Enabling user interface developers to experience accessibility limitations through visual,hearing, physical and cognitive impairment simulation

His paper presents a tool facilitating developers of user interfaces (UIs) to experience accessibility limitations that can be posed from various disabilities during the interaction of impaired users with their developments. In this respect, various aspects of visual, hearing, physical and cognitive impairments have been modelled through filters providing approximate, yet, realistic simulations over them. These filters have formed the basis for the developed tool, which can be used either on its own (as a standalone application), or be embedded in the NetBeans Integrated Development Environment. The tool, named DIAS, allows for impairment simulations to be performed over Java, mobile and web applications. Moreover, it integrates two of the most common assistive technologies (ATs), namely a screen reader and a magnifier. As a result, developers of UIs can not only experience how interaction would be affected from various impairments, but they can also understand how their developments would be perceived by impaired users through an AT. This work aims to provide an integrated, practical solution for impairment simulation, which could be easily adopted by developers, thus realistically increasing the possibilities for the future development of interactive applications that are more accessible to users with disabilities.     

Hand dexterity assessment based on mouse pointer trajectory measurements in children with learning disabilities

Universal Access in the Information Society - We present an investigation of the systematic differences in upper limb motor skills between children with learning disabilities (LDs) and children...     

A knowledge-based approach to user interface adaptation from preferences and for special needs

Moving between devices is omnipresent, but not for people with disabilities or those who require specific accessibility options. Setting up assistive technologies or finding settings to overcome a certain barrier can be a demanding task for people without technical skills. Context-sensitive adaptive user interfaces are advancing, although migrating access features from one device to another is very rarely addressed. In this paper, we describe the knowledge-based component of the Global Public Inclusive Infrastructure that infers how a device shall be best configured at the operating system layer, the application layer and the web layer to meet the requirements of a user including possible special needs or disabilities. In this regard, a mechanism to detect and resolve conflicting accessibility policies as well as recommending preference substitutes is a main requirement, as elaborated in this paper. As the proposed system emulates decision-making of accessibility experts, we validated the automatic deduced configurations against manual configurations of ten accessibility experts. The assessment result shows that the average matching score of the developed system is high. Thus, the proposed system can be considered capable of making precise decisions towards personalizing user interfaces based on user needs and preferences.     

The impact of computer self-efficacy,situational interest and academic self-concept in virtual communities of inquiry during the distance learning procedures through Second Life

The current study investigates a case where the online learning procedure in three-dimensional (3D) technologically-advanced environments of the Web 2.0 is growing at an exponential rate. In this occasion it is highly imperative need to understand students’ interactions in this innovative mode of e-Education that requires from educators and scholars not only analysis conceptually, but also an empirically-driven optimization. The community of inquiry (CoI) model (or framework) consists to be as one of the most prominent multi-dimensional constructs that it is widely used to represent several distinct dimensions of social presence, teaching presence and cognitive presence, as a unique and fundamental theoretical concept to measure students’ interactions in contemporary electronic environments. Although, the effectiveness of these multi-dimensional constructs creates a dilemma to researchers who want the breadth and comprehensiveness of this model for the precision and clarity of users’ (instructors and students) dimensions with other motivational and learning variables. To address this dilemma, the current empirical study presents statistical analyses from the “trinity” constructs of the CoI model by utilizing correlation and hierarchical regression analyses with two fundamental motivational (computer self-efficacy and situational interest) and another one learning (academic self-concept) variables. This study goes one step further and introduces the conspicuously indisputable intervention of a virtual (V)CoI and its utilization in multi-user virtual worlds, like Second Life (SL). The study findings of one hundred thirty-five (135) participants who enrolled in several online sessions unveiled that the situational interest was the only significant predictor of social presence. The computer self-efficacy was not a significant predictor of the CoI model, while on the other hand academic self-concept was a significant predictor in a revamped attempt to validate the strong relationship among constructs within it. According to the aforementioned reasons, it can be surmised that the successful combination of the VCoI in Second Life, surpassing irrefutable and inherent shortcomings to a future-driven sustainable use and growth.     

