Preserving spatial information and overcoming variations in appearance for face recognition

Local feature-based approaches mainly aim to achieve robustness to variations in facial images by assuming that only some parts of the facial images may be affected. However, such approaches may lose spatial information. In this study, a compromise feature extraction scheme is studied which extracts local features while preserving spatial information. The proposed scheme exploits an ensemble of classifiers where each member is constructed using randomly selected design parameters including the size, number and location of sub-images for local feature extraction. Experiments conducted on FERET and ORL databases have shown that proposed scheme surpasses the local feature-based reference systems which focus on either local information or preserving spatial information.     

Approximation by Stancu–Chlodowsky polynomials

In this paper, we investigate convergence and approximation properties of a Chlodowsky type generalization of Stancu polynomials (we called Stancu–Chlodowsky polynomials).The rates of convergence of this generalization are obtained by means of modulus of continuity and by using the $K$-functional of Peetre. We also present and prove theorems on weighted approximation and the order of approximation of continuous functions by these operators on all positive semi-axis.     

Schauder bases and approximation property in fuzzy normed spaces

Our aim in this article is to introduce and study the notion of weak and strong Schauder bases in fuzzy normed spaces. Further, we introduce strong and weak fuzzy approximation properties and set a relationship between these two new notions which may provide an acceleration to the structural analysis of fuzzy normed spaces.     

A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain

Cost estimation and effort allocation are the key challenges for successful project planning and management in software development. Therefore, both industry and the research community have been working on various models and techniques to accurately predict the cost of projects. Recently, researchers have started debating whether the prediction performance depends on the structure of data rather than the models used. In this article, we focus on a new aspect of data homogeneity, “cross- versus within-application domain”, and investigate what kind of training data should be used for software cost estimation in the embedded systems domain. In addition, we try to find out the effect of training dataset size on the prediction performance. Based on our empirical results, we conclude that it is better to use cross-domain data for embedded software cost estimation and the optimum training data size depends on the method used.     

Bandwidth-effective streaming of educational medical videos

Real-time delivery of medical videos requires high level of quality of service that shows no tolerance to loss and delay. In this study, how content-adaptive streaming can aid to maintain high-quality streaming sessions is investigated. The proposed strategy allocates a specific amount of bandwidth to streaming sessions and takes advantage of the general structure of instructional medical videos to decrease bandwidth consumption below this limit. The main contribution of the proposed mechanism is that it eliminates the need for rate adaptation which is common in traditional streaming by employing a proper bandwidth management scheme that enables constant bandwidth consumption below the allocated capacity. A streaming system based on the proposed mechanism has been implemented and its performance has been tested via emulation. Experimental results indicate that content-adaptive streaming successfully eliminates the need of rate adaptation during the delivery of critical data and hence preserves viewers’ satisfaction.     

Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming

Compressive strength and splitting tensile strength are both mechanical properties of concrete that are utilized in structural design. This study presents gene expression programming (GEP) as a new tool for the formulations of splitting tensile strength from compressive strength of concrete. For purpose of building the GEP-based formulations, 536 experimental data have been gathered from existing literature. The GEP-based formulations are developed for splitting tensile strength of concrete as a function of age of specimen and cylinder compressive strength. In experimental parts of this study, cylindrical specimens of 150 × 300 mm and 100 × 200 mm in dimensions are utilized. Training and testing sets of the GEP-based formulations are randomly separated from the complete experimental data. The GEP-based formulations are also validated with additional 173 data of experimental results other than the data used in training and testing sets of the GEP-based formulations. All of the results obtained from the GEP-based formulations are compared with the results obtained from experimental data, the developed regression-based formulation and formulas given by some national building codes. These comparisons showed that the GEP-based formulations appeared to well agree with the experimental data and found to be quite reliable.     

Derivation of empirical equations for neutronic performance in a thorium fusion breeder with various coolants using regression analysis

In this paper, regression analyses (RA) are presented for the neutronic calculation of ThO₂ mixed ²⁴⁴CmO₂ fuel with different neutronic parameters for various coolants, natural lithium, Li₂₀Sn₈₀ and Flinabe, respectively. The tritium breeding ratio (TBR), energy multiplication factor (M), total fission rate (Σ_f) and ²³²Th(n, γ) reaction is computed by XSDRNPM. In addition, this numerical results are estimated by RA depends on neutronic parameters and the empirical equations for neutronic performance are acquired. The results obtained by using XSDRNPM and the results of the RA, obtained empirical equations, are compared. The empirical equations indicate that RA can successfully be used for the prediction of the neutronic performance parameters in the hybrid reactor with a high degree of accuracy. In addition, correlation matrix is calculated to determined statistical relationships between variables TBR, M, Σ_f, and ²³²Th(n, γ).     

An interactive whiteboard student survey: Development,validity and reliability

The interactive whiteboard (IWB) has become a popular technology for instructors over the last decade. Though research asserts that the IWBs facilitate learning in different ways, there is a lack of studies examining actual IWB use in classroom settings based on learners’ perspectives by means of valid instruments. The purpose of this study is to develop a valid and reliable interactive whiteboard student survey in order to evaluate the IWB use based on perceptions of students who have been taught with IWBs. In establishing the theoretical base of the survey, the Technology Acceptance Model (TAM) and constructivist learning theories were considered. In addition, with respect to IWB use in classrooms, a number of studies emanated from countries such as the UK, the USA, and Australia were examined, and similar research questionnaires and findings and also reflections of students about IWBs were utilized to create an item pool. A 39-item survey was conducted among middle school students (N = 263) from the city of Istanbul in Turkey. Collected data was exposed to exploratory factor analysis and resulted in a 26-item, three-factor survey, whose factors were named as the perceived efficiency of IWB, perceived learning contribution and motivation, and the perceived negative effects of IWB. The results of the analyses illustrated that this new IWB student scale, which explains 50% of the total variance with a good level of Cronbach’s Alpha coefficient (0.93) is a valid and reliable instrument designed specifically for measuring the use of IWBs in real classroom settings. Besides, theoretical foundations of interactive whiteboard use, the development process of the instrument, and results of validity and reliability analyses were discussed in detail.     

A branch and bound algorithm for minimizing makespan on a single machine with unequal release times under learning effect and deteriorating jobs

We present a single-machine problem with the unequal release times under learning effect and deteriorating jobs when the objective is minimizing the makespan. In this study, we introduced a scheduling model with unequal release times in which both job deterioration and learning exist simultaneously. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and position in the sequence. A branch-and-bound algorithm incorporating with several dominance properties and lower bounds is developed to derive the optimal solution. A heuristic algorithm is proposed to obtain a near-optimal solution. The computational experiments show that the branch-and-bound algorithm can solve instances up to 30 jobs, and the average error percentage of the proposed heuristic is less than 0.16%.