The educational electronic book as a tool for supporting children’s emergent literacy in low versus middle SES groups

The effects of an educational electronic book (e-book) on 149 five- to six-year-old kindergarteners’ emergent literacy levels were researched in two SES groups: low (LSES) (79 children) vs. middle (MSES) (70 children). In each SES group, children were randomly assigned to four groups. Three groups were assigned to work individually in one of three e-book activity modes: “Read story only”, “Read with dictionary”, or “Read and play” during three similar activity sessions and the fourth group served as a control which received the regular program of the kindergarten. Pre- and post-intervention emergent literacy measures included word meaning, word recognition, and phonological awareness. Results show that word meaning of children from both middle and low SES improved following the educational e-book activity, regardless of mode. Second, LSES children’s emergent literacy levels showed relatively greater improvement rates than did those of the MSES children. Third, children in the “Read with dictionary” and “Read and play” activity modes showed more improvement in their emergent literacy levels than did those in the “Read story only” mode. Implications for future research and for education are discussed.     

Brain reading using full brain support vector machines for object recognition: there is no "face" identification area

Over the past decade, object recognition work has confounded voxel response detection with potential voxel class identification. Consequently, the claim that there are areas of the brain that are necessary and sufficient for object identification cannot be resolved with existing associative methods (e.g., the general linear model) that are dominant in brain imaging methods. In order to explore this controversy we trained full brain (40,000 voxels) single TR (repetition time) classifiers on data from 10 subjects in two different recognition tasks on the most controversial classes of stimuli (house and face) and show 97.4% median out-of-sample (unseen TRs) generalization. This performance allowed us to reliably and uniquely assay the classifier's voxel diagnosticity in all individual subjects' brains. In this two-class case, there may be specific areas diagnostic for house stimuli (e.g., LO) or for face stimuli (e.g., STS); however, in contrast to the detection results common in this literature, neither the fusiform face area nor parahippocampal place area is shown to be uniquely diagnostic for faces or places, respectively.     

Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

Spurious Valleys in the Error Surface of Recurrent Networks—Analysis and Avoidance

This paper gives a detailed analysis of the error surfaces of certain recurrent networks and explains some difficulties encountered in training recurrent networks. We show that these error surfaces contain many spurious valleys, and we analyze the mechanisms that cause the valleys to appear. We demonstrate that the principle mechanism can be understood through the analysis of the roots of random polynomials. This paper also provides suggestions for improvements in batch training procedures that can help avoid the difficulties caused by spurious valleys, thereby improving training speed and reliability.     

Addressing agent loss in vehicle formations and sensor networks

In this paper, we address the problem of agent loss in vehicle formations and sensor networks via two separate approaches: (1) perform a ‘self‐repair’ operation in the event of agent loss to recover desirable information architecture properties or (2) introduce robustness into the information architecture a priori such that agent loss does not destroy desirable properties. We model the information architecture as a graph G(V, E), where V is a set of vertices representing the agents and E is a set of edges representing information flow amongst the agents. We focus on two properties of the graph called rigidity and global rigidity, which are required for formation shape maintenance and sensor network self‐localization, respectively. For the self‐repair approach, we show that while previous results permit local repair involving only neighbours of the lost agent, the repair cannot always be implemented using only local information. We present new results that can be applied to make the local repair using only local information. We describe implementation and illustrate with algorithms and examples. For the robustness approach, we investigate the structure of graphs with the property that rigidity or global rigidity is preserved after removing any single vertex (we call the property as 2‐vertex‐rigidity or 2‐vertex‐global‐rigidity, respectively). Information architectures with such properties would allow formation shape maintenance or self‐localization to be performed even in the event of agent failure. We review a characterization of a class of 2‐vertex‐rigidity and develop a separate class, making significant strides towards a complete characterization. We also present a characterization of a class of 2‐vertex‐global‐rigidity. Copyright © 2008 John Wiley & Sons, Ltd.     

Study of folklore and mythological traditions using intellectual data mining

This work is devoted to the development and substantiation of intellectual data mining as applied to studying folklore and mythological traditions. The approach is based on use of the functions of distance between traditions. The examples of application of the methods developed to investigate the interrelation between folklore traditions of the American continent are considered.     

