Miracle-dz/UniFAR-FTUs huggingface dataset:
LFG contributions in second language acquisition research: the development of case in Russian L2

Learning a second language (L2) is a complex task, involving cognitive and affective factors, both personal and social. Hence theories of Second Language Acquisition (SLA) are many and varied. Among them, Processability Theory (PT) offers a principled transitional paradigm that deals specifically with grammatical development (cf. Pienemann, 1998; Pienemann, Di Biase & Kawaguchi, 2005). In this paper we will illustrate how LFG contributes substantially to the formulation of PT’s developmental hypotheses. Specifically it provides PT with two fundamental concepts, ensuring that the different parts of a sentence fit together: the different syntactic (i.e., lexical, phrasal or sentence) levels within or across which their elements require unification; and the different kinds of correspondences among a-, cand f-structures. Furthermore, within the PT framework, we will investigate the development of case in Russian L2. In this respect, the original proposal that we wish to make here is that King’s (1995) descriptive account of case assignments in Russian can also be applied in a developmental perspective. In particular, we will show that King’s four types of case assignments (semantic, configurational, lexical and grammatical functions) can be successfully interfaced with PT’s stages for the development of case, and thus constitute a helpful resource for a better understanding of the learners’ developmental process. Our hypotheses are then tested on cross-sectional data collected among 12 learners of Russian L2 at different proficiency levels and from a varied L1 background.
A Framework for Iris Partial Recognition based on Legendre Wavelet Filter

An increasing need for biometrics recognition system has grown substantially to address the issues of recognition and identification especially in highly dense areas such as airport, train stations and for financial transaction. Evidences of these can be seen in some airports and also the implementation of these technologies in our mobile phones. Among the most popular biometric technologies include facial, fingerprints and iris recognition. The iris recognition is considered by many researchers to be the most accurate and reliable form of biometric recognition, because iris can neither be surgically operated with a chance of losing slight nor change due to ageing. However, presently most iris recognition system available can only recognize iris image with frontal-looking and high-quality images. Angular image and partially capture image cannot be authenticated with existing method of iris recognition. This research investigates the possibility of developing a framework for recognition partially captured iris image. The research also adopts the Legendre wavelet filter for the iris feature extraction. Selected iris images from CASIA, UBIRIS and MMU database were used to test the accuracy of the introduced framework. A threshold for the minimum iris image required was established.
The influence of student internship work experience on their self-improvement and professionalism in Sport Management.

One of the major reasons provided for advocating for student internship has been the promise that it improves students in terms of practical orientation while at the same time providing for the acquisition of professionalism required in a specific field of expertise. Scant research attention has been given on this matter in the context of Sport Management in South Africa. The purpose of this study, therefore, is to fill this void by examining the influence of students’ internship work experience on their professionalism and the mediating role of students’ self-improvement in this relationship from a mentor perspective. Three hypotheses were formulated and data from a sample 144 mentors were collected to empirically test these hypotheses. The results showed that students’ internship work experience positively influence both their self-improvement and professionalism in a significant way. Implications of the findings are discussed and future research direction suggested.
Probability distribution equalization scheme for PAPR reduction in NC-OFDM systems

This Paper Investigates the effect of probability distribution equalization techniques on reducing the PAPR in NCOFDM widely used in Cognitive Radio Systems, and proposes the use of Arithmetic Coding to be used in NCOFDM for PAPR reduction. Simulation results show that the proposed Arithmetic Coding scheme can greatly reduce the PAPR of the NC-OFDM system. Furthermore, the results show that the Arithmetic Coding scheme outperforms the Huffman Coding scheme.
Adaptive sparse coding and dictionary selection

