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GA-SATIC: Semi-autoregressive Transformer Image Captioning Based on Geometric Attention 5

GA-SATIC: Semi-autoregressive Transformer Image Captioning B...

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5th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Li, Ziyi Li, Ma Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Image captioning is a cross-domain task involving image and natural language processing. Most of the current models follow an encoder-decoder architecture, where the encoder takes image feature vectors as input, and the decoder uses an autoregressive or non-autoregressive approach for decoding. However, most models extract the feature vectors of an image through region proposals of the object detector, without considering the relative spatial relationship between objects. When decoding, the autoregressive decoding method is adopted, that is, the next word is generated based on the generated word, and the generation is performed step by step, which will lead to high delay in the inference process. To solve this problem, scholars have proposed a non-autoregressive approach, which speeds up inference by generating all words in parallel, but reduces the quality of generated captioning. Aiming at the above problems, this paper proposes a semi-autoregressive Transformer model based on geometric attention. the encoder integrates the relative spatial relationship between the detected objects through geometric attention and appearance attention, so as to enhance spatial awareness;the decoder adopts a semi-autoregressive decoding method, which is serialized globally and paralleled locally, enabling the model to achieve a better trade-off between decoding speed and captioning accuracy. Extensive experiments and ablation studies on the MSCOCO dataset have shown that the model achieves better performance compared to state-of-the-art models. © 2022 ACM.

关键词： Signal encoding

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An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical architectures 27th

An MPI-based Algorithm for Mapping Complex Networks onto Hie...

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

作者： Predari, Maria Tzovas, Charilaos Schulz, Christian Meyerhenke, Henning Humboldt Univ Berlin Germany Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9783030856656;9783030856649

processing massive application graphs on distributed memory systems requires to map the graphs onto the system's processing elements (PEs). this task becomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. parallel partitioning tools do exist;yet, corresponding mapping algorithms or their public implementations all have major sequential parts or other severe scaling limitations. In this paper, we propose a parallel algorithm that maps graphs onto the PEs of a hierarchical system. Our solution integrates partitioning and mapping;it models the system hierarchy in a concise way as an implicit labeled tree. the vertices of the application graph are labeled as well, and these vertex labels induce the mapping. the mapping optimization follows the basic idea of parallel label propagation, but we tailor the gain computations of label changes to quickly account for the induced communication costs. Our MPI-based code is the first public implementation of a parallel graph mapping algorithm;to this end, we extend the partitioning library PARHIP. To evaluate our algorithm's implementation, we perform comparative experiments with complex networks in the million- and billion-scale range. In general our mapping tool shows good scalability on up to a few thousand PEs. Compared to other MPI-based competitors, our algorithm achieves the best speed to quality trade-off and our quality results are even better than non-parallel mapping tools.

关键词： Load balancing Process mapping Hierarchical architectures parallel label propagation

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Chinese description of videos incorporating multimodal features and attention mechanism 2021

Chinese description of videos incorporating multimodal featu...

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5th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Liu, Hu Wu, Junxiu Yuan, Jiabin Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China

ISBN: (纸本)9781450388634

Video description is a hot topic in the area of computer vision and natural language processing, which has made remarkable achievements in recent years. But most researches on video description are to generate English description while few on Chinese description. this paper explores the generation process of video Chinese description and proposes a model for video Chinese description, which introduces three complementary modal features and temporal attention mechanism based on the general encoder-decoder framework. the optimized video description model combined with an appropriate Chinese preprocessing method further improves Chinese descriptions' richness and accuracy. these works provide a valuable reference for future research on multilingual video description. We tested the proposed Chinese model on an expanded Chinese corpus of standard English dataset MSVD (Microsoft Research video description corpus) and studied the special processing methods of Chinese description generation. Experimental results show that the highest METEOR value obtained by the Chinese model proposed is still 6.6% higher than that of the best result on MSVD's Chinese corpus, and the model also has an advanced result in English environment.

关键词： Video Chinese description Multimodal features Attention mechanism Natural language processing

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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification 34

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

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34th international conference on VLSI Design / 20th international conference on Embedded Systems (VLSID)

作者： Sharma, Ayushparth Lata, Kusum LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9781665440875

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. this article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. the proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10x higher energy-efficiency compared to serial counterpart and reduced execution period by 70x. Clinical validation shows that parallel architecture introduces 5-7% error in nodule characteristic determination in comparison to serial one.

