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2022 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2022

作者： Meshram, Chandrashekhar Obaidat, Mohammad S. Imoize, Agbotiname Lucky Bahkali, Ismail Tambare, Parkash Hsiao, Kuei-Fang Jayawanti Haksar Government Post Graduation College Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States KASIT The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum44801 Germany King Abdulaziz University Department of Information Science Jeddah21589 Saudi Arabia WRAM Research Lab Pvt. Ltd. Nagpur440027 India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

Authentication mechanisms for Telecare Medicine Information Systems (TMIS) have been extensively researched to safeguard the privacy and integrity of sensitive user data, such as telephone numbers, health information, medical records, etc. This paper presents a new efficient authentication technique for TMIS leveraging convolution Chebyshev chaotic maps. Based on the findings of this investigation, the suggested technique is secure against password guessing, privileged insider, replay, stolen verifier, and man-in-the-middle attacks. Generally, the computational costs of the proposed authentication technique outperform other competing techniques in the existing literature. © 2022 IEEE.

关键词： Convolution

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Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

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Machine Intelligence research 2022年第5期19卷 366-411页

作者： Yang Wu Ding-Heng Wang Xiao-Tong Lu Fan Yang Man Yao Wei-Sheng Dong Jian-Bo Shi Guo-Qi Li Applied Research Center Laboratory Tencent Platform and Content Group Shenzhen 518057China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi′an Jiaotong UniversityXian710049China School of Artificial Intelligence Xidian UniversityXi'an 710071China Division of Information Science Nara Institute of Science and TechnologyNara 6300192Japan Peng Cheng Laboratory Shenzhen 518000China Department of Computer and Information Science University of PennsylvaniaPhiladelphia PA19104-6389USA Institute of Automation Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial *** has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation *** recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial ***,insightful views on the opportunities and challenges of efficiency are also highly required for the entire *** general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be *** this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired *** investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

关键词： Visual recognition deep neural networks(DNNS) brain-inspired methodologies network compression dynamic inference survey

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Practical limitations on robustness and scalability of quantum Internet

arXiv

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arXiv 2023年

作者： Sadhu, Abhishek Somayajula, Meghana Ayyala Horodecki, Karol Das, Siddhartha Raman Research Institute Karnataka Bengaluru560080 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Telangana Hyderabad500032 India Department of Computer Science Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad500104 India Institute of Informatics National Quantum Information Centre Faculty of Mathematics Physics and Informatics University of Gdańsk Wita Stwosza 57 Gdańsk80-308 Poland International Centre for Theory of Quantum Technologies University of Gdańsk Wita Stwosza 63 Gdańsk80-308 Poland

The realization of a desirably functional quantum Internet is hindered by fundamental and practical challenges such as high loss during transmission of quantum systems, decoherence due to interaction with the environment, fragility of quantum states under measurement, etc. We study the implications of these constraints by analyzing the limitations on the scaling and robustness of the quantum Internet. Considering quantum networks, we present practical bottlenecks for secure communication, delegated computing, and resource distribution among end nodes. In particular, we show that the device-independent quantum key distribution (DI-QKD) network of isotropic states with parameter p has a finite diameter depending on p. Motivated by the power of abstraction in graph theory (in association with quantum information theory), that allows us to consider the topology of a network of users spatially separated over long distances around the globe or separated as nodes within the small space over a processor or circuit within a same framework, we consider graph-theoretic quantifiers to assess network robustness and provide critical values of communication lines for viable communication over quantum Internet. We consider some quantum networks of practical interest, ranging from satellite-based networks connecting far-off spatial locations to currently available quantum processor architectures within computers, and analyze their robustness to perform quantum information processing tasks. Some of these tasks form primitives for delegated quantum computing, e.g., entanglement distribution and quantum teleportation, that are desirable for all scale of quantum networks, be it quantum processors or quantum Internet. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms

Enhancing Security and Scalability of Metaverse with Blockch...

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

作者： Anand Singh Rajawat S.B. Goyal Aarti Goyal Kavita Rajawat Maria Simona Raboaca Chaman Verma Traian Candin Mihaltan School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Mind Intellect Technology Indore India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world.

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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Telecommunications Conference (ITC-Egypt), International

作者： Abdelhameed Ibrahim Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah El-Sayed M. El-kenawy Faculty of Engineering Design and Information & Communications Technology (EDICT) School of ICT Bahrain Polytechnic Isa Town Bahrain Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform; the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance.

