检索结果-内蒙古大学图书馆

IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Fuwen Tian Meng Xu College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, and records within the same attribute column often exhibit high similarity, providing favorable conditions for data compression. Researchers have proposed numerous compression algorithms, but these algorithms can only achieve optimal compression results when applied to data with specific features. Therefore, it is crucial to select the compression algorithm that performs best based on the features of the data. We propose a novel method for selecting compression algorithms using Stacking, a technique that effectively enhances the prediction ability of classification models in machine learning. Our experimental results demonstrate that the stacked model achieves an accuracy of 92.9%, surpassing other models in terms of F1-Score and Kappa coefficient. Furthermore, our compression algorithm selection method outperforms other existing methods in terms of compression performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HC-MAE: Hierarchical Cross-attention Masked Autoencoder Integrating Histopathological Images and Multi-omics for Cancer Survival Prediction

HC-MAE: Hierarchical Cross-attention Masked Autoencoder Inte...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Suixue Hu, Xiangjun Zhang, Qingchen Hainan University School of Information and Communication Engineering Haikou China School of Computer Science and Technology Hainan University Haikou China Software College Shenyang Normal University Shenyang China

ISBN: (纸本)9798350337488

Accurate cancer survival prediction enables clinicians to tailor treatment regimens based on individual patient prognoses, effectively mitigating over-treatment and inefficient medical resource allocation. Recently, the integration of histopathological images and multi-omics data, together with deep learning, has become increasingly applied to predict cancer survival. However, current deep learning-based integration methods ignore the spatial relationships across various fields of view within gigapixel histopathological images, since they mainly focus on a specific field of view. Inspired by the hierarchical image pyramid transformer (HIPT), we propose a hierarchical cross-attention masked autoencoder (HC-MAE) to integrate histopathological images and multi-omics data for cancer survival prediction. Specifically, HC-MAE aggregates the representations learned from different fields of view, effectively capturing the fine-grained details and the spatial relationships within histopathological images. We conduct experiments to compare the HC-MAE method with current state-of-the-art methods on six cancer datasets sourced from The Cancer Genome Atlas (TCGA). The experimental results demonstrate that HC-MAE achieves superior performance on five out of six cancer datasets, significantly outperforming the compared methods. The code is available at https://***/SuixueWang/HC-MAE. © 2023 IEEE.

关键词： Hierarchical Cross-attention Histopathological Image Masked Autoencoder Multi-omics Survival Prediction

来源：评论

学校读者我要写书评

暂无评论

Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients 2

Empowering Donors: How Blockchain Technology Can Help Ensure...

引用

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

作者： Ajmal, Sahar Muzammil, Muteeb Bin Shoaib, Muhammad Mehmood, Muhammad Hassaan National University of Computer and Emmerging Sciences Department of Software Engineering Faisalabad Pakistan Riphah International University College of Computing Faisalabad Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied School Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential. © 2023 IEEE.

关键词： Transparency

来源：评论

学校读者我要写书评

暂无评论

Protecting Intellectual Property of EEG-based Model with Watermarking

Protecting Intellectual Property of EEG-based Model with Wat...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Tianhua Xu Sheng-hua Zhong Zhijiao Xiao College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Sharing learned models is crucial in research and the industry’s rapid development and progress. Meanwhile, as the Intellectual Property (IP) of the model proposer, the learned high-performance models must be protected to avoid being illegally copied or redistributed by malicious users. Unfortunately, even though the field of Electroencephalography (EEG) has made significant progress and the models are becoming increasingly complex, more work still needs to be done on protecting EEG-based models. The damage caused by model stealing and attack on the brain-computer interface (BCI) is more severe than in other fields. In this paper, we propose a method that protects the IP of EEG-based models with watermarking for the first time. Watermarks are embedded into three representative EEG-based models by designing a trigger set. On the premise of not sacrificing the primary task’s performance significantly, the models’ legality can be verified remotely through the trigger set. Furthermore, we demonstrate that the proposed model protection method is robust to various anti-watermarking attacks, such as fine-tuning, transfer learning, pruning, and watermark overwriting.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Medical Device Management System Using Smart Contract on Blockchain 9

A Medical Device Management System Using Smart Contract on B...