关键词： lung cancer detection delay structures medical image processing energy-efficient platform reconfigurability

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Ensemble model for ECG Classification

Ensemble model for ECG Classification

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international conference on Multimedia, Signal processing and Communication Technologies, (IMPACT)

作者： Abdul Haque Nadeem Akhtar Dept.of Computer Engineering Zakir Hussain College of Engineering & Technology Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Electrocardiography (ECG) is a relatively popular diagnostic technique that does not involve any kind of bodily invasion, and algorithms are increasingly supporting its interpretation. there has been a shortage of relevant datasets for training in this area of work and there will be lack of clearly defined evaluation procedure, which has slowed progress in the field of automated analysis of electrocardiograms (ECGs). these factors have made it difficult to compare the effectiveness of various algorithms. PTB-XL is a clinical 12-lead electrocardiogram dataset that was just released and is easily available. Here, we illustrate a range of results for this dataset., which covers a classification job based on a variety of ECG statements. this is done so that we may get around the issues that have been brought up. Among the deep learning-based time series classification approaches that were researched and examined, convolutional neural networks, and especially vgg16 and alexnet-based architectures, provide the greatest performance. the vgg16-based convolutional method achieves 0.8710 accuracy and 0.9448 AUC when applied to the ECG dataset. the ensemble model achieves the accuracy of 0.8690 and AUC of 0.9431. In the PTB-XL dataset, we used a 100 Hz sample rate for our training purposes. We split out the PTB-XL dataset into training, which is approx. 70% of the data, testing, which occupies 15% of the dataset, and the remaining 15% is for the validation set. the findings demonstrate that the strategy presented here is not only effective but also accurate when it comes to classifying ECG signals.

关键词： Training Biomedical equipment Convolution Computational modeling Signal processing algorithms Computer architecture Electrocardiography

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Hybrid Ladder Transformers with Efficient parallel-Cross Attention for Medical Image Segmentation 5

Hybrid Ladder Transformers with Efficient Parallel-Cross Att...

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5th international conference on Medical Imaging with Deep Learning, MIDL 2022

作者： Haozhe, Luo Changdong, Yu Selvan, Raghavendra Department of Computer Science and Technology Sichuan University China College of Information and Communication Engineering Harbin Engineering University China Department of Computer Science University of Copenhagen Denmark Department of Neuroscience University of Copenhagen Denmark

Most existing transformer-based network architectures for computer vision tasks are large (in number of parameters) and require large-scale datasets for training. However, the relatively small number of data samples in medical imaging compared to the datasets for vision applications makes it difficult to effectively train transformers for medical imaging applications. Further, transformer-based architectures encode long-range dependencies in the data and are able to learn more global representations. this could bridge the gap with convolutional neural networks (CNNs), which primarily operate on features extracted in local image neighbourhoods. In this work, we present a hybrid transformer-based approach for segmentation of medical images that works in conjunction with a CNN. We propose to use learnable global attention heads along with the traditional convolutional segmentation network architecture to encode long-range dependencies. Specifically, in our proposed architecture the local information extracted by the convolution operations and the global information learned by the self-attention mechanisms are fused using bi-directional cross attention during the encoding process, resulting in what we call a hybrid ladder transformer (HyLT). We evaluate the proposed network on two different medical image segmentation datasets. the results show that it achieves better results than the relevant CNN- and transformer-based architectures. © 2022 L. Haozhe, Y. Changdong & R. Selvan.

关键词： Network architecture

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Input Based Resource Allocation in Motion Estimation using Re-configurable Architecture 5

Input Based Resource Allocation in Motion Estimation using R...

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5th international conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Suhasini, S. SheelaLavanya, J. M. Parameswari, M. Manikandan, G. Nissi, S. Gracia Kings Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9781665426428

Reconfigurable engineering can dynamically assign the assets during runtime. It tends to be adequately utilized in computationally escalated applications like media processing. In media processing, video compression is one of the most computationally intensive applications. ME is the basic undertaking in video pressure as it devours enormous measure of computational time for finding the best block match by calculating Sum of Absolute Difference (SAD) of different blocks in successive video frames. To overcome this problem, its inherent parallel execution nature is analysed and mapped into customized parallel reconfigurable engineering to adequately deal with the force and asset usage by unique reconfiguration. Application of reconfigurations in the hardware for block matching and comparator modules based on the level of motion in the input video can produce substantial optimization in terms of power and resource utilization.

