Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient word reading: Automaticity of print-related skills indexed by rapid automatized naming through cusp-catastrophe modeling

ABSTRACT The study explored the moderating role of rapid automatized naming (RAN) in reading achievement through a cusp-catastrophe model grounded on nonlinear dynamic systems theory. Data were obtained from a community sample of 496 second through fourth graders who were followed longitudinally over 2 years and split into 2 random subsamples (validation and cross-validation groups). Results verified the superiority of the cusp-catastrophe over linear and logistic models and established RAN-digits performance as a significant bifurcation factor in both concurrent and longitudinal prediction models. These findings suggested that reduced serial naming speed below a critical level was associated with significantly reduced predictability of word reading efficiency from pseudoword decoding ability. Results were tentatively interpreted as documenting the importance of highly automatized coordination of reading-related component processes, indexed by RAN performance, for word reading efficiency during the early and middle stages of reading acquisition.
Semantic network analysis for unsupervised topic linking and labelling

We rely more and more on machines to organise, analyse and summarise the vast amount of textual digital information that is being produced at a rate never seen before. At the same time, we notice an increase in availability of structured knowledge that is understandable by both humans and machines. The integration between unstructured text and structured knowledge is crucial for availing of the knowledge contained in text. The research questions that we tackle in this thesis are essential for understanding how applications can effectively link text elements to external background knowledge, and how this background knowledge can assist humans in the interpretation of vast text collections. Towards this goal, this thesis deals primarily with two core problems: word-sense disambiguation and topic labelling. Word-sense disambiguation is a fundamental problem that needs to be dealt with by most systems that need to integrate text and background knowledge. In this thesis, we investigate two scenarios for word-sense disambiguation. The first scenario focuses on disambiguation with multiple sense inventories simultaneously, and has not been addressed before. We tackle this problem by proposing a versatile disambiguation approach that only requires a short textual definition of word senses. The second scenario addresses word-sense disambiguation with a pre-given semantic graph, DBpedia. We propose a new disambiguation algorithm that solely relies on graph proximity for solving this problem. The novelty lies in that no previous work took a semantic graph approach to disambiguation with DBpedia. The second core problem this thesis tackles is topic labelling. Topic labelling is necessary for displaying text mining results in a human interpretable way. Broadly, its goal is to find a phrase that captures the essence (gist) behind a group of related words (topic). Our approach exploits the structure of the semantic graph of DBpedia in order to solve this problem. The unifying high-level hypothesis behind our research is that structural properties of concepts reveal their semantic properties. All our findings show a substantial correspondence between distributional semantics and semantics captured in the structure of semantic networks. This opens new opportunities for integrating the knowledge extracted from text through text mining and background knowledge, as well as for leveraging the benefits of this integration. Throughout this thesis we evaluate our proposed methods through user studies, compare their performances to related work and discuss our findings.
Simulation of Real Time 2D DWT Structure

This research contains an introduction to the first works in this field, and describes the Discrete Wavelet Transform DWT which is the only kind that can be implemented in the digital computer. The research lists the important structures that are used to implement the digital filters which are the hart of DWT. The research contains problems that face us in my M.Sc thesis to implement DWT image structure processor. In my M.Sc thesis we solve the problem of zero padding but we solve the second problem (waiting the column processor until the row processor finish its process) by using pipelineing technique. The pipeline technique solves the problem partially. This research solve the second problem completely by proposing a new structure which make the Two Dimentional DWT (2D-DWT) structure process the video in real time without waiting the row processor. The research was build fast structure that can decompose image by using DWT. The speed performance of the structure was tested using (Simulink) in (Matlab7). The results obtained from simulation of the proposed 2D DWT structure are compared with my M.Sc thesis structure and the traditional structure to show how we improve the process speed of 2D DWT structure.
The Physiology and Computation of Pyramidal Neurons

