检索结果-内蒙古大学图书馆

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Wang, Jun Lei, Bohan Xu, Xiaoyin Zhuang, Yueting Zhang, Min School of Software Technology Zhejiang University Ningbo China College of Computer Science & Technology Zhejiang University Hangzhou China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350358780

Generative adversarial network (GAN) is widely used to augment training set in medical imaging. However, training GANs can be challenging, as they are susceptible to mode collapse. These challenges stem from the fact that deep neural networks can only represent continuous mappings, while in GAN, the generator aims to compute discontinuous transportation maps between the noise distribution and the data distribution. We propose a novel approach that can overcome mode collapse, and in turn, lead to improved capability by GAN to generate specific classes of medical images. Our method starts by mapping the data manifold to the latent space using an autoencoder (AE). Subsequently, we encode the sample labels and integrate them with the latent representations. Next, we employ extended semi-discrete optimal transport (SDOT) mapping, which maps a Gaussian distribution to the empirical latent distribution, thereby generating new latent representations with known labels. Finally, we employ GAN to establish the mapping from the continuous latent distribution induced by the extended SDOT mapping to the real data distribution, generating high-quality images. We conducted extensive experiments on the DermaMNIST and BloodMNIST datasets. The experimental results highlight the exceptional performance of our model in generating images belonging to specified classes. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Object Detection for UAV Images Based on Improved YOLOv6

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2023年第2期50卷

作者： Li, Yingwei Zhang, Xiaoxia School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

This paper, we have improved the YOLOv6 object detection network to better detect high-density, small target objects in UAV aerial images. First of all, we designed a mixed data augmentation method which adds background samples to the training, so that the model can better identify falsely detected and falsely detected targets without changing the computational complexity. In addition, to prevent the loss of feature information caused by small target features in the upsampling stage, we have strengthened the Feature Pyramid Networks (FPN) with Feature Alignment Module (FAM) and Feature Selection Module (FSM). Finally, we have formed the Transformer Prediction Head (TPH) by using the Transformer Encoder Block to replace the prediction head of the original model. This ensures accurately locate targets in high-density scenes. The experiment used the Visdrone2019-DET drone aerial photography dataset. Compared with the original YOLOv6 network, the average precision (AP50) of the improved network on the verification set when the IoU is 0.5 was 59.73%, which is 6.02% higher than before the improvement. Comparison results with other mainstream object detection algorithms show that the performance of our proposed algorithm is better than other detectors. Therefore, the algorithm proposed in this paper can effectively detect objects with high density and small targets in UAV aerial photography scenes. © 2023, IAENG International Journal of computer science. All Rights Reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Building the metaverse using digital twins at all scales,states,and relations

引用

Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

来源：评论

学校读者我要写书评

暂无评论

Vehicle And Pedestrian Detection Algorithm Based on Improved YOLOv5

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2023年第4期50卷 1401页

作者： Sun, Jiuhan Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

As urbanization progresses, urban road congestion has intensified, highlighting the need for effective vehicle and pedestrian detection as a cornerstone of public safety transportation. This area holds significant relevance in video surveillance and public safety domains. Despite its importance, achieving precise vehicle and pedestrian detection in complex road environments remains challenging. This paper presents a vehicle-pedestrian detection algorithm based on the improved YOLOv5. Key modifications include the integration of a small target detection layer and alterations to the feature pyramid using the feature fusion technique inherent to the weighted Bidirectional Feature Pyramid Network (BIFPN). This ensures efficient multi-scale feature fusion. A coordinated attention mechanism is introduced to preserve accurate target location data. Furthermore, the paper incorporates the SIOU metric to refine the localization loss function, bolstering both speed and edge regression accuracy. Experimental outcomes indicate that our improved YOLOv5 algorithm augments detection accuracy by 1.9% and achieves a detection speed of 67 FPS, which surpasses many competing target detection algorithms. © 2023, International Association of Engineers. All rights reserved.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

High-Performance GPU based Planetary-Scale Terrain Visualization 2022

High-Performance GPU based Planetary-Scale Terrain Visualiza...

引用

5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Masood, Zafar Zheng, Jiangbin Ahmad, Idrees Irfan, Muhammad Saeed, Amir School of Software Northwestern Polytechnical University Xi’an China Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan School of Automation and Electrical Engineering Beihang University China

ISBN: (纸本)9781450395465

High-performance visualization of planetary-scale multi-resolution terrain for high-fidelity display systems is a challenging task. Real-world terrain visualization requires real-time modeling and rendering of the large-scale multi-resolution model. This work proposes a novel method for high-performance multi-resolution terrain modeling and rendering using hardware tessellation. The proposed method submits patch primitives using geometry instancing for high-performance. Patch Level-of-detail (LOD) is simplified using a view-dependent simplification algorithm with error control. The proposed simplification algorithm simplifies the model using viewer information and local height variation for optimum triangulation. Patch simplified LOD is tessellated utilizing a fixed-function tessellator. Height grid-aligned post-tessellated vertices are displaced in 3D to construct accurate high-frequency surface details. Finally, multi-resolution satellite imagery and normal maps are utilized for real-world features display. We evaluated the proposed method for high display resolutions and achieved high rendering rate. The proposed method results are compared with clipmap-based and hardware tessellation based methods. © 2022 ACM.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Cyber Security Threats on Multifunctional Devices and Mitigation Techniques 2

Cyber Security Threats on Multifunctional Devices and Mitiga...