关键词： Zynq SoC SAD FPGA processing system programmable logic

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Optimal Analysis for Enterprise Financial Management Based on Artificial Intelligence and parallel Computing Method 5

Optimal Analysis for Enterprise Financial Management Based o...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Rajeswari, M. Kumar, N. Raman, P. Patjoshi, Pramod Kumar Singh, Vaishali Pundir, Sumit SRM Institute of Science and Technology Vadapalani Department of Management Studies TamilNadu Chennai India College of Management SRM Institute of Science and Technology Kattankulathur TamilNadu Chennai India Panimalar Engineering College Department of MBA TamilNadu Chennai India Centurion University of Technology and Management School of Management Odisha Bhubaneswar India Ambalika Institute of Management & Technology Luknow Department of Computer Science and Engineering Uttar Pradesh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350398267

To function in today's society, people need access to information technology for every aspect of their lives, from personal interactions to the creation of goods and services. As the Internet and the In tandem with the expansion of the country's information industry, an increasing number of businesses are opting to use an Automation of Financial Data Management with AI. Artificial intelligence (AI) is a new technological science that aims to mimic, extend, and increase human intellect via the study and development of ideas, methodologies, technologies, and application systems. Artificial intelligence (AI), which may be seen as a method of teaching computers to replicate and simulate human thinking patterns and behaviours, has been the primary focus of data processing in recent years. To better grasp the complexity of many challenges in the financial services industry, AI has become a significant solution for creating accuracy and accountability. this paper begins with a discussion of basic technology's blueprint and shape, optimises it, investigates the real-world requirements of SMEs in terms of financial management, concludes with system architecture and development architecture, and finally concludes with performing hypothetical tests testing the system's functional performance. the test results show that the system accomplishes its intended purposes and performs its primary operations, but that it still needs some tweaks and additions. Because of this, it is very important that all the information used in managing money is exact, correct, and reliable. © 2022 IEEE.

关键词： Behavioral research

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Efficiency Across Datasets: Insights into Video Compression Techniques

Efficiency Across Datasets: Insights into Video Compression ...

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Image processing and Capsule Networks (ICIPCN), international conference on

作者： A. Sreeram B. Umesh Chandra G. Sai Swetha Shilpa Bagade Electronics and Communication Engineering GRIET Hyderabad India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

In recent times, there has been notable progress in deep learning-based image compression, showcasing enhanced coding efficiency and subjective quality. However, comparatively less attention has been devoted to video compression employing deep neural networks. this paper introduces DeepLVCnet is a deep predictive video compression network that operates from end to end. With multiple framework theories, multiple scale arrangements, and a temporal context-adaptive entropy model, the method makes use of mode-selective uni-and bi-directional predictions. DeepLVCnet jointly compresses motion information and residual data through feature transformation layers within the multi-scale structure. A mode-selective architecture with uni-directional and bidirectional predictive modes is incorporated into DeepLVCnet, in contrast to earlier deep learning-based video compression techniques limited to P-frame or B-frame usage. Furthermore, utilizing temporal context information from reference frames during current frame coding, a unique temporal-context-adaptive entropy model is developed. this model is designed to enable parallel processing, offering computational and architectural advantages over autoregressive entropy models used in some CNN-based video compression methods. the overall objective is to enhance coding efficiency and subjective video enlargement quality through these innovations.

关键词： Training Technological innovation Image coding Computational modeling Video compression parallel processing Entropy

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Implementation of High-speed and High-Accuracy Convolutional Neural Network Accelerator for Target Detection Applications

Implementation of High-speed and High-Accuracy Convolutional...

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Innovative Trends in Information Technology (ICITIIT), international conference on

作者： Anandakumar Haldorai R Babitha Lincy M Suriya Minu Balakrishnan Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Coimbatore TamilNadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Deep Convolutional Neural Network (CNN) for real-time applications heavily depends on computing efficiency. the efficiency of the CNN algorithm has been improved by modifying Convolutional Neural Network accelerator. thus, the proposed work development of improved CNN accelerator using Residue Number System (RNS) and implement in FPGA for performance investigation of throughput, latency, accuracy. the non-weighted nature of RNS makes it suitable for high-speed applications. As a result, it requires less space to house input feature maps and kernels. In addition to that, the proposed method will allow for parallel computation and required less power consumption. According to test results, the planned CNN accelerator can run the AlexNet at 165.4 frames per second, 200.5 GOPs per second at its max, and 22.5 GOPs per Watt. Additionally, it can be changed to support VGG-16 for complicated object recognition.

关键词： Power demand Object detection parallel processing throughput Real-time systems Convolutional neural networks Object recognition

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