employed to reduce haptic data (i.e., force) rate for a typical teleoperation application. The approach selects only those samples for transmission which lie outside the perceptual deadband. The existing structure of the deadband has linear decision boundaries and assume that the just noticeable differences (JNDs) for increasing and decreasing change in a reference force stimulus are similar. This paper questions this assumption and searches for an asymmetric perceptual deadband using a datadriven approach. For the purpose, we design an experimental setup for collecting human haptic responses (perceived and nonperceived) of several users for a force range [3, 5] N. A machine learning classifier inspired from the Weber's law is trained to predict the labels of the responses and define a generalized linear perceptual deadband for each user. The results show that the generalized deadband do provide different increasing and decreasing JND (i.e., pointing towards asymmetry in force perception), but fails to improve the data reduction significantly as compared to the existing symmetric one.

关键词： Haptic data compression Perceptual deadband Just noticeable difference Weber fraction Prediction accuracy

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Applying Generative AI to Create SOP, Reducing API Costs Through Prompt compression and Evaluating LLM Responses with Tonic Validate RAG Metrics 4

Applying Generative AI to Create SOP, Reducing API Costs Thr...

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4th International conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vetriselvi, T. Mathur, Mihir Bhuvaneswari, M. Scope Vellore Institute of Technology Dept. of Iot Vellore India Scope Vellore Institute of Technology Dept. of Cse with Data Science Vellore India

ISBN: (纸本)9798331529635

Generative Artificial Intelligence (AI) utilizes existing data to create new forms of content, including text, images, and audio. One valuable application of generative AI in an IT Enabled Services (ITES) company is the development of Standard Operating Procedures (SOPs) by processing and consolidating existing SOPs through a Large Language Model. This paper explores how generative AI can generate content within standardized templates, improve the language of SOPs, and make them suitable for specific industries and processes, such as the pharmaceutical procure-to-pay process. It also addresses version control, helps users maintain consistency and compliance, extracts knowledge for onboarding, and provides interactive training on SOPs through Questions and Answers. The proposed workflow employs Azure OpenAI's GPT-3.5 turbo for generating responses, which are evaluated using Tonic Validate Retrieval Augmented Generation (RAG) metrics. Furthermore, the paper introduces Prompt compression approaches and selects one prompt compression approach to streamline the context retrieved for large language models while preserving the semantic meaning of the outputs. It also details strategies for reducing API call costs by over 10% for prompts of varying token sizes, ensuring high-quality responses from the GPT-3.5 turbo model according to RAG metrics. These criteria of accuracy, precision and cost reduction are considered to recommend the prompt compression approach for Large Language Models used in development of Standard Operating Procedures. © 2024 ieee.

关键词： API call cost Artificial Intelligence Azure OpenAI LLM (GPT-3.5 turbo) Generative AI Large Language Models NLTK Prompt compression Retrieval Augmented Generation (RAG) Standard Operating Procedures Stemming Tonic Validate word tokenize

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Proceedings of SC 2024: International conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of SC 2024: International Conference for High Pe...

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2024 International conference for High Performance Computing, Networking, Storage and Analysis, SC 2024

ISBN: (纸本)9798350352917

The proceedings contain 108 papers. The topics discussed include: versatile datapath soft error detection on the cheap for HPC applications;network-offloaded bandwidth-optimal broadcast and allgather for distributed AI;asynchronous distributed-memory parallel algorithms for influence maximization;EXO: accelerating storage paravirtualization with EBPF;understanding data movement patterns in HPC: a NERSC case study;hZCCL: accelerating collective communication with co-designed homomorphic compression;MProt-DPO: breaking the ExaFLOPS barrier for multimodal protein design workflows with direct preference optimization;scaling molecular dynamics with ab initio accuracy to 149 nanoseconds per day;and AutoCheck: automatically identifying variables for checkpointing by data dependency analysis.

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A parallel compression framework for fractal images using DCT block classification 21

A parallel compression framework for fractal images using DC...

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21st ieee International conference on Ubiquitous Computing and Communications / 5th ieee International conference on data Science and Computational Intelligence / 21st ieee International conference on Computer and Information Technology

作者： Pang, Jiahao He, Jie Guo, Hui Chen, Minglang Wuzhou Univ Guangxi Key Lab Alachine Vis & Intelligent Contro Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

Fractal image compression has higher space domain compression performance compared to the mainstream image compression algorithms using discrete cosine transform, but the search time for coding fractal image compression is too long and less practical. In this paper, we propose a fractal image block classification scheme based on discrete cosine transform, and use NVIDIA CUDA architecture to achieve parallel coding, which shows that this scheme can achieve 107 times faster speedup than the basic fractal scheme, and the coding time is significantly reduced, and the PSNR decreases only about 0.4, so this scheme has greater practicality.

关键词： fractal image compression DCT CUDA image block classification

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Federated Learning Scheme Based on Gradient compression and Local Differential Privacy

Federated Learning Scheme Based on Gradient Compression and ...

