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

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Big data clustering method based on parallel K-means 4

Big data clustering method based on parallel K-means

引用

4th ieee International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Liu, Haibo Bai, Yongbin Chen, Zhenhao Zhang, Zhenfeng China Satellite Maritime Tracking and Control Department Jiangsu Jiangyin214400 China

ISBN: (纸本)9798350359589

In the era of big data, traditional data clustering algorithms have gradually failed to meet the application requirements, and the optimization of data compression and parallelization methods has become a research hotspot. Based on the analysis of the traditional K-means clustering algorithm, this paper optimizes and improves the parallelized K-means algorithm, and proposes the Spark-Kmeans algorithm, which mainly retains the sample set distribution information by random sampling of large samples, and pre-clusters the samples in the nodes, and reclusters the pre-clustering in the convergence node. And it uses this as the initialization clustering center, so as to eliminate the problem of algorithm convergence instability caused by random initialization of the clustering center. Finally, single-node clustering and Spark-Kmeans clustering experiments are performed on the kdd-cup99 dataset and sklearn randomly generated dataset, and the effectiveness of the algorithm is verified by time-consuming, purity, error squared and indexes. © 2024 ieee.

关键词： cluster K-means parallel Spark

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Adaptive Model compression of Ensembles for Evolving data Streams Forecasting

Adaptive Model Compression of Ensembles for Evolving Data St...

引用

ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Boulegane, Dihia Cerquiera, Vitor Bifet, Albert LTCI Telecom Paris Grenoble France Dalhousie Univ Halifax NS Canada LTCI Telecom Paris Paris France

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Ensemble methods combining several models have shown superior predictive performance in data streams forecasting compared to individual models. Besides, they can cope with evolving data streams and concept drift as they allow adaptation. However, ensembles are renowned for their complexity and computational costs which makes them unsuitable in cases where both resources and time are limited such as IoT applications. In this paper, we propose to use model compression in the streaming setting in order to overcome the aforementioned drawbacks. We show that compressing a highly performing dynamic ensemble into an individual model leads to better predictive performance when compared to an individual learner while significantly reducing computational costs. We conduct an extensive experimental study on both real and synthetic time series to measure the impact of compression on both predictive performance and computational cost.

关键词： Model compression Knowledge Distillation Dynamic Ensemble Methods Time Series Forecasting data stream Mining

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Expediting In-Network Federated Learning by Voting-Based Consensus Model compression

Expediting In-Network Federated Learning by Voting-Based Con...

引用

ieee conference on Computer Communications (ieee INFOCOM)

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Guo, Song Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Macquarie Univ Fac Sci & Engn Sch Comp Sydney NSW Australia Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Federated learning

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Neural JPEG: End-to-End Image compression Leveraging a Standard JPEG Encoder-Decoder

Neural JPEG: End-to-End Image Compression Leveraging a Stand...

引用

data compression conference (DCC)

作者： Mali, Ankur Ororbia, Alexander G. Kifer, Daniel Giles, C. Lee Penn State Univ University Pk PA 16802 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781665478939

Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression. However, current methods either use additional post-processing blocks on the decoder end to improve compression or propose an end-to-end compression scheme based on heuris-tics. For the majority of these, the trained deep neural networks (DNNs) are not compatible with standard encoders and would be difficult to deploy on personal com-puters and cellphones. In light of this, we propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends, an approach we call Neural JPEG. We propose frequency domain pre-editing and post-editing methods to optimize the distribution of the DCT coefficients at both encoder and decoder ends in order to improve the stan-dard compression (JPEG) method. Moreover, we design and integrate a scheme for jointly learning quantization tables within this hybrid neural compression framework. In summary, our contributions are as follows:

关键词： Deep learning Image coding Quantization (signal) Frequency-domain analysis Neural networks Transform coding Rate-distortion

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Proceedings of 2024 ieee 13th data Driven Control and Learning Systems conference, DDCLS 2024

Proceedings of 2024 IEEE 13th Data Driven Control and Learni...

引用

13th ieee data Driven Control and Learning Systems conference, DDCLS 2024

ISBN: (纸本)9798350361674

The proceedings contain 379 papers. The topics discussed include: a transformer based network in monocular satellite pose estimation;controller dynamic linearization based data-driven adaptive control for a vapor-compression refrigeration system;cooperative awareness message generation interval prediction model based on Bayesian optimized long short-term memory neural network;preassigned time prescribed performance tracking control for high-order nonlinear systems with time-varying powers;gaussian reinforcement learning: optimal tracking control for uncertain linear systems;a velocity tracking method for quadruped robot with rhythm controller;model-free predictive control of hydraulic cylinder based on parameter prediction of extreme learning machine;and semi-supervised domain adaptation with feature separation for wind turbine anomaly detection.

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Steganography Audio Files Using EAS Method and AES CBC Algorithm for data Security 11

Steganography Audio Files Using EAS Method and AES CBC Algor...

