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We consider the problem of learning a policy used by an agent in a Markov decision process using state-action samples. We focus on a class of parameterized policies and use ℓ1-regularized logistic regression to train a policy that best fits the observed state-action pairs (demonstrations). We bound the difference in average reward of the trained and the original policy (regret) in terms of the generalization...
The coefficient of high resolution (HR) block and low resolution (LR) block is assumed to be equal in selecting the corresponding atoms of HR dictionary in previous super-resolution (SR) reconstruction algorithms, which may cause error matching and decrease the accuracy of HR coefficient estimation. A learning method of structural dictionary and mapping relation (LCDMR) is combined to compensate this...
Traditional image aesthetic evaluation method usually involves the extraction of a set of relevant image aesthetic features and classification by a classifier trained on the set of features. The system's performance greatly depends on the effectiveness of the features. However, most of these features are carefully hand-crafted for specific datasets and assumed strong prior knowledge. Therefore, these...
Stroke is a growing global public health issue in low and middle income countries, where prevalence rates for cerebrovascular events now exceed those in industrialized nations. Limited access to stroke rehabilitation, which includes prevention of physical inactivity and deconditioning to better manage risk factors for recurrent vascular events, remains a major barrier to care. In the Caribbean Community,...
Planning in education is a process that can determine the success of learning. This has major impact in engineering education, where students have to develop complex competences in order to make decisions that can influence the world and humankind. Teachers are reluctant regarding the effective practice of planning educational processes, identifying the lack of time and appropriate pedagogical training...
In this paper, a joint channel estimation and turbo equalization scheme is proposed with low complexity and high accuracy for doubly selective fading channels. Based on the traditional data frame, by adding several pre-training sequences, the spectral efficiency is improved in the proposed two stage scheme. In the first stage, with the adaptive subblock-wise inverse QR recursive least square (IQR-RLS)...
As technology infiltrates every aspect of students' daily lives, game design acquires a bigger and more influential impact on shaping students' personalities and development of learning competencies. Considering the continuously increasing research on the use of educational games in classrooms, the researcher identifies another great interest in the constructivistic perspective of game design for...
Although the Filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) systems are robust against double selective fading channels, it unfortunately is still sensitive to timing offset and carrier frequency offset (CFO) errors. To solve this synchronization problem, in this paper, we propose a new data-aided timing and CFO synchronization scheme, that exploits the conjugate-symmetry...
Formal Verification is a computationally expensive step in the verification of today's complex hardware designs. Effective results can be obtained from formal runs by planning ahead the effort and cost that are required for them. Additionally estimating in-advance the expected formal's complexity promotes a lot of potential tricks and clever setup techniques to overcome the initial push-button capacity...
Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the training set which feature has higher ranking...
This paper proposes a detection technique based on factor graph (FG) to estimate the position of radio wave emitter. To obtain accurate estimation, we combine received signal strength (RSS) and direction of arrival (DOA) schemes into a single factor graph, called joint RSS-DOA, where soft information as mean and variance of the estimated target position are exchanged between the two schemes. The performance...
Brain-Computer Interface (BCI) is a direct communication pathway between brain and external devices bypassing the natural pathway of nerves and muscles. BCI enables an individual to send commands to a peripheral device using his brain activity. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. Feasibility of detecting familiar...
In this paper we present a novel approach to human-robot control. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, named KURE in this paper, with the ability to adapt both towards the user and the robotic morphology. The aim is for a transparent mechanism connecting user and robot, allowing for a seamless integration of control...
A novel method for nonlinear stochastic time-varying systems identification based on multi-dimensional Taylor network with optimal structure is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as the time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output...
The majority of computational work on emotion in music concentrates on developing machine learning methodologies to build new, more accurate prediction systems, and usually relies on generic acoustic features. Relatively less effort has been put to the development and analysis of features that are particularly suited for the task. The contribution of this paper is twofold. First, the paper proposes...
In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete...
Ventricular Tachycardia (VT) is a dangerous arrhythmic event which can lead to sudden cardiac death if not detected and taken care of in time. This work uses non-linear features derived from Recurrence Quantification Analysis (RQA) along with Kolmogorov complexity, by analyzing the ECG signals, to train a classifier which can predict VT prior to their onset in remote continuous health devices. Compressed...
Tools used by clinicians to diagnose and treat insomnia typically include sleep diaries and questionnaires. Overnight polysomnography (PSG) recordings are used when the initial diagnosis is uncertain due to the presence of other sleep disorders or when the treatment, either behavioral or pharmacologic, is unsuccessful. However, the analysis and the scoring of PSG data are time-consuming. To simplify...
Methods for action recognition have evolved considerably over the past years and can now automatically learn and recognize short term actions with satisfactory accuracy. Nonetheless, the recognition of complex activities - compositions of actions and scene objects - is still an open problem due to the complex temporal and composite structure of this category of events. Existing methods focus either...
In this paper modified enhanced fuzzy min max (modified-EFMMN) has been proposed for pattern classification. The objectives of modified-EFMM are firstly, to lift the classification accuracy, secondly to reduce the network complexity and thirdly to utilize minimum number of features to provide classification decision. The modified-EFMM handles overlap among the different class hyperbox more stringently,...
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