检索结果-内蒙古大学图书馆

2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Ngoc-Kim-Khanh Nguyen, Thanh-Trung Nguyen, Tuan-Anh Sartori, Fabio Turchetto, Massimiliano Scotognella, Francesco Alfieri, Roberto Cassi, Davide Nguyen, Quang Bellingeri, Michele Sciences Van Lang University Faculty of Fundamental 69/68 Dang Thuy Tram Street Ward 13 Binh Thanh District Ho Chi Minh Viet Nam Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Ho Chi Minh City Viet Nam Università di Parma Dipartimento di Scienze Matematiche Fisiche e Informatiche via G.P. Usberti 7/a Parma43124 Italy Politecnico di Milano Dipartimento di Fisica Piazza Leonardo da Vinci 32 Milano20133 Italy Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh700000 Viet Nam

ISBN: (纸本)9781665461665

We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the 'facemask use' and 'vaccination policies' to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability β. We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability β=0, which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected. © 2022 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging

arXiv

引用

arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Transformer Network for Vehicle Detection

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

来源：评论

学校读者我要写书评

暂无评论

Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

arXiv

引用

arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Preliminary research Design on Sensor data Gathering for Air Quality Text Generation 4

Preliminary Research Design on Sensor Data Gathering for Air...

引用

4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Aulia, Indra Hizriadi, Ainul Seniman Muhibuddin Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Data Science and Intelligent System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Computer System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

Air pollution is one of the environmental problems faced by many countries (i.n. the developing countries). It often harms the health of people, plants, and animals as well as our environment. To decrease the risks, e ective and e cient technology for air quality monitoring is indispensable. In Indonesia, it is still limited in the case of the number of devices and the dissemination of air quality information. This paper describes the preliminary research design of air quality monitoring technology based on the Internet of Things (IoT) and Natural Language Generation (NLG) for solving these limitations. It de nes air quality knowledge in the form of a set of rules and presents the architecture design for data gathering and text generation. data gathering uses the related sensors which are able to collect the values of parameters as air quality indicators. Then, the collected values are identi ed to be used for generating the textual information in Indonesian language to social media, such as twitter. So, in this research, the proposed solutions are able to solve the problems properly. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

引用

arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

来源：评论

学校读者我要写书评

暂无评论

A method for detecting floating objects on water based on edge computing

A method for detecting floating objects on water based on ed...

引用

IEEE International Symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： He Li Shuaipeng Yang Jinjiang Liu Honglin Fang Zhumu Fu Rui Zhang Huimei Jia Lianmeng Lv Henan Costar Group Co. Ltd Nanyang Henan China College of Information Engineering Henan University of Science and Technology Luoyang Henan China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunication Beijing China Xi'an Hengpin Electronic Technology Co. Ltd Xi’an China

With the development and application of computer vision, many target detection networks are applied to the detection of floating objects in rivers. For the detection problems such as small targets easily missed and misdetected in water surface floating object detection tasks and difficult to deploy models. An edge computing-oriented approach to river floater detection is proposed. First, a four-fold down-sampling feature layer is added to the YOLOv5 network which enhances more target detail features and improves the detection capability of small objects. Second, CA (Coordinate Attention) is added to the Backbone to suppress background noise interference, and different pooling is used to accommodate different hierarchical features. Then, a bilinear interpolation method is adopted for up-sampling to avoid the loss of small object features. Design a data enhancement algorithm for small targets based on Mosaic to increase the number of small objects and enrich the training background. Finally, for the edge computing architecture platform, the channel pruning algorithm is used to prune and compress the model structure to adapt to the computing capability of edge devices. The experimental results show that the method can effectively improve the detection capability of the network for floating objects on the water surface. The detection accuracy can reach 93.6%, and the detection speed can be maintained at 36 frames per second, which can achieve high-precision real-time detection of floating objects on the water surface.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient Multi-Order Cascade Distillation Model for the Detection of Small Targets and Occluded Objects in Transmission Line Inspection

