检索结果-内蒙古大学图书馆

International Conference on Computing and Networking technology (ICCNT)

作者： Satvik Vats Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

To emphasize the division of umpire actions exactly into classes and attempts to catch functioning umpires relative to the setting of hockey, which comprises five classes. The work provides precious data about the model’s performance during the umpire’s different actions, achieved by thorough practice and description of the collected data. The discoveries stand for future reference in further developing the quality measures for each hockey umpire action detection indicator. The scores of the given Class 1 are precision and recall of 89.27% and 96.83% and F1-score of 92.89%. Class 2’s precision is 90.15%, recall is 95.00%, and F1-score is 92.51%. Class 3 has a precision of 92.65% with an accuracy of 87.74%, while its F1-score has been calculated to be 90.13%. Class 4 ‘s precision is 93.51%, recall at 89.89% and F1-Score at 91.67%. Last, class 5 has a luck score of 94.92%, a recall score of 96.18%, and an F1 score of 95.55%. Furthermore, it is pointed out that all sorts of averages, like macro, micro, and weighted, are seen during the application of the model to give a general look or overall performance. MicroF1 stems from the meaning of macro F1 and is the weighted average of the label’s accuracy, the overall accuracy, and the weighted accuracy. The weighted average precision, recall and F-score are 92.82%, 92.77%, and 92.74%. The central micro average results are the accuracy, recall and F1-score with the values 0.9277. This report consists of the experiments finished with the data and statistical analyses studied, highlighting its CNN-SVM technique in the hockey umpire action detection process.

关键词： Measurement Analytical models Accuracy Statistical analysis Neural networks Tsunami Data models Convolutional neural networks Sports

来源：评论

学校读者我要写书评

暂无评论

Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

arXiv

引用

arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

来源：评论

学校读者我要写书评

暂无评论

JavaBench: A Benchmark of Object-Oriented Code Generation for Evaluating Large Language Models

arXiv

引用

arXiv 2024年

作者： Cao, Jialun Chen, Zhiyong Wu, Jiarong Cheung, Shing-Chi Xu, Chang Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs’ capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5 benchmarks involve Java, resulting in an insufficient understanding of LLMs’ capability to generate Java code. Second, imbalanced code granularity. Function-/statement-level benchmarks account for over 83.3% of benchmarks. Only a mere handful extends to class-/project-levels, and all are limited to Python. Third, lacking advanced features. Existing benchmarks primarily assess basic coding skills (e.g., variables, operators, and control structures), while overlooking advanced Object-Oriented Programming (OOP) features (i.e., encapsulation, inheritance, and polymorphism). Considering the prevalence of these advanced features in real-world Java project development, constructing benchmarks to test LLMs on handling OOP features is necessary. To fill these gaps, we propose JavaBench, a project-level Java benchmark that exercises OOP features. It comprises four Java projects with 389 methods in 106 Java classes. The test coverage is up to 92%, and JavaBench is attested by 282 undergraduate students, reaching a 90.93/100 average score (i.e., pass rate against the test suite), ensuring the quality of documentation, code skeleton, and tests. To better evaluate LLM’s capability against JavaBench, we introduce a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics. Our extensive experiment yields several interesting findings. First, we noticed that regarding project-level Java programming, LLMs are far behind undergraduate students (no project can be correctly completed by any studied LLMs, and at most 41.17% Pass@5 in a more relaxed evaluation). Second, using method signature as prompt context may strike an ide

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Intracardiac Electrogram Signals Key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction Pattern recognition Testing

来源：评论

学校读者我要写书评

暂无评论

Weather Prediction(analysis) using Soft kind of Computing techniques

Weather Prediction(analysis) using Soft kind of Computing te...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Pogiri Revathi Santhoshi Bhavani G Karthika Priya Dharshini Gnanajeyaraman Rajaram Saravanan Siddhan Rajat Kumar Dwibedi Department of ECE GMR Institute of Technology Rajam Andhra Pradesh India Department of ECE Avanthi Institute of Engineering and Technology Thagarapuvalasa Andhra Pradesh India Saveetha Engineering college Saveetha nagarThandalam Chennai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of ECE Aarupadai Veedu Institute of Technology Vinayaka Mission’s Research Foundation(Deemed to be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Identifying weather conditions is a prevalent issue across all industries. Autonomous vehicles must accurately assess meteorological conditions to adapt their driving behavior. Contemporary farming relies on analyzing present weather patterns. A potential approach could involve implementing a system that detects weather conditions from images. Since no specialized sensors are required, the entire setup should be quite affordable. Transfer learning (TL) enables the development of picture categorization methods with a limited database. This work introduces 3 weather detection systems. The designs are built using InceptionV3, MobileNetV2, & ResNet50 designs. Their effectiveness is evaluated and explained.

