Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher's Error Correction: A Key Factor in Developing Iranian EFL Learners' Speech Accuracy

The purpose of the present study was to investigate the effect of the teacher’s error correction on the accuracy of the EFL learners’ speech. In this regard the proposed null hypothesis is: “There is no significant relationship between the teacher’s syntactic error correction and the EFL learners’ speech accuracy”. In order to test the null hypothesis, 95 students who study English as a Foreign language at Azad university, Mashhad branch participated in a TOEFL test, 40 of them were ranked as “intermediate” and were randomly assigned to the experimental and control groups. Both groups took part in an interview performed by the researcher as the pre-test. During the treatment procedure, in the experimental group, the syntactic errors of the students were corrected by the teacher as soon as they committed them in their speech. On the other hand the syntactic errors of the students’ speech in the control group were totally ignored by the teacher. After the treatment, which took 16 sessions, the students participated in another interview as the post-test. Both the pre-test and post-test interviews plus all the class discussions during the treatment were tape recorded. The interviews were scored by five raters. In order to compare the means of the post-test scores of the experimental and control groups a t-test was calculated. The observed t was 2.072 and t critical was 2.021. Since the observed t exceeded the t critical, the null hypothesis was rejected. We can conclude that “There is a significant relationship between the teacher’s syntactic error correction and the EFL learners’ speech accuracy”. The results of this study can be useful for language educators in dealing with their students’ syntactic errors in speaking.
Random Forests based feature selection for decoding fMRI data

In this paper we present a new approach for the prediction of a behavioral variable from Functional Magnetic Resonance Imaging (fMRI) data. The difficulty in this problem comes from the huge number of image voxels that may provide relevant information with respect to the limited number of available images. A very common solution consists in using feature selection techniques, i.e. to evaluate the significance of each individual brain region with respect to the target information, and then to use the best ranked features as input to a classifier, such as linear Support Vector Machines (SVM; we take this as the reference method). However, this kind of scheme ignores the correlations between features, so that it is potentially suboptimal, and it does not generally provide an interpretable pattern of predictive voxels. Based on Random Forests, our approach provides an accurate auto-calibrated framework for selecting a set of very few jointly informative regions. Comparisons with the reference method on real data show that our approach yields a little bit higher classification performance, but the real gain comes from the sparsity of our variable selection.
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition

Distributed representation of words, or word embeddings, have motivated methods for calculating semantic representations of word sequences such as phrases, sentences and paragraphs. Most of the existing methods to do so either use algorithms to learn such representations, or improve on calculating weighted averages of the word vectors. In this work, we experiment with spectral methods of signal representation and summarization as mechanisms for constructing such word-sequence embeddings in an unsupervised fashion. In particular, we explore an algorithm rooted in fluid-dynamics, known as higher-order Dynamic Mode Decomposition, which is designed to capture the eigenfrequencies, and hence the fundamental transition dynamics, of periodic and quasi-periodic systems. It is empirically observed that this approach, which we call EigenSent, can summarize transitions in a sequence of words and generate an embedding that can represent well the sequence itself. To the best of the authors’ knowledge, this is the first application of a spectral decomposition and signal summarization technique on text, to create sentence embeddings. We test the efficacy of this algorithm in creating sentence embeddings on three public datasets, where it performs appreciably well. Moreover it is also shown that, due to the positive combination of their complementary properties, concatenating the embeddings generated by EigenSent with simple word vector averaging achieves state-of-the-art results.
User Acceptance Model of Computer-Based Assessment: Moderating Effect of Self-Regulation.

Computer-based assessment (CBA) is an important area of e-learning research. Most studies of CBA technology add new constructs to existing user acceptance models and rarely consider the moderating effects. However, the self-regulation (SR) levels (i.e., high or low) in an e-learning environment substantially affect individual learning behaviour and performance. This study investigates the moderating effects of SR levels on the relationships between factors in the CBA acceptance model, which was based on the unified theory of acceptance and use of technology (UTAUT) model. The main findings are that, firstly, both the perceived performance expectancy and social influence significantly affect CBA behavioural intention in all students regardless of their SR level. Secondly, the effect of effort expectancy on CBA behavioural intention is significantly larger in low-SR students compared to high-SR students. Finally, behavioural intention significantly predicts CBA use behaviour in high-SR students but not in low-SR students.
INTERNET-BASED LANGUAGE LEARNING AND TEACHING

