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检索条件"任意字段=IEEE Data Compression Conference"

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A Novel Approach to Select an Optimal Text compression Method Based on Application 6

A Novel Approach to Select an Optimal Text Compression Metho...

引用

6th International conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Rahman, Md. Atiqur Nirob, Md. Asraful Sharker Akter, Mashreka Bin Ayan, Mariyam Payel, Israt Jahan East West University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331531973

Substantial amounts of text data are now produced on many platforms, especially via social media and in various industries. These data are challenging to store on a local computer or to send over the internet. compression is an effective way of reducing the size of the original data, saving storage space, and reducing transmission costs in cyberspace. Many text compressors have already been developed, some of which offer greater compression while others provide higher speeds. For data storage applications such as Google Drive, OneDrive, and Dropbox, the compression ratio is more significant over encoding and decoding time. However, in contrast, there are various applications, such as real-time messaging, where encoding time, decoding time, and compression ratio hold equal importance. The selection of a text compressor based on either the compression ratio or the compression or decompression speed is easy;however, the performance of a compressor depends on all of these parameters, and it is difficult to select a better compressor from a range of alternatives based on a combination of these parameters. In this work, we propose a technique for selecting the optimum text compressor by analyzing the data from each perspective. An analysis is also provided to confirm the authenticity of the proposed technique. © 2024 ieee.

关键词： bits per symbol (bps) compression speed (CS) decompression speed (DS) Text compression

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Effective and Efficient Big OLAP data Cube compression in Mobile Cloud Environments: The IQTS Algorithm

Effective and Efficient Big OLAP Data Cube Compression in Mo...

引用

2023 ieee International conference on Big data, Bigdata 2023

作者： Cuzzocrea, Alfredo Hajian, Mojtaba University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France Big Data Management and Analytics at University of Paris City Paris France

ISBN: (纸本)9798350324457

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and 'philosophy'. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm. © 2023 ieee.

关键词： Approximate Query Answering Big data Big OLAP Cubes Intelligent Big data Applications Mobile Clouds

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High Efficient compression: Model compression Method Based on Channel Pruning and Knowledge Distillation

High Efficient Compression: Model Compression Method Based o...

引用

2023 Asia-Europe conference on Electronics, data Processing and Informatics, ACEDPI 2023

作者： Lin, Junan Ye, Zekai Wang, Junhan Zhejiang Gongshang University College of Computer and Information Engineering Hangzhou China

ISBN: (纸本)9798350301151

With the development of neural network models in the field of deep learning image processing, more complex and higher performance neural network models can be used, but with it comes greater parameter quantities and computing power requirements. Although the model compression method has been updated in recent years, it still has problems such as inefficiency and reduced accuracy. Based on this, we propose a model compression method based on channel pruning and knowledge distillation, we sort and prune the L2 regularization weights of the convolutional layer filter containing a large number of calculations in the original model and prune it according to a certain proportion, and assist in restoring the accuracy by the knowledge distillation of the pruning model through the original model. Experimental results of the VGG16 model show that our pruning method reduced the parameters of the model by half and increased its processing speed to 2.7 times that of the original model, but the accuracy was reduced by about 10%. To solve this problem, we propose a knowledge distillation method to assist the training of the pruning model, so that the problem of model accuracy degradation has been improved and maintained at about 3.3%, to achieve a balanced state of model compression and accuracy reduction, thereby realizing the efficient compression of the model. © 2023 ieee.

关键词： Distillation

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High-fidelity 3D Model compression based on Key Spheres

High-fidelity 3D Model Compression based on Key Spheres

引用

data compression conference (DCC)

作者： Li, Yuanzhan Liu, Yuqi Lu, Yujie Zhang, Siyu Cai, Shen Zhang, Yanting Donghua Univ Visual & Geometr Percept Lab Shanghai Peoples R China

ISBN: (纸本)9781665478939

In recent years, neural signed distance function (SDF) has become one of the most effective representation methods for 3D models. By learning continuous SDFs in 3D space, neural networks can predict the distance from a given query space point to its closest object surface, whose positive and negative signs denote inside and outside of the object, respectively. Training a specific network for each 3D model, which individually embeds its shape, can realize compressed representation of objects by storing fewer network (and possibly latent) parameters. Consequently, reconstruction through network inference and surface recovery can be achieved. In this paper, we propose an SDF prediction network using explicit key spheres as input. Key spheres are extracted from the internal space of objects, whose centers either have relatively larger SDF values (sphere radii), or are located at essential positions. By inputting the spatial information of multiple spheres which imply different local shapes, the proposed method can significantly improve the reconstruction accuracy with a negligible storage cost. Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction. Experiments conducted on three datasets verify the superior performance of our method.

