Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital resources in the translation process – attention, cognitive effort and processing flow

ABSTRACT Digital resources are an important part of the translator’s work. Access to dictionaries, parallel texts, reference works, websites and other resources supports the comprehension and reformulation processes and they are an integrated part of most translators’ work processes. Despite the importance of digital resources in the translation process, little research has been carried out in this area. Eye tracking data and screen recording data from 18 professional translators are investigated to examine (1) translators’ use of digital resources with specific emphasis on attention and cognitive effort in translation involving digital resource consultation and (2) translators’ processing flow during translation involving digital resources.
Post-traumatic stress disorder among Syrian adolescent refugees in Jordan.

OBJECTIVE Studies among Syrian refugees in Turkey, Lebanon and Germany showed high prevalence rates of post-traumatic stress disorder (PTSD). However, data on PTSD among Syrian adolescent refugees in Jordan are scarce. Therefore, this study aimed to assess the prevalence and severity of PTSD, and its associated factors among Syrian schoolchildren refugees resettled in Jordan. METHODS This cross-sectional study was conducted among Syrian schoolchildren refugees aged 12-18 years. A stratified cluster random sample was used to select male and female schoolchildren in four cities with the highest density of Syrian refugees. The Child Post-Traumatic Stress Disorder Symptom Scale (CPSS) was used to measure PTSD severity. RESULTS The prevalence of moderate to severe PTSD among Syrian schoolchildren refugees in Jordan was 31%. This prevalence was higher in female adolescents compared with males (P = 0.001), and in children who had one or both parents died compared with those whose parents were living together (P = 0.006). The prevalence was lower in those living in Sahab city compared with Mafraq city (P = 0.006). CONCLUSION PTSD is a common condition among Syrian adolescent refugees, especially among females, and those who lost one or both parents. Screening for PTSD and other mental health illnesses is highly recommended. Gender-specific psychosocial support and educational programs should be implemented.
Image-set, Temporal and Spatiotemporal Representations of Videos for Recognizing, Localizing and Quantifying Actions

This dissertation addresses the problem of learning video representations, which is defined here as transforming the video so that its essential structure is made more visible or accessible for action recognition and quantification. In the literature, a video can be represented by a set of images, by modeling motion or temporal dynamics, and by a 3D graph with pixels as nodes. This dissertation contributes in proposing a set of models to localize, track, segment, recognize and assess actions such as (1) image set via aggregating subset features given by regularizing normalized CNNs, (2) image set via inter-frame principal recovery and sparsely coding residual actions, (3) temporally local models with spatially global motion estimated by robust feature matching and local motion estimated by action detection with motion model added, (4) spatiotemporal models 3D graph & 3D CNN to model time as a space dimension, (5) supervised hashing by jointly learning embedding and quantization, respectively. State-of-the-art performances are achieved for tasks such as quantifying facial pain and human diving. Primary conclusions of this dissertation are categorized as follows: (i) Image set can capture facial actions that are about collective representation;
Empirical Asset Pricing Via Machine Learning

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
The functional role of beta‐oscillations in the supplementary motor area during reaching and grasping after stroke: A question of structural damage to the corticospinal tract

Hand motor function is often severely affected in stroke patients. Non‐satisfying recovery limits reintegration into normal daily life. Understanding stroke‐related network changes and identifying common principles that might underlie recovered motor function is a prerequisite for the development of interventional therapies to support recovery. Here, we combine the evaluation of functional activity (multichannel electroencephalography) and structural integrity (diffusion tensor imaging) in order to explain the degree of residual motor function in chronic stroke patients. By recording neural activity during a reaching and grasping task that mimics activities of daily living, the study focuses on deficit‐related neural activation patterns. The study showed that the functional role of movement‐related beta desynchronization in the supplementary motor area (SMA) for residual hand motor function in stroke patients depends on the microstructural integrity of the corticospinal tract (CST). In particular, in patients with damaged CST, stronger task‐related activity in the SMA was associated with worse residual motor function. Neither CST damage nor functional brain activity alone sufficiently explained residual hand motor function. The findings suggest a central role of the SMA in the motor network during reaching and grasping in stroke patients, the degree of functional relevance of the SMA is depending on CST integrity.
Empirical Study on Influence Factors of Adaption Intention of Online Customized Marketing System in China

