Miracle-dz/UniFAR-FTUs huggingface dataset:
Access to Educational and Instructional Computer Technologies for Post‐secondary Students with Disabilities: lessons from three empirical studies

ABSTRACT Access issues based on three Canadian empirical studies of the use of computer and information technologies by college and university students with physical, sensory, and learning disabilities are presented. Data were obtained between fall 1997 and spring 1999 from: (1) focus groups with students with disabilities (n = 12); (2) structured interviews with students with disabilities (a = 37) and with post‐secondary personnel responsible for providing services to them (n = 30); (3) questionnaires completed by post‐secondary students with disabilities (n= 725). Findings indicate that the overwhelming majority of students with disabilities use computers and the Internet, but that 41 % of them need some type of adaptation to use computers effectively. Key findings emphasize advantages of computer technologies and delineate barriers to full access. Types of computer, information and adaptive technologies used by students with different disabilities are presented and emerging trends are highlighted. The goal is (1) to sensitize educational and instructional technologists, professors and planners involved in the implementation of educational media into post‐secondary education curricula and (2) to demonstrate that designing for accessibility from the outset creates a more equitable learning environment that provides opportunities for all students
Reviewing learning designs with HEART: a learning design support strategy

Substantial research and development efforts have been invested in producing digitallearning designs and learning objects, with seemingly limitless potential for sharing andportability. However, initiatives to disseminate and share learning designs have had limitedsuccess. This may be partly due to learning design being viewed as a product to be used in arange of teaching contexts, as is, without due consideration of learning design as acontextualised process driven by teachers’ pedagogical beliefs about the affordances,possibilities and relevance of digital teaching and learning resources. The HEART(HEaring And Realising Teaching-voice) learning design support strategy aims to supportteachers’ learning design practice by eliciting and depicting the pedagogical beliefsunderpinning a learning design or resource. In this session participants may trial an earlyprototype of the HEART website, and provide critical feedback on each stage of the supportstrategy.Keywords: Learning design, pedagogical dimensions, visualisation, teacher beliefs
High school mathematics marks as an admission criterion for entry into programming courses at a South African university

In this study, the assumption that good performance in mathematics in the final school year could be used as a pre-entry requirement to programming courses at universities in South Africa, is challenged. The extant literature reports positive relationships between mathematics performance and success in programming courses. As computer programming modules in higher education institutions (HEIs) are typically characterised by low success rates, it becomes important to eliminate potentially erroneous entry requirements. The low success rate in programming modules is ascribed to the abstract nature and content of programming courses, and the inadequacy of pre-university education to prepare students for the cognitive skills required for success in such programmes. This paper reports on a single independent variable, 'performance in high school mathematics', and its relationship to performance in two computer programming courses. The dataset comprised the school marks of four cohorts of students who were enrolled for the programming modules between 2012 and 2015. Firstly, we computed the point-biserial correlation between a dichotomous variable that indicated whether students had mathematics as a subject in Grade 12 or not, and their performance in the programming modules. Once we established that a relationship existed, the marks achieved in the final school year for mathematics, and performance in two programming modules were correlated. Results indicated that the school mathematics marks correlate only marginally, and that correlations were not significant, with performance in the two programming courses. We also correlated the school mathematical literacy marks with performance in the two programming courses, and found that a strong positive correlation that was significant existed with the second semester programming course. We conclude that the mark achieved for school mathematics cannot be considered as a valid admission criterion for programming courses in the South African context.
Recurrent neural network language models for automatic speech recognition

