Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ability of Supplying the Elliptical Items Omitted From a Written Text and its Impact on the Reading Comprehension of 4th Year Libyan University Students Studying EFL

This paper investigates the ability of supplying the elliptical items omitted from a written text and its impact on the reading comprehension of Libyan university students studying EFL. Ellipsis can be defined as the omission of one or more lexical items that are understood in the context, but which are required to make the sentence or utterance grammatically correct. An elliptical item could be nominal, verbal or clausal. 4 th year English Department students from the English Department- Faculty of Arts- Gharian in Al-Jabal Al-Gharbi University completed reading comprehension test. Seventy one out of a hundred and four students accepted to participate in the study. The test was modified to include many elliptical items and the students were asked to supply them. The results, which were statistically analyzed, suggested that the study participants were not able to supply the correct elliptical items and consequently had poor reading comprehension results. The mean marks achieved were only 33.8. This result may have many pedagogical implications.
Simulating Tsunami Shallow-Water Equations with Graphics Accelerated Hardware (GPU) and Radial Basis Functions (RBF)

The rapid growth curves in the speed of GPUs relative to CPUs and its ever-growing popularity have generated a new area in computational technology. We are concerned with modeling tsunami waves, where computational time is of extreme importance. We have employed the NVIDIA board 8600M GT on a MacPro laptop computer to test the efficacy of the GPU on the set of shallow-water equations. We have compared the relative speeds between CPU and GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions, which is a more novel method based on a gridless and a multi-scale, adaptive framework. For the NVIDIA 8600M GT we found a speed up by a factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. The time steps employed for the RBF method are larger than those used in finite-differences. This ratio of time steps, favoring the RBFs over the finite-difference points, increases with the number of grid points employed. Thus, in modelling tsunami waves over the same physical time transpired, RBF acting in concert with GPU would hold great promise because of the spectacular reduction in the computational time.
Enigma: an image retrieval system

The paper presents the functionality of a system which retrieves images on the basis of automatically generated indexes (i.e. semantic image representations, obtained by automatic image analysis, indicating the content of the images). The system consists of two parts: an off-line indexing part and an on-line image retrieval part. The indexing component is used to automatically generate semantic representations of images so that the image retrieval component can use this information to enable image retrieval. The man-machine communication of the image retrieval component is based on an iconical graphical query language to accomplish geographical query specification for image access. Experiments have been carried out on three different sets of images from the following domains: MRI images of the chest, electronic schemas and topographic maps. The experiments show encouraging results especially for domains which have a high degree of formality in their pictorial expression, such as electronic schemas and topographic maps, and to a less extent for domains having a weak degree of formality such as MRI images of the chest.
Assessment of the Relationship between CVD and CKD in a Community based Survey

Objective: The aim of this study was to assess the relationship between Chronic Kidney Disease (CKD) and cardiovascular Diseases (CVD) in a community based Survey. Methodology: This was a community based survey, investigated 2944 individuals for CKD and CVD in Northern Saudi Arabia. Results: Of the 2944 Glomerular infiltration rate was measured for 2700/2944 (91.7%). Of the 2700, Congestive heart failure (CHF), Heart attack, Stroke and Peripheral vascular disease (PVD), were identified in 27, 52, 41 and 58 patients, respectively. Conclusion: CVDs were highly connected to CKD among general population. There is strong relationship between CVD and CKD among those with undetected CKD. The most common CVDs were CHF, Heart Attack, Stroke and PVD.
Event based neutron activation spectroscopy and analysis algorithm using MLE and metaheuristics

Techniques used in neutron activation analysis are often dependent on the experimental setup. In the context of developing a portable and high efficiency detection array, good energy resolution and half-life discrimination are difficult to obtain with traditional methods (1) given the logistic and financial constraints. An approach different from that of spectrum addition and standard spectroscopy analysis (2) was needed. The use of multiple detectors prompts the need for a flexible storage of acquisition data to enable sophisticated post processing of information. Analogously to what is done in heavy ion physics, gamma detection counts are stored as two-dimensional events. This enables post-selection of energies and time frames without the need to modify the experimental setup. This method of storage also permits the use of more complex analysis tools. Given the nature of the problem at hand, a light and efficient analysis code had to be devised. A thorough understanding of the physical and statistical processes (3) involved was used to create a statistical model. Maximum likelihood estimation was combined with metaheuristics to produce a sophisticated curve-fitting algorithm. Simulated and experimental data were fed into the analysis code prompting positive results in terms of half-life discrimination, peak identification and noise reduction. The code was also adapted to other fields of research such as heavy ion identification of the quasi-target (QT) and quasi-particle (QP). The approach used seems to be able to translate well into other fields of research.
Depth of anaesthesia assessment based on time andfrequency features of simplified electroencephalogram (EEG)

