The use of social and salience cues in early word learning

This article explores young infants' ability to learn new words in situations providing tightly controlled social and salience cues to their reference. Four experiments investigated whether, given two potential referents, 15-month-olds would attach novel labels to (a) an image toward which a digital recording of a face turned and gazed, (b) a moving image versus a stationary image, (c) a moving image toward which the face gazed, and (d) a gazed-on image versus a moving image. Infants successfully used the recorded gaze cue to form new word-referent associations and also showed learning in the salience condition. However, their behavior in the salience condition and in the experiments that followed suggests that, rather than basing their judgments of the words' reference on the mere presence or absence of the referent's motion, infants were strongly biased to attend to the consistency with which potential referents moved when a word was heard. (c) 2006 Elsevier Inc. All rights reserved.

Understanding your digital identity

The term “Digital Identity” is used here to describe the persona a person projects across the internet. Your Digital Identity as perceived by other people is made up of material that you post yourself (for example photographs on Flickr and your own web page) but it also is made up of material other people put there about you (blog posts that mention you, photographs in which you are tagged). The “This is Me” project has developed resources that can be used by students and others to appreciate what their Digital Identity is and how they can control it to help present the persona with the reputation that they want.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

Children with Special Educational Needs as film makers: Using digital video to support Understandings of Narrative

The literature has identified issues around transitions among phases for all pupils (Cocklin, 1999) including pupils with special educational needs (SEN) (Morgan 1999, Maras and Aveling 2006). These issues include pupils’ uncertainties and worries about building size and spatial orientation, exposure to a range of teaching styles, relationships with peers and older pupils as well as parents’ difficulties in establishing effective communications with prospective secondary schools. Research has also identified that interventions to facilitate these educational transitions should consider managerial support, social and personal familiarisation with the new setting as well as personalised learning strategies (BECTA 2004). However, the role that digital technologies can play in supporting these strategies or facilitating the role of the professionals such as SENCos and heads of departments involved in supporting effective transitions for pupils with SEN has not been widely discussed. Uses of ICT include passing references of student-produced media presentations (Higgins 1993) and use of photographs of activities attached to a timetable to support familiarisation with the secondary curriculum for pupils with autism (Cumine et al. 1998).

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

Experience of international students from developing countries in negotiating technology for learning

International students are important economically and culturally, bringing diversity and an international perspective enriching learning experiences in classrooms. With the global transformations eLearning has become an important element of students’ higher education experience in developed countries. Although students of developed countries have digital exposure at an early age, many students from developing countries, on the journey of becoming international students, are inadequately prepared for eLearning. The lack of digital skills, prior experience, cultural differences and language barriers together with the drastic changes in learning environments require international students to not only adapt to the host environment but also to negotiate technology for learning. The scarcity of research exploring the eLearning experiences of international students from developing countries and the benefits of this understanding is discussed in an effort to promote research in this area.

Educational inclusiveness through Ludic engagement and digital creativity

This paper describes an approach to teaching and learning that combines elements of ludic engagement, gamification and digital creativity in order to make the learning of a serious subject a fun, interactive and inclusive experience for students regardless of their gender, age, culture, experience or any disabilities that they may have. This approach has been successfully used to teach software engineering to first year students but could in principle be transferred to any subject or discipline.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

The use of ICT by adults with learning disabilities in day and residential services

The use of Information and Communication Technology (ICT) by adults with learning disabilities has been positively promoted over the past decade. More recently, policy statements and guidance from the UK government have underlined the importance of ICT for adults with learning disabilities specifically, as well as for the population in general, through the potential it offers for social inclusion. The aim of the present study was to provide a picture of how ICT is currently being used within one organisation providing specialist services for adults with learning disabilities and more specifically to provide a picture of its use in promoting community participation. Nine day and 14 residential services were visited as part of a qualitative study to answer three main questions: What kinds of computer programs are being used? What are they being used for? Does this differ between day and residential services? Computers and digital cameras were used for a wide range of activities and ‘mainstream’ programs were used more widely than those developed for specific user groups. In day services, ICT was often embedded in wider projects and activities, whilst use in houses was based around leisure interests. In both contexts, ICT was being used to facilitate communication, although this was more linked to within-service activities, rather than those external to service provision.

Sport and re/creation: what skateboarders can teach us about learning

This paper explores the role of digital media and creativity in the processes of learning that occur in groups of urban skateboarders. In particular, it examines how the production and consumption of amateur videos contribute to both skaters’ mastery of the techniques of the sport and their integration into the culture of the sport. The data come from an ethnographic study of skateboarders in Hong Kong, which included in-depth interviews, participant observation and the collection of texts and artifacts like magazines, blog entries and amateur skating videos. Skateboarders use video in a number of ways that significantly impact their learning and integration into their communities. They use it to analyze tricks and techniques, to document the stages of their learning and socialization into the group, to set community standards, to build a sense of belonging with their ‘crews’ and to imagine ‘idealized futures’ for themselves and their communities. Understanding the value and function of such ‘semiotic mediation’ in learning and socialization into sport cultures, I suggest, can contribute to helping physical educators design tasks that integrate training in physical skills with opportunities for students to make meaning around their experiences of sport and physical education.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.