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8th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2023

作者： Jiepeng, Liu Zhenhua, Liu Chuanwen, Lin Guoyun, Li School of Artificial Intelligence and Big Data Hefei University China Hefei Public Security Bureau Science and Technology Informatization Detachment China

ISBN: (纸本)9781510666290

According to the national policy of proposing water pollution prevention and control regulations for the Chaohu Lake basin, to solve the current problem of chemical pollution in the Chaohu Lake basin, the relevant environmental data of the Chaohu Lake basin provided by Chengxin Environmental Testing Company were used, and a random forest algorithm based on high correlation filtering was proposed to construct a prediction model for the concentration of chemical pollution such as ammonia nitrogen in the Chaohu Lake basin. The method uses high correlation filtering to eliminate the strong correlation between water pollution data and adjusts the parameters of the random forest model to achieve the best prediction effect for the training data. The model was tested by real data sets, and the Bayesian ridge regression model, ordinary linear regression model, elastic network regression model, support vector machine model, and correlation vector machine model were used for comparison experiments, and the average relative error was used as the evaluation index. Finally, the random forest algorithm model with high correlation filtering achieved the best prediction results in the study of quantitative analysis of eutrophic chemical pollution such as ammonia nitrogen in the Chaohu Lake basin. © 2023 SPIE. All rights reserved.

关键词： Forecasting

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Reform of Smart Exhibition English Guiding from the Perspective of New Media based on Data Association Analysis Model

Reform of Smart Exhibition English Guiding from the Perspect...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Cai, Yan Bao, Xianghua Maanshan Technical College Anhui Province Maanshan 243000 China Science and Technology Informatization Detachment of Ma'anshan Public Security Bureau Anhui 243000 China

ISBN: (纸本)9781665410281

Taking the exhibition English curriculum reform of the college as an example, from the aspects of course positioning, course objectives, teaching content design, and course evaluation and assessment, explore the teaching mode of exhibition English curriculum based on data association analysis model, and use multi-dimensional information data space, data development, The relationship between data analysis and data association analysis is used to discuss the visual presentation design, and follow the research ideas of "data collection/data statistics/analysis/import/preprocessing/data mining"to construct the intelligent exhibition English guidance reform under the new media technology. © 2022 IEEE.

关键词： Exhibitions

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Histogram publishing method based on geometric mechanism 16

Histogram publishing method based on geometric mechanism

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16th IEEE International Conference on Computer science and Education, ICCSE 2021

作者： Tao, Tao Chen, Laibing Bao, Xianghua Wang, Aiguo Zhou, Kewu Anhui University of Technology School of Computer Science and Technology Ma'Anshan China Science and Technology Informatization Detachment of Ma' Anshan Public Security Bureau Ma'Anshan China

ISBN: (纸本)9781665414685

Histogram publishing method based on differential privacy has been widely concerned. However, most histogram publishing methods are based on the Laplace mechanism, which can not effectively avoid floating-point approximation attack. To address this problem, this paper proposes a histogram publishing method based on the geometric mechanism, and each bin of histogram is counted by an integer. Besides, the dynamic programming grouping strategy is adopted to effectively reduce the histogram publishing error in long queries. © 2021 IEEE.

关键词： Graphic methods

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Retraction Notice: Reform of Smart Exhibition English Guiding from the Perspective of New Media based on Data Association Analysis Model

Retraction Notice: Reform of Smart Exhibition English Guidin...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Yan Cai Xianghua Bao Maanshan Technical College Maanshan City Anhui Province Science and Technology Informatization Detachment of Ma’anshan Public Security Bureau Anhui China

ISBN: (数字)9781665410281

ISBN: (纸本)9781665410298

Taking the exhibition English curriculum reform of the college as an example, from the aspects of course positioning, course objectives, teaching content design, and course evaluation and assessment, explore the teaching mode of exhibition English curriculum based on data association analysis model, and use multi-dimensional information data space, data development, The relationship between data analysis and data association analysis is used to discuss the visual presentation design, and follow the research ideas of "data collection/data statistics/analysis/import/preprocessing/data mining" to construct the intelligent exhibition English guidance reform under the new media technology.

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Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion

arXiv

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arXiv 2023年

作者： Wang, Xiao Jin, Jiandong Li, Chenglong Tang, Jin Zhang, Cheng Wang, Wei The School of Computer Science and Technology Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation The School of Artificial Intelligence Anhui University Hefei230601 China Video Investigation Detachment of Hefei Public Security Bureau Hefei230031 China

Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained CNN (e.g., ResNet) as their backbone network for visual feature learning, which might obtain suboptimal results due to the insufficient employment of the relations between pedestrian images and attribute labels. In this paper, we formulate PAR as a vision-language fusion problem and fully exploit the relations between pedestrian images and attribute labels. Specifically, the attribute phrases are first expanded into sentences, and then the pre-trained vision-language model CLIP is adopted as our backbone for feature embedding of visual images and attribute descriptions. The contrastive learning objective connects the vision and language modalities well in the CLIP-based feature space, and the Transformer layers used in CLIP can capture the long-range relations between pixels. Then, a multi-modal Transformer is adopted to fuse the dual features effectively and feed-forward network is used to predict attributes. To optimize our network efficiently, we propose the region-aware prompt tuning technique to adjust very few parameters (i.e., only the prompt vectors and classification heads) and fix both the pretrained VL model and multi-modal Transformer. Our proposed PAR algorithm only adjusts 0.75% learnable parameters compared with the fine-tuning strategy. It also achieves new state-of-the-art performance on both standard and zero-shot settings for PAR, including RAPv1, RAPv2, WIDER, PA100K, and PETA-ZS, RAP-ZS datasets. The source code and pre-trained models will be released on https://***/Event-AHU/OpenPAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Visual languages

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