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TranskV: A Networking Support for Transaction processing in Distributed Key-value Stores 7

TranskV: A Networking Support for Transaction Processing in ...

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7th IEEE international conference on Big Data Computing Service and Applications, BigDataService 2021

作者： Eldakiky, Hebatalla Du, David Hung-Chang Department of Computer Science and Engineering University of Minnesota Twin Cities United States

ISBN: (纸本)9781665434836

through the massive use of mobile devices, data clouds, and the rise of Internet of things, enormous amount of data has been generated and analyzed for the benefit of society. NoSQL Databases and specially key-value stores become the backbone in managing these large amounts of data. Most of key-value stores ignore transactions due to their effect on degrading key-value store's performance. Meanwhile, programmable switches with the software-defined networks and the Programming Protocol-Independent Packet Processor (P4) lead to a programmable network where in-network computation can help accelerating the performance of applications. In this paper, we proposed a networking support for transaction processing in distributed key-value stores. Our system leverages the programmable switch to act as a transaction coordinator. Using a variation of the time stamp ordering concurrency control approach, the programmable switch can decide to proceed in transaction processing or abort the transaction directly from the network. Our experimental results on an initial prototype show that our proposed approach, while supporting transactions, improves the throughput by up to 4X and reduces the latency by 35% when compared to the existing architectures. © 2021 IEEE.

关键词： Concurrency control

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Analyzing and processing of Astronomical Images using Deep Learning Techniques 7

Analyzing and Processing of Astronomical Images using Deep L...

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7th IEEE international conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Vy, Sandeep Sen, Snigdha Santosh, K Global Academy of Technology Dept. CSE. GAT Bengaluru India India and Research Scholar IIIT Allahabad Dept. CSE. GAT Global Academy of Technology Bengaluru India

ISBN: (纸本)9781665428491

Deep Learning techniques are widely used in various use cases like image detection, pattern recognition, computer vision, and prediction, etc. these days, Convolutional neural networks(CNN) which is an efficient algorithm of Deep Learning are also extensively used in astronomical image processing. In this work, we implemented different architectural models like AlexNet, VGG16, ResNe50, InceptionV3,Xception to classify and predict the Redshift(z) values of unlabeled galaxy images taken from the EFIGI catalog and galaxy-zoo image dataset from the Kaggle website. In addition to these pre-built architectural models, we have introduced a novel, customized CNN classifier and Redshift(z) predictor models to study the behavior of CNN layers and to achieve reasonable accuracy by fine-tuning hyperparameters. Our customized CNN Classifier model achieved a considerably good accuracy of 92.3% with 87.3% validation loss in galaxy classification. Whereas in Redshift(z) prediction, our novel CNN Redshift(z) predictor model achieved a very low loss of 0.000158 when compared to other pre-built architectures. © 2021 IEEE.

关键词： Deep learning

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Efficient Peer-to-Peer Unicasting for VANET architectures via Enhanced Monolithic Protocols 7

Efficient Peer-to-Peer Unicasting for VANET Architectures vi...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media conference, SEEDA-CECNSM 2022

作者： Amponis, George Lagkas, thomas Karampatzakis, Dimitris Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Moscholios, Ioannis Goudos, Sotirios Sarigiannidis, Panagiotis Department of Computer Science International Hellenic University Kavala Campus Greece K3Y Ltd. Sofia Bulgaria Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Faculty of Science Engineering and Computing Kingston University London United Kingdom Department of Informatics and Telecommunications University of Peloponnese Tripolis Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

With the advent of vehicular networks and the corresponding requirements, developers are faced with new challenges associated with fundamental protocol attributes and capabilities. In our work, we consider ESP-NOW: a connectionless WiFi communication protocol developed by Espressif and featuring short packet (up to 250 bytes) transmission. It is different from more traditional WiFi protocols, as the upper five layers of the Open Systems Interconnection (OSI) stack are simplified to a single monolithic layer. the data does not need to travel through all the OSI-or Transmission Control Protocol (TCP)-specific layers, which measurably reduces over-head, processing delay all while increasing system responsiveness. the presented work targets the considerable research gap in regards to this protocol and constitutes an attempt to dissect it and provide experimental results, which prove to be promising for highly mobile clusters. Relevant developments find great applicability in ad hoc and vehicular communications, where real-time connectivity and sufficient coverage are pivotal in supporting reliable networking. In this paper, we also investigate the usage of a modified 802.11 standard to enable long-range and low-power interfacing amongst networked vehicular nodes. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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FPGA Implementation Of a High throughput Low Power Advanced Encryption Standard (AES-128) Cipher

FPGA Implementation Of a High Throughput Low Power Advanced ...