This paper studies the influence factors and the influence mechanism which effect adoption intention of online customized marketing system, which is based on TRI model and the theory of resistance to marketing persuasion as the main framework of the research model from two aspects of technical and marketing characteristics. According to empirical study based on 303 Chinese users, we found that the user’s optimism, creativity and system inadaptability directly influence on the adoption intention in terms of technology attribution, marketing supply appeal, marketing supply fitness, perceived controlling and perceived pressure influence on the adoption intention in terms of marketing attribute, meanwhile, recommendation fitness are influenced by customized label, customized participation and explanation of recommendation. Marketing supply attractiveness is influenced by product attributes, novelty and form of customized supply. And users’ concern about security of system has no significant influence on users’ adoption of online customized marketing system. The people at different education are different significantly in their adaption attention. Finally management suggestion for operators was given.
Analyzing El Niño–Southern Oscillation Predictability Using Long‐Short‐Term‐Memory Models

El Niño–Southern Oscillation (ENSO) can have global impacts, affecting daily temperature and precipitation, and extreme weather, such as hurricanes and tornadoes. Because of its importance, scientists strive to understand the processes that govern ENSO and develop models to predict its evolution and changes in variability. Here long‐short‐term‐memory models (LSTMs) were compared to linear regression models (LR) to explore the benefits of simple, deep neural networks in predicting ENSO, in addition to quantifying the relative importance of the sources of ENSO's predictability. The models use central Pacific sea surface temperatures (SST), equatorial Pacific warm water volumes, and western Pacific zonal winds as predictors, individually and in combinations, on monthly and daily resolutions, from 1‐ to 11‐month leads. By using these predictors, many characteristic time scales are encompassed—from days‐to‐weeks in the atmosphere, to months‐to‐seasons in the coupled system, and interseasonal‐to‐interannual in the subsurface ocean. Results show, with monthly input, predictions from LSTM were like predictions from LR. However, with daily SST at longer leads, LSTM exhibited some advantage over LR in terms of the correlation coefficient. This suggests that daily SST may contain some nonlinear element that improves LSTM predictability compared to LR. In addition, this suggests that more information, such as gridded data and additional variables, would likely improve predictability using LSTM, but results would be more difficult to interpret. Overall, LSTM may be appealing because once the computationally expensive training of LSTM is complete, the predictions employing the trained model can be relatively cheap to perform thereafter.
Comparative Analysis of two Stochastic Models subjected to Inspection and Scheduled Maintenance

The present paper analyses the comparison of two stochastic models of a cable manufacturing plant subjected to inspection and scheduled maintenance. Here, two models are compared on the basis of MTSF, steady state availability and profit function using Laplace transforms and software package Code-Blocks 13.12. Model 1 is a single unit system with scheduled maintenance whereas Model 2 is a single unit system with inspection of repairable failure, reinstallation/reconditioning failure and replaceable failure. The graphs have been plotted to decide which model is better than the other model for a particular condition. Using regenerative point technique and semi-Markov processes, the complete system is analyzed.
Investigating and Prioritizing Factors Affecting Health Literacy in University Students of Yazd Using Artificial Neural Network Technique

Introduction: Current university students are potential and actual parents of future generations. The level of their health literacy affects health and health literacy level of future generations. Therefore, the purpose of the present study was investigating and prioritizing the factors affecting the health literacy of students in Yazd (a city in central Iran) using an artificial neural network technique. Methods: This study was cross-sectional and descriptive and it was conducted on 400 university students in Yazd during autumn 2018. Data were collected using a questionnaire on health literacy and 14 independent variables. Data were analyzed with SPSS software via descriptive statistics, one sample T-test, and artificial neural network. Results: The mean of students' health literacy was 80.65±12.21. The most important factors affecting the level of students' health literacy were the grade, college, father's education level, age, and residence. Conclusion: Since grade had the greatest impact on health literacy of students, it is suggested that university officials consider the campus to hold classes, lectures, workshops and distribute appropriate resources such as brochures, booklets among students and also provide health information stations at locations such as dining room, university entrance, and dormitory to promote students' health literacy in lower grades
Maximization of Profit for a Problem of Location and Routing, with Price-sensitive Demands

This article seeks to study and solve a problem of profit maximization of a company by defining the location of an optimal number of facilities, allocation and routing of vehicles, and costs for home delivery to meet the demand of its customers. This study is based on an article in which, for the first time, a problem of location and routing and maximization of utilities with price-sensitive demands is integrated. This problem, unlike other studies that only minimize other metrics such as waiting times, route distances, and transportation costs, seeks a greater benefit by increasing profits by discriminating prices depending on the customer's location or adding an additional cost to retail sales. This paper presents a model for small instances based on the model proposed in the aforementioned article. Next, a two-phase heuristic is proposed that solves larger instances with a result close to that obtained in the previous article where a branch-and-price heuristic was used.
Semantic Annotation of Quantification in Natural Language

