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A Sequential Update Algorithm with parallel processing for Deep Learning-Based Beamforming in Real Environments 12

A Sequential Update Algorithm with Parallel Processing for D...

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12th international conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation

作者： Yoon, Sung-Wook Kwon, Oh-Wook Chungbuk Natl Univ Dept Intelligent Syst & Robot Cheongju South Korea

ISBN: (纸本)9781665423830

In this paper, we propose a deep learning-based sequential update beamforming algorithm for continuous speech enhancement in real environments. Previous beamforming algorithms have evaluated the performance using pre-segmented audio signals generated by mixing speech and noise in a completely overlapped manner. However, in real environments, speech utterances are sparsely uttered on the time axis and then input signals of beamformers become a continuous noisy speech stream with no speech most of the time. When a noise signal without speech is input to the previous beamforming algorithms, the performance is degraded. Considering these facts, we propose a robust block-wise sequential update algorithm in the real environment. In addition, by using parallel processing, the convergence speed of the update algorithm was increased, resulting in additional performance improvement. the evaluation set is composed of a mixture of speech utterances extracted from the CHiME3 evaluation set and the continuously-played background music extracted from MUSDB. As a speech recognizer, the Kaldi-based toolkit is used. We confirm that the proposed sequential update beamforming algorithm shows better performance than the baseline beamforming algorithm.

关键词： beamforming deep learning sequential update algorithm

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FPGA Implementation of 32-Bit parallel LMS Adaptive Beamforming Architecture using Two 8-Bit Stages

FPGA Implementation of 32-Bit Parallel LMS Adaptive Beamform...

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international conference on Computing and Networking Technology (ICCNT)

作者： Mohini R. Damodar Yogita Vaidya Electronics and Telecommunication VLSI & Embedded system COEP Technological university Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this research project analyses a hardware pipelined reduced complexity parallel least mean square (RC-pLMS) implementation for adaptive beamforming. the composite of every single stage of defects in RC-pLMS, which is composed of two massively parallel, least mean square (LMS) phases, produces the overall error signal (pLMS). By removing the second independent weight set, the pLMS has been further reduced in size, ending in a less complex pLMS architecture (RC-pLMS). Our proposed RCpLMS algorithm (DRC-pLMS) will utilise a pipelined hardware architecture because to this approach alongside the delay and sum relaxation approach. Synthesise and implementation outcomes show that the recommended design delivers a maximum operational frequency and its take maximum resource utilisations. the objective of this study is to improve efficiency by switching the DRC-pLMS design with two 8-bit input levels. At the end, this work was translated into Verilog HDL, produced on a Xilinx Vertex-5 FPGA, and all the parameters related to area, delay, and power were compared.

关键词： Frequency synthesizers Array signal processing Adaptive algorithms Mean square error methods Hardware Delays Hardware design languages Field programmable gate arrays Adders Convergence

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5th international Symposium on Intelligent Computing Systems, ISICS 2024

5th International Symposium on Intelligent Computing Systems...

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5th international Symposium on Intelligent Computing Systems, ISICS 2024

ISBN: (纸本)9783031829307

the proceedings contain 25 papers. the special focus in this conference is on Intelligent Computing Systems. the topics include: Using LLMs to Enhance Research Output Tracking in Weakly Categorized Research Areas: A Case Study to Classify Publications in Islamic Economics, Finance, Banking and Business;Evaluating AI-Generated Video Quality: A Novel Assessment Model;a Non-blind Audio Watermarking in 3D Mesh Models Using Daubechies Wavelet Technique;Assessing the Impact of Data Augmentation Techniques on YOLO’s Performance in PV Anomaly Detection;studying Factors Affecting Attitudes on Artificial Intelligence Voice Assistants in the United Arab Emirates;Conceptualizing a Generative AI-Based Solution for Emotional Intelligence Interventions: Application to Education and Training;dental Caries Segmentation from Orthopantomograms Using Optimized DeepLabV3+;convolutional Neural Networks for the Classification of Comet Assay Cells;AI Driven Carbon Emission Estimation and Forecasting with Recommended Solution;leveraging Deep Learning for Accurate Classification of Leptograpsus Crabs Based on Morphological Measurements;heart Disease Prediction Using Machine Learning;development and Implementation of a Learning Management System with Data Analytics for Enhanced Online Education;VidLyze: Video Content Toxicity Identification Tool Using Natural Language processing (NLP) and Machine Learning;Comprehensive Evaluation of GAN architectures for Acute Lymphoblastic Leukemia Classification;a Novel Classification of Authentication Schemes for Internet of Drones;carbonMetric: An Intelligent System for Calculating and Reducing Carbon Footprints in Urban Environments;evaluating Adan vs. Adam: An Analysis of Optimizer Performance in Deep Learning;quality Assessment of Fundus Images for Further Classification: Lesson from thesis;empowering Students: Leveraging No-Code and Training Models with Google Machine Learning for Accessible App Development.

