检索结果-内蒙古大学图书馆

Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems 12

学校读者我要写书评

暂无评论

Visual Cryptography and Blockchain for Protecting Against Ph...

12th International Conference and Exposition on Electrical and Power engineering, EPE 2022

作者： Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Malik, Shilpa Neagu, Bogdan Constantin Raboaca, Maria Simona Verma, Chaman Sandip University School of Computer Science and Engineering Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida201302 India Central Institute of Educational Technology Delhi India 'Gheorghe Asachi' Technical University of Iasi Department of Power Engineering Iasi700050 Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

A recent poll suggests that a lot of people don't vote because they feel like they already have too much to do. This is because of many things, like the fact that everyone has to go to a polling place, wait in a long line, and be mentally and physically tired before they can vote. We have pushed for people to be able to vote online, but there are a growing number of problems and risks that could come with this. When websites are attacked by things like phishing, it might be hard to log in. With the help of Visual Cryptography (VC), We proposed Visual Cryptography and Blockchain for Protecting Against Phishing Attacks on Electronic Voting Systems we made a voting system that lets people discuss and decide on important business ideas without giving away any information. © 2022 IEEE.

关键词： Cryptography

Access Control Based on Self-Sovereign Identity

学校读者我要写书评

暂无评论

Access Control Based on Self-Sovereign Identity

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Stefania Loredana Nita Marius Iulian Mihailescu Valentina Marascu Marius Rogobete Department of Computers and Cybersecurity Faculty of Information Systems and Cyber Security Military Technical Academy “Ferdinand I” Bucharest Romania Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science “Spiru Haret” University Bucharest Romania Low Temperature Plasma Department National Institute for Laser Plasma and Radiation Physics Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the rapidly growing interest in digital identity management, there is an important shift toward adopting verifiable credentials, often based on blockchain technology. This transition highlights a growing interest in the concept of Self-Sovereign Identity (SSI), a paradigm that empowers individuals with control over their own digital identities through the use of verifiable credentials. This paper introduces a novel access control framework that leverages the principles of SelfSovereign Identity to significantly enhance the security of system access based on the users’ credentials. By integrating the decentralized and user-centric nature of SSI, our proposed framework aims to address existing vulnerabilities in traditional access control systems. It provides a robust, scalable, and controlled mechanism that ensures higher levels of trust and security in digital interactions. This approach aligns with the broader digital identity trends favoring transparency, user autonomy, and trust.

关键词： Access control computers Privacy Computational modeling computer architecture Market research Blockchains

CLINICALLY RELEVANT LATENT SPACE EMBEDDING OF CANCER HISTOPATHOLOGY SLIDES THROUGH VARIATIONAL AUTOENCODER BASED IMAGE COMPRESSION

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Nasr, Mohammad Sadegh Hajighasemi, Amir Koomey, Paul Malidarreh, Parisa Boodaghi Robben, Michael Saurav, Jillur Rahman Shang, Helen H. Huber, Manfred Luber, Jacob M. Department of Computer Science and Engineering University of Texas Arlington United States Multi-Interprofessional Center for Health Informatics University of Texas Arlington United States Division of Internal Medicine Ronald Reagan University of California Los Angeles Medical Center United States Department of Bioengineering University of Texas Arlington United States

In this paper, we introduce a Variational Autoencoder (VAE) based training approach that can compress and decompress cancer pathology slides at a compression ratio of 1:512, which is better than the previously reported state of the art (SOTA) in the literature, while still maintaining accuracy in clinical validation tasks. The compression approach was tested on more common computer vision datasets such as CIFAR10, and we explore which image characteristics enable this compression ratio on cancer imaging data but not generic images. We generate and visualize embeddings from the compressed latent space and demonstrate how they are useful for clinical interpretation of data, and how in the future such latent embeddings can be used to accelerate search of clinical imaging data. © 2023, CC BY-NC-ND.

关键词： Image compression

RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Li, Yuheng Hu, Mingzhe Qiu, Richard L.J. Thor, Maria Williams, Andre Marshall, Deborah Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30308 United States Department of Biomedical Engineering Emory University AtlantaGA30308 United States Department of Computer Science and Informatics Emory University AtlantaGA30322 United States Department of Medical Physics Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Department of Radiation Oncology Icahn School of Medicine at Mount Sinai New YorkNY10029 United States

Deep learning-based segmentation of genito-pelvic structures in MRI and CT is crucial for applications such as radiation therapy, surgical planning, and disease diagnosis. However, existing segmentation models often struggle with generalizability across imaging modalities, and anatomical variations. In this work, we propose RoMedFormer, a rotary-embedding transformer-based foundation model designed for 3D female genito-pelvic structure segmentation in both MRI and CT. RoMedFormer leverages self-supervised learning and rotary positional embeddings to enhance spatial feature representation and capture long-range dependencies in 3D medical data. We pre-train our model using a diverse dataset of 3D MRI and CT scans and fine-tune it for downstream segmentation tasks. Experimental results demonstrate that RoMedFormer achieves superior performance segmenting genito-pelvic organs. Our findings highlight the potential of transformer-based architectures in medical image segmentation and pave the way for more transferable segmentation frameworks. © 2025, CC BY.