This article explores teachers and students benefits of using Internet-based Language Learning (IBLL) which includes teaching English Language. This paper presents advantages and disadvantages of using internet resources and computers to learn and teach English language. Until quite recently, Internet- based Language Learning was a relevant topic mostly to those with a special interest in that area. Recently, though, the internet has become so widespread in educational institutions and homes and its uses have expanded so dramatically that the majority of English language teachers have started the implications of the internet for language learning. This article provides brief overview of how internet resources can be used for language teaching. It focuses not on a technical description of hardware and software but rather on pedagogical questions that teachers have considered in using internet resources in the classroom.
BERT-based Lexical Substitution

Previous studies on lexical substitution tend to obtain substitute candidates by finding the target word’s synonyms from lexical resources (e.g., WordNet) and then rank the candidates based on its contexts. These approaches have two limitations: (1) They are likely to overlook good substitute candidates that are not the synonyms of the target words in the lexical resources; (2) They fail to take into account the substitution’s influence on the global context of the sentence. To address these issues, we propose an end-to-end BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources. Our approach first applies dropout to the target word’s embedding for partially masking the word, allowing BERT to take balanced consideration of the target word’s semantics and contexts for proposing substitute candidates, and then validates the candidates based on their substitution’s influence on the global contextualized representation of the sentence. Experiments show our approach performs well in both proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.
Learning to recognize human actions: from hand-crafted to deep-learning based visual representations

El reconocimiento de acciones es un reto de gran relevancia para la vision por computador. Los investigadores que se hallan trabajando en el campo aspiran a dotar a los ordenadores de la habilidad de percibir visualmente las acciones humanas - esto es observar, interpretar y comprender a partir de datos visuales los eventos que involucran humanos y que trascurren en el entorno fisico. Las apllcaclones de esta tecnologia son numerosas: interaccion hombre -maquina, e-salud, monltorlzacion/vldeovigllancia, u obtencion de vfdeocontenldo basado en contenido, entre otros. Los metodos de diseno manual han dominado el campo hasta los primeros exitos del aprendizaje profundo en el reconocimiento de acciones. Aunque estos ultimos obtuvieran peores resultados que los manualmente disenados, han mejorado lenta pero constantemente hasta convertirse hoy en estado del arte. No obstante, los metodos de diseno manual pueden resultar particularmente adecuados en ciertos escenarios, asi como cuando los datos de entrenamiento son escasos o, simplemente, para aportar un conocimiento adicional que las redes profundas no son capaces de aprender facilmente . Es por eso que a menudo encontramos ambos paradigmas combinados, consiguiendo una mejora de los resultados de los metodos en general. Esta tesis ha concurrido en el tiempo con este cambio de paradigma y, por tanto, lo refleja en sus dos partes claramente diferenciadas. En la primera parte, analizamos las posibles mejoras sobre los metodos ya existentes de diseno manual para el reconocimiento de acciones, y lo hacemos desde diferentes perspectivas. Haciendo uso de las trayectorias densas como fundamento de nuestro trabajo: primero, exploramos el uso de datos de entrada de diversas modalidades visuales y diferentes vistas para enriquecer los descriptores de las trayectorias. Segundo, nos centramos en la parte de clasificacion del reconocimiento de acciones, proponiendo un ensamblado de clasificadores que actuen sobre distintos conjuntos de caracteristicas y fusionando las salidas de estos con una estrategia basada en la Teorfa de Dempster -Shaf er. Y tercero, proponemos un nuevo metodo de diseno manual de extraccion de caracteristicas que construye una descripcion Intermedia para modelar de manera mas efectiva las dinamicas espaclotemporales de largo plazo presentes en los videos. La segunda parte de la tesis, empieza con un estudio exhaustivo de los metodos actuales de aprendizaje profundo para el reconocimiento de acciones. Analizamos las metodologias y presentamos una taxonomia que resume sus aspectos mas Importantes. Mas concretamente, analizamos y discutimos como las propuestas tratan la Informacion temporal de los datos. Por ultimo, pero no menos Importante, proponemos una nueva red de neuronas recurrente con conexiones residuales que integra de forma Implicita nuestras contribuciones previas en un marco teorico potente y experimentalmente prometedor.
A Novel Architecture for Mobile ECG Recognition Systems Using Hybrid Wavelet Transform Feature Extraction Schemes