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international conference on Signal processing and Integrated Networks (SPIN)

作者： Jaideep Kala Jeebananda Panda Lavi Tanwar Department of Electronics and Communication Delhi Technological University New Delhi India

Ensuring secure transmission and storage of digital information is critical for any organization to function properly. To address this issue, encryption algorithms are commonly used. Advanced Encryption Standard (AES) has been globally adopted as the mainstay cipher algorithm for securing transmission networks and storage devices due to its easy implementation and compatibility with both hardware and software applications. Another reason for its widespread popularity is its uncompromisable nature against existing brute force attacks, making it practically unbreakable on existing computing power. AES implementation for battery operated devices requires an algorithm with low power consumption and high-speed encryption/decryption of digital data. this paper proposes an FPGA implementation of a high throughput parallel pipelined 128-bit AES algorithm with a low power key expansion mechanism for iterative stages. A 128-bit symmetric key has been used for undertaking 10 rounds of transformations. All the encryption and decryption transformations are simulated using iterative design methodology in order to minimize hardware consumption. Xilinx Artix-7 FPGA device is used for hardware evaluation and Verilog HDL for programming. Simulation and synthesis task has been performed on Xilinx Vivado v2021.1 IDE. the results exhibit high-rate encryption of 68 Gb/s and low energy consumption of 7 pJ/bit.

关键词： Ciphers Energy consumption Power demand Software algorithms Signal processing algorithms throughput Hardware

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Prototyping of Low-Cost Configurable Sparse Neural processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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international conference on parallel and Distributed Systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. therefore, we design and prototype a low-cost configurable sparse Neural processing Unit (NPU). the NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. the computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. the buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors Neural networks Random access memory Prototypes

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Multiple Transform Selection concept modeling and implementation using Interface Based SDF graphs 21

Multiple Transform Selection concept modeling and implementa...

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14th Workshop on Design and architectures for Signal and Image processing, DASIP 2021, held jointly with the 16th HiPEAC conference

作者： Haggui, Naouel Belghith, Fatma Hamidouche, Wassim Masmoudi, Nouri Nezan, Jean-François Ietr Laboratory Insa Rennes France National School of Engineering of Sfax Tunisia

ISBN: (纸本)9781450389013

Recent studies predict that video data accounts for 82% of Internet traffic by 2022. this fact has motivated MPEG to define a new Video Coding Standard called Versatile Video Coding (VVC), which will be released by the end of 2020. VVC will offer the possibility to handle new video formats and to improve significantly video compression over its predecessor HEVC. Indeed, the objective is to reduce the necessary bit rate by half, at equivalent quality. these advances require the use of more complex algorithms, although the increase in complexity has been limited throughout the standardization process. In order to decrease the complexity of VVC and consequently the coding execution time, several methods have been introduced at different stages of the encoder. the aim of this paper is to explore the available parallelism of VVC to accelerate the coding and the decoding processes. this paper focuses on the transformation block and more specifically the new concept of Multiple Transform Selection (MTS) introduced by VVC. Moreover, a study of several granularity levels of Interface-Based Synchronous Dataflow (IBSDF) models and their impact on the performances obtained on x86 architectures is presented. IBSDF dataflow graph has been developed to reveal the available parallelism of MTS. the PREESM fast prototyping tool is then used for the mapping and the scheduling of MTS on virtual and real parallel architectures and for generating efficient parallel implementations on real architectures. PREESM has been used in this work to explore the potential parallelism offered by MTS and to prove the efficiency of MTS on multicore x86 architectures. Experimental results show a speed-up close to the optimum. © 2021 ACM.

