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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是261-270 订阅

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Detection of Underground Object Using Ground Penetrating Radar

Detection of Underground Object Using Ground Penetrating Rad...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： L. David William Raj Utsab Banerjee M. thilagaraj A. Suganya U. Ramani C. Ganesh MVJ College of Engineering Bengalur MVJ College of Engineering Bengaluru India Dr. Mahalingam College of Engineering and Technology Pollachi India K. Ramakrishnan College of Engineering Trichy India Sri Eshwar College of Engineering Coimbatore India

this paper presents the applications for the detection of buried Improvised Explosive Devices (IEDs) and buried fusing mechanisms for the sake of national security. the instrument is used for the erection of buried images in Ground Penetrating Radar. the identified underground objects like skeletons, treasure, cables, mines etc. are potentially measured. During the collection of Ground Penetrating Radar raw images, the unwanted impurities, or elements such as noise and clutter are obtained. Raw images are also called B-Scan images, where the sampling rate is 8+ frames per seconds. the immense data for the pre-processing techniques has been preferred. the underground radar image signalized by the hyperbola which is reflected from the underground objects. the focused work is to clarify the interpretation of hyperbolic pattern present in Ground Penetrating Radar data and the curve fitting and the neural networks techniques are used to evaluate the position of the object.

关键词： Ground penetrating radar Impurities Neural networks Radar imaging parallel processing Skeleton Object recognition

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Asynchronous Coarse-Grained Reconfigurable Computing Architecture for Matrix Inversion

Asynchronous Coarse-Grained Reconfigurable Computing Archite...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Tianli Wang Mingshu Chen Yunpeng Xing Caihong Li Anping He the School of Information Science and Engineering Lanzhou University Gansu CN

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Matrix inversion is a fundamental operator in MIMO (multiple-input multiple-output) technology, which is a key technology in wireless communication. MIMO technology is characterized by a strong correlation between its underlying operations and matrix inversion. Traditional Von Leuwen archi-tecture processors are limited by the performance bottleneck of sequential execution, which hinders the performance of matrix inversion and limits the development of MIMO technology. the paper proposes a self-timed dynamically reconfigurable architecture to address the performance bottleneck caused by sequential execution in matrix arithmetic. this bottleneck has limited the development of MIMO technology. the architecture consists of several coarse-grained operators interconnected by an on-chip network to form an array for massively parallel computation. the experiments were conducted using an FPGA platform. the results demonstrate that the proposed architecture in this paper has excellent solving performance while significantly reducing the power consumption of the entire system. Specifically, when solving the inverse matrix of a fourth-order matrix, the dynamic power consumption is only 1.976W.

关键词： Wireless communication Power demand Program processors Correlation Integrated circuit interconnections System-on-chip Reconfigurable architectures

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Surrounding Keyword-Aware Self-Attention Network for Visual Question Answering

Surrounding Keyword-Aware Self-Attention Network for Visual ...

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Zhongdai Wu Jinyang Yi Bing Han Dezhi Han State Key Laboratory of Maritime Technology and Safety Shanghai Ship and Shipping Research Institute Co. Ltd Shanghai China Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the emergence of the Transformer architecture, self-attention networks have gained widespread attention as efficient and parallel mechanisms for capturing global information across various domains. However, traditional self-attention networks face challenges in accurately localizing key textual elements and allocating importance to individual words in complex multimodal Visual Question Answering (VQA) tasks, resulting in suboptimal performance. To address this issue, we propose a Surrounding Keyword-aware Self-Attention Network (SKASAN) designed to focus on the local contextual information surrounding key textual elements while integrating global information, thereby more effectively capturing crucial information necessary for problem-solving. Specifically, we introduce varying degrees of Gaussian deviation to achieve localized attention focusing and leverage global attention across layer information to guide stacked feature layer extraction, thereby correcting the attention distribution. Experimental results on the VQA-v2 dataset demonstrate that our proposed SKASAN significantly improves the rationality of attention distribution, leading to notable performance enhancements.

关键词： Visualization Focusing Signal processing Feature extraction Transformers Question answering (information retrieval) Resource management Problem-solving Data mining Faces

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Text-Image Generation Using Visual-Language Transformer parallel Decoding

Text-Image Generation Using Visual-Language Transformer Para...