For years, the cardiovascular disease has severely endangered the human life. One key point of all critical cares is to continuously monitor whoever is in a high-risk of sudden attack, and to immediately alert for professionally on-time aids. Thus, in this research, a novel architecture to aid patients to be with caretakers wherever and whenever they are is proposed. This research describes instruments and components of the key design of the architecture including a continuous monitoring module integrating ECG measurement units with RF (Bluetooth) transmission module embedded in an easy-to-use openplatform mobile phone for relaying the signal to ECG recognition systems. The mobile system can interact with mobile doctors, patient’s relatives or even volunteers as well as emergency units, especially to deliver patient condition information and GPS location for further professional aids. In addition, to reduce the false alarm of ECG recognition, we proposed the hybrid scheme applying additional features extracted over a traditional wavelet transform technique resulting in a high recognition precision with PNN classification based on a well-known MIT-BIH arrhythmia database.
Joint Beamforming and User Association Scheme for Full-Dimension Massive MIMO Networks

In this paper, we study the joint cooperative beamforming and user association scheme for multi-cell multi-user full-dimension massive multiple-input multiple-out (MIMO) networks, where each user equipment is dynamically associated with one of the base stations. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and a binary vector that represents the user association decisions. The binary vectors are further transformed into binary beam association factors (BAFs) as indicators of both the beamforming and the user association decisions. A three-step Gaussian belief propagation (GaBP) based distributed solver is proposed to solve the non-convex NP-hard problem considered in this paper. By relaxing the binary BAFs, we first transform the optimization problem into a linear programming. Subsequently, GaBP is used to efficiently obtain a feasible solution on BAFs in parallel, and two mapping algorithms are proposed to achieve different performance-complexity tradeoffs. Simulation results show that the proposed cooperative beamforming methods significantly outperform the benchmarks in the literature in terms of the network capacity with a low computation complexity.
Applications of Data Mining in Healthcare

Peng, Bo. M.S., Purdue University, May 2019. Applications of Data Mining in Healthcare. Major Professor: George Mohler. With increases in the quantity and quality of healthcare related data, data mining tools have the potential to improve people’s standard of living through personalized and predictive medicine. In this thesis we improve the state-of-the-art in data mining for several problems in the healthcare domain. In problems such as drug-drug interaction prediction and Alzheimer’s Disease (AD) biomarkers discovery and prioritization, current methods either require tedious feature engineering or have unsatisfactory performance. New effective computational tools are needed that can tackle these complex problems. In this dissertation, we develop new algorithms for two healthcare problems: highorder drug-drug interaction prediction and amyloid imaging biomarker prioritization in Alzheimer’s Disease. Drug-drug interactions (DDIs) and their associated adverse drug reactions (ADRs) represent a significant detriment to the public health. Existing research on DDIs primarily focuses on pairwise DDI detection and prediction. Effective computational methods for high-order DDI prediction are desired. In this dissertation, I present a deep learning based model DI for cardinality-invariant and order-invariant high-order DDI prediction. The proposed models achieve 0.740 F1 value and 0.847 AUC value on high-order DDI prediction, and outperform classical methods on order-2 DDI prediction. These results demonstrate the strong potential of DI and deep learning based models in tackling the prediction problems of high-order DDIs and their induced ADRs. The second problem I consider in this thesis is amyloid imaging biomarkers discovery, for which I propose an innovative machine learning paradigm enabling precision medicine in this domain. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. I implement this paradigm using a newly developed
A Literature Survey on Intrusion Detection System in Manets using Machine Learning Techniques

Since, a decade of time Mobile Ad hoc Networks (MANETs) have come with wireless networking technology. Due to its dynamic in nature of MANETs, these are vulnerable to various attacks in all the OSI layers but research shows that in Network layer the attacks are effectively done by intruders. In this survey many of the attacks at Network layer are identified in MANETs by most of the researchers which is outlined in this paper. Mostly, AODV routing protocols and other protocols are used for transferring packets in the direction of the destination. These transferred packets data is then deposited within the log files, to surveillance these routing of packets from these Log files, the techniques used in MANETs are Data mining, Support Vector Machines (SVM), Genetic algorithms (GA) and other Machine learning approaches. Further, the methodologies and techniques proposed for detecting and predicting these attacks from various kinds of intrusions within the MANETS is discussed.
Zero-Shot Semantic Parsing for Instructions

We consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from 7 application domains (e.g. a calendar or a file manager), each example consisting of a triplet: (a) the application’s initial state, (b) an instruction, to be carried out in the context of that state, and (c) the state of the application after carrying out the instruction. We introduce a new training algorithm that aims to train a semantic parser on examples from a set of source domains, so that it can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and Liang (2015), and further augment the parser with features and a logical form candidate filtering logic, to support zero-shot adaptation. Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.
Flipped Classroom Method for the Teacher Training for Secondary Education: A Case Study in the University of Granada, Spain

Flipped Classroom methodology has strongly introduced in the classrooms of different educational levels, but above all in the University. This methodolo-gy could be defined as the reversal of roles in the classroom, where the stu-dent acquires theoretical knowledge outside of the classroom and becomes a space for resolving doubts and cooperative work. Thus, this paper analyzes the experience carried out at the University of Granada with students of the Master's Degree in Secondary School Training where Flipped Classroom methodology has been applied. The research methodology used is quantita-tive, so that an ad hoc questionnaire on a Likert scale was used to obtain the data. Among the obtained results, it is observed that the students perceive an improvement in their academic performance and the improvement of the dif-ferent personal and social skills. Finally, it can be concluded that this kind of experiences where the Flipped Classroom methodology is applied favors the development of skills depending of subject taught and the field of knowledge.
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References

Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses. Previous models are generally trained based on 1-to-1 mapping from an input query to its response, which actually ignores the nature of 1-to-n mapping in dialogue that there may exist multiple valid responses corresponding to the same query. In this paper, we propose to utilize the multiple references by considering the correlation of different valid responses and modeling the 1-to-n mapping with a novel two-step generation architecture. The first generation phase extracts the common features of different responses which, combined with distinctive features obtained in the second phase, can generate multiple diverse and appropriate responses. Experimental results show that our proposed model can effectively improve the quality of response and outperform existing neural dialogue models on both automatic and human evaluations.
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction

In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential and regional features of the text, and a complete word graph is further utilized to extract implicit features among all word pairs of the text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show that GraphRel maintains high precision while increasing recall substantially. Also, GraphRel outperforms previous work by 3.2% and 5.8% (F1 score), achieving a new state-of-the-art for relation extraction.
Visual Surveillance on DSP-Based Embedded Platforms Clemens

Visual surveillance has become an important topic of research in the last few years due to the increased need for security in public places and the unbowed trend to use digital cameras for surveillance purposes, or for integration of visual sensors in other objects of personal use, like mobile phones or PDAs. To perform dedicated operations at camera site, embedded platforms, so-called Smart Cameras, have become popular recently. These platforms are equipped with one or multiple video sensors and enough computational power to process the data stream onboard. Moreover, these platforms have additional advantages, like robustness against environmental stress or low power consumption. The use of smart sensors facilitates the building of large networks, and the local processing paradigm allows for extracting valuable information at site and transmission over low-cost and low-bandwidth communication channels. However, until now, little work has been done on the development and investigation of state-of-the-art algorithms for surveillance from the field of computer vision in respect of embedded systems. On this account, in this thesis we focus on the tasks of object detection and object recognition on smart cameras. We discuss several issues of algorithm development on embedded DSP-based platforms, especially the issues related to parallelism mechanisms and fixed-point arithmetic. Given an extensive overview about current work on object detection and object recognition, we investigate both fields of development in detail. After discussing the basic algorithm, we experimentally demonstrate the suitability of the approach, derived in this thesis, for performing object detection in real-time on a real-world traffic surveillance scenario, given a prototypical DSP-based hardware platform. Our results encourage the integration of our algorithm into a larger system for public surveillance. In the context of object recognition, we demonstrate, how state-of-the-art recognition technology can be used to deploy reasonable recognition capabilities on smart cameras. After proving the suitability on a moderate size object database, we show how our algorithm can be used in a traffic surveillance scenario for recognition and reacquisition of vehicles on public streets. The main advantages of our approach are the high accuracy and the minimization in communication necessary between adjacent camera motes. By investigating our approach in detail, we are able to draw general conclusions and statements about the suitability of different aspects and methods of software development on DSP-based embedded systems.