检索结果-内蒙古大学图书馆

Research on Prediction Methods of Prevalence Perception under Information Exposure

computers, Materials & Continua 2020年第12期65卷 2263-2275页

作者： Weijin Jiang Fang Ye Wei Liu Xiaoliang Liu Guo Liang Yuhui Xu Lina Tan School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Mobile E-Business Collaborative Innovation Center of Hunan ProvinceHunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China School of Bioinformatics University of MinnesotaTwin CitiesUSA

With the rapid development of information technology,the explosive growth of data information has become a common challenge and *** network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these *** dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad *** these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular *** simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.

关键词： Social network situational awareness adaptive prediction prediction of popularity

来源：评论

学校读者我要写书评

暂无评论

Towards a novel few-shot object counting paradigm with point-guided exemplar prompt in Industrial Internet of Things

引用

Future Generation computer Systems 2026年 174卷

作者： Gangzheng Zhai Shaojie Han Kun Chen Shihui Zhang School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Computer Virtual Technology and System Integration Qinhuangdao 066004 China

With the rapid development of computing infrastructure and the increasing demand for big data processing, object counting has emerged as a critical and challenging task. Few-Shot Object Counting (FSOC) aims to estimate the number of objects in any category based on a few visual exemplar prompts. Existing methods typically rely on bounding boxes to guide the model in understanding the correlation between visual exemplars and the query image, followed by regressing a density map for counting. However, despite the growing overall average performance, we contend that the exploration of more generic counting frameworks has not received adequate attention. In this work, we propose a novel Point-guided Exemplar Prompting Network (PEPNet), a new framework that uses point annotations as prompts to guide object counting. PEPNet consists of two core components: a Multi-scale Attention Fusion Module (MAFM) and an Iterative Encoding Matching Module (IEMM). MAFM integrates spatial and channel attention mechanisms to adaptively highlight critical regions while capturing multi-scale features, effectively balancing global context and local details. IEMM, for the first time, employs a point-guided prompting strategy to iteratively encode visual exemplars, suppressing irrelevant features and enhancing important ones. In particular, the multi-head similarity matching block in IEMM refines the matching process progressively, improving the correlation between exemplars and the query image, thereby boosting object recognition and counting accuracy. Extensive experiments on multiple benchmark datasets, including FSC-147, Val-COCO, Test-COCO, CARPK, and ShanghaiTech, demonstrate the effectiveness of PEPNet. Notably, on the FSC-147 validation set, our method achieves a performance improvement of 1.9% in Mean Absolute Error (MAE) and 12.3% in Root Mean Square Error (RMSE) compared to the state-of-the-art SPDCN. Additionally, on the test set, we observe performance improvements of 0.2% in MAE

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards self-interpretable review spammer group detection

引用

Information Sciences 2025年 716卷

作者： Huo, Chenghang Zhang, Fuzhi School of Information Science and Engineering Yanshan University Hebei Province Qinhuangdao China Hebei Key Laboratory of Computer Virtual Technology and System Integration Hebei Province Qinhuangdao China

Due to data imbalance, existing spammer group detection methods often yield suboptimal performance. Moreover, many of these approaches operate as black boxes, offering little to no interpretability for their detection results. To address these issues, we propose a self-interpretable approach for detecting review spammer groups. Our method begins by constructing a user-product bipartite graph with edge attributes. We integrate user review information with a novel fitness function to develop an adaptive genetic algorithm that effectively identifies high-quality candidate groups. Next, we introduce a hybrid graph neural network enhanced with active learning to generate vector representations of these candidate groups. We then design and construct a prototype layer and a group classification layer to detect spammer groups accurately. To provide interpretability, we incorporate prototype learning to create an interpretation mechanism that explains detection outcomes. Experimental results demonstrate that our method achieves substantial improvements in Precision@k and Recall@k at the top-1000 ranking, outperforming state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets by [11.53 %, 97.36 %, 51.37 %, 32.06 %] and [12.65 %, 54.65 %, 64.19 %, 47.37 %], respectively. Additionally, the Fidelity of our interpretability results under varying Sparsity levels is approximately 4 %, 9 %, 8 %, and 8 % higher than those of existing methods on the same datasets. © 2025 Elsevier Inc.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain

arXiv

引用

arXiv 2021年

作者： Chen, Guangyao Peng, Peixi Ma, Li Li, Jia Du, Lin Tian, Yonghong Department of Computer Science and Technology Peking University Peng Cheng Laborotory State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University AI Application Research Center Huawei

Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. However, the importance of the phase spectrum of the image for a robust vision system is still ignored. In this paper, we notice that the CNN tends to converge at the local optimum which is closely related to the high-frequency components of the training images, while the amplitude spectrum is easily disturbed such as noises or common corruptions. In contrast, more empirical studies found that humans rely on more phase components to achieve robust recognition. This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image. That is, the generated samples force the CNN to pay more attention to the structured information from phase components and keep robust to the variation of the amplitude. Experiments on several image datasets indicate that the proposed method achieves state-of-the-art performances on multiple generalizations and calibration tasks, including adaptability for common corruptions and surface variations, out-of-distribution detection, and adversarial attack. The code is released on github/iCGY96/APR. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning to See in the Dark with Events 16th

Learning to See in the Dark with Events

引用

16th European Conference on computer Vision, ECCV 2020

作者： Zhang, Song Zhang, Yu Jiang, Zhe Zou, Dongqing Ren, Jimmy Zhou, Bin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China SenseTime Research Beijing China Tsinghua University Beijing China