引用

11th International conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Taruk, Medi Hamdani, Hamdani Sepkardianto, Salas Mulawarman University Informatics Department Samarinda Indonesia

ISBN: (纸本)9798350355314

The ease of exchanging information can be misused by irresponsible parties to commit various cybercrimes such as data tapping. Therefore, this study was conducted with the aim of creating an Android-based application that is used to secure data. The method used to secure data is a combination of steganography techniques using EAS (Enhanced Audio Steganography) method with cryptography techniques using AES (Advanced Encryption Standard) mode CBC (Cipher Block Chaining) algorithm. Application of steganography is carried out using the EAS method, where data is inserted into certain bytes, so that it can maintain the quality of the cover media used. The cover media used is audio in W A V format. The application of cryptography is carried out using the AES mode CBC algorithm which is a popular symmetric algorithm because of its high performance and level of security. In addition, GZIP compression is also applied to perform data compression which aims to increase the storage capacity of the audio cover. The results of this study are to produce an Android-based application that is used to secure data by inserting data into the audio cover and can be extracted back into the original data. © 2024 ieee.

关键词： AES Android CBC Cryptography EAS Steganography

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BIM and IoT Sensors data Integration for Improving the Efficienty of Concrete Curing Process

BIM and IoT Sensors Data Integration for Improving the Effic...

引用

2024 ieee conference on Engineering Informatics, ICEI 2024

作者： Ranjha, Sagheer Ekambaram, Palaneeswaran Shi, Lei Dani, Sourabh Swinburne University of Technology School of Engineering Department of Civil and Construction Engineering MelbourneVIC Australia Swinburne University of Technology Trades and Engineering Technologies MelbourneVIC Australia Swinburne University of Technology School of Engineering MelbourneVIC Australia

ISBN: (纸本)9798331505776

Determining the early-age compression strength of concrete is an integral part of many construction projects. This paper investigates the feasibility of a method to monitor temperatures related to concrete curing using IoT and BIM technology. The research includes analysis of temperature data and whether it is practical to rely on sensor-based data gathering to determine the compressive strength of concrete. To achieve this, an arrangement of temperature sensors was fixed to a series of concrete core samples as they cured, and the temperature data feed was linked to a BIM platform. It was found that the integration between IoT and BIM technologies was functional and significantly contributed towards more cost effective, efficient and accurate practices. This is particularly important if predictions on future compressive strength can be made using current sensor information. Further testing would be required to determine the heat of hydration and accurately predict the compressive strength. © 2024 ieee.

关键词： Concretes

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Adaptive Methods for Tensor data compression 16

Adaptive Methods for Tensor Data Compression

引用

16th International conference on COMmunication Systems and NETworkS, COMSNETS 2024

ISBN: (纸本)9798350383119

With recent advancements in computational technology, technologies like artificial intelligence, and machine learning are gaining popularity among researchers and developers to realize various applications like image processing [27], natural language processing (NLP) [28], computer vision applications, etc. Neural network models such as Convolutional neural network (CNN), Deep neural network (DNN), and Recurrent neural networks (RNN) are used to perform highly complex tasks with high accuracy by training these models with millions © 2024 ieee.

关键词： federated learning Split computing tensor data encoding

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MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

引用

30th ieee International conference on Image Processing (ICIP)

作者： Lee, Yegi Kim, Shin Yoon, Kyoungro Lim, Hanshin Kwak, Sangwoon Choo, Hyon-Gon Konkuk Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9781728198354

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjontegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

关键词： Video Coding for Machines Video compression Image compression Machine-Attention MPEG VCM

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REGER: Reordering Time Series data for Regression Encoding 40

REGER: Reordering Time Series Data for Regression Encoding

引用

40th ieee International conference on data Engineering, ICDE 2024

作者： Xiao, Jinzhao He, Wendi Song, Shaoxu Huang, Xiangdong Wang, Chen Wang, Jianmin Tsinghua University China BNRist Tsinghua University China Timecho Ltd

ISBN: (纸本)9798350317152

Regression models are employed in lossless compression of time series data, by storing the residual of each point, known as regression encoding. Owing to value fluctuation, the regression residuals could be large and thus occupy huge space. It is worth noting that compared to the fluctuating values, time intervals are often regular and easy to compress, especially in the IoT scenarios where sensor data are collected in a preset frequency. In this sense, there is a trade-off between storing the regular timestamps and fluctuating values. Intuitively, rather than in time order, we may exchange the data points in the series such that the nearby ones have both smoother timestamps and values, leading to lower residuals. In this paper, we propose to reorder the time series data for better regression encoding. Rather than recomputing from scratch, efficient updates of residuals after moving some points are devised. The experimental comparison over various real-world datasets, either public or collected by our industrial partners, illustrates the superiority of the proposal in compression ratio. The method, REGression Encoding with Reordering (REGER), has now become an encoding method in an open-source time series database, Apache IoTDB. © 2024 ieee.

关键词： Time series

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