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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是171-180 订阅

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning 6

Research on Traffic Sign Detection and Recognition System Us...

引用

6th International conference on computer Communication and the Internet (ICCCI)

作者： Wang, Lung-Jen Suwattanapunkul, Taweelap Thalauy, Jarinya Jansengrat, Parinya Natl Pingtung Univ Dept Comp Sci & Informat Engn Pingtung Taiwan Natl Pingtung Univ Int Master Program Informat Technol & Applicat Int Coll Pingtung Taiwan Rajamangala Univ Technol Thanyaburi Dept Big Data Management & Analyt Klongluang Pathum Thani Thailand

ISBN: (纸本)9798350351774;9798350351767

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： Self-driving Car Traffic Sign Detection Traffic Sign Recognition TSDR Deep Ensemble Learning YOLOv5s MobileNet

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CRAFT: Common Router Architecture for Throughput Optimization 1

引用

23rd International conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Yan, Jiahua Wang, Mingyu Qin, Yao Yu, Zhiyi Sun Yat Sen Univ Sch Microelect Sci & Technol Guangzhou 510275 Peoples R China

ISBN: (数字)9789819707980

ISBN: (纸本)9789819707973;9789819707980

Agile development is considered as an emerging trend in hardware design, and NoC-based multi-IP integrated system has become the mainstream solution. The most critical challenge of this solution is to design a common efficient NoC with ultra-high throughput and low-cost routers to meet the communication characteristics of varied applications. Usually, NoC can not achieve both the gains from high throughput and low cost because high-throughput router architectures need complex virtual channel control logic to ensure that the power consumption and area of buffers will not be too large. Meanwhile, the low-cost router architecture is difficult to achieve higher throughput by increasing the number of buffers due to its simple control circuits. Moreover, most routers in NoC are optimized only for specific applications. To address the problem, this paper presents a "viaduct theory" for throughput optimization and realizes a common NoC router architecture for different scenario requirements (CRAFT router). Firstly, we improve the top boundary of the router throughput with the fully-connected crossbar switch, which is an expensive solution for high throughput. Secondly, we use a method similar to the ring-decouple theory in low-cost routers to reduce the number of router buffers while maintaining high throughput. The experimental results show that the throughput for the low-cost program is 15.4% higher than the conventional router with the same overhead. For the high-performance program, our router saves 50% hardware overhead compared with fully-connected routers with the same throughput;The maximum throughput that can be achieved is 26.3% higher than conventional routers, and 14.2% higher than the other state-of-the-art designs.

关键词： Common router Network on chip high throughput Low cost

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Improving Road Defect Detection Precision and Efficiency with Structural Pruning Techniques 21

Improving Road Defect Detection Precision and Efficiency wit...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Ouyang, Hang Li, Tailin Wang, Chongru Gu, Yuanting Yang, Fan Deng, Kaixin College of Computer Science and Cyber Security Chengdu University of Technology Sichuan Chengdu China College of Energy Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798331519254

Timely detection and treatment of road defects are essential to ensure the road safety, comfort, and the longevity. However, due to high usage frequency and complex, variable environmental conditions, road defects are inevitable. Therefore, establishing an efficient system for road defect detection is particularly critical. This paper investigates the current state of object detection technologies, analyzes various existing detection methods, and proposes a lightweight road defect detection model, based on YOLOv11n. In the experimental evaluation, the improved model was compared against multiple versions of YOLO lightweight models to assess its performance. By employing the DepGraph structural pruning method, the model size was reduced, leading to improved operational efficiency and reduced computational resource consumption while maintaining detection accuracy. Experimental results demonstrate that, compared to YOLOv11n, the improved model achieved a 2.2% increase in precision, a 2.8% increase in recall, a 2.3% improvement in mAP50, a 72.2% reduction in parameters, and a 50.8% decrease in computational load. This research aims to contribute to road safety and extend the lifespan of road infrastructure. © 2024 IEEE.

关键词： Lightweight model performance evaluation Road defect detection Structural pruning YOLOv11n

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Case2Code: Scalable Synthetic Data for Code Generation 31

Case2Code: Scalable Synthetic Data for Code Generation

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Shao, Yunfan Li, Linyang Ma, Yichuan Li, Peiji Song, Demin Cheng, Qinyuan Li, Shimin Li, Xiaonan Wang, Pengyu Guo, Qipeng Yan, Hang Qiu, Xipeng Huang, Xuanjing Lin, Dahua School of Computer Science Fudan University China Shanghai AI Laboratory China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs, which can be challenging to scale due to the dependence on a teacher model and high generation costs. In this paper, we focus on synthesizing code data at scale and propose a Case2Code task by exploiting the expressiveness and correctness of programs. Case2Code is an inductive inference task that aims to infer underlying code implementations by observing input-output examples or program behaviors, By incorporating LLMs to generate program inputs, and executing the program with these inputs to obtain the program outputs, we can synthesize diverse and high-quality Case2Code data at scale for training and evaluating code LLMs. Experimental results show that case-to-code induction is challenging for current representative LLMs if they are untrained. Models trained with Case2Code improve performance not only on distribution case-to-code induction but also various coding-generation tasks, demonstrating the great potential of large-scale synthetic data and inductive learning. © 2025 Association for Computational Linguistics.

