Miracle-dz/UniFAR-FTUs huggingface dataset:
Learner-Centred Design and Evaluation of Web-Based E-Learning Environments

Designing E-learning is a combination of pedagogical design, usability and information architecture. E-learning environments should have intuitive interfaces and clear information design, allowing learners to focus on learning. However, there is often a mismatch between what an on-line educator thinks the learner would learn, and what a learner thinks he will, and then has learned from the course. In addition, there is sometimes a mismatch between how an educator wants to teach and what is represented on the interface by the instructional designers. Such mismatches affect the learner's experience and his motivation for E-learning. In this paper, we will first discuss the source and nature of these mismatches. Next, we will discuss whether usability techniques in the HCI literature are appropriate for evaluating E-learning environments for the learner experience. We will then propose a combination of requirements elicitation and usability techniques for learner-centred design and evaluation of Web-based E-learning environments. The proposed methodology is based on our experience of conducting empirical studies for evaluating user-system interactions in E-Commerce contexts.
The Caenorhabditis elegans gene unc-89, required fpr muscle M-line assembly, encodes a giant modular protein composed of Ig and signal transduction domains

Mutations in the Caenorhabditis elegans gene unc-89 result in nematodes having disorganized muscle structure in which thick filaments are not organized into A-bands, and there are no M-lines. Beginning with a partial cDNA from the C. elegans sequencing project, we have cloned and sequenced the unc-89 gene. An unc-89 allele, st515, was found to contain an 84-bp deletion and a 10-bp duplication, resulting in an in- frame stop codon within predicted unc-89 coding sequence. Analysis of the complete coding sequence for unc-89 predicts a novel 6,632 amino acid polypeptide consisting of sequence motifs which have been implicated in protein-protein interactions. UNC-89 begins with 67 residues of unique sequences, SH3, dbl/CDC24, and PH domains, 7 immunoglobulins (Ig) domains, a putative KSP-containing multiphosphorylation domain, and ends with 46 Ig domains. A polyclonal antiserum raised to a portion of unc-89 encoded sequence reacts to a twitchin-sized polypeptide from wild type, but truncated polypeptides from st515 and from the amber allele e2338. By immunofluorescent microscopy, this antiserum localizes to the middle of A-bands, consistent with UNC-89 being a structural component of the M-line. Previous studies indicate that myofilament lattice assembly begins with positional cues laid down in the basement membrane and muscle cell membrane. We propose that the intracellular protein UNC-89 responds to these signals, localizes, and then participates in assembling an M-line.
Analyzing Patterns of Drug Use in Clinical Notes for Patient Safety

Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence. Traditional drug safety alerts may not cover usages that are not FDA approved. Therefore, analyzing patterns of off-label drug usage in the clinical setting is an important step toward reducing the incidence of adverse events and for improving patient safety. We applied term extraction tools on the clinical notes of a million patients to compile a database of statistically significant patterns of drug use. We validated some of the usage patterns learned from the data against sources of known on-label and off-label use. Given our ability to quantify adverse event risks using the clinical notes, this will enable us to address patient safety because we can now rank-order off-label drug use and prioritize the search for their adverse event profiles.
Serum IgG Antibody Levels to Periodontal Microbiota Are Associated with Incident Alzheimer Disease

