scholarly journals A Fast Deep Learning Method for Security Vulnerability Study of XOR PUFs

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1715
Author(s):  
Khalid T. Mursi ◽  
Bipana Thapaliya ◽  
Yu Zhuang ◽  
Ahmad O. Aseeri ◽  
Mohammed Saeed Alkatheiri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computing Time ◽  
Computational Time ◽  
Learning Method ◽  
Promising Alternative ◽  
Xor Gate ◽  
Physical Unclonable Functions ◽  
Learning Time ◽  
Time Required

Physical unclonable functions (PUF) are emerging as a promising alternative to traditional cryptographic protocols for IoT authentication. XOR Arbiter PUFs (XPUFs), a group of well-studied PUFs, are found to be secure against machine learning (ML) attacks if the XOR gate is large enough, as both the number of CRPs and the computational time required for modeling n-XPUF increases fast with respect to n, the number of component arbiter PUFs. In this paper, we present a neural network-based method that can successfully attack XPUFs with significantly fewer CRPs and shorter learning time when compared with existing ML attack methods. Specifically, the experimental study in this paper shows that our new method can break the 64-bit 9-XPUF within ten minutes of learning time for all of the tested samples and runs, with magnitudes faster than the fastest existing ML attack method, which takes over 1.5 days of parallel computing time on 16 cores.

Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽  
2020 ◽  
Author(s):  
Ulices Que-Salinas ◽  
Pedro Ezequiel Ramirez-Gonzalez ◽  
Alexis Torres-Carbajal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Distribution Function ◽  
Microscopic Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
State Variables ◽  
Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

scholarly journals Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

2019 ◽  
Vol 490 (4) ◽  
pp. 4770-4777 ◽  
Author(s):  
M Kovačević ◽  
G Chiaro ◽  
S Cutini ◽  
G Tosti
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gamma Ray ◽  
Classification Performance ◽  
Previous Method ◽  
Machine Learning Method ◽  
Learning Method ◽  
Large Area ◽  
Γ Ray ◽  
Automatic Technique

ABSTRACT Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to develop an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope γ-ray instrument. The final result of this study increased the classification performance by about 80 ${{\ \rm per\ cent}}$ with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

scholarly journals ARPEGE Cloud Cover Forecast Post-Processing with Convolutional Neural Network

2020 ◽  
Author(s):  
Florian Dupuy ◽  
Olivier Mestre ◽  
Mathieu Serrurier ◽  
Valentin Kivachuk Burdá ◽  
Michaël Zamo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Cloud Cover ◽  
Spatial Information ◽  
Weather Prediction ◽  
Ground Truth ◽  
Computational Time ◽  
Post Processing ◽  
Traditional Methods

AbstractCloud cover provides crucial information for many applications such as planning land observation missions from space. It remains however a challenging variable to forecast, and Numerical Weather Prediction (NWP) models suffer from significant biases, hence justifying the use of statistical post-processing techniques. In this study, ARPEGE (Météo-France global NWP) cloud cover is post-processed using a convolutional neural network (CNN). CNN is the most popular machine learning tool to deal with images. In our case, CNN allows the integration of spatial information contained in NWP outputs. We use a gridded cloud cover product derived from satellite observations over Europe as ground truth, and predictors are spatial fields of various variables produced by ARPEGE at the corresponding lead time. We show that a simple U-Net architecture (a particular type of CNN) produces significant improvements over Europe. Moreover, the U-Net outclasses more traditional machine learning methods used operationally such as a random forest and a logistic quantile regression. When using a large number of predictors, a first step toward interpretation is to produce a ranking of predictors by importance. Traditional methods of ranking (permutation importance, sequential selection, . . . ) need important computational resources. We introduced a weighting predictor layer prior to the traditional U-Net architecture in order to produce such a ranking. The small number of additional weights to train (the same as the number of predictors) does not impact the computational time, representing a huge advantage compared to traditional methods.