Anaesthesiology is a medical subject focusing on the use of drugs and other methods to deprive patients’ sensation for discomfort in painful medical diagnosis or treatment. It is important to assess the depth of anaesthesia (DoA) accurately since a precise as- sessment is helpful for avoiding various adverse reactions such as intraoperative awareness with recall (underdosage), prolonged recovery and an increased risk of post- operative complications for a patient (overdosage). Evidence shows that the depth of anaesthesia monitoring using electroencephalograph (EEG) improves patient treat- ment outcomes by reducing the incidences of intra-operative awareness, minimizing anaesthetic drug consumption and resulting in faster wake-up and recovery. For an accurate DoA assessment, intensive research has been conducted in finding 'an ulti- mate index', and various monitors and DoA algorithms were developed. Generally, the limitations of the existing DoA monitors or latest DoA algorithms include unsatis- factory data filtering techniques, time delay and inflexible. The focus of this dissertation is to develop reliable DoA algorithms for accurate DoA assessment. Some novel time-frequency domain signal processing techniques, which are better suited for non-stationary EEG signals than currently established methods, have been proposed and applied to monitor the DoA based on simplified EEG signals based on plenty of programming work (including C and other programming language). The fast Fourier transform (FFT) and the discrete wavelet transforms are applied to pre-process EEG data in the frequency domain. The nonlocal mean, mobility, permu- tation entropy, Lempel-Ziv complexity, second order difference plot and interval feature extraction methods are modified and applied to investigate the scaling behaviour of the EEG in the time domain. We proposed and developed three new indexes for identifying, classifying and monitoring the DoA. The new indexes are evaluated by comparing with the most popular BIS index. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. The results also demonstrate the advantages of proposed indexes in the cases of poor signal quality and the consistency with the anaesthetists’ records. These new indexes show a 3.1-59.7 seconds earlier time response than BIS during the change from awake to light anaesthesia and a 33-264 seconds earlier time response than BIS during the change from deep anaesthesia to moderate anaesthesia.
A Reversible Cardiomyopathy

Dilated cardiomyopathy is a diagnosis that usually carries a grave prognosis. However among the numerous cases of dilated cardiomyopathy lurk a few that are acute, fulminant and potentially reversible. It is a challenge for a cardiologist to suspect this and successfully treat one . It is gratifying when the patient recovers and goes home. Similarly myopathies are chronic debilitating diseases that are associated with considerable morbidity . It has been observed that some myopathies are reversible. The hall mark of such a myopathy is it’s acute fulminant onset. Researchers have recommended that whenever an acute onset myopathy is found a reversible cause should be looked for.
Approximate short-scan filtered-backprojection for helical CB reconstruction

Two approximate reconstruction algorithms are proposed for helical cone-beam CT. One involves rebinning of CB data into 2-D fan-beam sinograms, while the other one uses 3-D cone-beam backprojection. Both methods uses the short-scan idea of 2-D fan-beam CT to limit the obliqueness of the CB ray-sums involved in the reconstruction of each transaxial slice. Performances of the two algorithms are illustrated using the 3-D Shepp phantom and a disks phantom.
Anthropometric Markers in Relation to Postprandial Hyperinsulinemia in Middle-Aged Adults

Our objective was to evaluate the relationship between postprandial hyperinsulinemia with three anthropometric markers of adiposity; body mass index (BMI), waist hip ratio (WHR) and body fat percentage (BFP). We carry out a retrospective observational study reviewed medical records of 752 patients who attended outpatient endocrinology, no previous diagnosis of metabolic disease, but with a family history of metabolic risk in the period 2012 to 2014. We collected demographic, anthropometric measurements (BMI, WHR, and BFP) and postprandial insulin values (quartiles 1, 2, 3 and 4) between 30 to 60 minutes into oral glucose tolerance test. For testing the association between anthropometric measurements and postprandial insulin quartiles, we use a univariate and multivariate multinomial logistic regression model. The measure of association is presented as relative prevalence ratio (PR) with confidence intervals (CI) at 95%. The mean age was 37.5 years ± 14.8 and 66% of the participants were women. In the univariate analysis using quartile 1 as references, quartiles 3 and 4 were associated with BMI, WHR and BFP. In multivariate analysis, quartiles 3 and 4 remained associated with markers of adiposity. BMI; PR = 1.15, 95% CI (1.10 to 1.22) and PR = 1.25, 95% CI (1.19 to 1.32) for quartiles 3 and 4; respectively. WHR; PR = 3.25, 95% CI (2.17 to 4.87) and PR = 5.85, 95% CI (3.76 to 9.10) for quartiles 3 and 4 respectively. BFP; PR = 1.09, 95% CI (1.05 to 1.12) and PR = 1.14, 95% CI (1.10 to 1.18) for quartiles 3 and 4; respectively. In conclusion, the diagnosis of obesity must be carried out using the combination of measures of adiposity in order to evaluate the total fat content and distribution of body fat.
Performance of hybrid ARQ scheme based on bit-to-symbol mapping

