检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Zhiwei Long, Guodong Zhang, Chunxu Zhang, Honglei Jiang, Jing Zhang, Chengqi Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia College of Computer Science and Technology Jilin University Jilin130012 China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

In recent years, the integration of federated learning (FL) and recommendation systems (RS), known as Federated Recommendation Systems (FRS), has attracted attention for preserving user privacy by keeping private data on client devices. However, FRSs face inherent limitations such as data heterogeneity and scarcity, due to the privacy requirements of FL and the typical data sparsity issues of RSs. Models like ChatGPT are empowered by the concept of transfer learning and self-supervised learning, so they can be easily applied to the downstream tasks after fine-tuning or prompting. These models, so-called Foundation Models (FM), focus on understanding the human’s intent and perform following their designed roles in the specific tasks, which are widely recognized for producing high-quality content in the image and language domains. Thus, the achievements of FMs inspire the design of FRS and suggest a promising research direction: integrating foundation models to address the above limitations. In this study, we conduct a comprehensive review of FRSs with FMs. Specifically, we: 1) summarise the common approaches of current FRSs and FMs;2) review the challenges posed by FRSs and FMs;3) discuss potential future research directions;and 4) introduce some common benchmarks and evaluation metrics in the FRS field. We hope that this position paper provides the necessary background and guidance to explore this interesting and emerging topic. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Access Control Model for E-Medical Systems

Privacy-Preserving Access Control Model for E-Medical System...

引用

Big data and Privacy Computing (BDPC), International Conference on

作者： Rongsheng Cai Lanxiang Chen Yizhao Zhu Faculty of Data Science City University of Macau Macau China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou hina

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

With the high speed development of information technology, the healthcare system increasingly relies on cloud servers for data storage and complex computation, patient data privacy issues stem from healthcare systems' vulnerability to breaches, despite data encryption emphasis, insufficient focus on preventing unauthorized access risks compromises patient information confidentiality. Consequently, the need to protect privacy within the healthcare system has gained significant attentions. However, existing research primarily concentrates on data encryption processes, but there is comparatively less attention given to addressing potential privacy leaks concerning data visitors. In this paper, we propose a privacy-preserving access control model for healthcare cloud data visitors. Our model combines the RBAC (Role-Based Access Control) model with the PSI (Private Set Intersection) technique to ensure the privacy and security of data visitors. The performance analysis of our proposed solution demonstrates its relative efficiency and practicality when applied to E-medical systems.

关键词： Access control data privacy Privacy Protocols Medical services data models Encryption

来源：评论

学校读者我要写书评

暂无评论

Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

引用

2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

引用

11th International Conference on computer Engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data privacy

来源：评论

学校读者我要写书评

暂无评论

FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

引用

29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

引用

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Enhanced Precision and Safety in Minimally Invasive Surgery: The Role of AI and Robotics Integration

Enhanced Precision and Safety in Minimally Invasive Surgery:...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Rani Sunil Kedar Chetan G Puri Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe Wardha Maharashtra Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research(DU) Sawangi (Meghe) Wardha Maharashtra

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

A revolutionary development in surgical practice, the incorporation of robotics and artificial intelligence (AI) into minimally invasive surgery (MIS) improves patient outcomes, safety, and precision. This review article examines the development of minimally invasive surgery (MIS) and highlights its notable advantages over open surgery, including shorter recovery periods, less discomfort following surgery, and a decreased rate of complications. The article explores the function of robotic systems, in particular the da Vinci Surgical System, which has transformed surgical operations by giving doctors more dexterity and visualization using robotic arms and high-definition 3D imagery. Additionally, the application of AI technology is explored, demonstrating how it enhances real-time decision-making, intraoperative assistance, and preoperative planning, ultimately improving surgical results. The paper also discusses the ethical and regulatory issues raised by surgical robots' growing autonomy, highlighting the necessity of strong frameworks to guarantee patient safety and fair access to these cutting-edge technologies. This study highlights how artificial intelligence (AI) and robotics have the ability to change the surgical medicine landscape by combining the most recent research and clinical practices. It also calls for continued communication among stakeholders to effectively traverse the complexity of this rapidly developing sector.

关键词： Ethics Visualization Minimally invasive surgery Medical robotics Ubiquitous computing Safety Planning Complexity theory Artificial intelligence Robots

来源：评论

学校读者我要写书评

暂无评论

Optimizing hyperparameters for dual-attention network in lung segmentation

引用

Intelligence-Based Medicine 2025年 11卷 100221-100221页

作者： Wahyuningrum, Rima Tri Fadillah, Rizki Abdil Yunita, Indah Satoto, Budi Dwi Muntasa, Arif Sari, Amillia Kartika Rahardjo, Paulus Dewi, Deshinta Arrova Bauravindah, Achmad Department of Informatics Engineering Faculty of Engineering University of Trunojoyo Madura Indonesia Department of Health Faculty of Vocational Studies Universitas Airlangga Indonesia Department of Computer Science and Technology Faculty of Data Science and Information Technology INTI International University Malaysia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Indonesia

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis. © 2025 The Authors

关键词： Chest X-ray images DANet Epoch Hyperparameter Learning rate Lung segmentation Process innovation

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Responsible Gaming Systems

Privacy-Preserving Responsible Gaming Systems

引用

Big data and Privacy Computing (BDPC), International Conference on

作者： Yizhao Zhu Lanxiang Chen Faculty of Data Science City University of Macau Macau China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Responsible gaming aims at maintaining the integrity and sustainability of the gaming industry. However, The exchange of sensitive data, such as player and gaming related information, among different parties raises significant privacy concerns, and safeguarding privacy becomes a top priority consideration throughout the design and implementation of responsible gaming systems. In this paper, we introduce an innovative scheme that utilizes Oblivious Transfer (OT) and Private Set Intersection (PSI) protocols to address privacy concerns of responsible gaming systems. Our scheme proposes a system model that addresses privacy concerns related to player global limits and banned player list query and response, without requiring infrastructure changes, unlike blockchain-based solutions; Our proposed scheme not only addresses privacy concerns between the query user and the centralized database server but also mitigates privacy concerns between the query user and certificate authority (CA) by incorporating blind RSA signature scheme; Our proposed scheme achieves linear complexity in computation and communication, while remaining resilient against malicious adversaries under the random oracle model.

关键词： Industries data privacy Privacy Protocols databases Computational modeling Big data

来源：评论

学校读者我要写书评

暂无评论

Cultural-Aware AI Model for Emotion Recognition

Cultural-Aware AI Model for Emotion Recognition

引用

Iranian Machine Vision and Image Processing (MVIP)

作者： Mehrdad Baradaran Payam Zohari Abtin Mahyar Hossein Motamednia Dara Rahmati Saeid Gorgin Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran Department of Computer Engineering Khaje Nasir University Of Technology Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju South Korea

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multimodal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

关键词：

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：