The sparse coding is approximation/representation of sign als with the minimum number of coefficients using an overcomplete set of elementary functi o s. This kind of approximations/ representations has found numerous applications in source separation, denoising, coding and compressed sensing. The adaptation of the sparse approxima tion framework to the coding problem of signals is investigated in this thesis. Open prob lems are the selection of appropriate models and their orders, coefficient quantization and spars e pproximation method. Some of these questions are addressed in this thesis and novel metho ds developed. Because almost all recent communication and storage systems are digital, an ea sy method to compute quantized sparse approximations is introduced in the first part. The model selection problem is investigated next. The linea r model can be adapted to better fit a given signal class. It can also be designed based on some a priori information about the model. Two novel dictionary selection methods are separate ly presented in the second part of the thesis. The proposed model adaption algorithm, calle d Dictionary Learning with the Majorization Method (DLMM), is much more general than curre nt methods. This generality allowes it to be used with different constraints on the model . Particularly, two important cases have been considered in this thesis for the first time, Parsim onious Dictionary Learning (PDL) and Compressible Dictionary Learning (CDL). When the gener ative model order is not given, PDL not only adapts the dictionary to the given class of signa ls, but also reduces the model order redundancies. When a fast dictionary is needed, the CD L framework helps us to find a dictionary which is adapted to the given signal class withou increasing the computation cost so much. Sometimes a priori information about the linear generative model is given in format of a parametric function. Parametric Dictionary Design (PDD) gener at s a suitable dictionary for sparse coding using the parametric function. Basically PDD finds a p arametric dictionary with a minimal dictionary coherence, which has been shown to be suitab le for sparse approximation and exact sparse recovery. Theoretical analyzes are accompanied by experiments to val idate the analyzes. This research was primarily used for audio applications, as audio can be sh own to have sparse structures. Therefore, most of the experiments are done using audio sign al .
Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.
Learners’ self-reports of exposure to violence in South African schools: A gendered reflection

The pervasive worldwide phenomenon of gender-based violence in schools poses a threat to education as a vehicle of economic development and economic freedom. Gender violence in and around schools is a global problem with serious implications for the educational attainment, health and wellbeing of both girls and boys. This paper explores the gendered nature of violence in selected schools in six provinces in South Africa. A qualitative study following a communitycentred, capacity-building approach used focus group interviews to collect data from a purposive sample of learners aged between 13 and 17 years who were perpetrators or victims of violence. The aim was to give voice to learners about their experiences of violence in schools. Findings indicated a high incidence of gender-based violence in schools. Boys mainly drew on genderbiased discourses to orchestrate demeaning gendered comments, sexualised gestures, sexual harassment and bullying. Teachers’ assault of learners in the form of corporal punishment was also deeply implicated in both girls’ and boys’ reports of gender-based violence. Recommendations are made for gender-based awareness campaigns, which involve learners, parents and teachers, and the setting up of school-based structures for learner peer support as critical strategies for combating gender-based violence in schools. Keywords: Femininities, masculinities, power, management, gender, school violence
Predictors of willingness to register as an organ donor among dutch adolescents

Abstract In order to identify relevant determinants of organ donor registration among Dutch adolescents, a school-based cross-sectional survey was conducted among 145 high school students. Fifty-one percent of respondents indicated they were willing to register as organ donors and 80% reported a positive general attitude towards registration. Various misconceptions about the registration and donation procedure were identified. On average only moderate knowledge levels related to organ donation were found. In order of strongest association, negative outcome expectancies, past behaviour and experience, positive outcome expectancies, and social outcome expectancies proved to be significant predictors of willingness to register as organ donors. Self-efficacy was indirectly associated with willingness via outcome expectancies. Knowledge about organ donation was not significantly associated with willingness. The results suggest that in order to persuade adolescents to register as organ donors, refutational messages will have to be developed to counterargue the prevailing negative outcome expectancies related to organ donation and registration as an organ donor.
Enhanced symmetrical split ring resonator for metallic surface crack detection

An enhanced sensor based on symmetrical split ring resonator (SSRR) functioning at microwave frequencies has been proposed in order to detect and characterize the metal crack of the materials. This sensor is based on perturbation theory, in which the dielectric properties of the material affect the quality factor and resonance frequency of the microwave resonator. Conventionally, coaxial cavity, waveguide, dielectric resonator techniques have been used for characterizing materials. However, these techniques are often large, and expensive to build, which restricts their use in many important applications. Thus, the enhanced bio-sensing technique presents advantages such as high measurement sensitivity with the capability of suppressing undesired harmonic spurious and permits potentially metal crack material detection. Hence, using a High Frequency Structure Simulator (HFSS) software, the enhanced sensor is modeled and the reflection S11 is performed for testing the aluminum metal with crack and without crack at the frequency range of 100 MHz to 3GHz. Variation of crack width and depth has been investigated and the most obvious finding emerged from this study is that the ability of detecting a minimum of sub-millimeter crack width and depth which is a round 10 𝛍m width or depth where the minimum shift of reflected frequency is recorded at 6.2 MHz and 3 MHz for crack width and depth respectively. The enhanced SSRR provides high capability of detecting small crack defection by utilizing the interaction between coupled gap resonators and it is useful for various applications such as aircraft fuselages, nuclear power plant steam generator tubing, and steel bridges and for others that can be compromised by metal fatigue.
On the joint design of compressed sensing and network coding for wireless communications

