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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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International conference on image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Dcha: distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

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2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Ren, Erxiang Qu, Cheng Luo, Li Li, Yonghua Liu, Zheyu Yang, Xinghua Wei, Qi Qiao, Fei School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens Ai Beijing China College of Science Beijing Forestry University Beijing China Tsinghua University Department of Precision Instrument Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9783982674100

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed. © 2025 EDAA.

关键词： parallel processing systems

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A Heterogeneous KBA parallel Algorithm for the Cartesian Discrete Ordinates for Multizone Heterogeneous System

A Heterogeneous KBA Parallel Algorithm for the Cartesian Dis...

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

作者： Runhua Li Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Innovations in powerful high-performance computing (HPC) architecture are enabling high-fidelity whole-core neutron transport simulations at reasonable time. Especially, the currently fashionable heterogeneous architectures make the cost of such simulations at very low level. Neutron distribution of a reactor core is governed by the Boltzmann neutron transport equation (BTE), first viable solutions of which need tremendous computer resources. Among of the high-fidelity numerical methods, the discrete ordinates method (SN) is becoming popular in the reaction design community by taking a good balance between computational cost and accuracy. Recently, MT-3000, which is a multizone heterogeneous architecture with a peak double precision performance of 11.6 TFLOPS, is proposed. In this work, the BTE is solved by the SN with heterogenous Koch-Baker-Alcouffe (KBA) parallel algorithms based on the MT-3000 architecture. A communication mechanism has been established to efficiently transmit data among the acceleration cores and the CPU cores. The kernel computation procedure is largely accelerated by the vectorization and instruction pipelining techniques. Numerical experiments show that our formulation could achieve 1.37 TFLOPs with single MT-3000, that is 11.8% of its peak performance.

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A Decision-Based Median Filter with Adaptive Neighborhood Logic for Efficient Noise Reduction in VLSI-Oriented image processing Applications

A Decision-Based Median Filter with Adaptive Neighborhood Lo...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Y. Vishwa Sri M. Jagadeesh Chandra Prasad B. Hari Krishna Usthulamuri Penchalaiah Pala Mahesh Kumar N. Vamshi Krishna Department of Electronics and Communication Engineering Kommuri Pratap Reddy Institute of Technology (UGC - Autonomous) Hyderabad India Department of Electronics and Communication Engineering G. Narayanamma Institute of Technology and Science (UGC - Autonomous) Hyderabad India Department of Electronics and Communication Engineering St. Martin’s Engineering College (UGC-Autonomous) Hyderbad India Department of Electronics and Communication Engineering Geethanjali Institute of Science and Technology (UGC-Autonomous) Nellore India SAK Informatics Hyderabad India Sr. Multidisciplinary Engineer SAK Informatics Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In the realm of image processing, filters are essential for mitigating various types of noise, including random, salt and pepper, and Gaussian noise. The effectiveness of these filters is crucial, particularly in real-time applications and Very Large-Scale Integration (VLSI) hardware implementations. Traditional hardware-based filters often face challenges related to lookup table (LUT) requirements, route delays, and power consumption. This research focuses on developing a Decision-Based Median Filter (DBMF) designed to address these issues through a novel approach. The proposed DBMF integrates data comparator decision-based multiplexer logic with adaptive neighborhood logic. Initially, a decision-based multiplexer, managed by selection logic, differentiates between high and low values within a pair of numbers to ensure accuracy. This process is iterated across nine-pixel combinations to compute the median value. The proposed DBMF demonstrates superior performance in noise reduction and key hardware metrics, including latency, power consumption, and LUT usage, compared to existing advanced methods. Evaluation results show that the DBMF achieves a Peak Signal-to-Noise Ratio (PSNR) of 45.84, a Mean Squared Error (MSE) of 0.00477, and a Structural Similarity Index (SSIM) of 0.9816, indicating substantial improvements in filter performance and efficiency. These findings highlight the DBMF’s effectiveness in reducing noise and enhancing overall hardware performance.

关键词： Multiplexing Salt Filters Power demand Noise reduction Very large scale integration Hardware Table lookup Delays Logic

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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： V. Sathiyapriya K. Nethra Devi S. Deepa M. Marimuthu P. Manikandan S. Prabu Department of Computer Science and Engineering Knowledge Institute of Technology Kakapalayam India Department of Artificial Intelligence and Data Science Gnanamani College of Technology Namakkal India School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches.

关键词： image segmentation Accuracy Soft sensors Pancreatic cancer Medical services Feature extraction Prediction algorithms Whale optimization algorithms Classification algorithms Ensemble learning

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Optimized Vision Transformer Training using GPU and Multi-threading

Optimized Vision Transformer Training using GPU and Multi-th...

