Language-specific aspects of reading acquisition: the case of Russian

We have investigated Russian children’s reading acquisition during an intermediate period in their development: after literacy onset, but before they have acquired well-developed decoding skills. The results of our study suggest that Russian first graders rely primarily on phonemes and syllables as reading grain-size units. Phonemic awareness seems to have reached the metalinguistic level more rapidly than syllabic awareness after the onset of reading instruction, the reversal which is typical for the initial stages of formal reading instruction creating external demand for phonemic awareness. Another reason might be the inherent instability of syllabic boundaries in Russian. We have shown that body-coda is a more natural representation of subsyllabic structure in Russian than onset-rime. We also found that Russian children displayed variability of syllable onset and offset decisions which can be attributed to the lack of congruence between syllabic and morphemic word division in Russian. We suggest that fuzziness of syllable boundary decisions is a sign of the transitional nature of this stage in the reading development and it indicates progress towards an awareness of morphologically determined closed syllables. Our study also showed that orthographic complexity exerts an influence on reading in Russian from the very start of reading acquisition. Besides, we found that Russian first graders experience fluency difficulties in reading orthographically simple words and nonwords of two and more syllables. The transition from monosyllabic to bisyllabic lexical items constitutes a certain threshold, for which the syllabic structure seemed to be of no difference. When we compared the outcomes of the Russian children with the ones produced by speakers of other languages, we discovered that in the tasks which could be performed with the help of alphabetic recoding Russian children’s accuracy was comparable to that of children learning to read in relatively shallow orthographies. In tasks where this approach works only partially, Russian children demonstrated accuracy results similar to those in deeper orthographies. This pattern of moderate results in accuracy and excellent performance in terms of reaction times is an indication that children apply phonological recoding as their dominant strategy to various reading tasks and are only beginning to develop suitable multiple strategies in dealing with orthographically complex material. The development of these strategies is not completed during Grade 1 and the shift towards diversification of strategies apparently continues in Grade 2.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Early-onset and late-onset Alzheimer’s disease are associated with distinct patterns of memory impairment

The goal of this study was to investigate the specific patterns of memory breakdown in patients suffering from early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD). Twenty EOAD patients, twenty LOAD patients, twenty matched younger controls, and twenty matched older controls participated in this study. All participants underwent a detailed neuropsychological assessment, an MRI scan, an FDG-PET scan, and AD patients had biomarkers as supporting evidence of both amyloïdopathy and neuronal injury. Results of the neuropsychological assessment showed that both EOAD and LOAD groups were impaired in the domains of memory, executive functions, language, praxis, and visuoconstructional abilities, when compared to their respective control groups. EOAD and LOAD groups, however, showed distinct patterns of memory impairment. Even though both groups were similarly affected on measures of episodic, short term and working memory, in contrast semantic memory was significantly more impaired in LOAD than in EOAD patients. The EOAD group was not more affected than the LOAD group in any memory domain. EOAD patients, however, showed significantly poorer performance in other cognitive domains including executive functions and visuoconstructional abilities. A more detailed analysis of the pattern of semantic memory performance among patient groups revealed that the LOAD was more profoundly impaired, in tasks of both spontaneous recall and semantic recognition. Voxel-Based Morphometry (VBM) analyses showed that impaired semantic performance in patients was associated with reduced gray matter volume in the anterior temporal lobe region, while PET-FDG analyses revealed that poorer semantic performance was associated with greater hypometabolism in the left temporoparietal region, both areas reflecting key regions of the semantic network. Results of this study indicate that EOAD and LOAD patients present with distinct patterns of memory impairment, and that a genuine semantic impairment may represent one of the clinical hallmarks of LOAD.

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Offline Handwritten Malayalam Character Recognition Based on Chain Code Histogram

Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification

Pattern Analysis Techniques for the Recognition of Unconstrained Handwritten Malayalam Character Images

The objective of the study is to develop a hand written character recognition system that could recognisze all the characters in the mordern script of malayalam language at a high recognition rate

"Thematically Analysing Social Network Content During Disasters Through the Lens of the Disaster Management Lifecycle" & "Investigating Similarity Between Privacy Policies of Social Networking Sites as a Precursor for Standardization"

