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

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Optimizing large data transfers for the ALICE experiment in Run 3 22

Optimizing large data transfers for the ALICE experiment in ...

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22nd RoEduNet conference: Networking in Education and Research, RoEduNet 2023

作者： Weisz, Sergiu Grigoras, Costin Suiu, Alice-Florenta Betev, Latchezar Carabas, Mihai Tapus, Nicolae University Politehnica of Bucharest Faculty of Automatic Control and Computers Bucharest Romania Geneva Switzerland

ISBN: (纸本)9798350393651

The physics programme and scope of HEP experiments natu-rally grow with time, thus the computing requirements, both CPU and storage, increase. The 4 large LHC experiments (ALICE, ATLAS, CMS, and LHCb) are undergoing upgrade cycles, for ALICE and LHCb the upgrade was finalized in the years of the LHC long Shutdown 2, 2019-2021, and for ATLAS and CMS it is foreseen to take place in 2026-2028. The ALICE upgrade was characterized by a complete overhaul of the detector, data acquisition (DAQ) systems, and entire software stack. As a consequence, the experiment can take up to 100 times more events, compared to the previous setup, and is employing a new online data compression and calibration utility (O2) to reduce the data stream, which nonetheless is 4 times larger than before the upgrade. To manage the new more complex data paths, a new data management software was designed and deployed as part of the Grid software upgrade. This article present the ALICE data movement system from the data compression farm O2 to the different storage instances around the world, with a focus on transfer optimization and automating data transfers. © 2023 ieee.

关键词： data compression

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Association Rules Algorithm Based on Dual Support and compression Matrix 7

Association Rules Algorithm Based on Dual Support and Compre...

引用

7th ieee Information Technology and Mechatronics Engineering conference, ITOEC 2023

作者： Zhang, Suzhi Fan, Haidi Li, Pu Lin, Xiang School of Software Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9798350334197

Aiming at the characteristics of "more frequent and more ordinary"of traditional Chinese medicine materials, this paper proposes an association rule algorithm based on double support degree and compression matrix to improve the execution efficiency of drug search. Specifically, by introducing the principle of compression matrix, adding arrays to improve the matrix storage mode, and compressing the matrix according to the correlation theorem of the association rule, so as to reduce the number of scans of the matrix and the number of candidate sets generated. By setting the maximum and minimum support threshold, the generation of infrequent itemsets and overfrequent itemsets is limited to ensure the validity of the rules, thereby improving the practicability of the obtained rules. Experiments show that the proposed algorithm has obvious advantages in the data set with high support degree and high number of transactions. © 2023 ieee.

关键词： Matrix algebra

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Machine-Learning-Based Multidimensional Big data Analytics over Clouds via Multi-Columnar Big OLAP data Cube compression

Machine-Learning-Based Multidimensional Big Data Analytics o...

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2023 ieee International conference on Big data, Bigdata 2023

作者： Cuzzocrea, Alfredo Hafsaoui, Abderraouf Leung, Carson K. University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France University of Manitoba Dept. of Computer Science WinnipegMB Canada

ISBN: (纸本)9798350324457

This paper proposes a new theory on combining innovative Multidimensional Big data Analytics with well-known Machine Learning (ML) in order to magnify the expressive power and the accuracy of knowledge insights discovery from massive big datasets. At the level of enabling technology, with the goal of fully supporting this novel paradigm, the issue of managing and mining big OLAP data cubes over Clouds arises. Due to computational complexity requirements, the latter challenge is addressed by proposing an innovative solution for (1) representing big OLAP data cubes over Clouds via a multi-column-based representation, and (2) compressing the deriving multi-column representations for achieving the desired effectiveness and efficiency. This paper introduces the fundamental model of Machine-Learning-Based Multidimensional Big data Analytics, along with a reference architecture implementing it. © 2023 ieee.

