Deficits in orthographic knowledge in children poor at rapid automatized naming (RAN) tasks?

The degree to which orthographic knowledge accounts for the link between Rapid Automatized Naming (RAN) and reading is contested, with mixed results reported. This longitudinal study compared two groups of 10-11 year old children, a low RAN group (N=69) and matched controls (N=74) on various measures of orthographic knowledge. The low RAN group showed a deficit in orthographic knowledge, both at the level of sub-word letter sequences and of whole words, as well as an unexpected strength in orthographic learning. Our findings underline the persistence of RAN-related reading problems, and raise questions about reading strategies in this group.

An experimental comparison between rival theories of rapid automatized naming performance and its relationship to reading

Two studies investigated the degree to which the relationship between Rapid Automatized Naming (RAN) performance and reading development is driven by shared phonological processes. Study 1 assessed RAN, phonological awareness and reading performance in 1010 children aged 7-10 years. Results showed that RAN deficits occurred in the absence of phonological awareness deficits. These were accompanied by modest reading delays. In structural equation modeling, solutions where RAN was subsumed within a phonological processing factor did not provide a good fit to the data, suggesting that processes outside phonology may drive RAN performance and its association with reading. Study 2 investigated Kail's (1991) proposal that speed of processing underlies this relationship. Children with single RAN deficits showed slower speed of processing than closely matched controls performing normally on RAN. However, regression analysis revealed that RAN made a unique contribution to reading even after accounting for processing speed. Theoretical implications are discussed.

State-of-the-art for entity-centric repository and authoring environment for multimedia

Many projects, e.g. VIKEF [13] and KIM [7], present grounded approaches for the use of entities as a means of indexing and retrieval of multimedia resources from heterogeneous sources. In this paper, we discuss the state-of-the-art of entity-centric approaches for multimedia indexing and retrieval. A summary of projects employing entity-centric repositories are portrayed. This paper also looks at the current state-of-the-art authoring environment, Macromedia Authorware, and the possibility of potential extension of this environment for entity-based multimedia authoring.

Semi-automatic annotation and retrieval of visual content using the topic map technology

There are still major challenges in the area of automatic indexing and retrieval of multimedia content data for very large multimedia content corpora. Current indexing and retrieval applications still use keywords to index multimedia content and those keywords usually do not provide any knowledge about the semantic content of the data. With the increasing amount of multimedia content, it is inefficient to continue with this approach. In this paper, we describe the project DREAM, which addresses such challenges by proposing a new framework for semi-automatic annotation and retrieval of multimedia based on the semantic content. The framework uses the Topic Map Technology, as a tool to model the knowledge automatically extracted from the multimedia content using an Automatic Labelling Engine. We describe how we acquire knowledge from the content and represent this knowledge using the support of NLP to automatically generate Topic Maps. The framework is described in the context of film post-production.

Parallel Monte Carlo algorithms for information retrieval

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Using the web graph to influence application behaviour

The Web's link structure (termed the Web Graph) is a richly connected set of Web pages. Current applications use this graph for indexing and information retrieval purposes. In contrast the relationship between Web Graph and application is reversed by letting the structure of the Web Graph influence the behaviour of an application. Presents a novel Web crawling agent, AlienBot, the output of which is orthogonally coupled to the enemy generation strategy of a computer game. The Web Graph guides AlienBot, causing it to generate a stochastic process. Shows the effectiveness of such unorthodox coupling to both the playability of the game and the heuristics of the Web crawler. In addition, presents the results of the sample of Web pages collected by the crawling process. In particular, shows: how AlienBot was able to identify the power law inherent in the link structure of the Web; that 61.74 per cent of Web pages use some form of scripting technology; that the size of the Web can be estimated at just over 5.2 billion pages; and that less than 7 per cent of Web pages fully comply with some variant of (X)HTML.

Component processes of early reading, spelling, and narrative writing skills in Turkish: a longitudinal study

The study examined: (a) the role of phonological, grammatical, and rapid automatized naming (RAN) skills in reading and spelling development; and (b) the component processes of early narrative writing skills. Fifty-seven Turkish-speaking children were followed from Grade 1 to Grade 2. RAN was the most powerful longitudinal predictor of reading speed and its effect was evident even when previous reading skills were taken into account. Broadly, the phonological and grammatical skills made reliable contributions to spelling performance but their effects were completely mediated by previous spelling skills. Different aspects of the narrative writing skills were related to different processing skills. While handwriting speed predicted writing fluency, spelling accuracy predicted spelling error rate. Vocabulary and working memory were the only reliable longitudinal predictors of the quality of composition content. The overall model, however, failed to explain any reliable variance in the structural quality of the compositions

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.

Reading comprehension in autism spectrum disorders: The role of oral language and social functioning

Reading comprehension is an area of difficulty for many individuals with autism spectrum disorders (ASD). According to the Simple View of Reading, word recognition and oral language are both important determinants of reading comprehension ability. We provide a novel test of this model in 100 adolescents with ASD of varying intellectual ability. Further, we explore whether reading comprehension is additionally influenced by individual differences in social behaviour and social cognition in ASD. Adolescents with ASD aged 14-16 years completed assessments indexing word recognition, oral language, reading comprehension, social behaviour and social cognition. Regression analyses show that both word recognition and oral language explain unique variance in reading comprehension. Further, measures of social behaviour and social cognition predict reading comprehension after controlling for the variance explained by word recognition and oral language. This indicates that word recognition, oral language and social impairments may constrain reading comprehension in ASD.

Coargumenthood and the processing of reflexives

We report three eye-movement experiments and an antecedent choice task investigating the interpretation of reflexives in different syntactic contexts. This included contexts in which the reflexive and a local antecedent were coarguments of the same verbal predicate (John heard that the soldier had injured himself), and also so-called picture noun phrases, either with a possessor (John heard about the soldier’s picture of himself) or without (John heard that the soldier had a picture of himself). While results from the antecedent choice task indicated that comprehenders would choose a nonlocal antecedent (‘John’ above) for reflexives in either type of picture noun phrase, the eye-movement experiments suggested that participants preferred to initially interpret the reflexive in each context as referring to the local antecedent (‘the soldier’), as indexed by longer reading times when it mismatched in gender with the reflexive. We also observed a difference in the time-course of this effect. While it was observed during first-pass processing at the reflexive for coargument reflexives and those in picture noun phrases with a possessor, it was comparatively delayed for reflexives in possessorless picture noun phrases. These results suggest that locality constraints are more strongly weighted cues to retrieval than gender agreement for both coargument reflexives and those inside picture noun phrases. We interpret the observed time-course differences as indexing the relative ease of accessing the local antecedent in different syntactic contexts.