A variety of neural signals have been measured as correlates to consciousness. In particular, late current sinks in layer 1, distributed activity across the cortex, and feedback processing have all been implicated. What are the physiological underpinnings of these signals? What computational role do they play in the brain? Why do they correlate to consciousness? This thesis begins to answer these questions by focusing on the pyramidal neuron. As the primary communicator of long-range feedforward and feedback signals in the cortex, the pyramidal neuron is set up to play an important role in establishing distributed representations. Additionally, the dendritic extent, reaching layer 1, is well situated to receive feedback inputs and contribute to current sinks in the upper layers. An investigation of pyramidal neuron physiology is therefore necessary to understand how the brain creates, and potentially uses, the neural correlates of consciousness. An important part of this thesis will be in establishing the computational role that dendritic physiology plays. In order to do this, a combined experimental and modeling approach is used. This thesis beings with single-cell experiments in layer 5 and layer 2/3 pyramidal neurons. In both cases, dendritic nonlinearities are characterized and found to be integral regulators of neural output. Particular attention is paid to calcium spikes and NMDA spikes, which both exist in the apical dendrites, considerable distances from the spike initiation zone. These experiments are then used to create detailed multicompartmental models. These models are used to test hypothesis regarding spatial distribution of membrane channels, to quantify the effects of certain experimental manipulations, and to establish the computational properties of the single cell. We find that the pyramidal neuron physiology can carry out a coincidence detection mechanism. Further abstraction of these models reveals potential mechanisms for spike time control, frequency modulation, and tuning. Finally, a set of experiments are carried out to establish the effect of long-range feedback inputs onto the pyramidal neuron. A final discussion then explores a potential way in which the physiology of pyramidal neurons can establish distributed representations, and contribute to consciousness.
DEVELOPING THEORIES OF TEACHING ACADEMIC INDONESIAN TO NON–LANGUAGE MAJORS: WAYS OF COLLECTING AND ANALYZING DATA

Writing is the skill most neglected in language education in Indonesia. High school graduates in general are not ready to write academic Indonesian, let alone academic English. This paper discusses practices of teaching Indonesian at pre-college and college levels, theories and practice of writing, and language versus non-language specialists as writing instructors. The objectives of this paper are to demonstrate that: (1) different techniques of data collection generate different types of data, (2) the more data you have, the better you triangulate the findings, and (3) the quality of data is not only determined by its collecting techniques, but also by its relevance with research objectives. In this paper, I want to share the methodology of several studies on teaching writing at college levels I have conducted in the last eight years in English and non-English departments in Indonesia. The studies have revealed the following: (1) language education has failed to provide pre-college students with fundamentals of academic writing, (2) freshman Indonesian should be focused on developing academic or technical writing, (3) the success of teaching academic writing at college levels is dependent on the success of teaching writing at pre-college levels, (4) non-Indonesian language lecturers have the potential to be empowered to teach academic writing in non-language departments, (5) ethnic literature-based writing has the potential to revitalize the ethnic literature, and (6) collaborative writing including peer reviewing and teacher-student conferencing has been effective for coping with big classes of writing. Keywords: College Indonesian, collaborative writing, qualitative research
Media Multitasking and Role of Visual Hierarchy and Formatting Cues in Processing of Web Content

ii Dedication. iv Acknowledgements. v Vita. vi List of Tables. ix List of Figures. x Chapter 1: Introduction. 1 Chapter 2: Literature Review. 9 Chapter 3: Method, Results, and Discussion. 26 Chapter 4: General Discussion. 57 References. 68 Appendix A: Study Material. 76 Appendix B: Pretest Questionnaires. 85
On the pragmatic and semantic functions of Estonian sentence prosody

The goal of the dissertation was to investigate intonational correlates of information structure in a free word order language, Estonian. Information-structural categories such as focus or givenness are expressed by different grammatical means (e.g. pronoun, presence of accent, word order etc.) in different languages of the world (Chafe, 1976; 1987; Prince, 1981; 1992; Lambrecht, 1994; Gundel, 1999). The main cue of focus in intonation languages (e.g. English and German) is pitch accent (Halliday, 1967a; Ladd, 2008). In free word order languages, information structure affects the position of words in a sentence (E. Kiss, 1995) and sometimes it is even implied that word order in a free word order language might function like pitch accent in an intonation language (Lambrecht 1994: 240). The study reports on perception and production experiments on the effects of focus and givenness on Estonian sentence intonation. The aim of the experiments was to establish whether information structure has tonal correlates in Estonian, and if so, whether information structure or word order interacts more strongly with sentence intonation. A perception experiment showed that L1-Estonian listeners perceive pitch prominence as focus and accent shift as a change of sentence focus. A speech production study showed congruently that L1-Estonian speakers do use accent shift, and mark sentence focus with pitch accent. Another speech production experiment demonstrated that there is no phonetic difference between new information focus (e.g. “What did Lena draw? ” – “Lena drew a whale.”) and corrective focus (e.g. “Lena drew a lion.” – “No! She drew a whale”). The last experiment showed that given information is signalled with varying F0 range, if followed by focus, but without a pitch accent, if preceded by focus. All the experiments revealed that word order has a weak influence on sentence intonation. Sentence intonation interacts with focus and givenness in Estonian. As a conclusion, it is suggested that the pragmatic functions of word order, which apparently can be overridden by focus interpretation, are slightly different from the functions of pitch accent.
MODELING CHARACTERS VERSUS WORDS FOR MANDARIN SPEECH RECOGNITION