Background Periodontitis and Alzheimer disease (AD) are associated with systemic inflammation. This research studied serum IgG to periodontal microbiota as possible predictors of incident AD. Methods Using a case-cohort study design, 219 subjects (110 incident AD cases and 109 controls without incident cognitive impairment at last follow-up), matched on race-ethnicity, were drawn from the Washington Heights-Inwood Columbia Aging Project (WHICAP), a cohort of longitudinally followed northern Manhattan residents aged >65 years. Mean follow-up was five years (SD 2.6). In baseline sera, serum IgG levels were determined for bacteria known to be positively or negatively associated with periodontitis (Porphyromonas gingivalis, Tannerella forsythia, Actinobacillus actinomycetemcomitans Y4, Treponema denticola, Campylobacter rectus, Eubacterium nodatum, and Actinomyces naeslundii genospecies-2). In all analyses, we used antibody threshold levels shown to correlate with presence of moderate-severe periodontitis. Results Mean age was 72 years (SD 6.9) for controls, and 79 years (SD 4.6) for cases (p<0.001). Non-Hispanic Whites comprised 26%, non-Hispanic Blacks 27%, and Hispanics 48% of the sample. In a model adjusting for baseline age, sex, education, diabetes mellitus, hypertension, smoking, prior history of stroke, and apolipoprotein E genotype, high anti-A. naeslundii titer (>640 ng/ml, present in 10% of subjects) was associated with increased risk of AD (HR = 2.0, 95%CI: 1.1–3.8). This association was stronger after adjusting for other significant titers (HR = 3.1, 95%CI: 1.5–6.4). In this model, high anti-E. nodatum IgG (>1755 ng/ml; 19% of subjects) was associated with lower risk of AD (HR = 0.5, 95%CI: 0.2–0.9). Conclusions Serum IgG levels to common periodontal microbiota are associated with risk for developing incident AD.
A comprehensive study on the general performance properties of Viloft-blended knitted fabrics

Viloft fiber is used frequently in sportswear and underwear in recent years. The purpose of this study is to determine the advantages of Viloft blends used for sportswear, taking into account objective measurements and subjective wear trials. In the study, 1 × 1 rib-knitted fabrics were produced from 20 tex yarn made by blending Viloft fiber and natural, synthetic, or functional fibers. 100% cotton, 100% viscose, and 100% Coolmax, the most preferred fabrics for sportswear were produced as well. Tight-fitting T-shirts with short sleeves and round neck were produced from these fabrics. Physical, mechanical, and transfer properties of the fabrics were measured. Besides, wear trials were conducted with five volunteers using the T-shirts, and mean skin temperature, microclimate relative humidity, and pulse rates were measured. According to the results, the mechanical properties of Viloft-blended fabrics have not been superior than commercial fabrics. Viloft/wool fabrics showing high thermal resistance and water vapor resistance have had poor transfer properties. Viloft/Coolmax fabrics with low water vapor resistance and high moisture management properties have come to the forefront in terms of liquid moisture comfort among Viloft-blended fabrics. It has been observed that knitted fabrics produced by blending Viloft/wool are not preferable options for sportswear. Viloft/Coolmax, Viloft/Thermolite, and Viloft/polyester fabrics have been advantageous for all activity levels.
Identifying cortical sulci from localization, shape and local organization

In this paper we propose an approach to identify sulci from sulcal pieces. Our method is founded on the sulci localization, feature-based shapes and their local organization. The position data enable the devising of an easy handled 3D probabilistic atlas using SPAM models. Shapes and local sulci scheme are recognized thanks to SVR models (a regression version of support vector machine). All these aformentioned aspects are merged into a unified Markovian framework, which favours locally the most reliable information. The first model is used to strongly constrain label coverage over space and the second to reach coherence within sulci neighbourhood. The mixture outperforms both models taking the best of their local performances.
A postprocessing technique for block effect elimination using a perceptual distortion measure

One of the drawbacks of the discrete cosine transform (DCT) is visible block boundaries due to coarse quantization of the coefficients. In this paper, an algorithm for the reduction of blocking artifacts is presented. The proposed method allows to produce higher quality reconstructed images by adaptively filtering the video signal according to the noise visibility. A visual model is therefore defined for predicting the block edge visibility across each picture of the coded sequence. This model that accounts for the perceptual characteristics of the block distortion is described. Experimental results are presented for low bit-rate coded sequences. They show that the postfiltering operation yields significant results with enhanced visual quality.
Capabilities of data assimilation in correcting sea surface temperature in the Persian Gulf