引用

2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Farooq, Umer Sarwar, Irfan Iqbal, Muhammad Waseem Aqeel, Muhammad Khan, Wasim Ahmad Al Hamadi, Hussam Mutah University College of Engineering Department of Computer Engineering Jordan LGU Department of Computer Science Lahore Pakistan Superior University Department of Computer Science Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan NCBA&E Lahore School of Computer Science Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Dubai College of Engineering and IT PO Box 14143 United Arab Emirates

ISBN: (纸本)9798350335644

The risks discussed in this document are related to smart printers/ fax/ photocopiers/ or e-mail-enabled smart devices, also known as "document centers,". These devices can be purchased outright or rented every month. When an item reaches its end of life, it is either donated to a non-profit organization or returned to the manufacturer (OEM) or supplier at the end of a lease arrangement. These network-interconnected devices have an inherent susceptibility that most IT operations teams or information security professionals are unaware of. This effort will raise awareness of the dangers of losing a company's data. This discussion will focus on reported vulnerabilities, previous discussions, and given the author's view on the potential danger. Create instructions for potential users on how to use and disassemble these devices safely to prevent losing sensitive data. Lastly, the technological structure of two potential attack vectors against a document center of an organization will be demonstrated in this study. The study was conducted in government as well as the private sector, medium-sized enterprises (300 employees of each entity) settings, outside of normal business hours, with special authorizations and engagement, limiting the scope to a minimal set of devices. © 2023 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Lightweight Vehicle Detection Based on Improved Yolox-nano

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2023年第1期50卷 152页

作者： Jing, XiuPing Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning 114051 China

Vehicle detection based on deep learning plays a vital role in various fields, such as autopilot and intelligent transportation. Moreover, it presents a major development direction to computer vision in recent years. Lightweight vehicle detection aims to reduce the burden of computer storage and computing, including exploration of network structure and computing efficiency. But it is difficult to cope with complex and changeable traffic scenes. The urgent problem is to enhance lightweight network performance while maintaining inference speed. In this context, this study proposes an improved Yolox-nano to alleviate the above-mentioned problems by splitting the vanilla convolution unit into two parts and utilizing fewer convolution kernels to generate several feature maps. Subsequently, several linear transformations are further applied to generate cheap feature maps efficiently. The cheap convolutional unit Ghost Module can control feature map ratio of vanilla convolution and linear transformation. The activation function in Ghost Module is improved by SiLU, which has a more powerful nonlinear ability. In addition, the convolution and the batch normalization in Ghost Module can be fused in the pre-processing stage to speed up inference time. Experimental results show that the improved network provides 4.9% and 3.1% mAP on Pascal VOC and MS COCO vehicle datasets. The improved network enhances the performance and maintains an efficient inference speed, and detection results become further competitive using Yolox-nano © 2023, IAENG International Journal of computer *** Rights Reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks

引用

Big Data Mining and Analytics 2024年第4期7卷 1262-1272页

作者： Xingyi Li Junming Li Jun Hao Xingyu Liao Min Li Xuequn Shang School of Computer Science Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic *** omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning ***,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of ***,samples with imbalanced classes in driver gene identification hamper the performance of *** address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model *** reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.

关键词： cancer driver gene multiplex networks pan-cancer multiomics data graph neural networks negative sample inference

来源：评论

学校读者我要写书评

暂无评论

CRAYM: neural field optimization via camera RAY matching 24

CRAYM: neural field optimization via camera RAY matching

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Liqiang Lin Wenpeng Wu Chi-Wing Fu Hao Zhang Hui Huang College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computing Science Simon Fraser University

ISBN: (纸本)9798331314385

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

引用

computer Systems science & engineering 2023年第7期46卷 687-699页

作者： VDinesh SSrinivasan Gyanendra Prasad Joshi Woong Cho Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha University602105ChennaiIndia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time *** the same time,vehicles exhibit high velocity,leading to more vehicle *** of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection *** is considered one of the possible solutions for attaining effectual interaction in *** one such difficulty was reducing the cluster number under increasing transmitting *** article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for *** major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping *** addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given *** DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean *** experimental assessment of the DHOGO-EAC technique was carried out under distinct *** comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

关键词： Vehicular networks clustering evolutionary algorithm fitness function distance metric

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：