The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) for large vocabulary continuous speech recognition (LVCSR). RNNLMs are currently state-of-the-art and shown to consistently reduce the word error rates (WERs) of LVCSR tasks when compared to other language models. In this thesis we propose various advances to RNNLMs. The advances are: improved learning procedures for RNNLMs, enhancing the context, and adaptation of RNNLMs. We learned better parameters by a novel pre-training approach and enhanced the context using prosody and syntactic features. We present a pre-training method for RNNLMs, in which the output weights of a feed-forward neural network language model (NNLM) are shared with the RNNLM. This is accomplished by first fine-tuning the weights of the NNLM, which are then used to initialise the output weights of an RNNLM with the same number of hidden units. To investigate the effectiveness of the proposed pre-training method, we have carried out text-based experiments on the Penn Treebank Wall Street Journal data, and ASR experiments on the TED lectures data. Across the experiments, we observe small but significant improvements in perplexity (PPL) and ASR WER. Next, we present unsupervised adaptation of RNNLMs. We adapted the RNNLMs to a target domain (topic or genre or television programme (show)) at test time using ASR transcripts from first pass recognition. We investigated two approaches to adapt the RNNLMs. In the first approach the forward propagating hidden activations are scaled learning hidden unit contributions (LHUC). In the second approach we adapt all parameters of RNNLM. We evaluated the adapted RNNLMs by showing the WERs on multi genre broadcast speech data. We observe small (on an average 0.1% absolute) but significant improvements in WER compared to a strong unadapted RNNLM model. Finally, we present the context-enhancement of RNNLMs using prosody and syntactic features. The prosody features were computed from the acoustics of the context words and the syntactic features were from the surface form of the words in the context. We trained the RNNLMs with word duration, pause duration, final phone duration, syllable duration, syllable F0, part-of-speech tag and Combinatory Categorial Grammar (CCG) supertag features. The proposed context-enhanced RNNLMs were evaluated by reporting PPL and WER on two speech recognition tasks, Switchboard and TED lectures. We observed substantial improvements in PPL (5% to 15% relative) and small but significant improvements in WER (0.1% to 0.5% absolute).
Advance Resource Discovery System using Data Mining Technique

One of the challenges facing the current web is the efficient use of all the available information. The Web 2.0 phenomenon has favored the creation of contents by average users, and thus the amount of information that can be found for diverse topics has grown exponentially in the last years. Initiatives such as linked data are helping to build the Semantic Web, in which a set of standards are proposed for the exchange of data among heterogeneous systems. However, these standards are sometimes not used, and there are still plenty of websites that require naive techniques to discover their contents and services. This paper proposes an integrated system for content and service discovery and extraction. The system is divided into several layers where the discovery of contents and services is made in a representational stateless transfer system such as the web. It employs several web mining techniques as well as feature oriented modeling for the discovery of cross-cutting features in web resources. The system is used in a scenario of electronic newspapers. An intelligent agent crawls the web for related news, and uses services and visits links automatically according to its goal. This scenario illustrates how the discovery is made at different levels and how the use of semantics helps implement an agent that performs high-level tasks. Keywords-Agents, contents, discovery, information extraction,representational stateless transfer system (REST),
Arabic Handwritten Characters Recognition Using Convolutional Neural Network

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and large public databases. In this work, we model a deep learning architecture that can be effectively apply to recognizing Arabic handwritten characters. A Convolutional Neural Network (CNN) is a special type of feed-forward multilayer trained in supervised mode. The CNN trained and tested our database that contain 16800 of handwritten Arabic characters. In this paper, the optimization methods implemented to increase the performance of CNN. Common machine learning methods usually apply a combination of feature extractor and trainable classifier. The use of CNN leads to significant improvements across different machine-learning classification algorithms. Our proposed CNN is giving an average 5.1% misclassification error on testing data.
iGeneration And Their Acceptance of Technology

Technology is becoming increasingly embedded into our lives. We are seeing a push towards digitization and online access. This can be a challenge for some because users’ level of technical ability varies among the generations and other factors. Predicting technological innovations and how they might supplement, integrate with, or entirely replace existing technology is nearly impossible. These changes and innovations include many within the realm of education, including the relatively recent advent and increasing presence of e-Book sources and platforms. This study examines how higher education students across different generations are embracing electronic books in their studies. Students have more distractions than ever before. Using mobile devices smartly but having the ability to concentrate when you need to can be a challenge. Just because e-Books options are available and being increasingly adopted does not necessarily mean they are preferred by students. This study contributes to our understanding of their acceptance across different generations of students.
Creating an Academic Business English List: a Corpus Based Study

