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Research on Simulation and Optimization of C Language Code Running Efficiency Based on Wavelet Transform

Research on Simulation and Optimization of C Language Code R...

引用

2023 International conference on Telecommunications, Electronics and Informatics, ICTEI 2023

作者： Yang, Ren Guilin University of Electronic Technology School of Computer and Information Security Guangxi Guilin541000 China

ISBN: (纸本)9798350342963

As a widely used high-level programming language, C language is often used in many software development, such as operating system, computer vision processing, 3D scene synthesis, big data, artificial intelligence and so on, because of its excellent code running efficiency. In this paper, based on WT (Wavelet Transform), the running efficiency of C language code is simulated and optimized. In order to improve the performance and quality of programs, it is often necessary to go through repeated processes of optimization, modification and debugging. program optimization is an important part of learning C programming. The research results show that the average running time and standard deviation of the measurement program written in C language of this method are smaller than those written in AGA (Adaptive Genetic Algorithm) and GA (Genetic Algorithm) languages. The average running time of AGA and GA is 6. 24S and 9S, while that of this method is 4. 08S. However, in numerical comparison, this method is also superior to the other two algorithms, which shows that the advantages of this method are very consistent. © 2023 IEEE.

关键词： Genetic algorithms

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An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques 20

An Evaluation Of Hospital Accreditation From The Survey With...

引用

20th International Joint conference on computer Science and Software Engineering, JCSSE 2023

作者： Prapunwattana, Dittaphong Imsombut, Aurawan Suwannahitatorn, Picha Saethang, Thammakorn Kasetsart University Biomedical Data Science Master's Program Faculty of Science Bangkok Thailand Kasetsart University Faculty of Science Department of Computer Science Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9798350300505

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring. © 2023 IEEE.

关键词： Embeddings

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Scheduling Queue Resource Occupation Prediction Based on Hybrid Neural Network 3

Scheduling Queue Resource Occupation Prediction Based on Hyb...

引用

3rd International conference on Artificial Intelligence, Automation, and high-performance Computing, AIAHPC 2023

作者： Li, Lu Zheng, Yu Lv, ZhuoHeng Hao, WenJing School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

To evaluate the resource usage in the scheduler queue, assist users in selecting appropriate queues for fast calculation, and improve the throughput and utilization of the system in the high-performance computing platform, it is necessary to use the historical job data in the queue for data analysis, and then make timely and effective predictions on the number of nodes occupied by jobs. In this paper, a queue node occupancy prediction method based on improved convolutional neural network (CNN) and long short memory network (LSTM) is proposed. Cluster and group the historical data of high-performance clusters to obtain balanced samples. In the improved CNN-LSTM network, the weights of different channels are determined by replacing the pooling layer with the customized attention mechanism layer. Samples are selected and extracted, and L2 regularization is used to prevent over fitting training to obtain more accurate results of node resource occupation. The test results of historical operation data of a supercomputer show that the improved CNN-LSTM hybrid network model proposed in this paper is more accurate than CNN-LSTM hybrid network model, LSTM model, MLP model and random forest model. © 2023 SPIE.

关键词： Supercomputers

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LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation 31

LoRA-drop: Efficient LoRA Parameter Pruning based on Output ...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Zhou, Hongyun Lu, Xiangyu Xu, Wang Zhu, Conghui Zhao, Tiejun Yang, Muyun Faculty of Computing Harbin Institute of Technology China Department of Computer Science & Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761964

Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method. However, it still faces high computational and storage costs to models with billions of parameters. Most previous studies have tackled this issue by using pruning techniques. Nonetheless, these efforts only analyze LoRA parameter features to evaluate their importance, such as parameter count, size, and gradient. In fact, the output of LoRA directly impacts the fine-tuned model. Preliminary experiments indicate that a fraction of LoRA possesses significantly high output values, substantially influencing the layer output. Motivated by the observation, we propose LoRA-drop. Concretely, LoRA-drop evaluates the importance of LoRA based on the LoRA output. Then we retain LoRA for important layers and the other layers share the same LoRA. We conduct abundant experiments with models of different scales on NLU and NLG tasks. Results demonstrate that LoRA-drop can achieve performance comparable to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

IMAN: An Adaptive Network for Robust NPC Mortality Predictio...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huo, Yejing Huang, Guoheng Cheng, Lianglun He, Jianbin Chen, Xuhang Yuan, Xiaochen Zhong, Guo Pun, Chi-Man Guangdong University of Technology School of Computer Science and Technology China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Macao Polytechnic University Faculty of Applied Sciences China Guangdong University of Foreign Studies School of Information Science and Technology China

ISBN: (纸本)9798350386226

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom

关键词： Patient treatment

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Analysis of key parameters of continuous concrete mixer based on discrete element 3

Analysis of key parameters of continuous concrete mixer base...

