Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing and evaluating corpus-based feedback

This dissertation outlines the development of a system of corpus-based feedback and evaluates its effects on students’ error correction and language learning. The design of the corpus-based feedback used in the study was informed by two SLA theories: the Interactionist Approach and Skill-learning Theory. The dissertation theorizes all aspects of the feedback design and discusses the various features of corpus-based feedback from SLA theoretical perspectives. To evaluate the quality of corpus-based feedback, the study compared it to that of traditional coded feedback. Based on the logical framework for evaluating CALL corrective feedback and Chapelle’s evaluation criteria (2001), five research questions were developed concerning three qualities of CALL corrective feedback: language learning potential, learner fit, and impact. To address the five research questions concerning language learning potential, learner fit, and impact, the study employed a mixed-methods approach to collect quantitative and qualitative data from 90 participants to evaluate and compare the effects of corpus-based and coded feedback. The quantitative data consisted of scores measuring the immediate effects of feedback in error correction exercises, scores measuring learning gains demonstrated by the preand post-tests, and students’ ratings of the effects of feedback in Likert-scale questionnaires. The qualitative data consisted of think-aloud exercises and semi-structured interviews, which played an important role in describing participants’ learning experiences with the intervention. Compared to students using traditional coded feedback, students treated with corpus-based feedback had greater success correcting errors in two error correction practice exercises and transferring knowledge to correct errors in a new text during the
Predicting STOP-Sign Compliance at All-Way Stop Intersections in Close Proximity to Signalized Intersections

STOP-signs (at unsignalized intersections) that are in close proximity to signalized intersections are often violated by drivers while “speeding up” to go through the upstream or downstream signalized intersection that have the green interval upon approach. It is thought that if the distance between the upstream or downstream signalized and the AWSC intersection is long, drivers usually comply with the STOP signs at the AWSC intersection. This research determined driver compliance rates (CRs) at All-Way STOP Control (AWSC) intersections that are in close proximity to upstream or downstream signalized intersections and, explored the existence of a relationship between CR and the distance between a pair of signalized and AWSC intersections. Thirty (30) isolated segments with combinations of signalized and AWSC intersections in the District of Columbia were selected for the study. Field data (traffic volumes) were obtained at each intersection in addition to observation of driver compliance with STOP signs at AWSC intersections via video playback. In all, 13,956 observations were made at 57 AWSC intersections in 2017. The study showed that lower CRs were observed at AWSC intersections that are in closer proximity to the signalized intersections. Thus, the shorter the distance from the existing AWSC to signalized intersections, the lower the CR (or higher violation rate). Based on the data obtained, a non-linear relationship between CR and distance between pairs of intersections was developed. From the model, to achieve a minimum compliance rate of 95% at an AWSC, an optimal distance of approximately 1,300 feet between the intersections
Essays on bivariate option pricing via copula and heteroscedasticity models: a classical and bayesian approach

LOPES, L. P. Essays on bivariate option pricing via copula and heteroscedasticity models: a classical and bayesian approach. 2019. 94 p. Dissertação (Mestrado em Estatística – Programa Interinstitucional de Pós-Graduação em Estatística) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2019. This dissertation is composed of two main and independents essays, but complementary. In the first one, we discuss the option price under a bayesian perspective. This essay aims to price and analyze the fair price behavior of the call-on-max (bivariate) option considering marginal heteroscedastic models with dependence structure modeled via copulas. Concerning inference, we adopt a Bayesian perspective and computationally intensive methods based on Monte Carlo simulations via Markov Chain (MCMC). A simulation study examines the bias and the root mean squared errors of the posterior means for the parameters. Real stocks prices of Brazilian banks illustrate the approach. For the proposed method is verified the effects of strike and dependence structure on the fair price of the option. The results show that the prices obtained by our heteroscedastic model approach and copulas differ substantially from the prices obtained by the model derived from Black and Scholes. Empirical results are presented to argue the advantages of our strategy. In the second chapter, we consider the GARCH-in-mean models with asymmetric variance specifications to model the volatility of the assets-objects under the risk-neutral dynamics. Moreover, the copula functions model the joint distribution, with the objective of capturing non-linear, linear and tails associations between the assets. We aim to provide a methodology to realize a more realistic pricing option. To illustrate the methodology, we use stocks from two Brazilian companies, where our the modeling offered a proper fitting. Confronting the results obtained with the classic model, which is an extension of the Black and Scholes model, we note that considering constant volatility over time underpricing the options, especially in-the-money options.
Requirements elicitation for robotic and computer-assisted minimally invasive surgery