关键词： Machine learning algorithms Transforms Predictive models Nearest neighbor methods Prediction algorithms Boosting Data models

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An Efficient Digital Short Signature Scheme using CCM for HC-IoT Environments

An Efficient Digital Short Signature Scheme using CCM for HC...

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2022 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2022

作者： Meshram, Chandrashekhar Obaidat, Mohammad S. Imoize, Agbotiname Lucky Bahkali, Ismail Meshram, Akshaykumar Hsiao, Kuei-Fang Jayawanti Haksar Government Post Graduation College Department of Post Graduation Studies and Research in Mathematics M.P. Betul460001 India Computer Science Department Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States The University of Jordan KASIT Amman Jordan University of Science and Technology Beijing Beijing China University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum44801 Germany King Abdulaziz University Department of Information Science Jeddah21589 Saudi Arabia Yeshwantrao Chavan College of Engineering Department of Applied Mathematics M.S. Nagpur India University of Texas-Permian Basin Computer Science Department OdessaTX79762 United States

ISBN: (数字)9781665486170

ISBN: (纸本)9781665486170

With billions of life-improving applications proliferating the global space, the Human-Centered Internet of Things (HC-IoT) grips excellent promise. The Internet will comprise more than 50 billion linked things, comprising mobile devices, GPS devices, sensors, actuators, and all the smart things that can be imagined. The HC-IoT comprises many smart gadgets to distribute identified information via online platforms. The goal is to encrypt messages between the sender and receiver during data exchange with the right instruction to challenge any human malicious attack. Different signature-based schemes have been proposed to ensure secure communication of critical user information. Smart gadgets claim lightweight characteristics, posing remarkable strengths and safety. The core challenge with signature-based techniques is the incurred computational cost owing to the enormous resources required for signature and verification procedures. The current paper projects an efficient Digital Short Signature (DSS) Scheme that uses Conformal Chaotic Maps (CCM) for protected communication in HC-IoT-based smart devices. The security of the projected scheme is thoroughly connected to a CCM in the random oracle model (ROM). The proposed DSS-CCM scheme offers more security assurance than the existing signature systems. The presented scheme would find practical applications in smart cards and emerging wireless security systems. © 2022 IEEE.

关键词： Internet of things

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Conditional score-based diffusion models for solving inverse problems in mechanics

arXiv

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arXiv 2024年

作者： Dasgupta, Agnimitra Ramaswamy, Harisankar Murgoitio-Esandi, Javier Foo, Ken Li, Runze Zhou, Qifa Kennedy, Brendan F. Oberai, Assad A. Department of Aerospace & Mechanical Engineering University of Southern California Los AngelesCA90089 United States BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre NedlandsWA6009 Australia Centre for Medical Research The University of Western Australia PerthWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia PerthWA6009 Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Torun87-100 Poland Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Alfred E. Mann Department of Biomedical Engineering University of Southern California Los AngelesCA90089 United States

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen’s spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently. © 2024, CC BY.

关键词： Markov chains

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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CBCT Reconstruction using Single X-ray Projection with Cycle-domain Geometry-integrated Denoising Diffusion Probabilistic Models

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Pan, Shaoyan Peng, Junbo Gao, Yuan Lo, Shao-Yuan Luan, Tianyu Li, Junyuan Wang, Tonghe Chang, Chih-Wei Tian, Zhen Yang, Xiaofeng Emory University Department of Biomedical Informatics Department of Radiation Oncology Winship Cancer Institute AtlantaGA30322 United States Honda Research Institute USA San JoseCA95134 United States State University of New York at Buffalo AmherstNY14068 United States Johns Hopkins University BaltimoreMD21205 United States Memorial Sloan Kettering Cancer Center Department of Medical Physics New YorkNY10065 United States University of Chicago Department of Radiation & Cellular Oncology ChicagoIL60637 United States Emory University Department of Radiation Oncology Winship Cancer Institute Department of Computer Science and Biomedical Informatics Department of Biomedical Engineering AtlantaGA30322 United States

In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient’s CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-t

关键词： Radiotherapy

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Kartalol: A New Deep Neural Network Framework Based on Transfer Learning for Iris Segmentation and Localization Task: New Dataset for Iris Segmentation

SSRN

引用

SSRN 2022年

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Malek-Ashtar University of Technology Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature, each of which has somehow mitigated some of the mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture that has been initialized with a pre-trained MobileNetV2 model. In addition, we have collected 1000 images to study the detectors in iris recognition scenarios better. The provided dataset (KartalOl) is available free for the research community. In the proposed framework, to better generalizability, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Also, data augmentation technics are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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