检索结果-内蒙古大学图书馆

PKEST: Public-Key Encryption with Similarity Test for Medical Consortia Cloud Computing

IEEE Transactions on Cloud Computing 2025年第02期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Sichuan Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China West China Hospital Sichuan University West China Biomedical Big Data Center Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

Progressively Relaxed Knowledge Distillation

Progressively Relaxed Knowledge Distillation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaozhe Gu Ran Jin Mingliang Li College of Big Data and Software Engineering Zhejiang Wanli University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Knowledge Distillation (KD) enhances the generalization ability of a student model by transferring knowledge from a teacher model. However, literature suggests that the student may struggle to match the teacher’s predictions perfectly, even with sufficient capacity. A distillation scheme imposing strong constraints on the student may lead to over-regularization and performance degradation. On the other hand, a scheme with low supervision intensity can alleviate over-regularization but may lead to the student acquiring less potentially beneficial knowledge. Determining the optimal distillation scheme often requires significant effort and experimentation. Furthermore, the fact that the optimal distillation strategy may vary at different stages of the distillation process is also often *** this work, we introduce PRKD (Progressively Relaxed Knowledge Distillation), an advanced distillation framework that dynamically adjusts the supervision intensity from the teacher throughout the training process. Specifically, PRKD is built upon a gradual transition of the distillation scheme, progressing iteratively from a strong to a weak configuration. Through this transition, the student preserves essential knowledge from the teacher while simultaneously alleviating the potential for excessive regularization. Our comprehensive experiments on diverse datasets demonstrate the efficacy of PRKD, consistently surpassing other state-of-the-art methods.

关键词： Training Degradation Neural networks

来源：评论

学校读者我要写书评

暂无评论

DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference

arXiv

引用

arXiv 2024年

作者： Shuai, Zhihao Chen, Yinan Mao, Shunqiang Zhou, Yihan Zhang, Xiaohong School of Big Data and Software Engineering Chongqing University China

Weakly supervised medical image segmentation (MIS) using generative models is crucial for clinical diagnosis. However, the accuracy of the segmentation results is often limited by insufficient supervision and the complex nature of medical imaging. Existing models also only provide a single outcome, which does not allow for the measurement of uncertainty. In this paper, we introduce DiffSeg, a segmentation model for skin lesions based on diffusion difference which exploits diffusion model principles to extract noise-based features from images with diverse semantic information. By discerning difference between these noise features, the model identifies diseased areas. Moreover, its multi-output capability mimics doctors' annotation behavior, facilitating the visualization of segmentation result consistency and ambiguity. Additionally, it quantifies output uncertainty using Generalized Energy Distance (GED), aiding interpretability and decision-making for physicians. Finally, the model integrates outputs through the Dense Conditional Random Field (DenseCRF) algorithm to refine the segmentation boundaries by considering inter-pixel correlations, which improves the accuracy and optimizes the segmentation results. We demonstrate the effectiveness of DiffSeg on the ISIC 2018 Challenge dataset, outperforming state-of-the-art U-Net-based methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Research on performance optimization of virtual data space across WAN

引用

Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

来源：评论

学校读者我要写书评

暂无评论

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models 32

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion...

引用

32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

作者： Zhou, Xingchen He, Ying Richard Yu, F. Li, Jianqiang Li, You College of Computer Science and Software Engineering Shenzhen University China China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)9781956792034

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://*** for a better view of our results. © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study for Temperature Prediction by Machine Learning and Deep Learning

A Comparative Study for Temperature Prediction by Machine Le...

引用

2023 International Conference on Intelligent Computing and Control, IC and C 2023

作者： Zhao, Heng Chen, Yixing Shenzhen Technology University College of Big Data and Internet Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665457095

Time series prediction has been a hot research topic for decades. Temperature data is typical time series data, so temperature prediction is an important applications. In recent years, Machine Learning has been applied to time series prediction in many fields. Besides, the development of Deep Learning has made many new breakthroughs in these predictions. Generally, Deep Learning is considered to be superior to traditional Machine Learning in temperature prediction. But in fact, the traditional methods can also perform well for short-Term forecast in this regard. This research compares the performance of Linear Regression, SVR, CART, KNN and LSTM models in short-Term temperature prediction through three evaluation indicators: MAE, MSE and R2-score. The results explicate that the traditional model can also be better than the depth model in short-Term temperature prediction. © 2023 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

The Investigation and Implementation of Automated Pricing and Replenishment Decisions for Vegetable Commodities Relying on Time Series Forecasting and Linear Programming Models 7

The Investigation and Implementation of Automated Pricing an...

引用

7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Tong, Yi Zhou, Jinxuan Deng, Liang Zhang, Congao Guo, Tao Long, Xueling Wuhan Business University Department of Information Engineering Hubei430056 China Major in Software Engineering China Major in Data Science and Big DataTechnology China Major in Internet of Things Engineering China Major in Financial Management China

ISBN: (纸本)9798350370782

In modern supermarkets, given the limited shelf life and the propensity for rapid declines in the sales of perishable goods, such as vegetables, it becomes imperative to conduct daily restocking based on historical sales and consumer demand to preclude the occurrence of undersold items the following day. Consequently, conducting restocking and pricing strategies based on sales and demand holds significant research importance for supermarkets. This study initially employs a multiple linear regression model to investigate the impact on profit by adjusting total sales (Q), sales price (P), and each component of cost-plus pricing (C, T, L, A), thereby determining the relationship between total sales and cost-plus pricing for each vegetable category, as well as the functional relationship between total sales and cost-plus pricing for each vegetable category. Based on the derived functional relationship, time series prediction and linear programming models are utilized to forecast the market demand for each vegetable category over a one-week period, subsequently formulating the daily restocking plan and pricing strategy for each vegetable category in the subsequent week to maximize supermarket profit while satisfying market demand. Finally, an analysis of saleable vegetable items reveals that 29 vegetable items can be displayed, with a total profit of 846.35 yuan. Upon testing the sales data of 300,000 vegetables, compared to the conventional display method sans the use of this model, the overall profit margin surged by 269.75 yuan, an increment of 47% relative to the initial profit, effectively enhancing the operational efficiency and profitability of the supermarket. © 2024 IEEE.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

引用

8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Research on Collaborative Innovation Ability Training of software engineering Talents Based on the Industry-Education Integration

Research on Collaborative Innovation Ability Training of Sof...

引用

software Services engineering (SSE) IEEE International Conference on

作者： Jun Zeng Junhao Wen Bin Cai Qian Xiao The School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Addressing the common issues in the cultivation of collaborative innovation abilities for software engineering talents, such as imperfections in the industry-education integration system, inefficiencies in the management mechanism, and poor connections between the education chain, talent chain, and industry chain, this article introduces the implementation measures for cultivating the collaborative innovation abilities of software engineering talents from the perspectives of organizational models, training models, practical systems, and safeguard mechanisms under the context of industry-education integration.

关键词： Industries Training Technological innovation Quality assurance Biological system modeling Collaboration software

来源：评论

学校读者我要写书评

暂无评论

Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

引用

8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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：