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In recent years, various approaches have been investigated towards blind image quality assessment (IQA) with high accuracy and low complexity. In this paper we develop a pre-saliency map based blind IQA method, which takes advantage of saliency information in prior of quality prediction for performance enhancement by two steps. 1) We split the image into patches and design a convolution neural network...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
This article examines the problems of distance learning and one of their possible solution. An adaptive learning system is described that allows to provide an individual approach when passing a course of study. An example of the response of an adaptive learning system to the actions of students while performing tasks and its proposal for a level change is given.
Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Material-based computation is treated as an optimisation...
The implementation of channel estimation procedures in modern wireless networks exposes them to the risk of pilot contamination attacks. To protect the system from such types of malicious intervention, a recently proposed method, based on legitimate pilots from shifted constellations, can be applied. In this paper, the detection capability of this method is studied in both the presence and absence...
What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in videos. The goal of this paper is to examine datasets, evaluation metrics, algorithms, and potential future directions. We look at the qualitative attributes that define activities such as pose variability, brevity, and density...
Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a...
Riding on the waves of deep neural networks, deep metric learning has achieved promising results in various tasks by using triplet network or Siamese network. Though the basic goal of making images from the same category closer than the ones from different categories is intuitive, it is hard to optimize the objective directly due to the quadratic or cubic sample size. Hard example mining is widely...
Generalized frequency division multiplexing (GFDM) with the flexible structure is one of the promising candidates for the fifth generation wireless communication system. This paper focuses on training sequence design that is used in the estimation of in-phase(I) and quadrature(Q) imbalance parameters on GFDM receivers as well as frequency selective channel. Combining with the structure of low complexity...
Discriminative clustering has been successfully applied to a number of weakly supervised learning tasks. Such applications include person and action recognition, text-to-video alignment, object co-segmentation and co-localization in videos and images. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based...
Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations...
Understanding human activity and being able to explain it in detail surpasses mere action classification by far in both complexity and value. The challenge is thus to describe an activity on the basis of its most fundamental constituents, the individual postures and their distinctive transitions. Supervised learning of such a fine-grained representation based on elementary poses is very tedious and...
A novel projection twin support vector machine (PTSVM), termed as NPTSVM, is presented in this paper for binary classification. Although this method determines two projection vectors using the same way as PTSVM, it has more advantages than existing PTSVMs. First, NPTSVM does not have to calculate inverse matrices during the learning process, which makes the training speed of NPTSVM be much faster...
Previous research has shown how developers "selfadmit" technical debt introduced in the source code, commenting why such code represents a workaround or a temporary, incomplete solution. This paper investigates the extent to which previously self-admitted technical debt can be used to provide recommendations to developers when they write new source code, suggesting them when to "self-admit"...
Image classification domain has been an area which has attracted a lot of researchers over past years. Many classification methodologies for spatial image datasets has been developed. Artificial intelligence based approaches are getting popular now a days for getting the image classification task done in more efficient and correct way. The prime goal is to develop a classification mechanism which...
State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in...
In this paper, the development of Multilingual Phone Recognition System (MPRS) in the context of Indian languages is described. MPRS is a language independent Phone Recognition System (PRS) that could recognise the phonetic units present in a speech utterance of any language. We have developed two Bilingual and a quadrilingual PRS using four Indian languages — Kannada, Telugu, Bengali, and Odia. International...
In advanced wireless communication systems that require spectrally efficient modulation schemes, the modulated signal with a high peak-to-average power ratio (PAPR) drives the power amplifier (PA) to operate near the saturation region and introduces serious nonlinearity of the PA. Digital predistortion (DPD) is one of the most promising techniques for PA linearization. In this paper, we propose a...
Parallel and distributed processing is employed to accelerate training for many deep-learning applications with large models and inputs. As it reduces synchronization and communication overhead by tolerating stale gradient updates, asynchronous stochastic gradient descent (ASGD), derived from stochastic gradient descent (SGD), is widely used. Recent theoretical analyses show ASGD converges with linear...
This paper describes a fast and accurate semantic image segmentation approach that encodes not only segmentation-specified features but also high-order context compatibilities and boundary guidance constraints. We introduce a structured patch prediction technique to make a trade-off between classification discriminability and boundary sensibility for features. Both label and feature contexts are embedded...
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