Abstract 1: Social Networks such as Twitter are often used for disseminating and collecting information during natural disasters. The potential for its use in Disaster Management has been acknowledged. However, more nuanced understanding of the communications that take place on social networks are required to more effectively integrate this information into the processes within disaster management. The type and value of information shared should be assessed, determining the benefits and issues, with credibility and reliability as known concerns. Mapping the tweets in relation to the modelled stages of a disaster can be a useful evaluation for determining the benefits/drawbacks of using data from social networks, such as Twitter, in disaster management.A thematic analysis of tweets’ content, language and tone during the UK Storms and Floods 2013/14 was conducted. Manual scripting was used to determine the official sequence of events, and classify the stages of the disaster into the phases of the Disaster Management Lifecycle, to produce a timeline. Twenty- five topics discussed on Twitter emerged, and three key types of tweets, based on the language and tone, were identified. The timeline represents the events of the disaster, according to the Met Office reports, classed into B. Faulkner’s Disaster Management Lifecycle framework. Context is provided when observing the analysed tweets against the timeline. This illustrates a potential basis and benefit for mapping tweets into the Disaster Management Lifecycle phases. Comparing the number of tweets submitted in each month with the timeline, suggests users tweet more as an event heightens and persists. Furthermore, users generally express greater emotion and urgency in their tweets.This paper concludes that the thematic analysis of content on social networks, such as Twitter, can be useful in gaining additional perspectives for disaster management. It demonstrates that mapping tweets into the phases of a Disaster Management Lifecycle model can have benefits in the recovery phase, not just in the response phase, to potentially improve future policies and activities. Abstract2: The current execution of privacy policies, as a mode of communicating information to users, is unsatisfactory. Social networking sites (SNS) exemplify this issue, attracting growing concerns regarding their use of personal data and its effect on user privacy. This demonstrates the need for more informative policies. However, SNS lack the incentives required to improve policies, which is exacerbated by the difficulties of creating a policy that is both concise and compliant. Standardization addresses many of these issues, providing benefits for users and SNS, although it is only possible if policies share attributes which can be standardized. This investigation used thematic analysis and cross- document structure theory, to assess the similarity of attributes between the privacy policies (as available in August 2014), of the six most frequently visited SNS globally. Using the Jaccard similarity coefficient, two types of attribute were measured; the clauses used by SNS and the coverage of forty recommendations made by the UK Information Commissioner’s Office. Analysis showed that whilst similarity in the clauses used was low, similarity in the recommendations covered was high, indicating that SNS use different clauses, but to convey similar information. The analysis also showed that low similarity in the clauses was largely due to differences in semantics, elaboration and functionality between SNS. Therefore, this paper proposes that the policies of SNS already share attributes, indicating the feasibility of standardization and five recommendations are made to begin facilitating this, based on the findings of the investigation.

Are discourse comprehension and cognitive processes influenced by the type of language language (English and Spanish)? A cross-language study based on elaborative inferences generation

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Social Networking Sites as Technology Enhanced Learning Communities towards autonomous language learning

The following contribution pretends to cope with the demands of a globalised, post-modern environment through the design and implementation of an online international project where an SNS is used in order to join English as Second Language (ESL) students from different parts of the world. The design of the project appears around the implementation of the Bologna process in the Faculty of Education from the University of Girona where the basic prerequisite of all students to acquire English at the level B1 of the Common European Portfolio makes English a compulsory competence for communication among its higher education candidates in order to develop in the world. Together with the University of Girona, there is the International Educational and Resources Network (iEARN) which promotes the participation of schools around the world in online international projects

Language experience shapes the development of the mutual exclusivity bias

Halberda (2003) demonstrated that 17-month-old infants, but not 14- or 16-month-olds, use a strategy known as mutual exclusivity (ME) to identify the meanings of new words. When 17-month-olds were presented with a novel word in an intermodal preferential looking task, they preferentially fixated a novel object over an object for which they already had a name. We explored whether the development of this word-learning strategy is driven by children's experience of hearing only one name for each referent in their environment by comparing the behavior of infants from monolingual and bilingual homes. Monolingual infants aged 17–22 months showed clear evidence of using an ME strategy, in that they preferentially fixated the novel object when they were asked to "look at the dax." Bilingual infants of the same age and vocabulary size failed to show a similar pattern of behavior. We suggest that children who are raised with more than one language fail to develop an ME strategy in parallel with monolingual infants because development of the bias is a consequence of the monolingual child's everyday experiences with words.

