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Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly...
We present a transfer learning framework for no-reference image quality assessment (NRIQA) of tonemapped High Dynamic Range (HDR) images. This work is motivated by the observation that quality assessment databases in general, and HDR image databases in particular are “small” relative to the typical requirements for training deep neural networks. Transfer learning based approaches have been successful...
With the development of smart phones, more and more mobile phone malwares have came out in the market especially on the popular platforms such as Android, which can potentially cause harm to users' information. But how to effectively detect the new malwares and malicious software variants has been a difficult problem. In view of the traditional feature extraction method based on binary program, this...
In order to solve the problem of low intrusion detection rate and weak generalization ability of Intrusion Detection System (IDS), it proposes a new hybrid method based on the relationship of feature and spatial correlation for IDS. The proposed IDS reduces the dimension of network data flow by spatial correlation-based dimension reduction method (SCDR). It improves the effectiveness of intrusion...
The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
Recently, the multi-label learning has drawn considerable attention as it has many applications in text classification, image annotation and query/keyword suggestions etc. In recent years, a number of remedies have been proposed to address this challenging task. However, they are either tree based methods which has the expensive train costs or embedding based methods which has relatively lower accuracy...
This work presents methods to automatically find optimal parameter settings for convolutional neural networks (CNNs) by using an evolutionary algorithm called particle swarm optimization (PSO). Even though the parameter space is extremely large (> 10 20), we experimentally show that a better parameter setting can be found for Alexnet configuration for five different image datasets. We have also...
To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes...
Aiming at the problems of redundant construction, decentralized investment and extravagance in the current military resource allocation, this paper proposes a new combination evaluation model based on Analytic Hierarchy Process (AHP) and matter-element analysis. The model integrates the merits of AHP and matter-element analysis, and solves the common incompatibility problem of multi-index evaluation...
In this paper, we formed prediction intervals using historical similarities, found through the direct correlation. At first, a string of 5 to 20 recent samples is correlated with a long training string of samples. Then, the highest normalized correlation values and corresponding indexes are picked. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude...
For online car-hailing dispatch, we have presented Long Short-Term Memory neural networks (called LSTM) to forecast supply-demand gap. It is a new creative thinking to apply deep networks to model gap volatility, incorporating weather information, traffic condition and point of interest (POI) data, as well as twelve previous returns. As far as we know, this paper is the first attempt to train LSTM...
A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels...
This paper presents a demo proposal of a standalone smartphone application that can automatically analyse the signal quality of PCG, as it is recorded on a low-cost smartphonebased digital stethoscope. Features, related to the inherent pattern of the autocorrelated signal envelope, have been used for classifying and discarding the noisy portions from a continuous PCG. Our application has been successfully...
The present study used fuzzy triangulation number to analyze self-assessment (SA) of 86 nonnative novice and experienced Iranian English language teachers. Fuzzy triangulation number was used to provide a score for each item of a 7 scales SA questionnaire (SAQ). These fuzzy scores were then compared to the teachers' obtained scores in FCE test. The teachers' scores obtained from the listening and...
Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (MKL) attempt to learn the parameters for combining the kernels through sophisticated optimization procedures. In this paper, we propose an alternative approach that creates dense embeddings for data using the kernel similarities...
In this paper, we propose to extract robust video descriptor by training deep neural network to automatically capture the intrinsic visual characteristics of digital video. More specifically, we first train a conditional generative model to capture the spatio-temporal correlations among visual contents and represent them as an intermediate descriptor. A nonlinear encoder, with the functions of dimension...
Compressed beam-selection (CBS) exploits the limited scattering of the millimeter wave (mmWave) channel using compressed sensing and finds the best beam-pair with limited overhead. The CBS procedure can further benefit from the knowledge of some additional structure in the channel. As mmWave systems are envisioned to be deployed in conjunction with sub-6 GHz systems, we use the spatial information...
Hyperspectral images(HSIs) provide hundreds of narrow spectral bands for the land-covers, thus can provide more powerful discriminative information for the land-cover classification. However, HSIs suffer from the curse of high dimensionality, therefore dimension reduction and feature extraction are essential for the application of HSIs. In this paper, we propose an unsupervised feature extraction...
Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. In this paper, we propose a neural network-based multimodal data fusion framework named deep multimodal encoder (DME). Through our new objective function, both the intra- and inter-modal correlations of multimodal sensor data can be better exploited for recovering the missing values, and...
This paper presents a novel method for personalized video preference estimation based on early fusion using multiple users' viewing behavior. The proposed method adopts supervised Multi-View Canonical Correlation Analysis (sMVCCA) to estimate correlation between different types of features. Specifically, we estimate optimal projections maximizing the correlation between three features of video, target...
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