Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Tourism & Population. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

Progress on information and communication technologies in hospitality and tourism

Abstract: Purpose – The purpose of this paper is to establish the progress of information and communication technology (ICT) based on a review of papers published in tourism and hospitality journals between 2009 and 2013. Design/methodology/approach – Based on three major databases, 107 journal papers were retrieved and reviewed. The papers were grouped into two major categories, consumer and supplier, which generally comprise the key players in the industries. Findings – A content analysis showed that hospitality and tourism industries use ICT in different functional units and for different applications. This, in turn, indicates their wide adoption in the industry. Industrial implications are discussed. Practical implications – On the basis of the content analysis, industry practitioners can learn about up-to-date practices and decide how to take advantage of recent technological developments. Originality/value – A major contribution of this paper is the comprehensive review of recently published papers in tourism and hospitality journals from the perspectives of consumer and supplier.

Fast Compressive Tracking

Abstract: It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

Using support vector machines for long-term discharge prediction

Abstract: Accurate time- and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive moving-average (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river fl...