关键词： Training Surface reconstruction Solid modeling Three-dimensional displays Shape Image color analysis Fitting

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English Classroom Teaching Based on Improved Apriori Algorithm 2

English Classroom Teaching Based on Improved Apriori Algorit...

引用

2nd ieee International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zheng, Shu Hu, Zhongbin Chengdu College of Arts and Sciences Chengdu China

ISBN: (纸本)9798350317558

Classroom teaching is the primary organizational formation of school teaching and the basic way of obtaining student's creativity and quality-oriented education. English teachers work from various backgrounds which include primary and secondary schools, universities, and colleges accommodating their teaching techniques to suit the different learning requirements of their students. However, only by maximizing their capability in information age could teachers understand the students better and fulfill the requirements of student learning in the environment of a network. In this research, the Improved Apriori Algorithm (IAA) is proposed for English classroom teaching quality. The IAA is improved by utilizing a Boolean matrix for row-column compression which minimizes the transaction database scanning time and the Trie tree is employed to increase the search process. Initially, the questionnaire sample data is established to evaluate the teacher criteria for learning tasks. The data preprocessing is performed by employing data filtering and then the evaluation of filtered text is split into short sentences. Then, the Word2 vector approach is employed to extract the features. Finally, the IAA technique is performed for quality of English classroom teaching. When compared with the Apriori Algorithm (AA), the IAA achieves better student innovation ability and student satisfaction of 90% and 95% respectively. © 2024 ieee.

关键词： data filtering English classroom teaching quality Improved Apriori Algorithm Student Word2 vector

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BeiDou Short Message Transmission Method Based on High-quality Image compression and Reconstruction 3

BeiDou Short Message Transmission Method Based on High-quali...

引用

3rd International conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Gong, Nanxu Li, Zijun Tan, Jie Chen, Hu School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350335972

Limited by bandwidth, Beidou short message system has certain limitations in the application of image transmission. To improve the problem, this paper proposes a Beidou short message transmission method based on high-quality image compression and reconstruction. We introduce the current mainstream deep learning framework based on variational autoencoders as our baseline. In order to achieve high-quality image compression and reconstruction, firstly, the attention mechanism is added to make the network automatically pay attention to beneficial information. Secondly, the image quality discriminator is designed that can provide the image definition score explicitly and is differentiable. Furthermore, based on the discriminator, a two-stage training method is proposed: first, the discriminator is trained with the data set synthesized by the Gaussian Blur, then the weights of the discriminator are fixed and the output of the discriminator is used to guide the network to generate images with high quality. The effectiveness of the method is verified by a series of experiments. © 2023 ieee.

关键词： Image reconstruction

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CuSZp: An Ultra-fast GPU Error-bounded Lossy compression Framework with Optimized End-to-End Performance 23

CuSZp: An Ultra-fast GPU Error-bounded Lossy Compression Fra...

引用

2023 International conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Huang, Yafan Di, Sheng Yu, Xiaodong Li, Guanpeng Cappello, Franck University of Iowa Iowa City United States Argonne National Laboratory Lemont United States Stevens Institute of Technology Hoboken United States

ISBN: (纸本)9798400701092

Modern scientific applications and supercomputing systems are generating large amounts of data in various fields, leading to critical challenges in data storage footprints and communication times. To address this issue, error-bounded GPU lossy compression has been widely adopted, since it can reduce the volume of data within a customized threshold on data distortion. In this work, we propose an ultra-fast error-bounded GPU lossy compressor cuSZp. Specifically, cuSZp computes the linear recurrences with hierarchical parallelism to fuse the massive computation into one kernel, drastically improving the end-to-end throughput. In addition, cuSZp adopts a block-wise design along with a lightweight fixed-length encoding and bit-shuffle inside each block such that it achieves high compression ratios and data quality. Our experiments on NVIDIA A100 GPU with 6 representative scientific datasets demonstrate that cuSZp can achieve an ultra-fast end-to-end throughput (95.53x compared with cuSZ) along with a high compression ratio and high reconstructed data quality. © 2023 ACM.