引用

9th IEEE International Conference on Behavioural and Social Computing, BESC 2022

作者： Yao, Xin Zhao, Xiangguo He, Rui Nie, Hulin Nie, Haojie Sun, Shichao Northeastern University College of Software Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

With the increasing variety and quantity of medical devices, there is an urgent need for safe and efficient management of them. The traditional centralized management system has the problems of information asymmetry and low security. Blockchain technology can effectively guarantee the immutability and reliability of system information. However, the management of medical devices requires the participation of multiple parties and the process is complicated. The existing methods still can not simply realize the whole-process tracking management of medical devices. To address this problem, we design and implement a medical device management system using smart contract on blockchain. First, we design a complete medical device management process. Then, we design smart contracts to implement bidding purchase, contract communication, inventory management, and multi-department joint supervision of medical devices. Finally, we test the transaction processing performance of the system. The results show that our system is effective and feasible. © 2022 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

FFFN: Fashion Feature Fusion Network by Co-Attention Model for Fashion Recommendation

FFFN: Fashion Feature Fusion Network by Co-Attention Model f...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhantu Lin Xiaoyan Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Fashion complementary recommendation has always been crucial in the field of recommendation. In previous work, researchers did not pay attention to the connection and combinability between multi-dimensional image features of fashion items. To effectively utilize the advantages of high-level and low-level features in images, we propose a Fashion Feature Fusion Network (FFFN) to solve the fashion complementary recommended tasks, which extracts and combines the features of different dimensions in the neural network into a fusion feature. Then, we input the fused image features and the category text features of fashion items into the co-attention module to realize the guidance of the text attention information to the image attention information. Experimental results on different datasets show that our model has advantages compared to the state-of-the-art methods.

关键词： Neural networks Signal processing Feature extraction Acoustics Task analysis Speech processing

来源：评论

学校读者我要写书评

暂无评论

Numerical Embedding of Categorical Features in Tabular Data: A Survey

Numerical Embedding of Categorical Features in Tabular Data:...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qianxi Qiu Han Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Tabular data, which are also known as structured data, is one of the most common forms of data and can be frequently seen in fields such as healthcare, finance, recommendation systems and network security. In contrast to homogeneous data like image, audio and text, tabular data is usually heterogeneous and shows a small sample size and weak correlations between features. Besides, tabular data may contain not only numerical features but also categorical ones that can not be handled directly by the majority of those popular learning algorithms. The aforementioned characteristics make decision tree ensemble methods, such as random forests and gradient boosting trees, more suitable for processing tabular data than other models. In this context, it is essential to transform categorical features into numerical ones in the setting of category embedding. This paper surveys embedding methods for handling low-cardinality categorical features in tabular data. Specifically, these methods are put into two categories, namely, supervised and unsupervised encoding, which each is introduced in terms of the strategies of category encoding. Moreover, the characteristics of each method are summarized. On the basis of the summarization, further directions of category embedding are suggested within the setting of representation learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust Steganography Based on Polar Codes 13

Robust Steganography Based on Polar Codes

引用

13th International Conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Li, Jinsheng Liu, Ling Shenzhen University College of Computer Science and Software Engineering China

ISBN: (纸本)9781665407854

In this paper, we use polar codes to design the robust steganography for blind watermarking and non-blind watermarking problems, respectively. The proposed steganography enables the embedded cover sequence to effectively resist further disturbance or illegal attacks whose noise level is less than the embedding distortion. By systematically combining channel polarization and source polarization techniques, we show that our schemes can approach the embedding capacity from both theoretical and numerical simulation perspective. © 2021 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

引用

computers, Materials & Continua 2023年第10期77卷 1281-1301页

作者： Jigna Patel Anand Ruparelia Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabad382481India Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Centre for Inter-Disciplinary Research and Innovation University of Petroleum and Energy StudiesDehradun248001India Doctoral School University Politehnica of BucharestBucharest060042Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm ValceaRamnicu Valcea240050Romania Power Engineering Department Gheorghe Asachi Technical University of IasiIasi700050Romania

The overgrowth of weeds growing along with the primary crop in the fields reduces crop *** solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used *** application of herbicide is effective but causes environmental and health ***,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary *** by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this *** key objective of this study is to detect plant and non-plant(weed)parts from crop *** the help of object detection,the precise location of weeds from images can be *** dataset is collected manually from a private farm in Gandhinagar,Gujarat,*** combined approach of classification and object detection is applied in the proposed *** Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object *** have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed *** outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

关键词： Precision Agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN

来源：评论

学校读者我要写书评

暂无评论

Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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：