ISBN: (纸本)9783030585228

Imaging in the dark environment is important for many real-world applications like video surveillance. Recently, the development of Event Cameras raises promising directions in solving this task thanks to its High Dynamic Range (HDR) and low requirement of computational sources. However, such cameras record sparse, asynchronous intensity changes of the scene (called events), instead of canonical images. In this paper, we propose learning to see in the dark by translating HDR events in low light to canonical sharp images as if captured in day light. Since it is extremely challenging to collect paired event-image training data, a novel unsupervised domain adaptation network is proposed that explicitly separates domain-invariant features (e.g. scene structures) from the domain-specific ones (e.g. detailed textures) to ease representation learning. A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss. To evaluate the proposed approach, a novel large-scale dataset is captured with a DAVIS240C camera with both day/low light events and intensity images. Experiments on this dataset show that the proposed domain adaptation approach achieves superior performance than various state-of-the-art architectures. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing

arXiv

引用

arXiv 2021年

作者： Zhao, Dong Li, Jia Li, Hongyu Xu, Long The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences China The Peng Cheng Laboratory Shenzhen518000 China

Conventional CNNs-based dehazing models suffer from two essential issues: the dehazing framework (limited in interpretability) and the convolution layers (content-independent and ineffective to learn long-range dependency information). In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task. One of the prominent advantages of the new framework is that the purposively chosen complementary tasks can focus on learning weakly dependent complementary features, avoiding repetitive and ineffective learning of the networks. We design a new dehazing network based on such a framework. Specifically, we select the intrinsic image decomposition as the complementary tasks, where the reflectance and shading prediction subtasks are used to extract the color-wise and texture-wise complementary features. To effectively aggregate these complementary features, we propose a complementary features selection module (CFSM) to select the more useful features for image dehazing. Furthermore, we introduce a new version of vision transformer block, named Hybrid Local-Global Vision Transformer (HyLoG-ViT), and incorporate it within our dehazing networks. The HyLoG-ViT block consists of the local and the global vision transformer paths used to capture local and global dependencies. As a result, the HyLoG-ViT introduces locality in the networks and captures the global and long-range dependencies. Extensive experiments on homogeneous, non-homogeneous, and nighttime dehazing tasks reveal that the proposed dehazing network can achieve comparable or even better performance than CNNs-based dehazing *** Codes 68U10 (Primary) 94A08, 54H30 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Demulsification

来源：评论

学校读者我要写书评

暂无评论

virtual reality application for maintainability design: A case study of the practical project 29

Virtual reality application for maintainability design: A ca...

引用

29th European Safety and Reliability Conference, ESREL 2019

作者： Guo, Ziyue Zhou, Dong Hao, Aimin School of Reliability and Systems Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and System Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789811127243

Maintainability an important property of the product, which is mostly determined in the design stages. Maintainability design including many aspects, such as visibility, reachability, maintenance space, ergonomics and safety. The design effect of maintainability directly influences the maintenance activity during the service stage. Today, while designers can perform maintainability design with the help of computer-aided design (CAD) software, the complexity of maintainability design poses a significant challenge to designers in terms of time cost and knowledge requirements. This paper reports a case study on how to utilize virtual reality (VR) technology to help designers with design work. A workflow is established to instruct each specific implementation step of VR, from system level to component level. The entire design work can be divided into the simulation phase and the evaluation phase. For the aspects of the maintainability, corresponding evaluation criteria and methods are established. When the simulation is finished, the designer can analysis and evaluate the maintainability status with these criteria and methods. Following the established workflow, the integrate maintainability design work for several subsystems of a helicopter is conducted. The suspected design problems are classified into three levels according to their hazard ratings. The verification on the physical prototype proves that most suspected design defects do exist. The benefits and challenges of using VR to assist maintainability design are also discussed. © 2019 European Safety and Reliability Association. Published by Research Publishing, Singapore.

关键词： virtual reality

来源：评论

学校读者我要写书评

暂无评论

Multi-view Semantic Information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yiming Li Ruixuan Cong Sizhe Wang Mingyuan Zhao Yang Zhang Fangping Li Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R.China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R.China College of Information Science and Technology Beijing University of Chemical Technology Beijing P.R.China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R.China

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Transformer for Video-based Person Re-identification

arXiv

引用

arXiv 2021年

作者： Zhang, Tianyu Wei, Longhui Xie, Lingxi Zhuang, Zijie Zhang, Yongfei Li, Bo Tian, Qi Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Pengcheng Laboratory China Tsinghua University China University of Science and Technology of China China Xidian University China

Recently, the Transformer module has been transplanted from natural language processing to computer vision. This paper applies the Transformer to video-based person reidentification, where the key issue is to extract the discriminative information from a tracklet. We show that, despite the strong learning ability, the vanilla Transformer suffers from an increased risk of over-fitting, arguably due to a large number of attention parameters and insufficient training data. To solve this problem, we propose a novel pipeline where the model is pre-trained on a set of synthesized video data and then transferred to the downstream domains with the perception-constrained Spatiotemporal Transformer (STT) module and Global Transformer (GT) module. The derived algorithm achieves significant accuracy gain on three popular video-based person reidentification benchmarks, MARS, DukeMTMC-VideoReID, and LS-VID, especially when the training and testing data are from different domains. More importantly, our research sheds light on the application of the Transformer on highlystructured visual data. © 2021, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Research on the Influence of Improved K-shell Algorithm on Commodity Profit 2

Research on the Influence of Improved K-shell Algorithm on C...

引用

2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Chen, Jing Huang, Jincheng College of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao066004 China

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

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：