Compressed sensing and network coding techniques have seen a widespread interest in many disciplines during the last decade. Recently, a novel idea emerged for the combination of these areas in wireless communications to leverage the benefits from network coding while taking advantage of the correlations in the (sensory) data. The potential gains, such as lower latency for large‐scale sensing scenarios, reduced energy consumption, and a decrease in the amount of data during transmissions, are alluring to many use‐cases. However, a common issue one faces when joining both techniques is encountered in the fact that network coding is designed to operate over finite fields, whereas compressed sensing is mainly concerned with real numbers. This paper studies the impact of compressed sensing on network coding to enable one step decoding for the reconstruction of compressed data. We emphasize and discuss the design of the sensing and coding matrices, as well as the algorithms that enable accurate reconstructions. We employ the KL1p compressed sensing library and the NS3 simulator to evaluate this joint design. Our simulations show that using normalized coefficient matrices drawn from Gaussian distributions has higher efficiency and scalability including, but not limited to, multi‐hop networks, where the recoding feature of network coding can be exploited. Furthermore, the Subspace Pursuit algorithm outperforms the state‐of‐the‐art reconstruction algorithms, with respect to the reconstruction signal‐to‐noise ratio, by more than two folds compared to the other benchmark algorithms in cluster‐based Wireless Sensor Networks, where recoding using real network codes are involved.
Compatible cluster merging for fuzzy modelling

Making a fuzzy model of a dynamic process requires the tuning of many parameters. Doing this heuristically is tedious and time consuming. Clustering techniques provide an easier way for forming fuzzy model using measurements made on the system. However, the number of clusters and hence the number of rules the fuzzy rule-base must be determined a priori. It is usually not possible to determine beforehand the optimal number of rules in a rule-base. In this paper, a compatible cluster merging algorithm is suggested for finding the "optimal" number of rules in a rule base. It is based on the compatible cluster merging algorithm proposed recently. The original compatible cluster merging algorithm has certain undesired properties for fuzzy modelling. Hence, a modification is proposed and a modified compatible cluster merging algorithm is described. The new algorithm combines techniques from the original compatible cluster merging, fuzzy multicriteria decision making and heuristics. Examples are given that show the applicability of the proposed method. <<ETX>>
Certificate discovery using SPKI/SDSI 2.0 certificates

In this thesis, a framework was designed to implement certificate discovery in SPKI/SDSI. The process of discovering a valid certificate chain is shown to be analogous to a string re-writing problem of finding certain string derivations. Given this framework, an algorithm was devloped to solve the problem and is shown to a time polynomial in the number of certificates. Given the theoretical framework, two certificate discovery implementations were developed. The first, written in Perl, tooks a simple certificate format and generated the necessary sequence based upon the chain desired. The second implementation, in C, was written as part of a library that is designed for the SPKI/SDSI framework. Thesis Supervisor: Ronald L. Rivest Title: Edwin Sibley Webster Professor of Computer Science and Engineering
Respiratory Symptoms of California’s Dairy Workers Chelsea Eastman

While research has documented the adverse impact of agricultural work on the respiratory health of farmers, few studies have reported on the respiratory health of dairy workers. Additionally, we are not aware of any published studies addressing the health impacts associated with large dairies in the western United States. The present investigation is a cross-sectional survey carried out at 13 dairies throughout the San Joaquin Valley. Data were collected from 232 dairy workers and 52 employees of a control facility (a vegetable processing plant) during a three-month period in 2008. Survey data collected included: socioeconomic status, respiratory health history, respiratory exposure history, work history, and current health symptoms. The dairy and control populations were almost all immigrant Latino and were similar in demographics, with two primary exceptions: dairy workers had higher incomes and had lived in the United States longer than the control employees. A substantial proportion of workers had never sought medical attention in the United States. Dairy work in California was associated with a significantly increased prevalence of asthmatic symptoms but not with significantly increased chronic cough, phlegm, or wheezing.
Implementing a Wormhole Attack on Wireless Sensor Networks with XBee S2C Devices