Word based models are widely used in speech recognition since they typically perform well. However, the question of whether it is better to use a word-based or a character-based model warrants being for the Mandarin Chinese language. Since Chinese is written without any spaces or word delimiters, a word segmentation algorithm is applied in a pre-processing step prior to training a wordbased language model. Chinese characters carry meaning and speakers are free to combine characters to construct new words. This suggests that character information can also be useful in communication. This paper explores both word-based and character-based models, and their complementarity. Although word-based modeling is found to outperform character-based modeling, increasing the vocabulary size from 56k to 160k words did not lead to a gain in performance. Results are reported for the Gale Mandarin speech-to-text task. Index Terms: Speech recognition, language modeling, Mandarin Chinese, speech-to-text transcription
The impacts of entrepreneurship on export orientation and internationalisation: the moderating effects of family ownership and involvement

This study aims to explore the influence of entrepreneurship on a company's export orientation and the degree of internationalisation. The moderating effects of family ownership and the level of involvement are also evaluated. Export and internationalisation activities have become a prevalent, worldwide phenomenon, however, the influences of entrepreneurship on these activities have rarely been explored. This study integrates previous literature and those piecemeal study results into a more comprehensive framework. Two hundred Thai family business managers who intend to run their own businesses were sampled as the respondents of this study. The results indicate that entrepreneurship orientation, in terms of autonomy, innovativeness, risk taking, pro-activeness, and competitive aggressiveness, all have significant influence on export performance which further influence the level of internationalisation. Furthermore, the results also reveal that lower levels of power concentrated on single owner (not sure what is meant here) will serve as an important catalyst to facilitate export and internationalisation. Lower levels of family involvement can also facilitate export and internationalisation. Although entrepreneurship is a positive factor to facilitate export-orientated business performance in general, this study specifically indicates that Asian business, like that of Thailand, for example, would be more successful if internationalisation is facilitated without the family's interference while decentralising their business decision-making structure.
Evaluating Student Performance In A Freshman Graphics Course To Provide Early Intervention For Students With Visualization And/Or Design Intent Difficulties

All engineering students at Rensselaer Polytechnic Institute are required to take a onecredit course in solid modeling. This course, Engineering Graphics and Computer Aided Design (EG&CAD) teaches the skills of using a solid modeling system to create parts, small assemblies, and documentation. More importantly, EG&CAD also emphasizes the use of vectors in creating solid models and thereby provides students reinforcement of their linear algebra knowledge. The students normally take EG&CAD during their freshman year and then have the opportunity to use solid modeling in their sophomore and senior design projects as well as some special topic electives. In addition, several other courses are now using solid models as a way to demonstrate fundamental principles. With an increasing dependence on solid modeling skills required, it is imperative that the course content in EG&CAD be effectively delivered and absorbed.
Special issue on e-leadership: Editorial.

In its 44 volumes to date prior to this special issue on e-leadership since the founding of the journal in 1970, the British Journal of Educational Technology (BJET) has only published seven articles which contain the word ‘leadership’ in the title (or 15 with ‘lead*’ in the title; in the full text ‘leadership' = 387; ‘lead' = 1421), all emerging since 2006, and comprising mainly book reviews. These include Naeve et al., BJET Guest Editors (book review), 2006; Jameson et al., 2006; Latcham (book review), 2007; Fill (book review), 2007; Rushby, BJET Editor (book review), 2007; Hadjithoma-Garstka, 2011 and Jang and Ryu, 2011. However, since the presence or absence of the word ‘leadership’ is not a definitive indicator of the actual treatment of the subject area, the 24 BJET articles and reviews containing ‘leadership’ in the abstract and 391 articles and reviews containing ‘leadership’ in the full text of the article, including those in this special issue, are possibly more revealing of an underlying, as yet largely unrecognised, significance. In the context of making a case for the focus of this special issue, the point is that there appears to have been, at least on the surface, relatively little coverage of the field of ‘e-leadership’ of educational technology within the history of the journal, despite the enormity and consequence of this field.
A quality metric to improve wrapper feature selection in multiclass subject invariant brain computer interfaces