The robotic surgical systems and computer-assisted technologies market has seen impressive growth over the last decades, but uptake by end-users is still scarce. The purpose of this article is to provide a comprehensive and informed list of the end-user requirements for the development of new generation robot- and computer-assisted surgical systems and the methodology for eliciting them. The requirements were elicited, in the frame of the EU project SMARTsurg, by conducting interviews on use cases of chosen urology, cardiovascular and orthopaedics procedures, tailored to provide clinical foundations for scientific and technical developments. The structured interviews resulted in detailed requirement specifications which are ranked according to their priorities. Paradigmatic surgical scenarios support the use cases.
An automatic short-answer grading model for semi-open-ended questions

ABSTRACT Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This method performs well for closed-ended questions that have single or very limited numbers of correct answers. However, some short-answer questions ask students to express their own thoughts based on various facts; hence, they have no reference answers. Such questions are called semi-open-ended short-answer questions. Questions of this type often appear in reading comprehension assessments. In this paper, we developed an automatic semi-open-ended short-answer grading model that integrates both domain-general and domain-specific information. The model also utilizes a long-short-term-memory recurrent neural network to learn the representation in the classifier so that word sequence information is considered. In experiments on 7 reading comprehension questions and over 16,000 short-answer samples, our proposed automatic grading model demonstrates its advantage over existing models.
Context-Aware Modeling Using Semantic Web and Z Notation

Surveys in user context modeling have shown that the semantic web is one of the promising approach to represent and structure the contextual information captured from user’s surrounding environment in a context-aware application. A benefit of using semantic web language is that it enables application to reason user contextual information in order to get the knowledge of user’s behavior. However, regarding its notation format, semantic web is suitable for implementation level or to be consumed by application run-time. Context-aware application is a part of distributed computing system. In distributed computing system, the language used for specification should be distinguished from the implementation / run-time purpose. This is known as separation of modeling language. Regarding the context-aware application, for those who are concerned with specification of context modeling, the language that is used for specification should also be distinguished from the implementation one. This thesis aims at proposing the use of formal specification technique to develop a generic context ontology model of user’s behavior at the Computer and Information Sciences Department, Universiti Teknologi PETRONAS. Initially, the context ontology was written in OWL semantic web language. The further process is mapping onto a formal specification language, i.e. onto Z notation. As a result, specification of context ontology and its consistency checking have been developed and verified beyond the semantic web language environment. An inconsistency of context model has been detected during the verification of Z model, which cannot be revealed by current OWL DL reasoner. The context-aware designers might benefit from the formal specification of context ontology, where the designers could fully use formal verification technique to check the correctness of context ontology. Thus, the modeling approach in this thesis has shown that it could complement the context ontology development process, where the checking and refinement are performed beyond the semantic web reasoner
Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot

Since the launch of Landsat-1 in 1972, the scientific domain of geo-information has been incrementally shaped through different periods, due to technology evolutions: in devices (satellites, UAV, IoT), in sensors (optical, radar, LiDAR), in software (GIS, WebGIS, 3D), and in communication (Big Data). Land Cover and Disaster Management remain the main big issues where these technologies are highly required. Data fusion methods and tools have been adapted progressively to new data sources, which are augmenting in volume, variety, and in quick accessibility. This Special Issue gives a snapshot of the current status of that adaptation, as well as looking at what challenges are coming soon.
Visual-spatial Attention and Reading Achievement in Hong Kong Chinese Children: Evidence from a One-year Longitudinal Study