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parallel Approach to Sliding Window Sums 19th

Parallel Approach to Sliding Window Sums

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Snytsar, Roman Turakhia, Yatish Microsoft Corp One Microsoft Way Redmond WA 98052 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9783030389611;9783030389604

Sliding window sums are widely used for string indexing, hashing, time series analysis and machine learning. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost. We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). Implementing the algorithm for the bioinformatics application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5x.

关键词： Time series analysis

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SPAMeR: Speculative Push for Anticipated Message Requests in Multi-Core Systems 22

SPAMeR: Speculative Push for Anticipated Message Requests in...

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Proceedings of the 51st international conference on parallel processing

作者： Qinzhe Wu Ashen Ekanayake Ruihao Li Jonathan Beard Lizy John University of Texas at Austin United States of America Arm United States of America

ISBN: (纸本)9781450397339

With increasing core counts and multiple levels of cache memories, scaling multi-threaded and task-level parallel workloads is continuously becoming a challenge. A key challenge to scaling the number of communicating tasks (or threads) is the rate at which existing communication mechanisms scale (in terms of latency and bandwidth). architectures with hardware accelerated queuing operations have the potential to reduce the latency and improve scalability of moving data between processing elements, reducing synchronization penalties, and thereby improving the performance of task-level parallel workloads. While hardware queues reduce synchronization penalties, they cannot fully hide load-to-use latency, i.e., perfect pipelines often are not realized. there is the potential, however, for better overlap. If the inter-processor communication latency is equal to or less than the time spent processing a message at the consumer, any and all latency may be overlapped while the consumer is processing. We exploit this property to speedup parallel applications above and beyond existing hardware queues. In this paper, we present SPAMeR, a speculation mechanism built on top of a state-of-the-art hardware-driven message queue architecture. SPAMeR has the capability to speculatively push messages in anticipation of consumer message requests. Unlike pre-fetch approaches which predict what addresses to fetch next, with a queue we know exactly what data is needed next but not when it is needed; SPAMeR adds algorithms that attempt to predict this. We evaluate the effectiveness of SPAMeR with a set of diverse task-parallel benchmarks utilizing the gem5 full system simulator, and observe a 1.33 × average speedup.

关键词： parallel computing message queue multi-core system speculation

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Cloud-Driven AI: Enhancing Machine Learning Performance through Distributed Computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

the exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. this research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. this framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. these results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management Distributed computing Random forests

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Multilingual Sentiment Analysis using Deep-Learning architectures

Multilingual Sentiment Analysis using Deep-Learning Architec...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Praveen Dominic Niranjan Purushothaman Anish Skanda Anil Kumar A Prabagaran J Angelin Blessy A John School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. the posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. the objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. the idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. the pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). the larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. the non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.

关键词： Sentiment analysis Analytical models Social networking (online) Oils Government Bit error rate Machine learning

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Enhancing parallelization with OpenMP through Multi-Modal Transformer Learning

Enhancing Parallelization with OpenMP through Multi-Modal Tr...

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international conference on Computer Engineering and Applications (ICCEA)

作者： Yuehua Chen Huaqiang Yuan Fengyao Hou Peng Hu Dongguan University of Technology Dongguan China Institute of High Energy Physics Chinese Academy of Sciences Beijing China Spallation Neutron Source Science Center Dongguan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

the popularity of multicore processors and the rise of High Performance Computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely adopted shared-memory parallel programming model, is favored for its ease of use. However, it is still challenging to assist and accelerate automation of its parallelization. Although existing automation tools such as Cetus and DiscoPoP to simplify the parallelization, there are still limitations when dealing with complex data dependencies and control flows. Inspired by the success of deep learning in the field of Natural Language processing (NLP), this study adopts a Transformer-based model to tackle the problems of automatic parallelization of OpenMP instructions. We propose a novel Transformer-based multimodal model, ParaMP, to improve the accuracy of OpenMP instruction classification. the ParaMP model not only takes into account the sequential features of the code text, but also incorporates the code structural features and enriches the input features of the model by representing the Abstract Syntax Trees (ASTs) corresponding to the codes in the form of binary trees. In addition, we built a BTCode dataset, which contains a large number of C/C++ code snippets and their corresponding simplified AST representations, to provide a basis for model training. Experimental evaluation shows that our model outperforms other existing automated tools and models in key performance metrics such as F1 score and recall. this study shows a significant improvement on the accuracy of OpenMP instruction classification by combining sequential and structural features of code text, which will provide a valuable insight into deep learning techniques to programming tasks.