关键词： Databases Transfer learning Sensors Autonomous vehicles Meteorology Residual neural networks Farming

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Metasurface Architecture for Complete Wavefront Control in mmWave Programmable Wireless Environments

Reconfigurable Metasurface Architecture for Complete Wavefro...

引用

International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： A. Pitilakis O. Tsilipakos A. C. Tasolamprou A. Tsioliaridou N. V. Kantartzis S. Ioannidis M. Kafesaki C. Liaskos Foundation for Research and Technology Hellas Institute of Computer Science Heraklion Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece National Hellenic Research Foundation Theoretical and Physical Chemistry Institute Athens Greece Foundation for Research and Technology Hellas Institute of Electronic Structure and Laser Heraklion Greece Department of Physics National and Kapodistrian University of Athens Athens Greece School of Electrical & Computer Engineering Technical University of Crete Chania Greece Department of Materials Science and Technology University of Crete Heraklion Greece Computer Science and Engineering Department University of Ioannina Ioannina Greece

We present a scalable printed-circuit board (PCB) based reconfigurable metasurface (MS) architecture that can be used for complete wavefront manipulation in both polarizations. The operation relies on local control of the complex reflection coefficient in each cell and is enabled by lumped varactor and varistor loading elements assembled on the PCB. We design a unit cell for operation in the mmWave spectrum of 5G communications (28 GHz), evaluate its performance, and map its tunable lumped element configuration to the attained complex reflection coefficient. Finally, we study an MS-enabled programmable wireless environment for indoor communications that can be extended to imaging or holography.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Complex brain activity analysis and recognition based on multiagent methods

Complex brain activity analysis and recognition based on mul...

引用

作者： Zhang, Hao Lan Liu, Jiming Dowens, Margaret G. School of Computer and Data Engineering Ningbo Institute of Technology Zhejiang University Ningbo China Ningbo Research Institute Zhejiang University Ningbo China Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong School of English The University of Nottingham Ningbo Campus Nottingham China

Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition. © 2020 John Wiley & Sons, Ltd.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation

OAFormer: Learning Occlusion Distinguishable Feature for Amo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhixuan Li Ruohua Shi Tiejun Huang Tingting Jiang Advanced Institute of Information Technology Peking University Hangzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

The Amodal Instance Segmentation (AIS) task aims to infer the complete mask of occluded instance. Under many circumstances, existing methods treat occluded objects as unoccluded ones, and vice versa, leading to inaccurate predictions. This is because existing AIS methods do not explicitly utilize the occlusion rates of each object as supervision. However, occlusion information is critical for the methods to recognize whether the target objects are occluded. Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance. In this paper, a simple yet effective Occlusion-aware transformer-based model, OAFormer, is proposed for accurate amodal instance segmentation. The goal of OAFormer is to learn the occlusion discriminative features. Novel components are proposed to enable OAFormer to be occlusion distinguishable. We conduct extensive experiments on two challenging AIS datasets to evaluate the effectiveness of our method. OAFormer outperforms state-of-the-art methods by large margins.

关键词： Target recognition Signal processing Transformers Acoustics Artificial intelligence Task analysis Speech processing

来源：评论

学校读者我要写书评

暂无评论

Controlled Dropout for Uncertainty Estimation

Controlled Dropout for Uncertainty Estimation

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Mehedi Hasan Ibrahim Hossain Ashikur Rahman Saeid Nahavandi Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC Australia Dept. of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid-Order Distributed Sgd: Balancing Communication Overhead, Computational Complexity, and Convergence Rate for Distributed Learning

SSRN

引用

SSRN 2023年

作者： Omidvar, Naeimeh Hosseini, Seyed Mohammad Maddah-Ali, Mohammad Ali School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Twin CitiesMN United States

Communication overhead, computation load, and convergence speed are three major challenges in the scalability of distributed stochastic optimization algorithms in training large neural networks. In this paper, we propose the approach of hybrid-order distributed stochastic gradient descent (HO-SGD) which strikes a better balance between these three than the previous methods, for a general class of non-convex stochastic optimization problems. In particular, we advocate that by properly interleaving zeroth-order and first-order gradient updates, it is possible to significantly reduce the communication and computation overheads while guaranteeing a fast convergence. The proposed method guarantees the same order of convergence rate as in the fastest distributed methods (i.e., fully synchronous SGD) while having significantly less computational complexity and communication overhead per iteration, and the same order of communication overhead as in the state-of-the-art communication-efficient methods, with order-wisely less computational complexity. Moreover, it order-wisely improves the convergence rate of zeroth-order SGD methods. Finally, empirical studies demonstrate that the proposed hybrid-order approach also provides superior generalization and significantly higher test accuracies than all the baselines, owing to its new exploration mechanism. © 2023, The Authors. All rights reserved.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：