SSRN

引用

SSRN 2023年

作者： Qu, Zhaoyang Zang, Jiye Dong, Yunchang Qu, Nan Song, Siqi Jiang, Tao Li, Min Liang, Feng Li, Lingcong School of Computer Northeast Electric Power University Jilin132012 China Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center Jilin132012 China State Grid Jilin Electric Power Research Institute Changchun130012 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing Power Supply Company 210000 China Northeast Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Changchun130021 China

The proposed cascade distillation detection model improves the accuracy of small target defect detection and robustness in complex environments during UAV inspection of transmission lines. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. A high-order aggregated feature extraction network is introduced to enhance feature extraction of small and obscured targets. Lastly, a spatially deformable attention mechanism optimizes sampling point selection and attention weight calculation by aggregating nearby contextual information, improving bounding box adjustment. © 2023, The Authors. All rights reserved.

关键词： Electric lines

来源：评论

学校读者我要写书评

暂无评论

A Novel Statistical Preprocessing Approach for Hyperspectral Image Unmixing

A Novel Statistical Preprocessing Approach for Hyperspectral...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Fatemeh Kowkabi Ahmad Keshavarz Lalit Kumar College of Engineering Marvdasht Branch Islamic Azad University Marvdasht Iran IoT and Signal Processing Research Group ICT Research Institute Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran EastCoast Geospatial Consultants Armidale NSW Australia

ISBN: (纸本)9781665442503

Hyperspectral images have wide application in different commercial and military missions. However, these images suffer from the low spatial resolution, and hence, spectral unmixing has been proposed as a suitable technique to solve this deficiency. Recently, some preprocessing modules have been proposed which use the combination of spatial and spectral information of image pixels with the purpose of improving endmember extraction and spectral unmixing in hyperspectral images. In this paper, we propose a statistical preprocessing algorithm using weighted extended cumulative entropy (WECE) as the Edge Detection algorithm to identify spectrally pure pixels and KL distance estimation using the mean and covariance values of pure oversegments and their neighbors to find the spatially homogenous and spectrally pure pixels for the next endmember extraction algorithms. Moreover, the average of KL distances are applied due to the reduction of noise power and local spectral variability. The performance of the novel statistical preprocessing algorithm has been compared with the sate-of-the-art in terms of RMSE reconstruction and average SAD errors on two real hyperspectral images known as HYDICE urban and AVIRIS Indian Pines in combination with several endmember extraction (EE) algorithms and its efficiency is evaluated.

关键词： Image edge detection Geoscience and remote sensing Estimation Entropy data mining Spatial resolution Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Improving Chinese Character Representation with Formation Tree

SSRN

引用

SSRN 2024年

作者： Hong, Yang Li, Yinfei Qiao, Xiaojun Li, Rui Zhang, Junsong National Engineering Research Center for E-learning Central China Normal University No.152 Luoyu Road Hubei Wuhan430079 China Mind Art and Computation Group Department of Artificial Intelligence Xiamen University No. 4221 South Xiang’an Road Xiang’an District Fujian Xiamen361104 China National Engineering Research Center of Educational Big Data Central China Normal University No.152 Luoyu Road Hubei Wuhan430079 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism Fujian Xiamen China

Learning effective representations for Chinese characters presents unique challenges, primarily due to the vast number of characters and their continuous growth, which requires models to handle an expanding category space. Additionally, the inherent sparsity of character usage complicates the generalization of learned representations. Prior research has explored radical-based sequences to overcome these issues, achieving progress in recognizing unseen characters. However, these approaches fail to fully exploit the inherent tree structure of such sequences. To address these limitations and leverage established data properties, we propose Formation Tree-CLIP (FT-CLIP). This model utilizes formation trees to represent characters and incorporates a dedicated tree encoder, significantly improving performance in both seen and unseen character recognition tasks. We further introduce masking for to both character images and tree nodes, enabling efficient and effective training. This approach accelerates training significantly (by a factor of 2 or more) while enhancing accuracy. Extensive experiments show that processing characters through formation trees aligns better with their inherent properties than direct sequential methods, significantly enhancing the generality and usability of the representations. © 2024, The Authors. All rights reserved.

关键词： Character recognition

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：