A method of the wavelet transformation for estimation of geometrical parameters upon the diagnostic images

A problem of analyzing blood vessels on retinal images is considered. We suggest a method for extracting vessels using a wavelet transform. The wavelet transform is modified by adding a wavelet rotation parameter and normalization with respect to the signal energy within the wavelet carrier limits. The new methods, for constructing the vessel central lines based on the modified wavelet transform, are being used for vessels geometrical parameters estimation. The parameters of vessels that are located at the edge of the optic nerve disk are then used while diagnostic criteria formation for eye-ground pathological changes determining.     

Interoperability of the national system of electronic digital signatures

The reasons of the absence of interoperability of the National system of electronic digital signatures (EDS) are analyzed based on systems analysis. An attempt is made to substantiate the minimax (minimum costs and maximum results) way of solving interoperability problems in EDS infrastructure in both Ukraine and foreign states.     

Methods of handling of in-bulk agglomeration layer image representation for granulometric composition assessment

The analysis of methods of images handling is carried out, the method of dynamic threshold division which is based on the calculation of weight coefficient of points in the local area is suggested. The method of adaptive adjustment of the size of the local area of the image representation is carried out to define the threshold value. Also methods of binary recursive filtration and mathematical morphology to reduce interference are introduced. The article is published in the original. Evgenii Valentinovich Ershov. The first pro-rector, head of the department of PC software, professor of the Cherepovetsk State University. Finished postgraduate studies at the North-Western Polytechnic Correspondence Institute (the city of St. Petersburg) in 1993 and defended a Ph.D. thesis on specialty 05.02.11 “Methods of Control and Diagnostics in Mechanical Engineering.” Circle of scientific interests: the development of methods and principles for the building of multi-functional technical vision systems, their mathematical and software support. Five postgraduate students defended theses under his supervision. Author of 170 scientific and teaching works, including 4 monographs, 85 articles, of which 12 were published in reviewed journals, 39 theses of reports, 2 reports on SRD, 3 patents. Took part in 20 scientific and technical conferences in Moscow, St. Petersburg, Cherepovetsk, Kursk, Tambov, Vologda, Tula, Krasnoyarsk, Yoshkar Ola, and Kemerovo. Vera Vital’evna Selivanovskikh. Docent of the department of PC software of the Cherepovetsk State University. Finished postgraduate studies at the Cherepovetsk State University on specialty “Solid-State Physics” in 2000 and began to study the problem of the optic-electronic control over the quality of metallurgical industry products. Defended a Ph.D. thesis on specialty 05.13.06 “Automation and Management of Technological Processes and Productions” in 2007. Author of more than 30 scientific and teaching works, including 1 monograph, 7 articles, of which 2 were published in reviewed journals, 13 theses of reports. Took part in the scientific and technical conferences in Moscow, Cherepovetsk, Kursk, Tambov, Vologda, Tula, Yoshkar Ola. Oksana Georgievna Ganicheva. Docent of the department of PC software of the Cherepovetsk State University. Finished postgraduate studies at the Cherepovetsk State University and defended a Ph.D. thesis on specialty 05.13.06 “Automation and Management of Technological Processes and Productions” in 2007. Circle of scientific interests: the development and perfecting of methods for the optic-electronic control over the quality of metallurgical industry products. Author of more than 30 scientific and teaching works, including 1 monograph, 7 articles, of which 2 were published in reviewed journals, 10 theses of reports. Took part in the scientific and technical conferences in Cherepovetsk, Kursk, Vologda, Tula, Yoshkar Ola.     

Sensor management for tracking smart targets

We consider the problem of tracking a “smart” target. That is, a target that is aware it is being tracked and modifies its behaviour accordingly. To track such targets effectively it is necessary to modify the behaviour of the tracking sensor in response to the target. We consider that the best framework for such a problem is that of a mathematical game. In this paper, we consider an idealised version of this problem that illustrates some of the issues that can occur when attempting to track a smart target and the utility of a game theoretic framework.