ABSTRACT This study examined whether visual-spatial attention could predict Chinese reading achievement longitudinally beyond some well-established reading precursors. A total of 257 second- and third-grade Hong Kong Chinese-speaking children participated in the study. Visual-spatial attention was measured by visual search tasks, which assessed the skills in highly inefficient and efficient searches. Chinese character reading, word reading fluency and reading comprehension skills were measured 1 year later. Latent variable path analysis showed that the skill in inefficient search uniquely predicted later word reading after the model controlled for phonological awareness, vocabulary knowledge, orthographic awareness, age and nonverbal IQ; while its effect on reading comprehension was completely mediated by word reading. Moreover, the skill in highly efficient search was not a unique predictor of both reading skills. The findings indicated the roles of top-down visual-spatial attention for word- and text-level Chinese reading.
Driving inventory system simulations with limited demand data: Insights from the newsvendor problem

ABSTRACT Stochastic inventory system simulation is often the tool of choice by industry practitioners who struggle with the evaluation of the quality of proposed inventory targets using service levels. However, driving simulations with unknown input distribution parameters has its own challenges. In this paper, we focus on the newsvendor problem and quantify the amount of demand parameter uncertainty – the uncertainty around the unknown demand distribution parameters which are estimated from the limited historical demand data – in the confidence interval of the mean service level. We use this quantification to understand how the variance of the mean service level, due to the amount of the demand parameter uncertainty in the simulation output process, changes with the choice of Type-1 and Type-2 service-level criteria, the historical data length, the ratio of the unit shortage cost to the unit holding cost, and the distributional shape of the demand’s density function.
Enterprise cybersecurity training and awareness programs: Recommendations for success

ABSTRACT To help employees recognize and change their computing security behavior, organizations need to invest in cybersecurity training and awareness programs to encourage their employees’ active engagement in complying with their security policies. However, many organizations’ cybersecurity training and awareness programs fail to achieve their goals as employees feel bored with such training programs and lack enthusiasm to participate in them. Highlighting the criticality of the success of cybersecurity training and awareness programs in organizations, this paper identifies best practices and provides actionable insights (relating cyber awareness to employees’ personal life, reinforcing security procedures and guidelines, instilling a “relaxed alert” state of employees, and minimizing security fatigue for employees) that will help enterprises develop and implement economical, effective, and engaging cybersecurity training and awareness programs.
Automatic stenosis grading system for diagnosing coronary artery disease using coronary angiogram

The coronary angiogram is considered as the golden standard for diagnosing the coronary artery disease. The paper proposes a system that helps to describe the level of stenosis in coronary angiogram image by using mathematical morphology and thresholding technique. A novel method is introduced to determine the percentage of stenosis and its grading. Based on the diagnostic results, myocardial infarction (MI) is treated well-in-advance. A real time clinical dataset consisting of 25 conventional coronary angiographies with 865 frames is used to evaluate the performance of the proposed system. The execution of the proposed system is inspected by a cardiologist and confirmed the system has produced excellent segmentation and stenosis grading automatically. Sensitivity, specificity, accuracy and precision of the system are 94.74%, 83.33%, 92% and 94.74%, respectively with an average computational time of 0.84 sec. Kappa value also shows perfect system agreement for stenosis grading.
Procedure to Prepare and Model Speed Data Considering the Traffic Infrastructure, as Part of a Cyber-Physical System

This chapter investigates the relationship between traffic control infrastructure (traffic lights and speed bumps) and the vehicles’ travel speeds, for certain hours and days of the week. The authors propose the following procedures: (1) street segmentation, (2) clustering and categorization of speed data, (3) histograms’ comparison analysis, (4) outlier detection, (5) modeling, and (6) delivering info to the users. Comparing speed histograms, segments with matching infrastructure presented similarities, regardless of the day of the week. Two techniques to model data were employed: polynomial regression and multinomial logistic regression. The algorithms to predict the travel speed category were also developed. The first technique yields on average 91.3% of data categorized correctly, and the second gets 90.09%. The traffic lights and speed bumps, located on the street segments under consideration, were identified as variables causing different travel speeds. The procedure allows to incorporate more traffic elements and can also be applied to other geographical locations.
Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.
Learning to play the Game of Hearts using Reinforcement Learning and a Multi-Layer Perceptron

