Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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22 Mar 2010
TL;DR: The feasibility of performing impersonation attacks on the modulation-based and transient-based fingerprinting techniques are studied to improve access control in wireless networks, revent device cloning and complement message authentication protocols.
Abstract: Physical-layer identification of wireless devices, commonly referred to as Radio Frequency (RF) fingerprinting, is the process of identifying a device based on transmission imperfections exhibited by its radio transceiver. It can be used to improve access control in wireless networks, revent device cloning and complement message authentication protocols. This paper studies the feasibility of performing impersonation attacks on the modulation-based and transient-based fingerprinting techniques. Both techniques are vulnerable to impersonation attacks; however, transient-based techniques are more difficult to reproduce due to the effects of the wireless channel and antenna in their recording process. We assess the feasibility of performing impersonation attacks by extensive measurements as well as simulations using collected data from wireless devices. We discuss the implications of our findings and how they affect current device identification techniques and related applications.
220 citations
TL;DR: It is concluded that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB and suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.
Abstract: Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.
219 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Sigma is a parameter of the radial basis function: a smaller sigma corresponds to fewer support vectors, which affects model training and prediction accuracy [58]....
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TL;DR: A new deep learning approach to predict brain age from a T1-weighted MRI is presented and a GWAS of the difference between predicted and chronological age is carried out, revealing two associated variants.
Abstract: Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: $$N=12378$$, replication set: $$N=4456$$) yielded two sequence variants, rs1452628-T ($$\beta =-0.08$$, $$P=1.15\times{10}^{-9}$$) and rs2435204-G ($$\beta =0.102$$, $$P=9.73\times 1{0}^{-12}$$). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.
218 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...In regard to Gaussian processes regression (GPR) and relevance vector regression (RVR), the main reason why we originally only included SVR but not GPR and RVR was because all three of these methods are kernel regression methods [1] and we did not expect to see a large difference in accuracy between them....
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10 Aug 2008
TL;DR: It is shown how classical statistical tools such as Principal Component Analysis and Fisher Linear Discriminant Analysis can be used for efficiently preprocessing the leakage traces and evaluates the effectiveness of two data dimensionality reduction techniques for constructing subspace-based template attacks.
Abstract: The power consumption and electromagnetic radiation are among the most extensively used side-channels for analyzing physically observable cryptographic devices. This paper tackles three important questions in this respect. First, we compare the effectiveness of these two side-channels. We investigate the common belief that electromagnetic leakages lead to more powerful attacks than their power consumption counterpart. Second we study the best combination of the power and electromagnetic leakages. A quantified analysis based on sound information theoretic and security metrics is provided for these purposes. Third, we evaluate the effectiveness of two data dimensionality reduction techniques for constructing subspace-based template attacks. Selecting automatically the meaningful time samples in side-channel leakage traces is an important problem in the application of template attacks and it usually relies on heuristics. We show how classical statistical tools such as Principal Component Analysis and Fisher Linear Discriminant Analysis can be used for efficiently preprocessing the leakage traces.
218 citations
Cites background from "Pattern Recognition and Machine Lea..."
...A natural extension (described in the next section) is to exploit LDA [5,10], which allows projecting the traces in a subspace that maximizes the ratio between the inter- and intra-class variance....
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TL;DR: A deep neural network-based car-following model that takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs and tries to embed prediction capability or memory effect of human drivers in a natural and efficient way.
Abstract: In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers’ actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.
218 citations
Cites background from "Pattern Recognition and Machine Lea..."
...2) The Type of Transfer Function: As suggested in many literatures [31], we choose the sigmoid function as the transfer...
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...There exist many types of neural networks, but their basic principles are very similar [31]....
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