This paper proposes an approach to the semantic annotation of quantification in natural language. Its main purpose is to provide input for a project that aims to define an international standard that would be part of the ISO Semantic Annotation Framework (SemAF, ISO 24617). The proposed approach capitalizes on work in formal and computational semantics, notably on the theory of generalized quantifiers, on Discourse Representation Theory, and on neo-Davidsonian event-based semantics, and is compatible with the SemAF Principles of semantic annotation (ISO 24617-6:2016).
Actual Impact of GAN Augmentation on CNN Classification Performance

In industrial inspection settings, it is common that data is either hard or expensive to acquire. Generative modeling offers a way to reduce those costs by filling out scarce training data sets automatically. Generative Adversarial Networks (GANs) have shown incredible results in the field of artificial image data generation, but until recently were not ready for industrial applications, because of unclear performance metrics and instabilities. However, with the introduction of Wasserstein GAN, which comprises an interpretable loss metric and general stability, it is promising to try using those algorithms for industrial classification tasks. Therefore, we present a case study on a single digit image classification task of banknote serial numbers, where we simulate use cases with missing data. For those selected situations, different data generation algorithms were implemented incorporating GANs in various ways to augment scarce training data sets. As a measure of plausibility of those artificially generated data, we used the classification performance of a CNN trained on them. We analyzed the gains in classification accuracy when augmenting the training samples with GAN images and compare them to results with either more classically generated, rendered artificial data and near perfect training data situations, respectively.
Effectiveness of e-learning portal from students' perspective

Purpose E-learning has become an increasingly prevalent learning approach in higher educational institutions due to the fast growth of internet technologies in India. This paper aims to mainly focus on evaluating the effectiveness of the e-learning experience from students’ perceptive. Design/methodology/approach “Survey” method has been used to collect the data with the help of a structured questionnaire from the students who have registered on COURSERA (www.coursera.org/) website for e-learning. The questionnaire consisted of two sections e-learning system and e-learning effectiveness. E-learning system included items related to system quality, information quality and service quality. E-learning effectiveness dimension included user satisfaction and net benefits. The items in this section were measured on a five-point Likert scale ranging from strongly disagrees to strongly agree. The data collected have been analyzed using the SPSS version 17.0 and AMOS version 21.0. Findings Results show that system quality and service quality contribute more in e-learning system compared to information quality. Students perception may be that information available on the website may not be very useful as it’s a one-way mode of communication. The researcher also found that the three dimensions (system quality, service quality and information quality) of e-learning system contributes to user satisfaction and net benefits. Students are satisfied with e-learning websites and intent to continue to use it in future as well. They also found it beneficial as it helps them in career growth and making them employable. Originality/value This paper proposed a second-order model of e-learning system and a second-order model e-learning effectiveness. E-learning system has been defined by three first-order constructs: a system quality, service quality and information quality. E-learning effectiveness has been defined by two first-order constructs: a user satisfaction and net benefits. The predictability of the proposed model is high to explain the impact of e-learning system on e-learning effectiveness.
Results of the European Performance Project on the Development of Measurement Techniques for Thin-Film Pv Modules

The EC-funded PERFORMANCE integrated project begun in January 2006 includes a sub-project dedicated to improving the comparability of thin-film PV module performance measurements among European test laboratories and to filling existing gaps of knowledge for defining specific measurement procedures. The need for such improvement is underlined by the results of international round-robin tests with PV modules in the past years that revealed significant discrepancies in measurements on thin-film and multi-junction devices. A comprehensive test programme was conducted with different types of thin-film PV modules reflecting the current state of the art of commercially available thin-film technologies. Module types covering the following technologies were selected for the round-robin test: a-Si (single, tandem, triple), a-Si/ Âµc-Si (hybrid), CI(G)S and CdTe. Due to possible variations in electrical performance due to temperature conditions or dark storage (transport, packaging) a test procedure was defined that included a short-term pre-conditioning procedure to be performed prior to the output power characterisations in individual test laboratories. Evaluation of reported data revealed poor comparability of results and in particular a high spread for PMAX values in the range of 6%. Besides uncertainties caused by current mismatches of layers for multi- junction devices and uncertainties related to the reference devices, pre-conditioning must be regarded as another important source of uncertainty. We found that measurement discrepancies could be partly explained by the failure of some test labs to comply with the defined pre-conditioning procedure
Visualization of World-Scale Evidence on Tax Avoidance and Tax Rate Convergence