The human thalamus processes syntactic and semantic language violations

Numerous linguistic operations have been assigned to cortical brain areas, but the contributions of subcortical structures to human language processing are still being discussed. Using simultaneous EEG recordings directly from deep brain structures and the scalp, we show that the human thalamus systematically reacts to syntactic and semantic parameters of auditorily presented language in a temporally interleaved manner in coordination with cortical regions. In contrast, two key structures of the basal ganglia, the globus pallidus internus and the subthalamic nucleus, were not found to be engaged in these processes. We therefore propose that syntactic and semantic language analysis is primarily realized within cortico-thalamic networks, whereas a cohesive basal ganglia network is not involved in these essential operations of language analysis.

fMRI delineation of working memory for emotional prosody in the brain: commonalities with the lexico-semantic emotion network

Decoding emotional prosody is crucial for successful social interactions, and continuous monitoring of emotional intent via prosody requires working memory. It has been proposed by Ross and others that emotional prosody cognitions in the right hemisphere are organized in an analogous fashion to propositional language functions in the left hemisphere. This study aimed to test the applicability of this model in the context of prefrontal cortex working memory functions. BOLD response data were therefore collected during performance of two emotional working memory tasks by participants undergoing fMRI. In the prosody task, participants identified the emotion conveyed in pre-recorded sentences, and working memory load was manipulated in the style of an N-back task. In the matched lexico-semantic task, participants identified the emotion conveyed by sentence content. Block-design neuroimaging data were analyzed parametrically with SPM5. At first, working memory for emotional prosody appeared to be right-lateralized in the PFC, however, further analyses revealed that it shared much bilateral prefrontal functional neuroanatomy with working memory for lexico-semantic emotion. Supplementary separate analyses of males and females suggested that these language functions were less bilateral in females, but their inclusion did not alter the direction of laterality. It is concluded that Ross et al.'s model is not applicable to prefrontal cortex working memory functions, that evidence that working memory cannot be subdivided in prefrontal cortex according to material type is increased, and that incidental working memory demands may explain the frontal lobe involvement in emotional prosody comprehension as revealed by neuroimaging studies. (c) 2007 Elsevier Inc. All rights reserved.

Autistic symptomatology and language ability in autism spectrum disorder and specific language impairment

Background: Autism spectrum disorders (ASD) and specific language impairment (SLI) are common developmental disorders characterised by deficits in language and communication. The nature of the relationship between them continues to be a matter of debate. This study investigates whether the co-occurrence of ASD and language impairment is associated with differences in severity or pattern of autistic symptomatology or language profile. Methods: Participants (N = 97) were drawn from a total population cohort of 56,946 screened as part of study to ascertain the prevalence of ASD, aged 9 to 14 years. All children received an ICD-10 clinical diagnosis of ASD or No ASD. Children with nonverbal IQ 80 were divided into those with a language impairment (language score of 77 or less) and those without, creating three groups: children with ASD and a language impairment (ALI; N = 41), those with ASD and but no language impairment (ANL; N = 31) and those with language impairment but no ASD (SLI; N = 25). Results: Children with ALI did not show more current autistic symptoms than those with ANL. Children with SLI were well below the threshold for ASD. Their social adaptation was higher than the ASD groups, but still nearly 2 SD below average. In ALI the combination of ASD and language impairment was associated with weaker functional communication and more severe receptive language difficulties than those found in SLI. Receptive and expressive language were equally impaired in ALI, whereas in SLI receptive language was stronger than expressive. Conclusions: Co-occurrence of ASD and language impairment is not associated with increased current autistic symptomatology but appears to be associated with greater impairment in receptive language and functional communication.

Efficient FPGA-based regular expression pattern matching

An approach to the automatic generation of efficient Field Programmable Gate Arrays (FPGAs) circuits for the Regular Expression-based (RegEx) Pattern Matching problems is presented. Using a novel design strategy, as proposed, circuits that are highly area-and-time-efficient can be automatically generated for arbitrary sets of regular expressions. This makes the technique suitable for applications that must handle very large sets of patterns at high speed, such as in the network security and intrusion detection application domains. We have combined several existing techniques to optimise our solution for such domains and proposed the way the whole process of dynamic generation of FPGAs for RegEX pattern matching could be automated efficiently.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.