Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Demotivation: Understanding Resistance to English Language Learning - The Case of Vietnamese Students

Demotivation in English language learning was investigated, using Vietnam as a case study, with three main foci: (i) the reasons (i.e., the demotives) underlying demotivation; (ii) the degree of influence of different demotives; and (iii) students’ experiences in overcoming demotivation. Using stimulated recall essays from 100 university students of their foreign language learning experiences, the findings indicated that demotivation was a significant issue for EFL learning, and a framework for discussing the different sources of demotives was developed. While some categories of demotives occurred more frequent than others, no category appeared to be more or less difficult to overcome. Rather, students’ awareness of the role of English language and their determination to succeed were critical factors in overcoming demotivation.

Teacher Use of Learners' Native Language in the Foreign Language Classroom

This article reports on an exploratory study into the use of students' native language (NL) by teachers in the foreign language (FL) classroom. The project was undertaken by four teachers of beginner French at the University of Queensland. The teachers' aim was to investigate the use of NL in a context which actively promotes an immersion approach to FL teaching. The audio recordings of the teachers' speech were transcribed to provide data for estimating the amount of NL, and for analyzing the various instances of NL use. The study indicates that the activity type is a significant variable affecting NL amount. It also isolates two strategic uses of NL, translating FL words into NL, and contrasting NL and FL forms, both of which involve intrasentential code switching with NL words embedded in an FL sentence. The study suggests that these strategies may facilitate acquisition during immersion in FL, but experimental research is needed to test the hypothesis that translation and contrast facilitate learning of FL vocabulary and grammar.

Perceptions of kanji learning strategies: Do they differ among Chinese character and alphabetic background learners

This study investigates three important issues in kanji learning strategies; namely, strategy use, effectiveness of strategy and orthographic background. A questionnaire on kanji learning strategy use and perceived effectiveness was administered to 116 beginner level, undergraduate students of Japanese from alphabetic and character backgrounds in Australia. Both descriptive and statistical analyses of the questionnaire responses revealed that the strategies used most often are the most helpful. Repeated writing was reported as the most used strategy type although alphabetic background learners reported using repeated writing strategies significantly more often than character background learners. The importance of strategy training and explicit instruction of fundamental differences between character and alphabetic background learners of Japanese is discussed in relation to teaching strategies. [Author abstract]

Beyond the middle: A report about literacy and numeracy development of target group students in the middle years of schooling: Volume 1

The report was commissioned by the Department of Education, Science and Training to investigate the perceived efficacy of middle years programmes in all States and Territories in improving the quality of teaching, learning and student outcomes, especially in literacy and numeracy and for student members of particular target groups. These target groups included students from lower socio-economic communities, Aboriginal and Torres Strait Islander communities, students with a language background other than English, rural and remote students, and students struggling with the transition from middle/upper primary to the junior secondary years. The project involved large scale national and international literature reviews on Australian and international middle years approaches as well as an analysis of key literacy and numeracy teaching and learning strategies being used. In the report, there is emergent evidence of the relative efficacy of a combination of explicit state policy, dedicated funding and curriculum and professional development frameworks that are focused on the improvement of classroom pedagogy in the middle years. The programs that evidenced the greatest current and potential value for target group students tended to have developed in state policy environments that encouraged a structural rather than adjunct approach to middle years innovations. The authors conclude that in order to translate the gains made into sustainable improvement of educational results in literacy and numeracy for target groups, there is a need for a second generation of middle years theorising, research, development and practice.

Beyond the middle: A report about literacy and numeracy development of target group students in the middle years of schooling: Volume 2 (Appendices)

This research project was commissioned by the Commonwealth Department of Education, Science and Training to investigate the perceived efficacy of middle years programs in all States and Territories in improving the quality of teaching, learning and student outcomes - especially in literacy and numeracy and for student members of particular target groups. The latter groups included students from lower socio-economic communities, Aboriginal and Torres Strait Islander (Indigenous) communities, students with a Language Background Other than English (hereafter LBOTE), rural and remote students, and students struggling with the transition from middle/upper primary to the junior secondary years.

Does affective learning exist in the absence of contingency awareness?

The proposal that affective learning, the learning of likes and dislikes, can exist in the absence of contingency awareness, whereas signal learning, the learning of stimulus relationships, cannot, was investigated in a differential conditioning paradigm that was embedded in a visual masking task. Startle magnitude modulation and changes in verbal ratings served as measures of affective learning, whereas skin conductance was taken to reflect signal learning. Awareness was assessed online with an expectancy dial and in a postexperimental questionnaire. Both between-subject comparisons of verbalizers and nonverbalizers and within-subject comparisons of verbalizers before and after verbalization failed to reveal any evidence for learning, whether affective or otherwise, in the absence of knowledge of the stimulus contingencies. (C) 2001 Academic Press.

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.