Brain computer interface systems based on electroencephalograph (EEG) signals have limitations which challenge their application as a practical device for general use. The signal features generated by the brain states we wish to detect possess a high degree of inter-subject and intra-subject variation. Additionally, these features usually exhibit a low variation across each of the target states. Collection of EEG signals using low resolution, non-invasive scalp electrodes further degrades the spatial resolution of these signals. The majority of brain computer interface systems to date require extensive training prior to use by each individual user. The discovery of subject invariant features could reduce or even eliminate individual training requirements. To obtain suitable subject invariant features requires search through a high dimension feature space consisting of combinations of spatial, spectral and temporal features. Poorly separable features can prevent the search from converging to a usable solution as a result of degenerate classifiers. In such instances the system must detect and compensate for
The Biodiversity Informatics Landscape: Elements, Connections and Opportunities

There are a multitude of biodiversity informatics projects, datasets, databases and initiatives at the global level, and many more at regional, national, and sometimes local levels. In such a complex landscape, it can be unclear how different elements relate to each other. Based on a high-level review of global and European-level elements, we present a map of the biodiversity informatics landscape. This is a first attempt at identifying key datasets/databases and data services, and mapping them in a way that can be used to identify the links, gaps and redundancies in the landscape. While the map is predominantly focused on elements with a global scope, the sub-global focus at the European-level was incorporated in the map in order to demonstrate how a regional network such as the European Biodiversity Observation Network (EU BON) can usefully contribute to connecting some of the nodes within the landscape. We identify 74 elements, and find that the informatics landscape is complex in terms of the characteristics and diversity of these elements, and that there is high variability in their level of connectedness. Overall, the landscape is highly connected, with one element boasting 28 connections. The average "degrees of separation" between elements is low, and the landscape is deemed relatively robust to failures since there is no single point that information flows through. Examples of possible effort duplication are presented, and the inclusion of five policy-level elements in the map helps illustrate how informatics products can contribute to global processes that define and direct political targets. Beyond simply describing the existing landscape, this map will support a better understanding of the landscape’s current structure and functioning, enabling responsible institutions to establish or strengthen collaborations, work towards avoiding effort duplication, and facilitate access to the biodiversity data, information and knowledge required to support effective decision-making, in the context of comparatively limited funding for biodiversity knowledge and conservation. To support this, we provide the input matrix and code that created this map as supplementary materials, so that readers can more closely examine the links in the landscape, and edit the map to suit their own purposes.
Parallel Solver for Blood Flow in Human Vascular System

In order to simulate blood flow in human vascular system, the unsteady, three-dimensional, incompressible Navier-Stokes equations are solved numerically. The solver is capable of dealing with moving boundaries and moving grids. A second-order in time and third-order upwind finite volume method for solving time-accurate incompressible flows utilizing pseudo-compressibility technique is used. For parallel execution, the flow domain is partitioned. Communication between the subdomains of the flow on Riken’s VPP/700E supercomputer is implemented using MPI message-passing library. The code is capable of running on both shared and/or distributed memory architectures.
Training Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are powerful sequence models that were believed to be difficult to train, and as a result they were rarely used in machine learning applications. This thesis presents methods that overcome the difficulty of training RNNs, and applications of RNNs to challenging problems. We first describe a new probabilistic sequence model that combines Restricted Boltzmann Machines and RNNs. The new model is more powerful than similar models while being less difficult to train. Next, we present a new variant of the Hessian-free (HF) optimizer and show that it can train RNNs on tasks that have extreme long-range temporal dependencies, which were previously considered to be impossibly hard. We then apply HF to character-level language modelling and get excellent results. We also apply HF to optimal control and obtain RNN control laws that can successfully operate under conditions of delayed feedback and unknown disturbances. Finally, we describe a random parameter initialization scheme that allows gradient descent with momentum to train RNNs on problems with long-term dependencies. This directly contradicts widespread beliefs about the inability of first-order methods to do so, and suggests that previous attempts at training RNNs failed partly due to flaws in the random initialization.