检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Generating tripartite entanglement via spatial overlap of identical particles

Generating tripartite entanglement via spatial overlap of id...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Donghwa Lee Tanumoy Pramanik Young-Wook Cho Hyang-Tag Lim Seungbeom Chin Yong-Su Kim Center for Quantum Information Korea Institute of Science and Technology (KIST) Seoul Republic of Korea Division of Nano & Information Technology KIST School Korea University of Science and Technology Seoul Republic of Korea State Key Laboratory for Mesoscopic Physics & Collaborative Innovation Center of Quantum Matter Peking University Beijing China Department of Physics Yonsei University Seoul Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

We report experimental generation of all the genuine three-party entangled states, i.e., GHZ and W states, of identical particles via spatial overlap. It shows that the indistinguishability is crucial for generating e... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Quantum entanglement Lasers and electrooptics Electrooptical waveguides

来源：评论

学校读者我要写书评

暂无评论

Recording provenance of workflow runs with RO-Crate

arXiv

引用

arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

来源：评论

学校读者我要写书评

暂无评论

Multi-Tier Computing-Enabled Digital Twin in 6G networks

arXiv

引用

arXiv 2023年

作者： Wang, Kunlun Tang, Yongyi Duong, Trung Q. Khosravirad, Saeed R. Dobre, Octavia A. Karagiannidis, George K. The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Innovation Laboratory of Digital GuangXi Smart Infrastructures Guangxi Information Center Nanning530000 China The School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT7 1NN United Kingdom The Nokia Bell Laboratories Murray Hill NJ07964 United States The Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNLA1C 5S7 Canada The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54 124 Greece Lebanon

Digital twin (DT) is the recurrent and common feature in discussions about future technologies, bringing together advanced communication, computation, and artificial intelligence, to name a few. In the context of Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns, arising from the large volumes of data exchanges. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing. Specifically, low latency data transmission, efficient resource allocation, and validated security strategies of multi-tier computing systems are used to solve the operational problems of the DT system. In this paper, we introduce the architecture and applications of DT using examples from manufacturing, the Internet-of-Vehicles and healthcare. At the same time, the architecture and technology of multi-tier computing systems are studied to support DT. This paper will provide valuable reference and guidance for the theory, algorithms, and applications in collaborative multi-tier computing and DT. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic data interchange

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative networks

引用

IEEE Transactions on Communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Earthworm optimization algorithm based cascade LSTM-GRU model for android malware detection

Cyber Security and Applications

引用

Cyber Security and Applications 2025年 3卷

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha Bansal, Shavi Attar, Razaz Waheeb Alhomoud, Ahmed Psannis, Konstantinos Department of Computer Science and Information Engineering Asia University Taiwan Taichung 413 China School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India University of Economics and Human Science Warsaw Poland Ronin Institute Montclair NJ United States Hong Kong Metropolitan University (HKMU) Hong Kong Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Insights2Techinfo India UCRD Chandigarh University Chandigarh India Management Department College of Business Administration Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha 91911 Saudi Arabia University of Macedonia Greece

The rise in mobile malware risks brought on by the explosion of Android smartphones required more efficient detection techniques. Inspired by a cascade of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, optimized using the Earthworm Optimization Algorithm (EOA), this study presents an android malware detection model. The paper used random forest model for feature selection. With a 99% accuracy and the lowest loss values, the proposed model performs better than conventional models including GRU, LSTM, RNN, Logistic Regression, and SVM. The findings highlight the possibility of proposed method in improving Android malware detection, thereby providing a strong answer in the changing scene of cybersecurity. © 2024

关键词： Android Earthworm optimization algorithm GRU LSTM Malware detection

来源：评论

学校读者我要写书评

暂无评论

Imaging-Free, Few-Shot, Three-Dimensional Focusing on Point-Like Emitters in Confocal Microscopy

Imaging-Free, Few-Shot, Three-Dimensional Focusing on Point-...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Swetapadma Sahoo Junyue Jiang Jaden Li Kieran Loehr Chad E. Germany Jincheng Zhou Bryan K. Clark Simeon I. Bogdanov Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois USA Nick Holonyak Jr. Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign Urbana Illinois USA Illinois Quantum Information Science and Technology Center University of Illinois Urbana-Champaign Urbana Illinois USA Zhejiang University-University of Illinois at Urbana-Champaign Institute Haining China Department of Physics University of Illinois Urbana-Champaign Urbana IL USA

We introduce a rapid, noise-robust, three-dimensional focusing framework for as-is confocal microscopes. We show automated real-time focusing on nanoscale emitters for SNR down to 1, and position tracking with a preci... 详细信息

ISBN: (纸本)9798350369311

关键词： Microscopy Laser noise Focusing Lasers and electrooptics Real-time systems Nanoscale devices Noise robustness Electro-optical waveguides Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Optimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization

引用

Informatics in Medicine Unlocked 2024年 44卷

作者： Bhuvaneswari Ramakrishnan, Akshay Sridevi, M. Vasudevan, Shriram K. Manikandan, R. Gandomi, Amir H. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirapalli India Asia Pacific and Japan Intel India Pvt. Ltd Bengaluru India Faculty of Engineering & Information Technology University of Technology Sydney Sydney NSW Australia University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary

A brain tumour is a malignant condition that spreads extremely quickly and requires rapid detection. In recent years, it has become apparent that deep learning is a promising technique for classifying brain tumours. To identify these tumours, this work proposes a hybrid CNN architecture, implementing InceptionV3, ResNet-50, VGG16, and DenseNet. To evaluate this approach, a total of 3929 images was obtained from Kaggle, including 2556 non-tumorigenic and 1373 tumorigenic specimens. Initially, the method extracts features from MRI pictures, then segments the tumour using the mask images. Subsequently, the segmented tumour image is fused with the original image, and finally the fused image is classified with the assistance of four distinct CNN models, specifically InceptionV3, ResNet, DenseNet, and VGG16. To enhance the performance of the hybrid architecture, these models were further optimized with oneAPI, which allows a comparative analysis of each individual model for classification. To evaluate the effectiveness of medical image classification models, a variety of evaluation criteria have been utilized extensively. We highlight the use of mean Intersection over Union (mIoU) as a more acceptable and intuitive statistic for evaluating the balance between true positives, false positives, and false negatives. This is in response to recent breakthroughs that have been made. © 2023 The Authors

关键词： Brain tumour Deep learning oneAPI Optimization Tumorigenic

来源：评论

学校读者我要写书评

暂无评论

Quantum Optics Applications of Hexagonal Boron Nitride Defects

arXiv

引用

arXiv 2024年

作者： Çakan, Aslı Cholsuk, Chanaprom Gale, Angus Kianinia, Mehran Paçal, Serkan Ateş, Serkan Aharonovich, Igor Toth, Milos Vogl, Tobias Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Munich80799 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney UltimoNSW2007 Australia University of Technology Sydney UltimoNSW2007 Australia Department of Physics Izmir Institute of Technology Izmir35430 Turkey

Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

引用

arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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：