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Artificial Intelligence (CAI), IEEE conference on

作者： Jonathan Ledet Ashok Kumar Dominick Rizk Rodrigue Rizk KC Santosh Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA USA Department of Electrical Engineering and Computer Science Catholic University of America Washington DC USA Department of Computer Science Applied AI Research Lab University of South Dakota Vermillion SD USA

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Traditional Convolutional Neural Networks (CNNs) often struggle with capturing intricate spatial relationships and nuanced patterns in diverse datasets. To overcome these limitations, this work pioneers the application of Vision Transformer (ViT) models which have gained significant attention in the field of computer vision for their ability to capture long-range dependencies in images through self-attention mechanisms. However, training large-scale ViT models with a massive number of parameters poses computational challenges. In this paper, we present an optimized approach for training ViT models that leverages the parallel processing capabilities of Graphics processing Units (GPUs) and optimizes the computational workload distribution using multi-threading. The proposed model is trained and tested on the CIFAR-10 dataset and achieved an outstanding accuracy of 99.92% after 100 epochs. The experimental results demonstrate the effectiveness of our approach in optimizing training efficiency compared to existing methods. This underscores the superior performance of ViT models and their potential to revolutionize image classification tasks.

关键词： Training Computer vision Multithreading Computational modeling Neural networks Graphics processing units Convolutional neural networks

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S2CCT: Self-Supervised Collaborative CNN-Transformer for Few-shot Medical image Segmentation

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongzhou Zhou Ziqi Shu Weixing Xie Junfeng Yao Qingqi Hong Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Institute of Artificial Intelligence Xiamen University Xiamen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S 2 CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks; (2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation; (3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S 2 CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon.

关键词： image segmentation Three-dimensional displays Superresolution Collaboration Organizations Feature extraction Transformers Medical diagnosis Medical diagnostic imaging Knowledge transfer

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Fusion of Pyramid Real image Denoising Network and Residual Encoder-Decoder for Cutting-Edge image Restoration for Advancing Industry 7th

Fusion of Pyramid Real Image Denoising Network and Residual ...

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7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Kumar, Chevella Anil Neelima, N. Pravallika, Ambati Kumar, Chinthakindi Kiran VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Mallareddy College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819609239

image denoising is a vital process in improving the visual quality by eliminating unnecessary noise from images, to restore the original clarity images. Conventional methods often assume Gaussian-distributed noise, which may not represent accurately for real-world noise complexities. This proposed research focuses on optimizing blind noise, prevalent in real-world images using deep learning techniques. The denoising model learns the features directly from the noisy samples by capturing the noise distribution within the image. This study employs the deep learning fusion-based method by integrating the noise prioritization using the Pyramid Real image Denoising Network (PRID Net), multi-scale feature extraction, and Residual Encoder-Decoder Network (RED net) to reduce the blind noise. Both PRID network and RED network use the convolution neural network to efficiently lower the noise in order to enhance the noisy image’s quality. The experimental analysis compares the proposed model with the individual PRID and RED network models in terms of objective measurements such as the Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Signal-to-Noise Ratio (SNR) on various datasets such as RENOIR, SIDD, and NIND datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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Multi-Layer Feature Division Transferable Adversarial Attack

Multi-Layer Feature Division Transferable Adversarial Attack

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zikang Jin Changchun Yin Piji Li Lu Zhou Liming Fang Xiangmao Chang Zhe Liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adversarial examples. However, existing state-of-the-art feature-level attacks only destroy a single intermediate layer, this severely limits the transferability of adversarial examples. And all of these attacks have a vague distinction between positive and negative features. By contrast, we propose the Multi-layer Feature Division Attack (MFDA), which aggregates multi-layer feature information on the basis of feature division to attack. Extensive experimental evaluation demonstrates that MFDA can significantly boost the adversarial transferability and quantitatively distinguish the effects of positive and negative features on transferability. Compared to the state-of-the-art feature-level attacks, our improvement methods with MFDA increase the average success rate by 2.8% against normally trained models and 3.0% against adversarially trained models.

关键词： Aggregates Closed box Signal processing Acoustics Speech processing

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 11

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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11th International conference on Computing and Pattern Recognition, ICCPR 2022

作者： Sun, Pingping Qiang, Baohua Liu, Zhiguang Yang, Xianyi Xi, Guangyong Liu, Weigang Chen, Ruidong Zhang, Shihao Guilin University of Electronic Technology China The 54th Research Institute of Cetc Hebei Key Laboratory of Intelligent Information Perception and Processing China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods. © 2022 ACM.

关键词： Alignment

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