检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Zhang, Qijian Cong, Runmin Li, Chongyi Cheng, Ming-Ming Fang, Yuming Cao, Xiaochun Zhao, Yao Kwong, Sam The Department of Computer Science City University of Hong Kong Hong Kong The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Computer Science Nankai University Tianjin300071 China School of Information Technology Jiangxi University of Finance and Economics NanchangJiangxi330032 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://***/ rmcong/DAFNet_TIP20 © 2020, CC BY-NC-SA.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

引用

arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： information management

来源：评论

学校读者我要写书评

暂无评论

Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 11 November 2022

引用

International Journal of Intelligent Systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

来源：评论

学校读者我要写书评

暂无评论

Golden grain: Building a secure and decentralized model marketplace for MLaaS

arXiv

引用

arXiv 2020年

作者： Weng, Jiasi Weng, Jian Cai, Chengjun Huang, Hongwei Wang, Cong The College of Information Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design. © 2020, CC BY.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

引用

computers, Materials & Continua 2018年第5期55卷 201-211页

作者： Junjia Chen Wei Lu Yuileong Yeung Yingjie Xue Xianjin Liu Cong Lin Yue Zhang School of Data and Computer Science Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China Center for Faculty Development and Educational Technology Guangdong University of Finance and EconomicsGuangzhou 510320China Department of Computer Science College of Engineering and Computer ScienceEastern Lake AvenueOrlandoFLUSA.

In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information *** most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding *** this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial ***,the distortion maps can be calculated for cover and stego images and the difference between them is *** this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence *** proposed scheme first generates the corresponding distortion maps for cover and stego *** the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego ***,support vector machine,based on the gaussian kernel,is used to classify the *** with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.

关键词： Binary image steganalysis informational security embedding distortion distortion level map co-occurrence matrix support vector machine.

来源：评论

学校读者我要写书评

暂无评论

Curtailing the spread of COVID-19 in Nigeria using digital framework and data driven approaches

EngMedicine

引用

EngMedicine 2024年第2期1卷 22-30页

作者： Bolanle Adefowoke Ojokoh Benjamin S.Aribisala Oluwafemi A.Sarumi Tobore V.Igbe Abimbola H.Afolayan Arome J.Gabriel Abiola Ezekiel Taiwo Olatunji M.Omisore Uchechukwu M.Chukwuocha Olusola T.Babalola Tolulope A.Adebayo Department of Information Systems Federal University of TechnologyP.M.B.704 AkureOndo StateNigeria Department of Computer Science Lagos State UniversityOjoLagos StateNigeria Department of Mathematics and Computer Science University of Marburg35037 MarburgGermany Center for Diabetes Technology Department of Psychiatry&Neurobehavioral SciencesUniversity of VirginiaCharlottesvilleVA 22903USA Department of Cyber Security Federal University of TechnologyP.M.B.704 AkureOndo StateNigeria Faculty of Engineering Mangosuthu University of TechnologyDurban 4026South Africa Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Department of Public Health Federal University of TechnologyIhiagwaOwerriNigeria Department of Computer Science Federal University of TechnologyP.M.B.704 AkureOndo StateNigeria

The onset of the coronavirus disease 2019(COVID-19)pandemic had profound economic and societal repercussions *** nations managed to mitigate the spread of the virus sufficiently early through implementing digital frameworks and other containment ***,many countries in Africa faced significant challenges due to limited technological infrastructure for infection *** study investigated the efficacy of digital frameworks,with a specific focus on contact tracing apps,in stemming the spread of COVID-19 within *** investigated the potential impact of the use of these apps on reducing infection rates and improving public health ***,this study provided insights into the practical implementation of such frameworks and the challenges associated with their nationwide deployment.A structured user-oriented questionnaire was distributed to participants across different geopolitical zones in *** study employed association rule mining and descriptive statistics,setting a support threshold of 0.001 and confidence of 0.8,to uncover patterns of interest among *** analysis using Barnes–Hut t-Distributed Stochastic Neighbor Embedding was conducted to derive reliable inferences from the *** findings revealed adoption rates of 69.7%and 93.8%for the minimum and maximum,respectively,in favor of using a COVID-19 contact tracing ***,71.6%of participants expressed concerns about the privacy of their personal information,whereas 74%showed an interest in receiving daily reports on estimated COVID-19 infection in their *** study presents empirical evidence highlighting the potential of contact tracing apps as valuable tools for monitoring and curbing the spread of COVID-19 and related diseases in Nigeria.