Predicting the quality of water and air is a particular challenge for forecasting systems that support them. In order to represent the small-scale phenomena, a high-resolution model needs accurate capture of air and sea circulations, significant for forecasting environmental pollution. Data assimilation is one of the state of the art methods to be used for this purpose. Due to the importance of thermal structure in monitoring the variations of environmental phenomena, the present study has used Sea Surface Temperature (SST) in data assimilation method to optimize this parameter. SST is one of the most important factors to conduct researches on the ocean, the atmosphere, and their interaction, not to mention monitoring and forecasting air and ocean phenomena as well as commercial and fishing communities and weather forecasts. This study has aimed to present a satellite-derived SST based on pathfinder advanced very high resolution radiometer (AVHRR) data assimilating in FVCOM (finite volume community ocean model) on the Persian Gulf to examine the effect of data assimilation by using the Cressman scheme. The performance of this method has been compared to the optimal interpolation SST (OISST) data, via both visual comparisons and statistical parameters. Applying assimilation method improves correlation coefficient of the model from 0.92 to 0.99. Results demonstrate that the modeled SST has been completely reconstructed by the data assimilated experiment via the Cressman scheme for this region. The spatial and temporal pattern of SST reveals a significant improvement in the entire domain during the investigated period in the gulf.
Introducing an agricultural land capability framework for land reform beneficiaries

This study incorporated remote sensing, geographic information system (GIS), and geostatistics to assess land capability in two sites acquired through land reform program. The relationships and interactions between components and parameters that are necessary for analysing the capability of agricultural land were investigated for an effective management system. The parameters taken into consideration were biophysical environment (soil texture, organic matter content, soil depth etc.), socio-economic and policy. The primary data included climatic data and soil samples obtained at 1 m depth; the secondary data included satellite imagery, toposheet, ancillary data, and agricultural statistics. The resulting land capability maps showed that the study areas have moderate to very high capability for summer crops. It is concluded that in order for land reform to contribute to sustainable livelihoods for land reform beneficiaries, agricultural land capability study should be integrated in land reform policy before the acquirement of land to land beneficiaries.
Optimal CUR matrix decompositions

The CUR decomposition of an m×n matrix A finds an m×c matrix C with a small subset of c < n columns of A, together with an r×n matrix R with a small subset of r < m rows of A, as well as a c×r low rank matrix U such that the matrix CUR approximates the input matrix A, that is, ||A --- CUR||2F ≤ (1 + ε)||A --- Ak||2F, where ||. ||F denotes the Frobenius norm, 0 < ε < 1 is an accuracy parameter, and Ak is the best m × n matrix of rank k constructed via the SVD of A. We present input-sparsity-time and deterministic algorithms for constructing such a CUR matrix decomposition of A where c = O(k/ε) and r = O(k/ε) and rank(U) = k. Up to constant factors, our construction is simultaneously optimal in c, r, and rank(U).
The Chemical and Genetic Characteristics of Szechuan Pepper (Zanthoxylum bungeanum and Z. armatum) Cultivars and Their Suitable Habitat

Szechuan peppers, famous for their unique sensation and flavor, are widely used as a food additive and traditional herbal medicine. Zanthoxylum bungeanum and Z. armatum are both commonly recognized as Szechuan peppers, but they have different tastes and effects. The chemical components, genetic characteristics, and suitable habitat of six cultivars were analyzed in this study. The results indicated that Z. armatum contained a larger proportion of volatile oil, whereas Z. bungeanum produced a more non-volatile ether extraction. The average content of volatile oil and non-volatile ether extract of Z. armatum were 11.84 and 11.63%, respectively, and the average content of volatile oil and non-volatile ether extract of Z. bungeanum were 6.46 and 14.23%, respectively. Combined with an internal transcribed spacer 2 (ITS2) sequence characters and chemical PCA results, six cultivars were classified into their own groups, for the two species in particular. The temperature in January and July were the most significant ecological factors influencing the contents of the Z. armatum volatile oil. However, annual precipitation, temperature in January and relevant humidity had a significant positive correlation with the content of non-volatile ether extract in Z. bungeanum. Thus, the most suitable areas for producing Z. bungeanum cultivars ranged from the Hengduan Mountains to the Ta-pa Mountains, and the regions suitable for Z. armatum cultivars were found to be in the Sichuan Basin and Dalou-Wu mountains. The predicted suitable habitat could be used as a preliminary test area for Szechuan pepper cultivar production.
Robust multi-feature visual tracking with a saliency-based target descriptor

