scholarly journals Deep neural network model of haptic saliency

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna Metzger ◽  
Matteo Toscani ◽  
Arash Akbarinia ◽  
Matteo Valsecchi ◽  
Knut Drewing
Keyword(s):  
Neural Network ◽  
Spatial Distribution ◽  
Spatial Frequency ◽  
Surface Relief ◽  
Deep Neural Network ◽  
Physical Stimulus ◽  
Haptic Exploration ◽  
Variational Autoencoder ◽  
Stimulus Features ◽  
Physical Features

AbstractHaptic exploration usually involves stereotypical systematic movements that are adapted to the task. Here we tested whether exploration movements are also driven by physical stimulus features. We designed haptic stimuli, whose surface relief varied locally in spatial frequency, height, orientation, and anisotropy. In Experiment 1, participants subsequently explored two stimuli in order to decide whether they were same or different. We trained a variational autoencoder to predict the spatial distribution of touch duration from the surface relief of the haptic stimuli. The model successfully predicted where participants touched the stimuli. It could also predict participants’ touch distribution from the stimulus’ surface relief when tested with two new groups of participants, who performed a different task (Exp. 2) or explored different stimuli (Exp. 3). We further generated a large number of virtual surface reliefs (uniformly expressing a certain combination of features) and correlated the model’s responses with stimulus properties to understand the model’s preferences in order to infer which stimulus features were preferentially touched by participants. Our results indicate that haptic exploratory behavior is to some extent driven by the physical features of the stimuli, with e.g. edge-like structures, vertical and horizontal patterns, and rough regions being explored in more detail.

scholarly journals Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2528 ◽  
Author(s):  
Yanqing Yang ◽  
Kangfeng Zheng ◽  
Chunhua Wu ◽  
Yixian Yang
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Positive ◽  
Detection Rate ◽  
Deep Neural Network ◽  
Training Data ◽  
Fine Tuning ◽  
Detection Methods ◽  
Detection Rates ◽  
Variational Autoencoder

Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.

scholarly journals Unsupervised learning of haptic material properties

2021 ◽  
Author(s):  
Anna Metzger ◽  
Matteo Toscani
Keyword(s):  
Neural Network ◽  
Spatial Structure ◽  
Unsupervised Learning ◽  
Material Properties ◽  
Deep Neural Network ◽  
Perceptual System ◽  
Tactile Receptors ◽  
Haptic Exploration ◽  
Latent Space

AbstractWhen touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e. latent space) allows for classification of material categories (i.e. plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, distances between these categories in the latent space resemble perceptual distances, suggesting a similar coding. We could further show, that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
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