This paper considers coded packet retransmission diversity scheme that reduce the probability of error. The proposed retransmission diversity scheme employs bit-interleaved coded modulation (BICM) and varies the bit-to-symbol mapping among multiple transmissions of the same packet (e.g., ARQ). The obtained mappings for forthcoming transmissions is chosen such that the sum of the magnitudes of the LLR of the bits forming the symbols in different (re)transmissions is maximized. Our performance results show that the proposed scheme performs better (in terms of BER) than a retransmission scheme transmitting same mappings all the time (e.g., Chase combining).
Visualization Systems Supporting the Reading of Arabic Document for non Arabic Speakers

This paper addresses a reading comprehension problem and visualization as the suggested solution. When one read, one would intend to understand the text being read. It is absolutely an exception in the context of one Arabic document called the Qur’an. Thus, v isualization techniques are then useful to support the understandability of the document for non-Arabic speakers such as the Malaysian and Indonesians. Descriptions and review of four prototype systems that use visualization techniques: scatter plot, trivariate plot, network diagrams, directory approach, hyperbolic and tree structure. This paper discusses reading and visualization theories, the problems encountered while reading the Arabic document (the Qur’an), comparative discussions on the prototype systems, and functional requirement proposed to enhance the prototype.
A Remark on the Economics of Quantifier Raising

Quantifier Raising is often considered to be a relatively local operation, though it has been argued that nonlocal applications are licensed by generating new scope interpretations (Fox 2000, Reinhart 2006) or resolving antecedent-contained deletions (Fox 2002, Wilder 2003, Cecchetto 2004). In this article, an account of the restricted distribution of sloppy pronouns in antecedent-contained deletions leads to the conclusion that exceptional applications of Quantifier Raising are in fact not licensed purely by virtue of QR’s escaping the antecedent for ellipsis.
A machine learning perspective on predictive coding using PAQ8 and new applications

The goal of this thesis is to describe a state-of-the-art compression method called PAQ8 from the perspective of machine learning. We show both how PAQ8 makes use of several simple, well known machine learning models and algorithms, and how it can be improved by exchanging these components for more sophisticated models and algorithms. We also present a broad range of new applications of PAQ8 to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and lossy compression using features from the field of deep learning.
Memetic algorithm using multiple surrogates for complex engineering design optimization

Complex engineering design (CED) optimization problems in science and engineering commonly have large design spaces. In such design spaces, typically thousands of exact fitness evaluations are required to locate a near optimal design. Often in photonics, electromagnetic, aerospace, biomedical and microwave circuits detailed design processes, variable-fidelity analysis codes are employed to strike a balance between design cost, time and estimation accuracy. Nevertheless, in analysis and design optimization processes where high-fidelity analysis codes are used, each exact fitness evaluation requiring the simulation of analysis codes may cost hours of supercomputer time. Therefore, the overwhelming part of the total run time in CED optimization is usually taken up by the simulation of analysis codes. This often poses a serious impediment to the practical application of high-fidelity analysis codes and evolutionary algorithms to CED optimization problems. In this dissertation work, the research focus has been placed on the use of multiple surrogate models in standard memetic algorithm (MA) to mitigate the costly CED optimization process. In this thesis, a novel hierarchical surrogate-assisted memetic algorithm (HSAMA) combining both global and local surrogate models for accelerating the optimization process is proposed and described. The performance of the proposed algorithm is analyzed by using a series of commonly used benchmark test functions. Furthermore, the proposed algorithm is also applied to aerodynamic shape design. Numerical results show that the HSAMA algorithm is capable of achieving good designs efficiently under a limited computational budget. Further, the impact of uncertainty introduced by approximation errors, i.e., ‘curse and blessing of uncertainty ’, is illustrated and demonstrated on surrogate-assisted memetic algorithm (SAMA). Inspired by this finding, a novel multi-surrogates assisted memetic i ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
The wrapper algorithm: surface extraction and simplification

