Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Towards quantum reversible ternary coded decimal adder

Quantum ternary logic is a promising emerging technology for the future quantum computing. Ternary reversible logic circuit design has potential advantages over the binary ones like its logarithmic reduction in the number of qudits. In reversible logic all computations are done in an invertible fashion. In this paper, we propose a new quantum reversible ternary half adder with quantum cost of only 7 and a new quantum ternary full adder with a quantum cost of only 14. We termed it QTFA. Then we propose 3-qutrit parallel adders. Two different structures are suggested: with and without input carry. Next, we propose quantum ternary coded decimal (TCD) detector circuits. Two different approaches are investigated: based on invalid numbers and based on valid numbers. Finally, we propose the quantum realization of TCD adder circuits. Also here, two approaches are discussed. Overall, the proposed reversible ternary full adder is the best between its counterparts comparing the figures of merits. The proposed 3-qutrit parallel adders are compared with the existing designs and the improvements are reported. On the other hand, this paper suggested the quantum reversible TCD adder designs for the first time. All the proposed designs are realized using macro-level ternary Toffoli gates which are built on the top of the ion-trap realizable ternary 1-qutrit gates and 2-qutrit Muthukrishnan–Stroud gates.     

Hierarchical Classifiers for Robust Topological Robot Localization

This paper presents a novel appearance-based technique for topological robot localization and place recognition. A vocabulary of visual words is formed automatically, representing local features that frequently occur in the set of training images. Using the vocabulary, a spatial pyramid representation is built for each image by repeatedly subdividing it and computing histograms of visual words at increasingly fine resolutions. An information maximization technique is then applied to build a hierarchical classifier for each class by learning informative features. While top-level features in the hierarchy are selected from the coarsest resolution of the representation, capturing the holistic statistical properties of the images, child features are selected from finer resolutions, encoding more local characteristics, redundant with the information coded by their parents. Exploiting the redundancy in the data enables the localization system to achieve greater reliability against dynamic variations in the environment. Achieving an average classification accuracy of 88.9% on a challenging topological localization database, consisting of twenty seven outdoor places, demonstrates the advantages of our hierarchical framework for dealing with dynamic variations that cannot be learned during training.     

A blur-robust descriptor with applications to face recognition

Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition by a fusion of image-formation models and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then, under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard data sets.     

Classification of primitive shapes using brain–computer interfaces

Brain–computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert–Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann–Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naïve classification rate of 20%) over ten subjects (accuracy range of 36%–54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.     

Failure analysis of unidirectional polymeric matrix composites with two serial pin loaded-holes

The objective of this study is to investigate the effects of geometrical and physical parameters on failure modes and failure loads in unidirectional polymeric matrix composites with two serial pin loaded holes, analytically and experimentally. It is assumed that all of unidirectional fibers in the laminate lie in one direction while loaded by a load p ₀ at infinity, parallel to the direction of the fibers. To derive equilibrium equations based on a Shear-Lag theory, a rectangular arrangement of fibers is considered and with the proper use of boundary and boundness conditions, stress and displacement fields are computed within the laminate, along with the surrounding pinholes. Finally by using the Hashin criterion failure modes and failure loads are estimated. To validate analytical results based on shear-lag theory, an experimental program is carried out. A very good agreement is observed between two procedures. Based on results, in small sizes of two pins, the dominant failure mode is bearing and with the increasing of hole sizes, failure modes are changed to tension and shear modes.     

Development of a void ratio-moisture ratio-net stress framework for the prediction of the volumetric behavior of unsaturated granular materials

Despite extensive research on the behavior of unsaturated fine-grained materials, there is still a lack of understanding of the volumetric behavior of unsaturated granular materials. In this research, a model has been developed to predict the fundamental volumetric behavior of unsaturated granular materials through loading and wetting state paths. In this regard, a loading-wetting surface was developed in a space of void ratio-moisture ratio-net stress. A distinctive feature of the proposed model is the relative simplicity in obtaining the model parameters using conventional geotechnical testing equipment. Two types of recycled granular materials, commonly applied in unbound pavements were used, namely, recycled crushed brick (CB) and excavation waste rock (WR). The uniqueness of the developed surface was evaluated by employing a number of loading and wetting state paths. The results indicate that the developed surface is unique in its loading state paths; however, it only shows uniqueness in its wetting state paths for stress levels greater than 2000 kPa. The proposed model seeks to introduce the application of the unsaturated soil mechanics theory, for predicting the behavior of granular materials in the field, by providing a practical and cost-effective methodology.     

Microstructural and precipitation characterization in Nb-Mo microalloyed steels: Estimation of the contributions to the strength

The influence of coiling temperature on the final microstructure and precipitation has been analyzed in several low carbon Nb and Nb-Mo microalloyed steels. A throughout characterization of the complex microstructures has been performed using electron backscattered diffraction, measuring low and high angle unit sizes, microstructural substructure, as well as quantifying the homogeneity. An important microstructural refinement is observed for all compositions as the coiling temperature decreases. Regarding precipitation, the coiling temperature strongly modifies the size and density of the fine precipitates, being 550 °C the optimal coiling temperature for the Nb-Mo steels. The addition of Mo to Nb steels provides a refinement of the precipitates and, therefore, enhances their contribution to strengthening. Considering all the microstructural and precipitation quantification data, the yield strength was estimated and the contribution of the different mechanisms calculated. The grain size contribution is proven to be the most important factor regarding strengthening, followed by dislocation density and precipitation especially at low coiling temperatures and Nb-Mo steels.     

High Gain Modified Dual-Band and Dual-Circularly Polarized Array Antenna

Wireless Personal Communications - A novel design of double-layer dual-band circularly polarized array antennas (DDCPAAs) is presented in this paper. First, a DDCP single antenna is introduced as...