检索结果-内蒙古大学图书馆

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Gadug Sudhamsu Ankita Agarwal Rekha Dhivrani K.S. Bhanu Jeyalaxmi M Ashwini R. Nawadkar Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Boundary-Aware Network for Abdominal Multi-Organ Segmentation

arXiv

引用

arXiv 2022年

作者： Hu, Shishuai Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China

Automated abdominal multi-organ segmentation is a crucial yet challenging task in the computer-aided diagnosis of abdominal organ-related diseases. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of abdominal organs remains challenging, due to the varying sizes of abdominal organs and the ambiguous boundaries among them. In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable organ sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Abdominal Multi-Organ Segmentation (AMOS) Challenge dataset and achieved an average Dice score of 89.29% for multi-organ segmentation on CT scans and an average Dice score of 71.92% on MRI scans. The results demonstrate that BA-Net is superior to nnUNet on both segmentation tasks. © 2022, CC BY-NC-ND.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

arXiv

引用

arXiv 2022年

作者： Yang, Wenyuan Shao, Shuo Yang, Yue Liu, Xiyao Liu, Ximeng Xia, Zhihua Schaefer, Gerald Fang, Hui The School of Electronics Engineering and Computer Science Peking University China The School of Computer Science and Engineering Central South University Changsha410083 China The College of Computer and Data Science Fuzhou University Fuzhou350116 China The College of Cyber Security Jinan University Guangzhou510632 China The Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To our best knowledge, it is the first scheme to embed the watermark to models under the Secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack. © 2022, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Accessible Authentication Using Keyword Search through Homomorphic Encryption 1

Accessible Authentication Using Keyword Search through Homom...

引用

1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vekariya, Vipul Manjunath, R. Gill, Jasmeen Gopianand, M. Kumar, Avneesh Jha, Amaresh Parul University Department of CSE PIET Gujarat Vadodara India Department of Data Science & Mathamatics Bangalore India RIMT University Department of CSE Godindgarh Mandi India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Galgotias University Department of Computer Application Uttar Pradesh Greater Noida India School of Modern Media UPES Dehradun India

ISBN: (纸本)9781665456876

data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use. © 2022 IEEE.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

arXiv

引用

arXiv 2022年

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of the proposed framework. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

An Advanced data Fabric Architecture Leveraging Homomorphic Encryption and Federated Learning

arXiv

引用

arXiv 2024年

作者： Rieyan, Sakib Anwar News, Raisul Kabir Muntasir Rahman, A.B.M. Khan, Sadia Afrin Jawad Zaarif, Sultan Tasneem Alam, Golam Rabiul Hassan, Mohammad Mehedi Ianni, Michele Fortino, Giancarlo Department of Computer Science and Engineering School of Data and Sciences BRAC University 66 Mohakhali Dhaka1212 Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Informatics Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning. © 2024, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm 1

An Efficient Algorithm to Classify the Mitotic Cell using An...

引用

1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Seetha, J. Ramadass, Sudhir Dilipkumar, S. Sundaram, Ajith Saritha, G. Hkbk College of Engineering Department of Ece Bangalore India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Data Analyst Sterck Systems Chennai India Vit Department of Analytics Scope Vellore India School of Business Selection Grade Kochi India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9798350397840

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer;however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful. © 2022 IEEE.

关键词： Deposits

来源：评论

学校读者我要写书评

暂无评论

A THEORY OF INITIALISATION’S IMPACT ON SPECIALISATION

arXiv

引用

arXiv 2025年

作者： Jarvis, Devon Lee, Sebastian Dominé, Clémentine Carla Juliette Saxe, Andrew M. Mannelli, Stefano Sarao School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa Center for Computational Neuroscience Flatiron Institute Simons Foundation Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre UCL United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology University of Gothenburg Sweden Machine Intelligence and Neural Discovery Institute University of the Witwatersrand South Africa CIFAR Azrieli Global Scholar CIFAR

Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network’s tendency to reuse learned features across tasks. However, this explanation heavily relies on the premise that neuron specialisation occurs, i.e. the emergence of localised representations. Our investigation challenges the validity of this assumption. Using theoretical frameworks for the analysis of neural networks, we show a strong dependence of specialisation on the initial condition. More precisely, we show that weight imbalance and high weight entropy can favour specialised solutions. We then apply these insights in the context of continual learning, first showing the emergence of a monotonic relation between task-similarity and forgetting in non-specialised networks. Finally, we show that specialization by weight imbalance is beneficial on the commonly employed elastic weight consolidation regularisation technique. Copyright © 2025, The Authors. All rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Event Tagger with Automatic Speech Recognizer for Audio Multi-task Scenarios by Distillation with Pre-Trained Large Models

Enhancing Event Tagger with Automatic Speech Recognizer for ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jianfeng Cheng Ye Liu Jian Yin Liangdao Wang Yan Pan Artificial Intelligence Department Lizhi Inc. Beijing & Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the continuous expansion of robotics and digital humans in practical applications, the demand for the auditory system is becoming deeper, usually requiring more efficient speech recognition framework capabilities to handle multiple tasks and use fewer resources. In previous audio processing frameworks, each type of audio processing task typically requires constructing a standalone deep network model for training, which results in more training data and higher training time when constructing models for multi-task audio scenarios simultaneously. The recent improvement of audio models based on transformers have brought about methods that can handle multiple audio tasks concurrently. However, recent related methods still require retraining multi-task targets with an amount of data, and achieve the general effect after training for multi-task scenarios than the simple combination of standalone methods processed separately. In order to better build a model that can handle multiple audio tasks, we propose a novel framework of distillation through pre-trained large models for enhancing event tagger with automatic speech recognizer. Through multiple rounds of experiments on several audio datasets, it has been verified that the proposed framework can achieve better results than the baseline for multitasking, and comparative results with less parameters compared to the baselines for single-task scenarios.

关键词： Training Adaptation models Analytical models Training data Speech recognition Speech enhancement Multitasking Transformers data models Robots

来源：评论

学校读者我要写书评

暂无评论

OCI Runtime Comparison and Analysis Study 5

OCI Runtime Comparison and Analysis Study

引用

5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Jeon, Sung Hwan Shim, Simon Chung, Haejin Nah, Yunmook Dept. of Data Science Graduate School Dankook University Yongin-si Korea Republic of Dept. of Computer Engineering San Jose State University San Jose United States Cha Mirisa College Duksung Women's University Seoul Korea Republic of Dept. of Applied Computer Eng. Dankook University Yongin-si Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

Containers are one of the technologies required for cloud services. Because containers use resources directly on the host, they are lighter and faster than traditional virtual machines. However, it has the disadvantage of poor security and isolation. To solve this problem, security-oriented runtimes such as Kata, gVisor, and Firecracker have emerged. Each runtime is created differently, so performance and resource usage are different. In this study, performance and evaluation of each lowlevel runtime are conducted. © 2022 IEEE.

关键词： Containers

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：