关键词： computerized tomography

Encrypted Data Caching and Learning Framework for Robust Federated Learning-based Mobile Edge Computing

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2023年

作者： Nguyen, Chi-Hieu Saputra, Yuris Mulya Hoang, Dinh Thai Nguyen, Diep N. Nguyen, Van-Dinh Xiao, Yong Dutkiewicz, Eryk The School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia The College of Engineering and Computer Science VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventional FedAvg approach when the straggling probability varies between 20% and 80%. Moreover, the proposed

关键词： Mobile edge computing

Unification of Finite Symmetries in Simulation of Many-body Systems on Quantum computers

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bastidas, Victor M. Fitzpatrick, Nathan Joven, K.J. Rossi, Zane M. Islam, Shariful Van Voorhis, Troy Chuang, Isaac L. Liu, Yuan Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Quantinuum Terrington House 13-15 Hills Road CambridgeCB2 1NL United Kingdom Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics North Carolina State University RaleighNC27606 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems often results in an exponential overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems;however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry groups into the simulation of many-body systems on quantum computers. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and ab initio electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first quantization on noisy hardware, and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreps of a symmetry group. In addition, we present a discussion of major open problems regarding the use of symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers. The efficiency of the proposed symmetry-adapted subroutine holds the promi

关键词： Quantum computers

Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Das, Manisha Gupta, Deep Radeva, Petia Bakde, Ashwini M. The Department of Electronics and Comm. Engineering Visvesvaraya National Institute of Technology Nagpur40010 India The Departament de Mathematics and Informatics Universitat de Barcelona Barcelona08007 Spain The Computer Vision Center Cerdanyola08193 Spain The Department of Radio-Diagnosis All India Institute of Medical Sciences Nagpur441108 India

Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion methods by executing feature extraction, feature selection, and feature fusion tasks, simultaneously. However, most of the existing convolutional neural network (CNN) architectures use conventional pooling or strided convolutional strategies to downsample the feature maps. It causes the blurring or loss of important diagnostic information and edge details available in the source images and dilutes the efficacy of the feature extraction process. Therefore, this paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. This helps in preserving the fine edge detail information present in both the source images and enhances the visual perception of fused images. Further, the proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images and preserves the diagnostic information effectively. Substantial experiments are conducted which demonstrate that the proposed method provides improved visual and quantitative results as compared to the other state-of-the-art fusion methods. © 2023, CC BY.

关键词： Feature extraction

Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Heidari, Moein Aghdam, Ehsan Khodapanah Molaei, Amirali Jia, Yiwei Jose, Abin Roy, Rijo Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://***/mindflow-institue/Awesome-Transformer. © 2023, CC BY.

关键词： Deep neural networks

Assessing in silico Tools for Accurate Pathogenicity Prediction in CHD Nucleosome Remodelers

学校读者我要写书评

暂无评论

SSRN

SSRN 2025年

作者： Rabouhi, Nazim Guindon, Simon Coleman, Emilia Aisha van Heesbeen, H.J. Greenwood, Celia M.T. Lu, Tianyuan Campeau, Philippe M. CHU Sainte-Justine Research Center University of Montreal MontrealQC Canada Gina Cody School of Engineering and Computer Science Concordia University MontrealQC Canada Department of Informatics UQAM University MontrealQC Canada Lady Davis Institute for Medical Research Jewish General Hospital Montreal Canada Gerald Bronfman Department of Oncology Department of Epidemiology Biostatistics & Occupational Health McGill University MontrealQC Canada Department of Population Health Sciences School of Medicine and Public Health University of Wisconsin–Madison MadisonWI United States Department of Biostatistics and Medical informatics School of Medicine and Public Health University of Wisconsin-Madison MadisonWI United States

Chromodomain Helicase DNA-binding (CHD) proteins compose a family of chromatin remodelers that play crucial roles in DNA repair and gene expression regulation, neural stem cell differentiation and chromatin integrity. Genetic variants in CHD chromatin remodelers are associated with neurodevelopmental disorders with features like autism spectrum disorder and intellectual disability. Consequently, the determination of variant pathogenicity in clinical genetic tests for individuals bearing CHD variants is crucial. In this study, we compared the efficiency of multiple pathogenicity prediction tools, which are valuable resources for the identification and annotation of potentially disease-causing variants, to assess the most accurate in silico tool capable of distinguishing pathogenic CHD variants from benign ones. We have focused specifically on genes that share high structural and functional similarity and are strongly linked to pathogenic mutations. Here, we evaluated a range of pathogenicity prediction tools and compared their output with pathogenicity conclusions reported in the literature and genomic databases. Our findings showed that the top performing tools were BayesDel, ClinPred, AlphaMissense, ESM-1b and SIFT. BayesDel, specifically with its addAF component, was overall the most robust tool for CHD variant pathogenicity prediction. We also suggest incorporating SnpEff’s high-impact variant identification capabilities for the development of a hybrid tool that would enhance the classification of CHD variants. Our study emphasizes the need for continuous evaluation and integration of updated prediction tools, including emerging AI approaches. This research also emphasizes the importance of gathering better clinical and mechanistic data on the deleteriousness of pathogenic variants to improve clinical diagnostics’ accuracy. © 2025, The Authors. All rights reserved.

关键词： Gene expression regulation