In this article a robust multi-feature tracking algorithm based on visual saliency mechanism and particle filter is proposed in order to avoid the tracking failure of single feature in complex environment. The target template is composed of visual saliency feature and other two features extracted from VISible (VIS) and InfraRed (IR) frames initially; more specifically, compressive sensing method is employed under the particle filtering framework in order to reduce the computation. Meanwhile, this target template is adaptively updated according to Bhattacharyya coefficients. Experimental results demonstrate that this algorithm is more accurately and effectively under conditions of illumination variation, clutter, as well as similar background and occlusions with good robustness than other existing tracking algorithms.
Psychological Predictors of Symptoms of Urinary Tract Infection and Bacteriuria in Patients Treated with Clean Intermittent Catheterization: A Prospective 7-Year Study

Objective: One of the major clinical problems in relation to clean intermittent catheterization (CIC) are symptoms of urinary tract infection (UTI) and bacteriuria. The aim of the present work was to study to what extent measures of psychological well-being, distress and neurotic personality traits in patients performing CIC could predict symptoms of UTI or bacteriuria 7 years later. Patients and Methods: Included in the study were 170 patients with a mean age of 57 years, who had used CIC for 105 months. CIC was practised due to neurogenic bladder dysfunction in two thirds of the patients, and non-neurogenic dysfunction in the remaining patients. All patients had been clinically examined 7 years prior to inclusion. Information about subjective symptoms of UTI and examination of bacteriuria in urine samples had been assessed. All subjects had also undergone repeated psychometric evaluations of psychosocial function, well-being and distress (General Health Questionnaire; GHQ-28; Spielberger State-Trait Anxiety Inventory; STAI X-1) and neuroticism (Eysenck Personality Questionnaire; EPQ-N). The results from selected assessments done 7 years earlier were used to predict reported UTI and findings of bacteriuria in urine samples in 1995. Results: Clinical UTI symptoms were reported by 35% of the patients. These patients had significantly higher GHQ-28 scores in 1988, 1989 and 1995 compared to the remaining 65%. Patients without clinical UTI during 1988/89, but with symptoms suggesting UTI in 1995, had significantly higher GHQ-28, STAI-X1 and EPQ-N scores in 1988 compared to those who did not report symptoms 7 years later. Patients with UTI during 1988/89 who were free of symptoms of UTI in 1995 had scores similar to those who were free of symptoms of UTI both in 1988/89 and in 1995. GHQ-28, STAI X-1 and EPQ-N scores in 1988/89 did not predict current bacteriuria, found in 62% of the urine samples in 1995. Conclusion: Social function, well-being and distress are significant predictors of long-term urinary tract complaints, but not of bacteriuria. This finding indicates that the aetiology of UTI complaints and of actual bacteriuria is different. Complaints of UTI may often express distress and not symptomatic infection. Physicians’ non-recognition of distress may lead to unnecessary and aggressive interventions for assumed infection such as the prescription of antibiotics. Identification and treatment of psychological distress and impaired social function should become routine in the clinical follow-up of CIC patients. Such a routine could reduce urinary tract complaints, lower the unnecessary use of antibiotics and increase the tolerability of CIC. Prospective intervention studies are needed to verify this hypothesis.
High-Level Concepts for Affective Understanding of Images