The wrapper algorithm (Gueziec and Hummel, 1994) is a rapid and efficient algorithm for extracting and representing surfaces defined as isosurfaces in digitized volumetric data. Unlike the marching cubes algorithm (Lorensen and Cline, 1987), and in agreement with the Payne-Toga algorithm (1990), the authors further decompose the cubical mesh associated with the sampled data into tetrahedral cells. They guarantee the resulting surface representation to be closed and oriented, defined by a valid triangulation of the surface of the body, which in turn is presented as a collection of tetrahedra, some of which are only partly filled. The surface is extracted as a collection of closed triangles, where each triangle is an oriented closed curve contained within a single tetrahedron. The authors' method is an extension of the work of Doi and Koide (1991). In particular, the surface extraction process is followed by a simplification algorithm, which typically provides between 10 and 20 to 1 compression ratios in the surface representation while respecting a maximum distance bound to the original surface, and without noticeable degradation in surface fidelity. The authors display surfaces of the cranium from CT-scans and cortical surfaces from MR-scans at full resolution as well as synthetic surfaces, which have been extracted and simplified by their method. <<ETX>>
Bridging Game-Programming into theK-12 Curriculum

The fact that the U.S. students lag behind in math and science performances continues to be a burning issue for the nation. In the past decade, although ample studies offered a significant variety of fun computing projects to motivate students in math and science learnings, this issue has been disregarded perhaps due to misaligned curriculum, or due to perhaps educators who lacked the required technical skills. To remedy this problem, this study investigated how the perspectives of the non-computer science educators changed after learning game-programming and how it could be fitted into the K-12 curriculum. Fourteen non-computer science educators and/or administrators in the K – 16 educational systems who made up a cohort at Sam Houston State University, Master of Education/Instructional Technology Program participated in this study. The participants were required to learn two free Web 2.0 game-programming applications and reflect on an article related to reviving interest in math and science as part of their program. Qualitative data consisted of online reflections, and peer-review processes through Facebook. A quantitative component was added to the analysis. The findings indicated that: (a) the perspectives of the participants changed from negative to positive as they reflected on their own game-programming learning experiences; (b) participants came to understand how game programming could build up students’ logical concepts and critical thinking skills improving performances in math, science, and other subjects; and (c) due to the benefits of logical concepts and critical thinking skills game programming could have immense benefits if built into the K-12 curriculum.
Finite State Automata Evolution Using Modular Architecture

The automatic creation of finite automata has long been a goal of the evolutionary computation community. The previous works in the evolution of finite state automata were limited to the evolution of strictly non-modular FSA. Here, a modular architecture to evolve FSA is proposed and a genetic programming procedure for evolving such structures is presented. Results on the Tomita Language benchmark indicate that the proposed procedure is indeed capable of successfully evolving modular FSA and that such modularity can result in a significantly increased rate of optimization.
Information theoretic approach to tactile encoding and discrimination

The human sense of touch integrates feedback from a multitude of touch receptors, but how this information is represented in the neural responses such that it can be extracted quickly and reliably is still largely an open question. At the same time, dexterous robots equipped with touch sensors are becoming more common, necessitating better methods for representing sequentially updated information and new control strategies that aid in extracting relevant features for object manipulation from the data. This thesis uses information theoretic methods for two main aims: First, the neural code for tactile processing in humans is analyzed with respect to how much information is transmitted about tactile features. Second, machine learning approaches are used in order to influence both what data is gathered by a robot and how it is represented by maximizing information theoretic quantities. The first part of this thesis contains an information theoretic analysis of data recorded from primary tactile neurons in the human peripheral somatosensory system. We examine the differences in information content of two coding schemes, namely spike timing and spike counts, along with their spatial and temporal characteristics. It is found that estimates of the neurons’ information content based on the precise timing of spikes are considerably larger than for spikes counts. Moreover, the information estimated based on the timing of the very first elicited spike is at least as high as that provided by spike counts, but in many cases considerably higher. This suggests that first spike latencies can serve as a powerful mechanism to transmit information quickly. However, in natural object manipulation tasks, different tactile impressions follow each other quickly, so we asked whether the hysteretic properties of the human fingertip affect neural responses and information transmission. We find that past stimuli affect both the precise timing of spikes and spike counts of peripheral tactile neurons, resulting in increased neural noise and decreased information about ongoing stimuli. Interestingly, the first spike latencies of a subset of afferents convey information primarily about past stimulation, hinting at a mechanism to resolve ambiguity resulting from mechanical skin properties. The second part of this thesis focuses on using machine learning approaches in a robotics context in order to influence both what data is gathered and how it is represented by maximizing information theoretic quantities. During robotic object manipulation, often not all relevant object features are known, but have to be acquired from sensor data. Touch is an inherently active process and the question arises of how