Past studies have shown that second language (L2) learners need to know between 95% to 98% of the vocabulary in a text in order to understand its content (e.g. Hirsh & Nation, 1992; Laufer, 1989; Laufer & Ravenhorst-Kalovski, 2010; Nation, 2006; Schmitt et al., 2011; Staehr, 2008). Other studies tried to identify exactly what words these students need to know in order to achieve this level of comprehension, and researchers created frequency lists that could be used to teach vocabulary (e.g., Browne, Culligan, & Phillips, 2013; Nation, 2012; West 1953). However, as General English (GE) frequency lists are not very efficient for teaching specialized vocabulary, other lists were created in order to solve this issue (Coxhead, 2000; Hsu, 2011a; Konstantakis, 2007). This thesis study investigated the vocabulary needed by undergraduate students in business in order to understand their textbooks at this 95-98% level of text coverage. A corpus based on undergraduate business core courses textbooks was compiled by the researcher in order to extract a list of the most used vocabulary excluding the BNC/COCA 3K and proper nouns (which are considered to be already known by these learners). The Academic Business English List (ABEL) was created, and it consists of 840 word families which cover 2.86% of the ABEL corpus. With knowledge of these 840 word families, the BNC/COCA 3K and proper nouns, text coverage reached 96.94%. The ABEL list can be used by materials creators and business English teachers in order to prepare students for their undergraduate business programs.
Image Inpainting Based On Geometric Similarity

Abstract This paper proposed a new image inpainting method for restoring a large damaged area in the images called the “geometric image inpainting (GII) method”. The method was developed in response to several common problems encountered in the image inpainting process and provided a high-quality result compared to human vision. In the field of image inpainting, there are three key issues: texture analysis, repairing order and reconstruction of lost image pixels. Though there have been a lot of researches published, few combined all three key issues in their studies. We propose a novel image inpainting method integrating all three key issues at the same time. Experiments showed that the proposed image inpainting method offered better results than other existed methods.
Unbiased Sampling of Facebook

The popularity of online social networks (OSNs) has given rise to a number of measurement studies that provide a first step towards their understanding. So far, such studies have been based either on complete data sets provided directly by the OSN itself or on Breadth-First-Search (BFS) crawling of the social graph, which does not guarantee good statistical properties of the collected sample. In this paper, we crawl the publicly available social graph and present the first unb iased sampling of Facebook (FB) users using a MetropolisHastings random walk with multiple chains. We study the convergence properties of the walk and demonstrate the uniformity of the collected sample with respect to multiple metrics of interest. We provide a comparison of our crawling technique to baseline algorithms, namely BFS and simple random walk, as well as to the “ground truth” obtained through truly uniform sampling of userIDs. Our contributions lie both in the measurement methodology and in the collected sample. With regards to the methodology, our measurement technique (i) applies and combines known results from random walk sampling specifically in the OSN context and (ii) addresses system implementation aspects that have made the measurement of Facebook challenging so far. With respect to the collected sample: (i) it is the first repre sentative sample of FB users and we plan to make it publicly available; (ii) we perform a characterization of several ke y properties of the data set, and find that some of them are substantially different from what was previously believed based on non-representative OSN samples.
Linear vs. Pictorial Access to On-screen Text and Computer Attitude

Learners often process pictures poorly when learning with text and pictures. Thus, fostering pictorial information processing might be advantageous. In an experiment, learners received computerized instructions that consisted of static pictures and on-screen text relating to the physiology of vision using one of two methods of access to text. Pictorial access to text by clicking on parts of pictures was hypothesized to be superior to linear access by clicking on forward and backward buttons. The type of access was tested for moderating effects of learn- ers' attitude towards computers. Results indicate that pictorial access to text mainly fosters knowledge when learners have a negative attitude towards computers. Positive-attitude learn- ers exhibited no learning benefits from pictorial access to on-screen texts. The CTML is based on three assumptions about learning with texts and pictures. The dual channel as- sumption states that pictorial and verbal information is processed separately in a visual-pictorial and an audito- ry-verbal channel, respectively (see Fig. 1). They are functionally independent of each other, but may interact. Written words are initially processed in the visual-pictorial channel before they move to the other channel in working memory. The limited capacity assumption suggests that the processing capacity of the two channels is limited. Within limits, learners must accomplish all the processing necessary for learning. The CLT especially emphasizes the role of working memory in learning and the problem of occupying working memory by useful and unnecessary processes. The components of cognitive load are intrinsic, germane and extraneous. Intrinsic load is created by task complexity and is mainly needed for understanding. Germane load goes beyond under- standing and is bound to constructing mental representations and storing them. Extraneous load results from the presentation manner of the material and is often the main source that is detrimental to learning. As long as working memory capacity has not been fully occupied by these loads, some capacity will still be available for processing. The active learning assumption states that learners must process information actively to achieve meaningful learning. Active processing includes the selection of relevant information from words and pictures, the mental organization of this information to form coherent verbal and pictorial representations, and the mental
Adjoint-Based Probabilistic Method for Source Identification in Water Distribution Systems