This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a blackbox, we use HLCs generated by pretrained CNNs in an explicit way to investigate the relations/associations between these HLCs and a (small) set of Ekman's emotional classes. As a proof-of-concept, we first propose a linear admixture model for modeling these relations, and the resulting computational framework allows us to determine the associations between each emotion class and certain HLCs (objects and places). This linear model is further extended to a nonlinear model using support vector regression (SVR) that aims to predict the viewer's emotional response using both low-level image features and HLCs extracted from images. These class-specific regressors are then assembled into a regressor ensemble that provide a flexible and effective predictor for predicting viewer's emotional responses from images. Experimental results have demonstrated that our results are comparable to existing methods, with a clear view of the association between HLCs and emotional classes that is ostensibly missing in most existing work.
The Infrequency-Posttraumatic Stress Disorder Scale (Fptsd) for the MMPI-2: Development and Initial Validation With Veterans Presenting With Combat-Related PTSD

Researchers have identified difficulties associated with the use of traditional Minnesota Multiphasic Personality Inventory-2 (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) validity scales with survivors of traumatic events. A new scale, the Infrequency-Posttraumatic Stress Disorder scale (Fptsd), was created from MMPI-2 items that were infrequently endorsed by 940 male combat veterans presenting for treatment at the posttraumatic stress disorder (PTSD) clinics of 2 Veterans Affairs Medical Centers. A variety of statistical methods were implemented that preliminarily established Fptsd's validity with a validation sample of 323 additional PTSD-diagnosed combat veterans. Results indicate that, relative to previously established validity and overreporting scales (F, Fb, and Fp), Fptsd was significantly less related to psychopathology and distress and better at discriminating simulated from genuinely reported PTSD. Clinical implications are discussed concerning the use of Fptsd to assess disability-seeking veterans suspected of overreporting PTSD symptoms.
The supercomputer industry in light of the Top500 data

The Top500 list, which has been updated semiannually for the past decade (1995-2005), ranks the 500 most powerful computers installed worldwide. Analyzing this data gives an impartial look at the supercomputer industry's current state and development trends, and sheds light on the challenges the industry faces.
How to quickly find a witness

The subfield of itemset mining is essentially a collection of algorithms. Whenever a new type of constraint is discovered, a specialized algorithm is proposed to handle it. All of these algorithms are highly tuned to take advantage of the unique properties of their associated constraints, and so they are not very compatible with other constraints. In this paper we present a more unified view of mining constrained itemsets such that most existing algorithms can be easily extended to handle constraints for which they were not designed a-priori. We apply this technique to mining itemsets with restrictions on their variance --- a problem that has been open for several years in the data mining community.
The Simulation and Optimization of Aspheric Plastic Lens Injection Molding

For the purpose of reducing the volumetric shrinkage and volumetric shrinkage variation, the process in injection molding of aspheric plastic lens was simulated, and several process parameters which include holding pressure, melt temperature, mold temperature, fill time, holding pressure time and cooling time were optimized by using an orthogonal experimental design method. Finally, the optimum process parameters and the influence degree of process parameters on the average volumetric shrinkage and the volumetric shrinkage variation are obtained.
Deep Recurrent Neural Networks for Supernovae Classification

We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae (code available at https://github.com/adammoss/supernovae). The observational time and filter fluxes are used as inputs to the network, but since the inputs are agnostic, additional data such as host galaxy information can also be included. Using the Supernovae Photometric Classification Challenge (SPCC) data, we find that deep networks are capable of learning about light curves, however the performance of the network is highly sensitive to the amount of training data. For a training size of 50% of the representational SPCC data set (around 104 supernovae) we obtain a type-Ia versus non-type-Ia classification accuracy of 94.7%, an area under the Receiver Operating Characteristic curve AUC of 0.986 and an SPCC figure-of-merit F1 = 0.64. When using only the data for the early-epoch challenge defined by the SPCC, we achieve a classification accuracy of 93.1%, AUC of 0.977, and F1 = 0.58, results almost as good as with the whole light curve. By employing bidirectional neural networks, we can acquire impressive classification results between supernovae types I, II and III at an accuracy of 90.4% and AUC of 0.974. We also apply a pre-trained model to obtain classification probabilities as a function of time and show that it can give early indications of supernovae type. Our method is competitive with existing algorithms and has applications for future large-scale photometric surveys.