关键词： Digital technology COVID-19 Contact tracing Mobile app Data analytics

来源：评论

学校读者我要写书评

暂无评论

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

引用

arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Secure Cryptography Infrastructures in the Cloud

Secure Cryptography Infrastructures in the Cloud

引用

IEEE Global Communications Conference

作者： Dawei Chu Kaijie Zhu Quanwei Cai Jingqiang Lin Fengjun Li Le Guan Lingchen Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Digital Switching System Engineering & Technological R&D Center CHINA Data Assurance and Communication Security Research Center Chinese Academy of Sciences Department of Electrical Engineering and Computer Science The University of Kansas USA Department of Computer Science University of Georgia USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

information systems are deployed in clouds as virtual machines (VMs) for better agility, elasticity and reliability. It is necessary to safekeep their cryptographic keys, e.g., the private keys used in TLS and SSH, against various attacks. However, existing virtualization solutions do not improve the cryptography facilities of in-cloud systems. This paper presents SECRIN, a secure cryptography infrastructure for VMs in the cloud. SECRIN is composed of a) virtual cryptographic devices implemented in VM monitors (VMMs), and b) a device management tool integrated in the virtualization management system. A virtual device receives requests from VMs, computes with cryptographic keys within the VMM and returns results. The keys appear only in the VMM's memory space, so that they are kept secret even if the VMs were compromised. With the management tool, the operator of virtualization management systems assigns virtual cryptographic devices to a VM as well as other resources, while the tenant (or owner) of a VM still holds proper controls on the keys. The virtual devices work compatibly with live migration, and the cryptographic computations are not interrupted when the VMs are moving from a host to another. We develop the SECRIN prototype with KVM-QEMU and oVirt. Experimental results show that, it works compatibly with existing virtualization solutions, provides reliable cryptographic computing services for applications, and is secure against attacks happening in VMs.

关键词： Cryptography Virtualization Cloud computing Virtual machine monitors Password Reliability Tools

来源：评论

学校读者我要写书评

暂无评论

An Acute Kidney Injury Prediction Model Based on Ensemble Learning Algorithm

An Acute Kidney Injury Prediction Model Based on Ensemble Le...

引用

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Yuan Wang Yake Wei Qin Wu Hao Yang Jingwei Li College of Computer Science and Information Engineering Tianjin University of Science and Technology Population and Precision Health Care (Tianjin) Ltd Center for Cyber Security University of Electronic Science and Technology of China Department of critical care medicine Sichuan University State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences

Acute Kidney Injury (AKI), a common disease in Intensive Care Unit (ICU) patients, is related to high cost, morbidity and mortality. The early prediction of AKI is critical for improving patients' outcome. However, sparse clinical data and highly imbalanced dataset bring great challenges to AKI prediction. Among existed machine learning algorithms, ensemble learning often stands out with its good performance on complex classification problems. Boosting algorithm is one of the best ensemble learning algorithms. Therefore, we develop a prediction model based on it aiming to forecast AKI ahead 24 hours and 48 hours. We also adopt the way that is analogous to text modeling transforming heterogeneous time series reflecting patients' medication information into multidimensional vector to overcome problem brought by sparse data. Additionally, since the imbalanced dataset would affect predictive performance, we artificially construct a more balanced dataset based on the original dataset to initialize the model. According to the experimental results, our model works well on the ICU patients dataset (AUC 24h ahead: 0.80 48h ahead 0.77). We also verify that medication information improves model performance (24h ahead: AUC 0.75 to 0.80 48h ahead: AUC 0.75 to 0.77) and find the optimal ratio of number between classes when initializing model for AKI prediction.

关键词： Predictive models Sensitivity Prediction algorithms Drugs Feature extraction Machine learning algorithms Kidney

来源：评论

学校读者我要写书评

暂无评论

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

arXiv

引用

arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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：