United Arab Emirates

The United Arab Emirates (UAE) is part of the geographic region known as the Middle East. With a land mass of 82,000 square kilometres, predominantly desert and mountains it is bordered by Oman, Saudi Arabia and the Arabian Gulf. The UAE is strategically located due to its proximity to other oil rich Middle Eastern countries such as Kuwait, Iraq, Iran, and Saudi Arabia. The UAE was formed from a federation of seven emirates (Abu Dhabi, Dubai, Sharjah, Ras Al Khaimah, Ajman, Fujuriah, and Um Al Quain) in December 1971 (Ras Al Khaimah did not join the federation until 1972) (Heard-bey, 2004, 370). Abu Dhabi is the political capital, and the richest emirate; while Dubai is the commercial centre. The majority of the population of the various Emirates live along the coast line as sources of fresh water often heavily influenced the site of different settlements. Unlike some near neighbours (Iran and Iraq) the UAE has not undergone any significant political instability since it was formed in 1971. Due to this early British influences the UAE has had very strong political and economic ties with first Britain, and, more recently, the United States of America (Rugh, 2007). Until the economic production of oil in the early 1960’s the different Emirates had survived on a mixture of primary industry (dates), farming (goats and camels), pearling and subsidies from Britain (Davidson 2005, 3; Hvit, 2007, 565) Along with near neighbours Kuwait, Bahrain, Oman, Qatar and Saudi Arabia, the UAE is part of the Gulf Cooperation Council (GCC), a trading bloc. (Hellyer, 2001, 166-168).

A human-simulated immune evolutionary computation approach

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

A variable step-size FxLMS algorithm for feedforward active noise control systems based on a new online secondary path modelling technique

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANC applications an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. This paper also proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Benefiting new version of FxLMS algorithm and not continually injection of white noise makes the system more desirable and improves the noise attenuation performance. Comparative simulation results indicate effectiveness of the proposed approach.

Population structure and patterns of genetic variation in a pearl oyster (Pinctada radiata) native to the Arabian Gulf

The study assessed natural levels and patterns of genetic variation in Arabian Gulf populations of a native pearl oyster to define wild population structure considering potential intrinsic and extrinsic factors that could influence any wild structure detected. The study was also the first attempt to develop microsatellite markers and to generate a genome survey sequence (GSS) dataset for the target species using next generation sequencing technology. The partial genome dataset generated has potential biotechnological applications and for pearl oyster farming in the future.

An international survey of daily disposable contact lens prescribing

Purpose The aim was to determine the extent of daily disposable contact lens prescribing worldwide and to characterise the associated demographics and fitting patterns. Methods Up to 1,000 survey forms were sent to contact lens fitters in up to 40 countries between January and March every year for five consecutive years (2007 to 2011). Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the survey form. Survey data collected since 1996 were also analysed for seven nations to assess daily disposable lens fitting trends since that time. Results Data were collected in relation to 97,289 soft lens fits, of which 23,445 (24.1 per cent) were with daily disposable lenses and 73,170 (75.9 per cent) were with reusable lenses. Daily disposable lens prescribing ranged from 0.6 per cent of all soft lenses in Nepal to 66.2 per cent in Qatar. Compared with reusable lens fittings, daily disposable lens fittings can be characterised as follows: older age (30.0 ± 12.5 versus 29.3 ± 12.3 years for reusable lenses); males are over-represented; a greater proportion of new fits versus refits; 85.9 per cent hydrogel; lower proportion of toric and presbyopia designs and a higher proportion of part-time wear. There has been a continuous increase in daily disposable lens prescribing between 1996 and 2011. The proportion of daily disposable lens fits (as a function of all soft lens fits) is positively related to the gross domestic product at purchasing power parity per capita (r2 = 0.55, F = 46.8, p < 0.0001). Conclusions The greater convenience and other benefits of daily disposable lenses have resulted in this modality capturing significant market share. The contact lens field appears to be heading toward a true single-use-only, disposable lens market.

Respite & Recoery at Al Wakra

International Design Competition for Qatar Psychiatric Hospital. The scheme for the Al Wakra Respite and Recovery Centre delivers on an all-in attitude toward evidence-based design. It sets new benchmarks in so many ways: the way it allows excellent separation between patient cohorts without unnecessary or visible restrictions; the way it allows sharing of most of the clinical kit and spaces; the way services reticulation and facilities management takes place without compromising security and safety; the ways It abandons the institutional axioms that are still so ubiquitous elsewhere, so it can appear as the friendly, welcoming and wholesome; the way it allows incredible flexibility to allow changes or flexion on the fly; the way it has such ‘good bones’ for more structural changes as the future unfolds. But most importantly, the scheme will be exemplary in the way the building itself plays a role in promoting the recovery and mental well-being of its residents. Like no other, the Centre will rise to the challenges of supporting and inspiring an exemplary mental health service and promote the well-being of the patients. The 160 bed scheme allows for 43,000m2 of landscape, packed with wholesome things to do and experience. The Aspire zone even has stables and a falconry, both to celebrate the love that Qatari people have for horses and birds.

Obtaining single document summaries using latent dirichlet allocation

In this paper, we present a novel approach that makes use of topic models based on Latent Dirichlet allocation(LDA) for generating single document summaries. Our approach is distinguished from other LDA based approaches in that we identify the summary topics which best describe a given document and only extract sentences from those paragraphs within the document which are highly correlated given the summary topics. This ensures that our summaries always highlight the crux of the document without paying any attention to the grammar and the structure of the documents. Finally, we evaluate our summaries on the DUC 2002 Single document summarization data corpus using ROUGE measures. Our summaries had higher ROUGE values and better semantic similarity with the documents than the DUC summaries.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Discriminative feature analysis and selection for document classification

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.

Feature selection for unsupervised learning

In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor search using all the features we need to select relevant features. We propose feature selection using Non-negative Matrix Factorization(NMF) and its application to nearest neighbor search. Recent clustering algorithm based on Locally Consistent Concept Factorization(LCCF) shows better quality of document clustering by using local geometrical and discriminating structure of the data. By using our feature selection method we have shown further improvement of performance in the clustering.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.