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6th international conference on Intelligent Computing and Signal processing (ICSP)

作者： Peng Fang Fan Wang Chongqing Three Gorges University School of Electronic and Information Engineering Chongqing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Autoregressive generative models have shown their superiority in the fields of vision and language, such as high accuracy, high fidelity, and strong stability. Most existing methods are trained around text-guided image generation tasks and image-guided text generation tasks. However, not only can these two generative tasks be trained on the same dataset, but both require the alignment of visual and textual representations in the same semantic space. In order to be able to learn better generalization of image and text representations, we unify these two generation tasks in an autoregressive generative model and enhance the quality of text and image generation through bidirectional generation goals. In order to take the 2D-structure of the image into account. To this end, we propose a visual-linguistic Transformer autoregressive generative model. the visual-language Transformer model adopts a novel decoding strategy that takes the 2D-structure of the image into account without affecting the generation of the 1D sequence structure of the text. through improved training strategies, we can not only generate highly accurate text descriptions, but also enhance the model's image understanding and generate high-quality images.

关键词： Training Visualization Accuracy Image synthesis Computational modeling Signal processing Transformers Stability analysis Decoding Iterative decoding

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2021 9th international conference on Information and Communication Technology, ICoICT 2021

2021 9th International Conference on Information and Communi...

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9th international conference on Information and Communication Technology, ICoICT 2021

ISBN: (纸本)9781665404471

the proceedings contain 121 papers. the topics discussed include: sentiment analysis on marketplace review using hybrid lexicon and SVM method;spam detection on Indonesian beauty product review;accessibility and response time analysis on the COVID-19 website in Indonesia;SVM parallel concept test with SMO decomposition on cancer microarray dataset;tone detection system design for targets with frequency drift;building an ID card repository with progressive web application to mitigate fraud based on the twelve-factor app methodology;ransomware detection on bitcoin transactions using artificial neural network methods;information cascade mechanism and measurement of Indonesian fake news;and implementation of simulated annealing-support vector machine on QSAR study of indenopyrazole derivative as anti-cancer agent.

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Exploiting Co-execution with OneAPI: Heterogeneity from a Modern Perspective 27th

Exploiting Co-execution with OneAPI: Heterogeneity from a Mo...

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27th international European conference on parallel and Distributed Computing (Euro-Par)

作者： Nozal, Raul Bosque, Jose Luis Univ Cantabria Dept Comp Sci & Elect Santander Spain

ISBN: (纸本)9783030856656;9783030856649

Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addresses these issues. In this paper, oneAPI is provided with co-execution strategies to run the same kernel between different devices, enabling the exploitation of static and dynamic policies. On top of that, static and dynamic load-balancing algorithms are integrated and analyzed. this work evaluates the performance and energy efficiency for a well-known set of regular and irregular HPC benchmarks, using an integrated GPU and CPU. Experimental results show that co-execution is worthwhile when using dynamic algorithms, improving efficiency even more when using unified shared memory.

关键词： Heterogeneous computing parallel computing Co-execution Load balancing SYCL oneAPI DPC plus Scheduling HPC

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NEURAL ARCHITECTURE SEARCH ON IMAGENET IN FOUR GPU HOURS: A thEORETICALLY INSPIRED PERSPECTIVE 9

NEURAL ARCHITECTURE SEARCH ON IMAGENET IN FOUR GPU HOURS: A ...

引用

9th international conference on Learning Representations, ICLR 2021

作者： Chen, Wuyang Gong, Xinyu Wang, Zhangyang Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX United States

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or approximations. Can we select the best neural architectures without involving any training and eliminate a drastic portion of the search cost? We provide an affirmative answer, by proposing a novel framework called training-free neural architecture search (TE-NAS). TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK) and the number of linear regions in the input space. Both are motivated by recent theory advances in deep networks and can be computed without any training and any label. We show that: (1) these two measurements imply the trainability and expressivity of a neural network;(2) they strongly correlate with the network's test accuracy. Further on, we design a pruning-based NAS mechanism to achieve a more flexible and superior trade-off between the trainability and expressivity during the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completes high-quality search but only costs 0.5 and 4 GPU hours with one 1080Ti on CIFAR-10 and ImageNet, respectively. We hope our work inspires more attempts in bridging the theoretical findings of deep networks and practical impacts in real NAS applications. Code is available at: https://***/VITA-Group/TENAS. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Graphics processing unit

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Real Time Crime Data Analysis with Hadoop and MapReduce: A Case Study From Sri Lanka

Real Time Crime Data Analysis with Hadoop and MapReduce: A C...