Purpose Firm tax avoidance has gathered substantial public attention in both practice and academic literature. We study tax avoidance among firms and countries using the visualization of accounting data of all of listed firms from 148 countries from 1985 to 2015. Design/methodology/approach Over 30 years of financial data on all listed firms from 148 countries and provides world-scale evidence on tax avoidance and tax rates convergence. The research design uses exploratory data analysis and reproducible research using data visualization tools, namely, R with dplyr, ggplot2, and googleVis (Geo Chart and Motion Chart). Findings We provide world-scale evidence on (1) firms’ tax avoidance, showing a non-random distribution of firms’ effective tax rates and return on assets using visualization from 1985 to 2015, and (2) downward convergence trend of firms’ effective and statutory tax rates among 58 countries over the past 20 years. Originality/Value This study makes two key contributions to the literature. First, this is the first to provide world-scale evidences on the tax avoidance and convergence trends of tax rates. Second, we use the visualization method, which allows easily understanding of what is happening around the world. Research Implications Our results highlight the tax avoidance issue, which can no longer be ignored, and facilitate a collaborative discussion between non-specialists and professionals with objective evidence from visualization.
Proximal Operator Splitting for Multi-Constraint Dictionary Learning

Although the dictionary learning (DL) problem has been extensively studied for about 15 years since the work of Olshausen, the DL problem with multi-constraints on the dictionary atoms has not yet been paid attentions. This paper first explore the DL problem using the newly emergence methods-the proximal splitting methods, such as the iterative shrinkage-thresholding algorithm (ISTA), the fast ISTA (FISTA) and the augmented Lagrange multiplier method (ALMM). Then propose a calculation method, called proximal operator splitting, to split the proximal operator with multi-constraints into several sub-proximal operator. Using this method, the existing proximal splitting methods can be easily extended to deal with the DL problem with multi-constraints. Experiments show that ALMM is a more efficient method than ISTA and FISTA. At last, compare the learned dictionaries of ALMM with the state-of-the-art methods, K-SVD and Majorization. The experimental results show that ALMM outperforms K-SVD and Majorization for correctly chosen constraints.
Breaking High-Resolution CNN Bandwidth Barriers With Enhanced Depth-First Execution

Convolutional neural networks (CNNs) now also start to reach impressive performance on non-classification image processing tasks, such as denoising, demosaicing, super-resolution, and super slow motion. Consequently, CNNs are increasingly deployed on very high-resolution images. However, the resulting high-resolution feature maps pose unseen requirements on the memory system of neural network processing systems, as on-chip memories are too small to store high-resolution feature maps, while off-chip memories are very costly in terms of I/O bandwidth and power. This paper first shows that the classical layer-by-layer inference approaches are bounded in their external I/O bandwidth versus on-chip memory tradeoff space, making it infeasible to scale up to very high resolutions at a reasonable cost. Next, we demonstrate how an alternative depth-first network computation can reduce I/O bandwidth requirements up to ${>}200\times $ for a fixed on-chip memory size or, alternatively, reduce on-chip memory requirements up to ${>}10\,000\times $ for a fixed I/O bandwidth limitation. We further introduce an enhanced depth-first method, exploiting both line buffers and tiling, to further improve the external I/O bandwidth versus on-chip memory capacity tradeoff and quantify its improvements beyond the current state of the art.
On the Distributed Method of Multipliers for Separable Convex Optimization Problems

In this paper, we present a novel method for convex optimization in distributed networks called the distributed method of multipliers (DMM). The proposed method is based on a combination of a particular dual lifting and classic monotone operator splitting approaches to produce an algorithm with guaranteed asymptotic convergence in undirected networks. The proposed method allows any separable convex problem with linear constraints to be solved in undirected networks. In contrast to typical distributed approaches, the structure of the network does not restrict the types of problems that can be solved. Furthermore, the solver can be applied to general separable problems, those with separable convex objectives and constraints, via the use of an additional primal lifting approach. Finally, we demonstrate the use of DMM in solving a number of classic signal processing problems including beamforming, channel capacity maximization and portfolio optimization.