scholarly journals Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing

2020 ◽  
Author(s):  
Haotian Guo ◽  
Xiaohu Song ◽  
Ariel B. Lindner
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Rna Processing ◽  
Binding Sites ◽  
Transcriptional Networks ◽  
Promising Alternative ◽  
Wide Range ◽  
Flanking Sequences ◽  
Structural Commonality

AbstractRNA-based regulation offers a promising alternative of protein-based transcriptional networks. However, designing synthetic riboregulators with desirable functionalities using arbitrary sequences remains challenging, due in part to insufficient exploration of RNA sequence-to-function landscapes. Here we report that CRISPR-Csy4 mediates a nearly all-or-none processing of precursor CRISPR RNAs (pre-crRNAs), by profiling Csy4 binding sites flanked by > 1 million random sequences. This represents an ideal sequence-to-function space for universal riboregulator designs. Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score ≈ 0.93). Inspired by exhaustive probing of palindromic flanking sequences, we designed anti-CRISPR RNAs (acrRNAs) that suppress processing of pre-crRNAs via stem stacking. We validated machine-learning-guided designs with >30 functional pairs of acrRNAs and pre-crRNAs to achieve switch-like properties. This opens a wide range of plug-and-play applications tailored through pre-crRNA designs, and represents a programmable alternative to protein-based anti-CRISPRs.

scholarly journals ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

2019 ◽  
Vol 15 (2) ◽  
pp. 141-148
Author(s):  
Sri Rahayu ◽  
Fitra Septia Nugraha ◽  
Muhammad Ja’far Shidiq
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Side Effects ◽  
20Th Century ◽  
High Accuracy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Good Classification ◽  
Auc Value

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

scholarly journals Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI

2020 ◽  
Vol 32 (4) ◽  
pp. 731-737
Author(s):  
Akinari Onishi ◽  
◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Learning Method ◽  
Eeg Signals ◽  
Recording Time ◽  
Auditory P300 ◽  
Eeg Data ◽  
Time Required

Brain-computer interface (BCI) enables us to interact with the external world via electroencephalography (EEG) signals. Recently, deep learning methods have been applied to the BCI to reduce the time required for recording training data. However, more evidence is required due to lack of comparison. To reveal more evidence, this study proposed a deep learning method named time-wise convolutional neural network (TWCNN), which was applied to a BCI dataset. In the evaluation, EEG data from a subject was classified utilizing previously recorded EEG data from other subjects. As a result, TWCNN showed the highest accuracy, which was significantly higher than the typically used classifier. The results suggest that the deep learning method may be useful to reduce the recording time of training data.

scholarly journals Model Selection For Forecasting Rainfall Dataset

2021 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Amri Muhaimin ◽  
Hendri Prabowo ◽  
Suhartono
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Seasonal Pattern ◽  
Information Criteria ◽  
Rainfall Time Series ◽  
Machine Learning Method ◽  
Learning Method ◽  
Hourly Rainfall

The objective of this research is to obtain the best method for forecasting rainfall in the Wonorejo reservoir in Surabaya. Time series and causal approaches using statistical methods and machine learning will be compared to forecast rainfall. Time series regression (TSR), autoregressive integrated moving average (ARIMA), linear regression (LR), and transfer function (TF) are used as a statistical method. Feedforward neural network (FFNN) and deep feed-forward neural network (DFFNN) is used as a machine learning method. Statistical methods are used to capture linear patterns, whereas the machine learning method is used to capture nonlinear patterns. Data about hourly rainfall in the Wonorejo reservoir is used as a case study. The data has a seasonal pattern, i.e. monthly seasonality. Based on the cross-validation and information criteria, the results showed that DFFNN using the time series approach has a more accurate forecast than other methods. In general, machine learning methods have better accuracy than statistical methods. Furthermore, additional information is obtained, through this research the parameter that best to make a neural network model is known. Moreover, these results are also not in line with the results of M3 and M4 competition, i.e. more complex methods do not necessarily produce better forecasts than simpler methods.

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