关键词： Input output programs

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Extending a high-performance Prover to higher-Order Logic 29th

Extending a High-Performance Prover to Higher-Order Logic

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29th International conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS)

作者： Vukmirovic, Petar Blanchette, Jasmin Schulz, Stephan Vrije Univ Amsterdam Amsterdam Netherlands Univ Lorraine LORIA INRIA CNRS Nancy France DHBW Stuttgart Stuttgart Germany

ISBN: (纸本)9783031308192;9783031308208

Most users of proof assistants want more proof automation. Some proof assistants discharge goals by translating them to first-order logic and invoking an efficient prover on them, but much is lost in translation. Instead, we propose to extend first-order provers with native support for higher-order features. Building on our extension of E to lambda-free higher-order logic, we extend E to full higher-order logic. The result is the strongest prover on benchmarks exported from a proof assistant.

关键词： computer circuits

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NeRFLiX: high-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer

NeRFLiX: High-Quality Neural View Synthesis by Learning a De...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhou, Kun Li, Wenbo Wang, Yi Hu, Tao Jiang, Nianjuan Han, Xiaoguang Lu, Jiangbo CUHK Shenzhen SSE Shenzhen Peoples R China SmartMore Corp Hong Kong Peoples R China CUHK Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Neural radiance fields (NeRF) show great success in novel view synthesis. However, in real-world scenes, recovering high-quality details from the source images is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the synthetic novel views produced by NeRF models still suffer from notable rendering artifacts, such as noise, blur, etc. Towards to improve the synthesis quality of NeRF-based approaches, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm by learning a degradation-driven inter-viewpoint mixer. Specially, we design a NeRF-style degradation modeling approach and construct large-scale training data, enabling the possibility of effectively removing NeRF-native rendering artifacts for existing deep neural networks. Moreover, beyond the degradation removal, we propose an inter-viewpoint aggregation framework that is able to fuse highly related high-quality training images, pushing the performance of cutting-edge NeRF models to entirely new levels and producing highly photo-realistic synthetic views.

关键词： 3D from multi-view and sensors

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HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning 12

HIV Inhibitory Activity Classification with Morgan Fingerpri...

引用

12th International conference on Information and Communication Technology, ICoICT 2024

作者： Verna Siswanto, Jocelyn Angelina Sunarya, Caroline Calista, Agnes Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350363432

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set. © 2024 IEEE.

关键词： Diseases

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Developing a Dynamic Graph Network for Interpretable Analysis of Multi-Modal MRI Data in Parkinson's Disease Diagnosis

Developing a Dynamic Graph Network for Interpretable Analysi...

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45th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Li, Fanshi Wang, Zhihui Guo, Yifan Liu, Congcong Zhu, Yanjie Zhou, Yihang Li, Jun Liang, Dong Wang, Haifeng Chinese Acad Sci Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Guangdong Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Res Ctr Med AI Shenzhen Guangdong Peoples R China Shenzhen Second Peoples Hosp Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350324471

Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.

关键词： computer aided diagnosis

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Fine-Grained Annotation Through Multi-Model Fusion and Class-Specific performance Optimization in Object Detection 21

Fine-Grained Annotation Through Multi-Model Fusion and Class...

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21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hongzhi, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798331519254

Automatic annotation is essential for large-scale data labeling in object detection, but traditional single-model methods often lack accuracy and reliability. Multi-model systems address this by leveraging the strengths of multiple detectors but face two key challenges: identifying whether predictions from different models refer to the same target and effectively integrating those predictions. This paper introduces a fine-grained annotation method and a distributed system architecture to address these issues. A target matching mechanism inspired by non-maximum suppression (NMS) consolidates predictions across models by evaluating bounding box overlaps. A dynamic decision-making algorithm assigns adaptive voting weights based on classspecific model performance and uncertainty, ensuring precise and robust label assignments. The distributed architecture, combining a storage layer, search engine, and scheduling servers, efficiently handles largescale annotation tasks. This approach highlights the benefits of multi-model integration, adaptive decision-making, and scalable design for reliable automatic annotation in object detection. © 2024 IEEE.

关键词： Search engines

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Diamagnetically Levitated and Trapped Graphite Mechanical Resonators 38

Diamagnetically Levitated and Trapped Graphite Mechanical Re...

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38th IEEE International conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wang, Yunong Gage, Alexander Lee, Jaesung Feng, Philip X.-L. University of Florida Herbert Wertheim College of Engineering Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Central Florida College of Engineering & Computer Science Department of Electrical and Computer Engineering OrlandoFL32816 United States

ISBN: (纸本)9798331508890

We report on studying diamagnetic levitation and rigid body resonances of millimeter- to centimeter-scale trapped graphite mechanical resonators, by combining theoretical analysis with experimental demonstrations. Harnessing the strong diamagnetic susceptibility of pyrolytic graphite, we demonstrate stable levitation of square graphite thin plates with varying dimensions and masses above permanent magnets, without the need of active control. The resonance motions are excited by dielectric gradient forces and detected via an ultrasensitive optical interferometry system at room temperature. We observe rigid body resonance modes with frequencies between 25 Hz and 50 Hz and with their quality (Q) factors from 30 to 70. The Q factors are primarily constrained by air damping and eddy current damping. This work represents an initial step toward developing high-stability, anchor-less levitating resonant systems with large masses, paving the way for high-performance resonant sensors with minimal energy dissipation and exceptional thermal and mechanical isolation with very small power consumption. © 2025 IEEE.

关键词： Damping

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