A New Methodology for Efficient Classification of Multispectral Satellite Images Using Neural Network Techniques

A methodology based on self-organizing feature maps and indexing techniques for time and memory efficient neural network training and classification of large volumes of remotely sensed data is presented. Results on land-cover classification of multispectral satellite images using two popular neural models show orders of magnitude of speedup with respect to both training and classification times. The generality of the proposed methodology is demonstrated with a dramatic improvement of the classification time of the k-nearest neighbors statistical classifier.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Comparing hybrid learning with traditional approaches on learning the Microsoft Office Power Point 2003 program in tertiary education

The purpose of this study was to determine the effectiveness of a hybrid learning approach to deliver a computer science course concerning the Microsoft office PowerPoint 2003 program in comparison to delivering the same course content in the form of traditional lectures. A hundred and seventy-two first year university students were randomly assigned into two teaching method groups: traditional lecture instruction (TLI) and hybrid lecture instruction (HLI). Each group received six 95-min periods of instruction divided into 4 sections: a) 5-min brief outline of the key learning points, b) 40-min lecture on general knowledge c) 45-min constructivist-inspired learning activities and d) 5-min summary on key learning points. In the beginning and the end of this study students completed a 17-item multiple choice knowledge test. Two-way analysis of variances (ANOVA), with repeated measures on the last factor, were conducted to determine effect of method groups (TLI, HLI) and measures (pre-test, post-test) on knowledge test. The measures main effect was significant, as well as the groups x measures interaction effect. Two independent-samples t test were conducted to follow up the significant interaction. Differences in mean ratings of knowledge performance between the two teaching groups were not significantly different at first measure, while the TLI method group yielded a significantly lower mean rating at second measure. The findings indicated that HLI approach might be a superior option for undergraduate students on learning the Microsoft office PowerPoint 2003 program.     

Kernel-based classification using quantum mechanics

This paper introduces a new nonparametric estimation approach inspired from quantum mechanics. Kernel density estimation associates a function to each data sample. In classical kernel estimation theory the probability density function is calculated by summing up all the kernels. The proposed approach assumes that each data sample is associated with a quantum physics particle that has a radial activation field around it. Schrödinger differential equation is used in quantum mechanics to define locations of particles given their observed energy level. In our approach, we consider the known location of each data sample and we model their corresponding probability density function using the analogy with the quantum potential function. The kernel scale is estimated from distributions of K-nearest neighbours statistics. In order to apply the proposed algorithm to pattern classification we use the local Hessian for detecting the modes in the quantum potential hypersurface. Each mode is assimilated with a nonparametric class which is defined by means of a region growing algorithm. We apply the proposed algorithm on artificial data and for the topography segmentation from radar images of terrain.     

Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval

An efficient novel strategy for color-based image retrieval is introduced. It is a hybrid approach combining a data compression scheme based on self-organizing neural networks with a nonparametric statistical test for comparing vectorial distributions. First, the color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network. The similarity between two images is then assessed as commonality between the corresponding representative color distributions and quantified using the multivariate Wald–Wolfowitz test. Experimental results drawn from the application to a diverse collection of color images show a significantly improved performance (approximately 10–15% higher) relative to both the popular, simplistic approach of color histogram and the sophisticated, computationally demanding technique of Earth Mover’s Distance.     

Experimental Validation of a Helicopter Autopilot Design using Model-Based PID Control

Autonomous helicopter flight provides a challenging control problem. In order to evaluate control designs, an experimental platform must be developed in order to conduct flight tests. However, the literature describing existing platforms focuses on the hardware details, while little information is given regarding software design and control algorithm implementation. This paper presents the design, implementation, and validation of an experimental helicopter platform with a primary focus on a software framework optimized for controller development. In order to validate the operation of this platform and provide a basis for comparison with more sophisticated nonlinear designs, a PID controller with feedforward gravity compensation is derived using the generally accepted small helicopter model and tested experimentally.