One of the most dangerous threats to Wireless Sensor Networks (WSN) are wormhole attacks, due to their capacity to manipulate routing and application data in real time and cause important damages to the integrity, availability, and confidentiality of network data. An empirical method to launch a successful attack on IEEE 802.15. 4/Zigbee devices with source routing enabled is adopted in this work to find signatures for detecting wormhole attacks in real environments. It uses the KillerBee framework with algorithms for packet manipulation through a malicious node to capture and inject malicious packets in victim nodes. Besides, a reverse variant of wormhole attack is presented and executed. To evidence the realization of this threat by the attacking software, the experimental framework includes XBee S2C nodes. The results include recommendations, detection signatures and future work to face wormhole attacks involving source routing protocols like DSR.
A New Ranking based Semantic Hashing Method for Deep Image Retrieval

Visual search in millions of samples in high-dimensional feature space is computationally expensive and challenging. A natural solution is to reduce the dimension of image representation by mapping each sample into compact binary code. In this paper, we propose a Ranking Based Semantic Hashing (RBSH) method to tackle with this problem. Observing that semantic structures carry complementary information, this paper takes advantage of semantic supervision for training high quality hashing, the semantic mapping between the high-dimensional feature space of samples and the reduced representation space of binary code, with the help of pre-trained word2vec. Specifically, the proposed method learns the mapping based on two criteria: the contrastive ranking loss and the orthogonality constraint. The former preserves the ordering of relative similarity in image pairs, while the latter makes different bit in the hash stream as orthogonal as possible. Extensive experimental study has been conducted on VOC2012 and ILSVRC2014 image sets, demonstrating that the proposed approach generally outperforms the state-of-the-art hashing techniques based methods in image search.
Real-time 3D work-piece tracking with monocular camera based on static and dynamic model libraries

Purpose This paper aims to propose a novel real-time three-dimensional (3D) model-based work-piece tracking method with monocular camera for high-precision assembly. Tracking of 3D work-pieces with real-time speed is becoming more and more important for some industrial tasks, such as work-pieces grasping and assembly, especially in complex environment. Design/methodology/approach A three-step process method was provided, i.e. the offline static global library generation process, the online dynamic local library updating and selection process and the 3D work-piece localization process. In the offline static global library generation process, the computer-aided design models of the work-piece are used to generate a set of discrete two-dimensional (2D) hierarchical views matching libraries. In the online dynamic library updating and selection process, the previous 3D location information of the work-piece is used to predict the following location range, and a discrete matching library with a small number of 2D hierarchical views is selected from dynamic local library for localization. Then, the work-piece is localized with high-precision and real-time speed in the 3D work-piece localization process. Findings The method is suitable for the texture-less work-pieces in industrial applications. Originality/value The small range of the library enables a real-time matching. Experimental results demonstrate the high accuracy and high efficiency of the proposed method.
Comparisons of Classification Methods in Efficiency and Robustness

Classification arises in a wide range of applications. A variety of statistical tools have been developed for learning classification rules from data. Understanding of their relative merits and comparisons helps users to choose a proper method in practice. This thesis focuses on theoretical comparison of model-based classification methods in statistics with algorithmic methods in machine learning in terms of the error rate. Extending Efron’s comparison of logistic regression with the LDA under the normal setting, we compare classification methods based on the limiting behaviour of the classification boundary of each method. In doing so, we contrast such algorithmic methods as the support vector machine and boosting with the LDA and logistic regression and study their relative efficiencies. The analytical results also indicate some bias in the support vector machine and its variants, and we propose a proper modification for removing the bias. Besides the comparison of classification methods in efficiency, we study their robustness to model-misspecification such as non normal setting and mislabeling. In addition to the theoretical study, we also present results from numerical experiments under various settings for comparisons of finite-sample performance.