引用

3rd International conference on Mechatronics and Mechanical Engineering, ICMME 2024

作者： Zhang, Hao Palaoag, Thelma D. Department of Computer Science University of the Cordilleras Baguio City Philippines Shaanxi Polytechnic Institute Shaanxi Xianyang City China

ISBN: (纸本)9781510681651

The existing research on the key parameters of the continuous mixer is mostly based on the dry and rigid concrete material. Facing the safety of firefighting operators in high-risk environments, this paper investigates a robot for mixing engineering composites, which can automatically monitor the homogeneity of the mixing and, together with an automatic material feeding system, can realize automated production. The mixing time is short, and the concrete maturity is poor when preparing the ready-mixed concrete. Based on the discrete element method, this study determined the simulation conditions of ready-mixed concrete, analyzed the key parameters of the continuous mixer, carried out the orthogonal simulation experiment of the multi-factor structural parameters of the continuous mixer, studied the influence of different structural parameters on the mixing time, mixing uniformity and productivity, and determined the best parameters: The blade installation Angle is 25°, the stirring linear speed is 2.0m/s, the central intersection area accounts for 15%, and the axial clearance of adjacent blades is 1.5D. It is beneficial for improving the performance of continuous mixers and the quality of concrete. © 2024 SPIE.

关键词： Concrete mixing

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Jointing high-State and Low-State Feature Reconstruction Networks and Discriminator Training for Track Anomaly Detection 4

Jointing High-State and Low-State Feature Reconstruction Net...

引用

4th International conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Wang, Ziyi Ye, Yun Xie, Hongxing Zheng, Xingke Zhang, Liyan Chen, Enguo Xu, Sheng Guo, Tailiang College of Physics and Information Engineering Fuzhou University Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China

ISBN: (纸本)9798331528850

Railway safety inspections are critical to prevent catastrophic incidents, yet existing anomaly detection methods often suffer from insufficient sample data and overgeneralization. This paper proposes an improved anomaly detection method called HLFRN-D, which integrates dual-state feature reconstruction networks trained alongside a discriminator. HLFRN-D mitigates decoder overgeneralization by shifting the discriminator's focus from pixel-level abnormalities to evaluating the reconstruction performance of the networks. An anomaly generation module and a novel loss function further enhance robustness. Experimental results show that HLFRN-D achieves superior performance, with an AUROC score of 97.6% and PRO score of 94.4% on MVTec AD, and 99.3% AUROC and 97.7% PRO on track datasets, highlighting its effectiveness in detecting diverse anomaly types and locations. © 2024 IEEE.

关键词： Semantic Segmentation

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Research and Implementation of Dynamic Gesture Recognition System based on ZYNQ 5

Research and Implementation of Dynamic Gesture Recognition S...

引用

5th International conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Li, Jie Liu, Qingying Li, Zengzhen Chen, Wei Chongqing Engineering Research Center of New Energy Storage Devices and Applications Chongqing University of Arts and Sciences Chongqing402160 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510662520

At present, gesture has become an important channel of human-computer interaction, and gesture recognition has been widely used in various fields. In this paper, the dynamic gesture recognition technology is studied from algorithm and system implementation for portable devices which require high real-time performance. The algorithm mainly uses the region of interest extraction based on face recognition, skin color detection based on HCrCg color space and gesture motion track marking based on scanline seed filling algorithm. The system is implemented by Xilinx ZYNQ, and a SOPC system architecture based on ARM Cortex-A9 hard core and ARM Cortex-M3 soft core and FPGA is proposed. The scanline seed filling algorithm with long running time is designed as a hardware accelerator to improve the running speed. Through the test of the prototype, the recognition accuracy can reach 95.75% in a simple background and 90.83% in a complex background. The average running time of the system is only 0.68 seconds, which is more than 30% faster than using pure software method. The system has good performance in recognition accuracy and running speed. © 2023 SPIE.

关键词： Gesture recognition

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Application and research of intelligent inspection robots in hydropower stations 3

Application and research of intelligent inspection robots in...

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3rd International conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Huang, Yingying Cao, Hanhua Gu, Changle Guangdong Huaxia Senior Technical School Guangzhou510935 China Guangzhou Xinhua University Guangzhou510520 China

ISBN: (纸本)9781510680449

A machine vision based inspection robot system is designed in this paper to address the issues of high cost and difficulty in ensuring detection accuracy in traditional manual pumped storage station crack and seepage detection. Construct a convolutional neural network that integrates cross entropy and Dice cost function, and establish an evaluation function based on total pixel accuracy, intersection to union ratio, and F1 score to ensure accurate detection of common cracks. In order to verify the effectiveness of the robot inspection system, the convolutional neural network was tested and compared with common computer vision and manual detection methods in terms of performance. The comparison results show that the neural network constructed in the article has made significant progress in both detection accuracy and detection efficiency. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer vision

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2023 International conference on Emerging Trends in Networks and computer Communications, ETNCC 2023 - Proceedings

2023 International Conference on Emerging Trends in Networks...

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2023 International conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

ISBN: (纸本)9798350314878

The proceedings contain 43 papers. The topics discussed include: blockchain-powered solution to safeguard IoT devices against attacks;strategies for developing acoustic model to pronounce names in low-resourced languages;detection of ransomware attacks using weight of evidence technique;an efficient multihop edge enabled architecture for time constraint application;predicting student attrition: a case study of the university of Namibia bachelor of accounting (chartered accountancy) program;an efficient security solution for IoT and cloud security using lattice-based cryptography;Vivaldi MIMO antenna using oval shape structure tor for 24GHz-34GHz millimeter-wave communication frequency band;performance evaluation of variant calling tools for human and microbial genomes;and GitHub copilot: a threat to high school security? exploring GitHub copilot’s proficiency in generating malware from simple user prompts.

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