关键词： Errors

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Self-Supervised Augmented Vision Transformers for Remote Physiological Measurement 8

Self-Supervised Augmented Vision Transformers for Remote Phy...

引用

8th International conference on Cloud Computing and Big data Analytics (ICCCBDA)

作者： Pang, Liyu Li, Xiaoou Wang, Zhen Lei, Xueyi Pei, Yulong Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai Peoples R China Shanghai Univ Med & Hlth Sci Coll Med Instruments Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9781665455336

Video based heart rate (HR) measurement conducting remote photoplethysmography (rPPG) exhibits immense prospects and achieves creditable results in constrained scenarios. (1) Background: Non-contact facial video-based HR estimation is a constantly evolving direction, in which deep learning models achieved state-of-the-art performance. However, these methods usually have higher computational burden, which are difficult to deploy on mobile or edge devices directly;(2) Methods: To address this problem, we propose a lightweight model for HR estimation based on rPPG. This model uses vision transformer to serves as the backbone, and the contraction factor is adopted to reduce the model complexity. (3) Results: The proposed model is tested on the challenging VIPL-HR-V2 dataset. The results show that the proposed model can greatly reduce the model complexity while attaining reasonable accuracy.

关键词： heart rate measurement remote photoplethysmography (rPPG) vision transformer neural network compression non-contact physiological signal processing contrast learning

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Modelling of a technical diagnostic system for a generator based on an internal combustion engine(ІСЕ) 5

Modelling of a technical diagnostic system for a generator b...

引用

5th ieee KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Zaichenko, Stefan Derevianko, Denys Korol, Serhii Shevchuk, Nataliia Bielokha, Halyna Wenjun, Zhang Department of Automation of Electrical and Mechatronic Complexes National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv Ukraine Department of Power Supply National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv Ukraine Thermometry Laboratories Institute of Engineering Thermophysics of NAS of Ukraine Kyiv Ukraine Department of economics and entrepreneurship National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv Ukraine

ISBN: (纸本)9798350384499

This paper presents a new approach to diagnose autonomous power-generating equipment derived from an ІСЕ by means of computer modeling of the performance of the technical diagnosis system. The essence of the proposed method is the possible mathematical determination of diagnostic parameters of the system, which allows analytically determining the degree of wear of the cylinder-piston group. Determining the currents of the electric starter allows One to establish the functional parameter - the real degree of compression. Determining the compression ratio allows One to set the energy indicators of the generator. The use of this method made it possible to establish the dependence of specific energy indicators on the technical condition of the generating plant. As a result of the conducted research, a multi-level hierarchical multi-component model of the system of technical diagnostics of the generator based on the ІСE was created, which allows to predict the diagnostic parameters of the research object and select the necessary diagnostic tools. The calculated data obtained from the developed mathematical model of an electric generator with a single-cylinder engine with spark ignition based on real parameters shows the high adequacy of the proposed mathematical model. ©2024 ieee.

关键词： compression ratio (machinery)

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Energy Efficiency Enhancement of SCM-Based Systems: Write-Friendly Coding

Energy Efficiency Enhancement of SCM-Based Systems: Write-Fr...

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作者： Chen, Yi-Shen Wu, Chun-Feng Chang, Yuan-Hao Kuo, Tei-Wei National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan

With the advent of the Internet of Things (IoT), more and more wearable devices have been developed and integrated into our daily lives. Energy efficiency is critical for these devices because they are typically run on energy-constrained resources like batteries or energy harvesters. The storage-class memory (SCM) technologies and data compression techniques could improve their energy efficiency via reducing data movements and squeezing the data size, respectively, where data compression is especially important for IoT and embedded systems to reduce the volume of data for the space-constrained memory/storage devices. Nevertheless, both of them cannot be aware of their inherent characteristics for further minimization of energy consumption. To this end, a write-friendly coding scheme is proposed in this work that jointly manages both techniques to yield energy-efficient SCM-based systems. Moreover, a novel design of ignorable bits is presented to partially skip write operations after completing data compression without sacrificing data accuracy. We evaluate the proposed scheme via a series of intensive experiments, the experimental results of which indicate that the proposed coding scheme reduces energy consumption by up to 45% under the investigated benchmarks. © 1982-2012 ieee.

关键词： data compression

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