关键词： Deep learning Training Codes Automation Accuracy parallel programming Multicore processing

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MICRoFuzz: An Efficient Fuzzing Framework for Microservices

MICRoFuzz: An Efficient Fuzzing Framework for Microservices

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IEEE/ACM international conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP)

作者： Peng Di Bingchang Liu Yiyi Gao Ant Group Hangzhou China Ant Group Beijing China

ISBN: (数字)9798400705014

ISBN: (纸本)9798350352177

Fuzzing is a widely adopted technique in the software industry to enhance security and software quality. However, most existing fuzzers are specifically designed for monolithic software architectures and face significant limitations when it comes to serving distributed Microservices applications (Apps). these limitations primarily revolve around issues of inconsistency, communication, and applicability which arise due to the differences in monolithic and distributed software architecture. this paper presents a novel fuzzing framework, called MICRO-Fuzz, specifically designed for Microservices. Mocking-Assisted Seed Execution, Distributed Tracing, Seed Refresh and Pipeline parallelism approaches are adopted to address the environmental complex-ities and dynamics of Microservices and improve the efficiency of fuzzing. MICROFuzz has been successfully implemented and deployed in Antgroup 1 , a prominent FinTech company. Its per-formance has been evaluated in three distinct industrial scenarios: normalized fuzzing, iteration testing, and taint verification. throughout five months of operation, MICROFuzz has diligently analyzed a substantial codebase, consisting of 261 Apps with over 74.6 million lines of code (LOC). the framework's effectiveness is evident in its detection of 5,718 potential quality or security risks, with 1,764 of them confirmed and fixed as actual security threats by software specialists. Moreover, MICROFuzz significantly increased line coverage by 12.24% and detected new paths by 38.42% in the iteration testing.

关键词： Industries Software architecture Pipelines Microservice architectures Software quality Fuzzing parallel processing

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5th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

5th International Conference on Microelectronics, Electromag...

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5th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

ISBN: (纸本)9789811538278

the proceedings contain 81 papers. the special focus in this conference is on Microelectronics, Electromagnetics and Telecommunication. the topics include: A Tapered Microstrip-Fed Steering-Shaped Super-Wideband Printed Monopole Antenna;High-throughput VLSI architectures for VLSI Signal processing;reform Initiatives for Electrical Distribution Utilities in Jharkhand, India;FPGA Performance Evaluation of Present Cipher Using LCC Key Generation for IoT Sensor Nodes;Performance Comparison Analysis of GTTPC and AHTPC Technique for WBAN with Mobile Scenario;Estimation of FSO Link Availability for Visakhapatnam Coastal Region;CPW-Fed Monopole Antenna for RCS Reduction in X-Band Applications;Performance Improvement of Solar PV Maximum Power Point Tracking Using Sliding Mode Control Algorithm;surfactant Effect on Bandgap and Crystallite Size of ZnO–TiO2–CeO2 Nanocomposites;TEC Variation Analysis During Near Peak Solar Year Activity Over Indian Subcontinent;a Review on Schemes for Interconnecting Microgrids of Urban Buildings;performance Analysis of Multiwalled Carbon Nanotube, Composite Multiwalled Carbon Nanotube, and Copper-Based Antenna in X-Band Applications;wideband Printed Antenna Design Techniques: A Comprehensive Study;comparison of Various algorithms for Side Lobe Level Reduction in 5G Antenna Arrays;analysis of Electromagnetic Shielding Effectiveness Properties of Al6061 Metal Matrix Composites at X-Band for Aerospace Applications;a Comprehensive Study and Evaluation of Recommender Systems;design, Simulation and Experimental Validation of Patch Antenna in S-Band Satellite Communication;performance Efficient Floating-Point Multiplication Using Unified Adder–Subtractor-Based Karatsuba Algorithm;Implementation of Program Page, Read Page and Block Erase Operations in NAND Flash Memory Controller;Design of Low Standby Power 10T SRAM Cell with Improved Write Margin;preface.

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