The multi-agent card game Hearts is learned by the use of Monte Carlo learning and a Multilayer Perceptron. This card game has imperfect state information and is therefore harder to learn than perfect information games. A few different parameters will be looked at to attempt to find out which combination is most promising. Most importantly, two activation functions, namely Sigmoid and a Leaky version of the Rectified Linear Unit (ReLU), will be compared and a variation in the amount of hidden layers will be studied. After experimentation it is concluded that the Sigmoid function is outperformed by the ReLU. Multiple hidden layers seem to slow the learning process down and do not improve performance.
Artificial Intelligence in Light-Source Design

The advent of light-emitting diode (LED) light sources has led to a new freedom in the design of light-source spectra, and it is now possible to optimise for different source performance parameters, which is the principal aim of the authors ’ work. LEDs and lasers are real or potential light sources, and are inherently monochromatic, that is, narrow-band sources, with typical optical bandwidths in the range 20 – 40 nm (nanometres) for LEDs and 1 – 5 nm for diode lasers. Mixtures of three or more can be used to produce nominally white light of the type acceptable for general purpose lighting. It is a characteristic of all types of sources that there is a trade-off between good colour properties and high efficiencies, and the methods described here are directed towards an optimum combination of such parameters. This chapter will explain the use of differential evolution (DE) as a highly effective heuristic approach to optimisation, and proceeds to explain the structure and operation of a DE algorithm designed as an optimisation tool for such purposes.
Hierarchical forecasting of engineering demand at KLM Engineering & Maintenance

Engineering demand for KLM Engineering and Maintenance consists of a varied collection of tasks for different customers and aircraft on routine and non-routine basis. Every combination of these characteristics presents a unique demand subset that in turn has its own behaviour. A total of 1716 different subsets of demand are present each of which could require its own forecast. Accurately forecasting these different series has proven to be challenging with the current forecasting approach. Leading to difficulties in accuratly matching engineering capacity to demand We propose a framework that applies multiple models and leverages the benefits of forecast combination and reconciliation. Different forecasting models are able to extract different information from the demand subset resulting in different forecasts. Using multiple models ensures that we are able to extract sufficient information from all the different subsets without risking model miss-specification. Combining the resulting forecasts creates forecasts that combine the information extracted by the different models. By leveraging multiple sources of information combined forecasts are more accurate than single forecasts. We then select the most accurate forecast through the mean absolute scaled error (MASE), resulting in a single best forecast for each of the demand subsets. Each of the 1716 different subsets end up with an individual forecast but these are incoherent between the levels of aggregation. The forecasts for each aircraft type demand do not sum equal to the forecast of total demand. Both the sum and total demand represent the same data but their forecasts are based on different information and therefore do not align. Reconciliation aligns all the forecasts on all levels by introducing minimal errors. As a result we end up with a single forecast aligned for all different subsets of demand. this is important to enable aligned decisions for capacity on both tactical and operational levels. Testing the proposed framework indicate that significant improvements are possible. 20% gains are achieved, in terms of the MASE, by the proposed framework compared to the current approach. By translating the results to yearly totals we find that our approach consistently forecasts the yearly total within 1% of the actual values. Reducing the error margin in 2017 by 89% from an overestimation of 9,3 FTE to 0,9 FTE. The increased accuracy allows better and more flexible decisions over capacity.
SCSE: Boosting Symbolic Execution via State Concretization

Symbolic execution is capable of automatically generating tests that achieve high coverage. However, its practical use is limited by the scalability problem. To mitigate it, this paper proposes State Concretization based Symbolic Execution (SCSE). SCSE speeds up symbolic execution via state concretization. Specifically, by introducing a concrete store, our approach avoids invoking the constraint solver to check path feasibility at conditional instructions. Intuitively, there is no need to check the feasibility of a path along a concrete execution since it is always feasible. With state concretization, the number of solver queries greatly decreases, thus improving the efficiency of symbolic execution. Through experimental evaluation on real programs, we show that state concretization helps to speed up symbolic execution significantly. key words: state concretization, symbolic execution, constraint solving