Water system sensors are becoming increasingly more efficient and effective at discovering water quality changes in distribution systems. This paper describes a method for determining the source of the water quality change. We use publicly available software (EPANET) and a conditioning method to probabilistically locate the contamination source. Prior work has shown that a similar method is effective for a distribution system with pipes, nodes, and a steady-state flow field. In this work, we demonstrate the effectiveness of this approach in a more complex distribution system with a pump, a tank, and transient flow conditions. 2.
Long‐term spatiotemporal trend analysis of precipitation and temperature over Turkey

Monthly precipitation and temperature data for Turkey were obtained from the Climate Research Unit Time Series (CRU TS V3.23) dataset (1901–2014) and analysed. The records of 81 grids, each located in a province, were converted into annual and seasonal data. The Mann−Kendall test was used to determine trend significance. The Theil−Sen slope estimator was used for estimating the magnitude of the trend. The Pettitt−Mann−Whitney test was used on the annual data series to identify the ‘most probable change year’ for precipitation and temperature. The spatial pattern of the trend was analysed using a geographical information system environment. The results showed that the annual areal precipitation over the whole of Turkey is not significantly increasing with a magnitude of 0.11 mm year−1, but the annual areal temperature is increasing significantly with a magnitude of 0.88 °C century−1. The grid analysis indicated that annual precipitation is decreasing in the southeastern Anatolia and Mediterranean regions and increasing in the Marmara and Black Sea regions, whilst other regions have no specific pattern. Although a trend is present in most of the grids, insignificance is dominant. Annual precipitation has no dominant change year with 1969 being the most probable. Seasonal precipitation has increasing and decreasing trends in the same regions that also show such trends in annual precipitation. Annual temperature has a significant, increasing trend in all regions. 1993 was found as the most probable change year. Seasonal temperature showed an increasing trend in all regions. The highest increasing trend for temperature was found in the summer.
Interpreting and Assessing the Answers Given by the CAS Expert: A Reaction Paper.

ed the general factored form for the factorization of x − 1 , for integral values of n. Then, they proceeded to fill in the activity table (see Figure 1 below) for n = 2 to 13 in x − 1 , according to the general rule for factoring that they had just generalized. They were, however, surprised to see the factorizations that the CAS produced. __________________________________________________________________________ Toward a generalization (activity with paper & pencil and with calculator) 1. In this activity each line of the table below must be filled in completely (all three cells), one row at a time. Start from the top row (the cells of the three columns) and work your way down. If, for a given row, the results in the left and middle columns differ, reconcile the two by using algebraic manipulations in the right hand column. Factorization using paper and pencil Result produced by FACTOR command Calculation to reconcile the two, if necessary
Automatic exercise sequencing-based algorithmic skills

In any learning systems and especially automated assessment tools, the most common task is to evaluate the students' performance using training exercise. The selection of the next exercise is generally performed as a static set of exercises or free by students. But, it would clearly be advantageous if this exercise selection process were to be automated based on their previous performances. Therefore, the focus of this paper is the development of a method capable of determining exercise progression and sequencing during a training session based on the students' past performance. A dynamic planning of algorithmic exercises was developed based on a semantic and pedagogical description to be used in training exercise.
Off-current characteristics of conductivity modulated TFT

This paper reports the leakage current characteristics of Conductivity Modulated Thin Film Transistor (CMTFT) fabricated using polycrystalline silicon. The transistor uses the idea of conductivity modulation in the offset region to obtain a significant reduction in on-state resistance. Experimental on-state and off-state current-voltage characteristics of the CMTFT have been compared with those of the conventional offset drain device. The devices were fabricated using a low temperature process (620/spl deg/C) which is highly desirable for large area electronic applications.
An adaptive predistorter for TDMA transmitters using a heterodyne architecture

This report presents results on the development of an adaptive predistorter for a generic TDMA transmitter A heterodyne architecture was used with the quadrature modulator and demodulator implemented as digital circuits. Previously reported results have indicated that increasing the look-up table (LUT) size improves the ACPR. Our results showed an opposite trend. Typical adjacent channel power rejection (ACPR) values of -57 dB were achieved using a 16 point LUT. The small LUT size may be attributed to memory effects inherent in a heterodyne system.
Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

Abstract Edge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.