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2023 ieee International conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Zhao, Lun Hu, Siquan Shi, Zhiguo University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350399028

As a deep learning framework for distributed deployment, federated learning allows users to complete model training while retaining the original data, which meets the local data privacy protection needs of each user. Differential privacy, as a classic data desensitization technology, is often used to improve privacy protection. Many studies have applied it to machine learning, but it is accompanied by the problem of model utility. In addition, in the traditional federated learning model training, there are frequent gradient exchanges between the user and the central server, which poses a huge challenge to the communication overhead of the model and affects the training efficiency of the model. In order to solve these problems, we propose a new federated learning scheme, firstly propose a StaComp compression algorithm to compress the gradient parameters before the user uploads them, use the local differential privacy mechanism to add noise to protect the user's local data. Finally, we evaluate our scheme on the MNIST dataset. Experiments show that the scheme of this article is close to the classic FedAvg algorithm in the aspect of model accuracy, and is better than other methods based on differential privacy. At the same time, it significantly reduces the training time, improves the convergence speed of the model, and saves communication costs. © 2023 ieee.

关键词： data privacy

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Exploring Various data Security Methods for Enhancing Security in Cloud Computing 2

Exploring Various Data Security Methods for Enhancing Securi...

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2nd International conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rubi Rani, Sunita Saroha, Vinod Kr. Beniwal, Sonal Bhagat Phool Singh Mahila Vishwavidyalaya Department of Computer Science Eng. & IT Sonipat India

ISBN: (纸本)9798331519056

Today in the information technology (IT) field the continued advancement in technology cloud computing plays a crucial role in sharing, storing and retrieving data from anywhere anytime. In the present day, the large number of devices connected through the internet creates big amounts of data and raises security and privacy issues. This paper target is a comprehensive review of various existing data security techniques such as cryptography, steganography, homomorphic encryption, machine learning, deep learning, Deoxyribonucleic Acid (DNA) cryptography, quantum cryptography and blockchain that are used in cloud environments for safe transmission and storage of data. The paper identifies the key points of each technique and shows the compression among them for a better understanding the usability of each technique. It provides the knowledge of which technique is suitable when and how and describes the current status of data security technology. The earlier work regarding different security methods and their identification has been enveloped together in this paper for presenting detailed insight. © 2024 ieee.

关键词： Cloud security

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Weight Distribution Based Model compression Method for Object Detection System 4

Weight Distribution Based Model Compression Method for Objec...

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4th International conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Zhang, Junrong Zhang, Minghe Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China

ISBN: (纸本)9798350343618

Artificial intelligence-based object detection is an essential field in computer vision applications such as autonomous driving, security surveillance, medical image analysis, intelligent transportation, and human-computer interaction. However, such state-of-the-art algorithm is computing and resources intensive, which limits its deployment especially on resources constrained devices such mobile phones, drones, and autonomous vehicles. In this paper, we propose a compression method to overcome such bottleneck. Our approach reduces the computing complexity by automatically zeroing out certain percentage of weights in convolutional layers. Through extensive experiments on the well-known YOLOv5s model, 60% computing can be saved during the inference process without sacrificing the (mean Average Precision) mAP. © 2023 ieee.

关键词： Autonomous vehicles

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Unlocking the Path Towards Automation of Tiny Machine Learning for Edge Computing

Unlocking the Path Towards Automation of Tiny Machine Learni...

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International conference on Smart Applications, Communications and Networking (SmartNets)

作者： Samaras, Georgios Mertiri, Marinela Xezonaki, Maria-Evgenia Psaromanolakis, Nikolaos Theodorou, Vasileios Bozios, Theodoros Intracom Telecom Peania Greece

ISBN: (纸本)9798350385328;9798350385335

ThirstyAI and resource-constrained environments have become evergrowing challenges for Machine Learning (ML), especially large-scale Deep Learning (DL) models. This paper introduces NanoDeploy Automator, an Automated Machine Learning (AutoML) subsystem for the TANDEM native Artificial Intelligence (AI) edge platform, designed for efficient and reliable Tiny Machine Learning (TinyML), addressing the need for smaller models with minimal loss of accuracy and automated model compression and deployment at the edge. This work has been conducted in scope of project TANDEM and is currently expanded and adapted, forming the basis for the TinyML mechanisms of project SUNRISE-6G. The paper showcases the novel application of Time Generative Pre-Trained Transformer (TimeGPT) in real-world Internet of Things (IoT) scenarios, emphasizing three key contributions: a cutting-edge Automated TinyML (AutoTinyML) pipeline, the integration of TimeGPT in IoT, and a comprehensive evaluation of time series forecasting and anomaly detection techniques. Evaluation results demonstrate the efficiency of the AutoTinyML pipeline in terms of accuracy and model compression. The proposed system holds promise for diverse applications, leveraging the power of AutoTinyML, and reflects a significant stride towards democratizing access to accurate predictions and reducing uncertainty in edge computing scenarios, eliminating the need for a dedicated team of ML engineers.

关键词： Automated Machine Learning Tiny Machine Learning Artificial Intelligence Big data Internet of Things Edge Computing Edge Intelligence

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