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international conference on Information Technology Research (ICITR)

作者： Dhanuka Dayawansa Hamza Nauzad Sithara Kumarasingha Uthpala Isuranga Prasanna S. Haddela Samadhi Rathnayake Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Modern law enforcement faces significant challenges in managing and analyzing the exponential growth of crime data, which often includes unstructured, high-volume, and real-time information. Traditional relational database systems struggle to handle these complexities, limiting the ability of agencies to derive actionable insights for proactive measures. this research addresses these challenges by proposing a scalable distributed framework utilizing Hadoop and MapReduce for real-time crime data analysis. the objectives include enabling efficient ETL (Extract, Transform, Load) processes, implementing a robust star schema for structured data storage, and providing actionable insights through an integrated real-time dashboard. the methodology employs a multi-node Hadoop cluster for parallel processing, optimizing data integration and analysis capabilities. Results demonstrate significant improvements in processing speed, fault tolerance, and scalability, validated through the framework's application in Sri Lanka's crime data analysis. Findings reveal enhanced resource allocation, crime pattern identification, and operational efficiency for law enforcement. this research establishes a cost-effective, high-performance solution to modern criminological data challenges, with future potential for predictive analytics and machine learning integration.

关键词： Data analysis Law enforcement Scalability Fault tolerant systems Stars Predictive models parallel processing Real-time systems Resource management Predictive analytics

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Transfer mechanism of data decryption authority in JointCloud computing 12

Transfer mechanism of data decryption authority in JointClou...

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12th IEEE international conference on Joint Cloud Computing (IEEE JCC) / 9th IEEE international conference on Mobile Cloud Computing, Services, and Engineering (IEEE MobileCloud)

作者： Xiang, Liangliang Qin, Ying Shi, Peichang Li, Rui Yang, Shangzhi Parallel & Distributed Proc Lab PDL Sci & Technol Changsha Peoples R China Natl Univ Def Technol Engn Res Ctr Natl Fundamental Software Coll Comp Changsha 410073 Peoples R China Tianjin Univ Tianjin 300350 Peoples R China

ISBN: (纸本)9781665434799

Data security sharing in the Jointcloud has always been a difficult problem for researchers. Some cloud data sharing solutions have a series of problems, such as high communication overhead, difficult key management, data tampering, single cloud failure, etc. We propose a data-sharing mechanism based on blockchain and threshold proxy re-encryption to solve the problems listed above. the proxy re-encryption algorithm we adopted is improved based on the Conditional Proxy Broadcast Re-Encryption. By combining the secret sharing scheme to realize trust decentralization. the re-encryption computing is delivered to multiple cloud service providers, which reduces the centralization problem caused by single cloud processing. At the same time, blockchain is used to manage metadata information and data access control rights to solve data loss and tampering problems. Experimental results show that our mechanism achieves low re-encryption computing overhead.

关键词： data sharing JointCloud proxy reencryption blockchain

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Towards a Privacy, Secured and Distributed Clinical Data Warehouse Architecture 9th

Towards a Privacy, Secured and Distributed Clinical Data War...

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9th international conference, FDSE

作者： thantilage, Ranul Deelaka Le-Khac, Nhien-An Kechadi, M-Tahar Univ Coll Dublin Sch Comp Sci Dublin Ireland Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9789811980688;9789811980695

Reputed organisations are always prompting Data Warehouses (DWs), which are essential for storing and mining their historical datasets. When it comes to the healthcare industry, DWs are becoming ever so imperative, as efficient storage for medical data is vital for one's health while mining it and seeking new insights. While clinical datasets are very complex, their timely integration and analysis are crucial to providing excellent care for patients. this research aims to provide an efficient data warehousing solution with multiple privacy and security measures integrated by design. Securing the data at all stages: during data input, exploration, pre-processing, selection, analysis, and presentation, is very challenging. this research explores data security from a holistic perspective and possible distributed analysis mechanisms while streamlining data sharing between healthcare centres to increase efficiency and better patient treatments. this study also considers security and privacy issues at all stages of the data warehousing process (data lifecycle) to ensure its correct handling and use. We focus on distributed clinical data warehouse architectures. We also describe the main requirements of a clinical data warehouse for the whole data lifecycle, Data Capture, Acquisition Management, Archiving, Sharing, Reporting, Analysis, and Privacy and Security. the proposed architecture is evaluated considering existing state-of-the-art concerning data analysis and sharing capabilities while ensuring data security and privacy.

关键词： Data warehouse Clinical data Cardiology architectures Data security Data privacy Clinical data warehouse requirements

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