关键词： Video signal processing

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Distributed Deep Learning with Data parallelism for Diabetic Retinopathy Classification 1

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7th international conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Patil, Sanskruti S. Patil, Mahesh S. Chickerur, Satyadhyan Giraddi, Shantala Shahapur, Seetharam N. Hadalageri, Anup School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India Center for High Performance Computing KLE Technological University Karnataka Hubballi580031 India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

the large-scale model training is typically slower and necessitates high-performance workstations and Distributed Deep Learning (DDL). the DDL models trained on a massive volume of data can outperform single accelerators being used, i.e., the performance of deep learning (DL) models can be enhanced by using distributed and parallel deep learning methods. But the DDL models need to be redesigned and evaluated for particular hardware resources and applications, requiring the literature on various DDL model evaluations for assistance. Authors in the proposed work design and evaluate DDL with data parallelism for classifying Diabetic retinopathy (DR) images. A performance comparative study on the DDL strategy implemented on DenseNet is presented where the model is trained on multi-GPUs with counts from 1 to 8. the results show that the model takes less time when trained on multi-GPUs compared to single GPU. the detailed evaluation analysis of DDL with data parallelism is presented in this paper. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Combining Computer Vision and Word processing to Classify Film Genres

Combining Computer Vision and Word Processing to Classify Fi...

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Digital Signal processing and its Applications (DSPA), international conference on

作者： Nikita Andriyanov Vitaly Dementev Data Analysis and Machine Learning Department Financial University under the Government of the Russian Federation Moscow Russia Radio Engineering Telecommunications and Information Security Department Ulyanovsk State Technical University Ulyanovsk Russia

the paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie Database, IMDB (https://***/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. the paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric.

关键词： Measurement Deep learning Computer vision Computational modeling Computer architecture Motion pictures Transformers

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MRLKG: Multimodal Representation Learning with Keyword-Based Graph for Hashtag Recommendation

MRLKG: Multimodal Representation Learning with Keyword-Based...

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Machine Learning and Natural Language processing (MLNLP), international conference on

作者： Mian Muhammad Yasir Khalil Qingxian Wang Bo Chen Muhammad Arslan Rauf Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

the increasing volume of social media content has made hashtag recommendation a critical task for enhancing content discoverability and user engagement. this paper presents MRLKG (Multimodal Representation Learning with Keywordbased Graph) for hashtag recommendation. Our approach integrates cross-modality joint representation with a keyword-guided graph. the joint embedding provides a contextually rich representation of the content. In parallel, a keyword-guided Graph Convolutional Network (GCN) generates a refined representation of each microblog post content by emphasizing key concepts and their relational context with other microblog posts, essential for understanding the broader context and trends that hashtags often represent. By integrating the contextually rich joint embedding provided by transformers with the KeyBERT-GCN's focus on key concepts and their relational structure, the model can recommend hashtags that are both contextually relevant and aligned with the key trends represented by other microblog posts. Experimental results demonstrate the effectiveness of our method, showing improved performance over existing approaches. this method offers a robust solution for hashtag recommendation by leveraging both contextual understanding and graph-based reasoning.

关键词： Representation learning Social networking (online) Graph convolutional networks Blogs Network analyzers Market research Transformers Natural language processing Cognition Context modeling

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Exploiting Combination of Spectral Features in Light Weight Neural Network for ABE on Limited Data

Exploiting Combination of Spectral Features in Light Weight ...

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international conference on Signal processing and Information Security (ICSPIS)

作者： Ritujoy Biswas Karan Nathwani Mohammed Krini Electrical Engineering Indian Institute of Technology Jammu Jammu India Laboratory for Signal Processing Aschaffenburg University of Applied Sciences Aschaffenburg Germany

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

this paper proposes an approach to extend the bandwidth of 4 KHz speech signals upto 8 KHz using a simple feed-forward neural network (FFNN). We employ the commonly used Short-Time Fourier Transform (STFT) values as features and boost its resolution by incorporating Mel Frequency Cepstral Coefficients (MFCCs). Apart from this feature-level fusion, a layer-level fusion is also examined to demonstrate the expansion in bandwidth. this is an advantage over traditional artificial bandwidth extension (ABE) methods using neural nets, which are heavily data-driven and require high model complexity. the proposed approach exploits the combination of relevant spectral features to avoid the requirement of highly complex architectures. this is because the combined features capture more intricate features relating the 4 KHz signal to the 8 KHz one. this facilitates the deployment of the technique on resourceconstrained devices, even with limited data. these reconstructed high pass (8 KHz) audio signals are perceptually better than the original 4 KHz bandwidth signals. Additionally, the performance of the proposed approach exceeds that of state-of-the-art methods in the literature. this approach has potential applications in enhancing the quality by extending the bandwidth of audio signals in telecommunication systems and also finds application in cochlear implant (CI) users.

关键词： Performance evaluation Cochlear implants Fourier transforms Information security Bandwidth Telecommunications Feedforward neural networks Complexity theory Mel frequency cepstral coefficient Signal resolution

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