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Quantity-Simulation-Analysis Method based Novel RSA Timing Attack Algorithm for Single-Chip Microcomputer Platform

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Quantity-Simulation-Analysis Method based Novel RSA Timing A...

IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Cong Li Qiang Han Tao Zhang Bingbing Lei Yu He School of Computer Science and Engineering North Minzu University Yinchuan Ningxia China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University (IGIPLab) Yinchuan Ningxia China School of Computer Science and Engineering Macau University of Science and Technology Macao SAR China

There are limitations in storage and computational capacity on the single-chip microcomputer platform under the secure edge computing paradigm. A higher success rate is possible via collecting sensitive information on the time side channel by multivariate statistical analysis to crack the RSA private key when attackers decrypt ciphertexts. We proposed a quantity-simulation-analysis (QSA) method to construct Markov model for RSA timing attack tasks, which firstly quantizes the decrypt process to obtain the time-consuming characteristics, then simulates the machine instruction cycles through parallel computing to analyze Markov model with more precise state transition matrix. On this basis, a novel timing attack algorithm with fuzzy clustering state transition probability matrix of the higher order Markov model on different step sizes is proposed, compared with some algorithms from other literatures taking an exhaustive search attack algorithm as a benchmark. Experimental results show that the algorithm achieves better results in terms of success rate.

关键词： Analytical models Correlation Computational modeling Hidden Markov models Clustering algorithms Markov processes Microcomputers

A computer vision system for saw blade condition monitoring

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Procedia CIRP 2021年 104卷 1107-1112页

作者： Nicolas Jourdan Tobias Biegel Volker Knauthe Max von Buelow Stefan Guthe Joachim Metternich Institute of Production Management Technology and Machine Tools (PTW) Otto-Berndt-Str. 2 6428 Darmstadt Germany Interactive Graphics Systems Group (GRIS) Fraunhoferstr. 5 64283 Darmstadt Germany Fraunhofer-Institute for Computer Graphics Research (IGD) Fraunhoferstr. 5 64283 Darmstadt Germany

Tool condition monitoring is a key component of predictive maintenance in smart manufacturing. Predicting excessive tool wear in machining processes becomes increasingly difficult if different materials need to be processed. We propose a novel computer vision-based system for saw blade condition monitoring that is independent of the processed materials and combines deep learning with classic computer vision. Our approach allows for accurate condition monitoring of blade wear which can further be used for predictive maintenance. Additionally, the system can classify different defect types such as missing blade teeth, thus preventing the production of scrap parts.

关键词： Condition monitoring computer vision deep learning saw blade wear

EFaR 2023: Efficient Face Recognition Competition

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

Wide-range Angle-sensitive Plasmonic Color Printing on Lossy-Resonator Substrates

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arXiv 2023年

作者： Chowdhury, Sarah N. Simon, Jeffrey Nowak, Michal P. Pagadala, Karthik Nyga, Piotr Fruhling, Colton Bravo, Esteban Garcia Maćkowski, Sebastian Shalaev, Vladimir M. Kildishev, Alexander V. Boltasseva, Alexandra Elmore Family School of Electrical & Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47906 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń87-100 Poland Department of Computer Graphics Technology Purdue University 401 N. Grant St Knoy Hall West LafayetteIN47907 United States

We demonstrate a sustainable, lithography-free process for generating non fading plasmonic colors with a prototype device that produces a wide range of vivid colors in red, green, and blue (RGB) ([0-1], [0-1], [0-1]) color space from violet (0.7, 0.72, 1) to blue (0.31, 0.80, 1) and from green (0.84, 1, 0.58) to orange (1, 0.58, 0.46). The proposed color-printing device architecture integrates a semi-transparent random metal film (RMF) with a metal back mirror to create a lossy asymmetric Fabry-Pérot resonator. This device geometry allows for advanced control of the observed color through the five-degree multiplexing (RGB color space, angle, and polarization sensitivity). An extended color palette is then obtained through photomodification process and localized heating of the RMF layer under various femtosecond laser illumination conditions at the wavelengths of 400 nm and 800 nm. Colorful design samples with total areas up to 10 mm2 and 100 μm resolution are printed on 300-nm-thick films to demonstrate macroscopic high-resolution color generation. The proposed printing approach can be extended to other applications including laser marking, anti-counterfeiting and chromo-encryption. © 2023, CC BY.

关键词： Color

Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

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Minimal Cases for Computing the Generalized Relative Pose us...

International Conference on computer Vision (ICCV)

作者： Banglei Guan Ji Zhao Daniel Barath Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China Computer Vision and Geometry Group ETH Zürich Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

ISBN: (纸本)9781665428132

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.

关键词： computer vision Motion estimation Computational modeling Pose estimation Benchmark testing Cameras Image sequences

Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

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SSRN

SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

Efficient dataflow modeling of peripheral encoding in the human visual system

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arXiv 2021年

作者： Brown, Rachel Dutell, Vasha Walter, Bruce Rosenholtz, Ruth Shirley, Peter McGuire, Morgan Luebke, David NVIDIA Research Vision Science UC Berkeley Computer Graphics Cornell University Brain and Cognitive Sciences CSAIL Massachusetts Institute of Technology NVIDIA Research

computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer's visual field, especially with the rise of high resolution and wide field-of-view display devices. However, while the ongoing study of foveal vision is advanced, much less is known about how humans process imagery in the periphery of their vision-which comprises, at any given moment, the vast majority of the pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, we present a dataflow computational model of peripheral encoding that is more efficient than prior pooling-based methods and more compact than contrast sensitivity-based methods. Further, we account for the explicit encoding of "end stopped" features in the image, which was missing from previous methods. Finally, we evaluate our model in the context of perception of textures in the periphery. Our improved peripheral encoding may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics. © 2021, CC BY.

关键词： Budget control

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

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arXiv 2024年

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center ALGORITMI/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Change Bit-Depth Procedure for Data Measured Preprocessing before Compression

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Change Bit-Depth Procedure for Data Measured Preprocessing b...

IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Alexey V. Levenets Vladislav V. Bondarenko Ruslan I. Bahenov Oleg I. Belozerov Svetlana I. Belozerova Sergey A. Bogatenkov Dept. Automation and System Engineering Pacific National University Khabarovsk Russia Dept. Information Systems and Mathematics Sholom-Aleichem Priamursky State University Birobidzhan Russia Dept. Computer Technology and Computer Graphics Far Eastern State Transport University Khabarovsk Russia Dept. Information Technology in Economics South Ural State University Chelyabinsk Russia

ISBN: (纸本)9781665464819

The paper proposes to change the bit-depth of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors’ research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods.

关键词： Current measurement Software Encoding Autocorrelation Matlab