关键词： Big data Big data Analytics Big OLAP Cubes Machine Learning Machine-Learning-Based Multidimensional Big data Analytics Multi-Columnar Big OLAP Cube compression Multi-Columnar Representation of Big OLAP Cubes Multidimensional Big data Analytics

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High-Order Intra Prediction for Future Video Coding

High-Order Intra Prediction for Future Video Coding

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data compression conference (DCC)

作者： Lin, Kai Zhang, Jiaqi Li, Junru Jia, Chuanmin Gao, Wen Peking Univ Inst Digital Media Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665478939

Intra prediction acts a significant role in removing the spatial redundancy in the hybrid coding framework. Versatile Video Coding (VVC) employs a set of angular intra modes to generate directional contents based on the linear projection hypothesis. However, the linear based predictor is not expressive enough for generating high-fidelity patterns with nonlinear structure. To compensate for that, we propose a high-order intra prediction (HOIP) for future video coding in this paper. In particular, the HOIP is modeled by a quadratic extrapolation function. To be compatible with the present intra prediction mechanism, the quadratic function can be formulated by two angular intra modes. To reduce the encoding complexity, we further propose a search pruning strategy to find the most appropriate pair-wise modes, and it can be flexibly extended for higher coding performance. The extensive experimental results demonstrate the effectiveness of the proposed method. Up to 0.6% BD-rate saving is obtained with the moderate complexity increment.

关键词： Video coding Extrapolation Termination of employment data compression Predictive models Encoding Complexity theory

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A Method for Compressed Storage and Retrieval of Triplet data Based on Cuckoo Hashing 21

A Method for Compressed Storage and Retrieval of Triplet Dat...

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21st International Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Pengcheng, Liu Qinzhi Shibin, Zhang Yan, Chang Lili, Yan School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China

ISBN: (纸本)9798331519254

This paper proposes a method for compressing and retrieving triple data based on optimized cuckoo hash. Firstly, the optimized cuckoo hash algorithm is utilized to quickly establish node indexes. Then, the relationship information of triples is stored in the form of a sparse matrix according to the relationship type. Finally, the double compression algorithm k2-Rel is employed to compress the relationship attribute matrix of stored triple information, aiming to reduce storage space and lower storage costs. Experimental results demonstrate that the optimized cuckoo hash algorithm significantly reduces the time for building node indexes. Furthermore, utilizing the k2-Rel compression algorithm for compressing the relationship information between nodes substantially decreases storage space, ultimately achieving efficient storage and retrieval of triple data. © 2024 ieee.

关键词： Cuckoo hash data compression Knowledge graph Triple data

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RAW data: A Key Component for Effective Deepfake Detection

RAW Data: A Key Component for Effective Deepfake Detection

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Husseinil, Sahar Dugelay, Jean-Luc Department of Digital Security EURECOM Biot France Docaposte Biometrics Lab Biot France

ISBN: (纸本)9798350368741

Current deepfake detection methods are prone to overfitting to specific deepfake artifacts and often struggle with genuine images that have undergone compression and other image processing operations. These processes can obscure indicators of forgery, leading to inaccurate decisions. This paper aims to redefine the boundary between real and fake images by narrowing the definition of authentic samples to a stage closer to the radiance of the scene as captured by the sensor, prior to any transformations by an Image Signal Processor (ISP). Our proposed method bypasses ISP processing steps, such as denoising, white balance, and de-mosaicing, which are embedded in camera hardware. This unaltered preservation makes raw data an ideal starting point for deepfake detection. Given the scarcity of large-scale datasets designed for training on raw images, we propose a methodological approach to train our model on raw image data. Our method demonstrated state-of-the-art performance on the CDF dataset and showed competitive results across other RGB domain deepfake detection datasets. The model developed in this study is available at https://***/DeepFaux/Deepfake-Detection-with-RAW-data. © 2025 ieee.

关键词： deepfake detection image manipulation ISP

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A Learning-based Approach for Martian Image compression

A Learning-based Approach for Martian Image Compression

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ieee International conference on Visual Communications and Image Processing (VCIP)

作者： Ding, Qing Xu, Mai Li, Shengxi Deng, Xin Shen, Qiu Zou, Xin Beihang Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

ISBN: (纸本)9781665475921

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. Image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.

关键词： Image compression Martian image deep learning

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Fast Partition Mode Decision via a Plug-in Fully Connected Network for Video Coding

Fast Partition Mode Decision via a Plug-in Fully Connected N...

引用

data compression conference (DCC)

作者： Zhang, Jiaqi Wang, Meng Jia, Chuanmin Wang, Qi Wang, Shanshe Ma, Siwei Gao, Wen Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665478939

Flexible coding unit partitioning such as quad-tree nested binary-tree and ternary-tree adopted by the emerging enhanced compression model (ECM) brings promising coding performance improvement. Meanwhile, the computational complexity increases dramatically, which may block the exploration and validation of new coding tools. This paper investigates a partition mode early pruning scheme via a fully connected network to reduce the encoding complexity for the ECM. In particular, we carefully select features and devise the fully connected network, which could seamlessly cooperate with the encoder, revealing promising learning and inference capability. Experimental results demonstrate that the proposed method achieves 15%similar to 50% encoding time savings with moderate bit-rate increasing on the ECM, and the extra complexity regarding the fully connected network and feature extraction is negligible.

关键词： Video coding Computational modeling data compression Feature extraction Encoding Computational complexity

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Energy-Based DCT Approach for PPG compression 44

Energy-Based DCT Approach for PPG Compression

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gupta, Shresth Singh, Anurag Sharma, Abhishek Int Inst Informat Technol Naya Raipur India

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

With the rapid development of communications, information technology, and Internet of Things (IoT), photoplethysmography (PPG) has achieved prevalence in telemedicine and remote health monitoring using wearable devices. As these devices are resource-constrained, efficient compression techniques are necessary for optimal storage and power consumption management with sustained clinical morphology of PPG signal. This work presents a new approach for PPG compression based on the energy of discrete cosine transform (DCT). With better data compression, a significant reduction of noise is also obtained in the reconstructed signal. The performance evaluation is done on multiple publicly available databases, an average compression ratio of 37.46 is achieved on MIMIC-II database with percentage root-mean-square difference of 0.0688, which is significantly higher than the existing PPG-compression approaches in the literature.

关键词： Performance evaluation Power demand databases Wearable computers Telemedicine Noise reduction Ubiquitous computing

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Enabling Rapid data Transfer in Medical IoT Systems with a High-Throughput PRESENT Cipher Hardware Framework

Enabling Rapid Data Transfer in Medical IoT Systems with a H...

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2024 International conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Reddy, Uma Rangaiah, Yagateela Pandu Nagpal, Amandeep Goyal, Himanshu Rai Sundari, Mandapati Venkata Rama Adnan, K. Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Hyderabad Dundigal500043 India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India GRIET Bachupally Telangana Hyderabad India Hilla University College Babylon Iraq

ISBN: (纸本)9798350355338

The timely and dependable sharing of essential medical information is dependent on fast and secure data transfer in the arena of Medical Internet of Things (IoT) technologies. In order to facilitate quick data transmission in Medical IoT devices, a high-throughput PRESENT Cipher hardware architecture is presented here. The suggested technique is comprised of various integral parts, such as the design of the hardware framework, the selection of the algorithm, the management of the keys, and the optimization of the performance. Important features of the proposed technique, such encryption, data compression, and error correction, are represented by mathematical equations. At its heart, our method is the PRESENT Cipher Algorithm, which is lauded for its efficiency and its high level of security. This technique is quite effective at encrypting data, protecting it while in transit. data compression methods like Run-Length Encoding (RLE) are used to decrease data size and improve efficiency. ReedSolomon codes provide error correction and hence ensure the reliability of transmitted data. Encryption keys are also protected by means of key management and other security procedures. It is determined how successful the suggested approach is by defining and evaluating a variety of performance assessment measures such as encryption speed, energy efficiency, data transmission rate, latency, resource usage, data integrity, compatibility, and scalability. The results show that the suggested technique is superior to the status quo methods in many respects. It is well-suited to the requirements of Medical IoT systems because to its faster encryption speed, reduced energy usage, larger data transmission speeds, and enhanced data integrity. © 2024 ieee.

关键词： data integrity

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