Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Human Resources Management Practices in the Aviation Industry on Employees’ Belonging to Work and Turnover Intention: Mediating Role of Perceived Supervisor Support

The importance of human resources in today's enterprise environment is an undeniable fact. The importance of human resources varies according to the dynamics of the industries. Enterprises in the aviation industry; whether it is an airline, airport operator, ground handler, they operate in the same technological environment with similar national and international regulations and similar enterprise models. Therefore, it can be said one of the most important factors that make enterprise successful in the industry is human resources. Human resources practices in enterprises are directly related to employees' motivation, efficiency, loyalty to the organization and turnover intention, consequently enterprise profitability. When the management organization studies on the aviation industry are analyzed, a limited number of studies on human resources practices, more specifically perceived supervisor support, exist. Motivated by this absence, this study focuses on human resource management practices in the aviation industry and the impact of these practices on employees’ sense of belonging to work and turnover intention, with the mediating role of perceived supervisor support. Keywordsbelonging to work; turnover intention; perceived
The Effects of Undervaluation and Cash Holdings on the Likelihood of a Share Repurchase Decision

This study investigates the effects of undervaluation and cash holdings on the likelihood of a share repurchase decision using data from Indonesian listed companies for the period 2009–2015. In contrast to previous studies, this study measures undervaluation using residuals of the Ohlson (1995) model of valuation based on yearly and panel regressions. The results of this study show that undervaluation and cash holding increase the likelihood of a share repurchase decision. Furthermore, this study also finds that cash holding strengthens the effect of undervaluation on the likelihood of a share repurchase decision. The study suggests that future research should avoid direct application of the price-to-book ratio as the undervaluation proxy since it has measurement error. The findings of this research can be employed in investment strategies and supportive regulation development. JEL Classification: G11, G12, G32
A 20-Year Consecutive Case Series From a Tertiary Cancer Center

Note: This article was posted on the Archives Web site as an Early Online Release. Early Online Release articles have been peer reviewed, copyedited, and reviewed by the authors. Additional changes or corrections may appear in these articles when they appear in a future print issue of the Archives. Early Online Release articles are citable by using the Digital Object Identifier (DOI), a unique number given to every article. The DOI will typically appear at the end of the abstract.
Learning Sequential Mobility and User Preference for new Location Recommendation in Online Social Networks

The fast expansion during the recent years of online social networks, such as Twitter, Facebook, or Foursquare, is making available an enormous and continuous stream of user-generated contents including information on human mobility within urban context. In particular, online social networks allows for the collection of geo-tagged data obtained through the GPS readings of phones through which users have the possibility to tag posts, photos and videos with geographical coordinates. In this context, recommending the future position of a mobile object is key for the implementations of several applications aiming at improving mobility within urban areas. The paper proposes a location recommendation approach that exploits geo-tagged data on social networks. The approach integrates user preference, sequential mobility and geographic constraints. The recommendation task is formulated as a similarity problem among the visiting and mobility profiles of users, accounting the mobility sequentiality in the patterns. Two ranking metrics are introduced to predict places the user could like. The metrics are then combined into an overall recommendation ranking function. The candidate locations are then ranked according to the two similarity measures. The experimental results obtained by using a real-world dataset of tweets show that the proposed method is effective in recommending unseen locations, outperforming representative state-of-the-art approaches.
EVALUATION OF NUTRITIVE VALUE OF SOME NATIVE FORAGE SPECIES IN TIKUR INCINNI DISTRICT, OROMIA, ETHIOPIA

Conservation and sustainable use of forage species is important for increasing livestock productivity and ensuring food security in mixed crop-livestock production systems of smallholder farmers. This study was conducted to evaluate nutritive values of selected grass and forbs in Tikur Inchini District West Shoa Zone, Oromia, Ethiopia. Ten selected herbaceous species were identified by knowledgeable local farmers. Sample of the selected species were collected at 50% flowering, full flowering and post flowering stages and their nutritive values were analyzed at Holeta Research Center, Animal Nutrition Laboratory following established procedures. Semi-structured questionnaire was prepared and perceptions of farmers were documented. The data were analyzed using excel spreadsheet, and SPSS version 20. Mean CP of forbs (7.68%) was higher than that of grasses (6.58%). On the other hand, mean NDF and ADF content were higher in grasses (65.04 and 49.03%, respectively) compared with the contents in forbs (62.13 and 45.41%, respectively). The result showed that there was significant difference (p<0.05) between the grass and forb species. The low CP content in the study area could be due to continued rangeland degradation as a result of overgrazing. It was noted that there was positive correlation between the indigenous knowledge of farmers and laboratory results on nutritive value of some forage species. Farmers perceived that highly desirable forage species such as Sporobolus affinis, Satureja punctata, and Cynodon spp were threatened due to expansion of crop land and overstocking. Conservation and sustainable use of forage species, rangeland rehabilitation, improving livestock management to minimize overgrazing and increasing options for feed availability from diverse sources were recommended as a result of this study.
The influence of the fluid dielectric constant on the shear strength of a unsaturated soil

Results of triaxial tests performed in saturated and unsaturated compacted soil specimens with different interstitial fluids are presented. Tests were carried out in order to study the influence of the fluid relative dielectric constant, r, on the soil shear strength of a granite-gneiss clayey residual soil from Salvador, Bahia, Brazil. It is shown that the soil shear strength is affected not only by the interstitial fluid saturation degree (or suction) but it is also a nonlinear function of the interstitial fluid value of r. The shear strength of the saturated samples decreased with r, following the order (air r = 1, diesel r = 2.13, ethanol r = 24.3 and water r = 80), whereas fluids with higher dielectric constants presented a more pronounced increase in shear strength under drying (replacement of the interstitial liquid with air). An empirical model is proposed to predict soil shear strength as a function of ( rw f), the difference between the relative dielectric constant of the water and the interstitial fluid. Good adherence between experimental and fitted results was obtained.
An Interactive Platform to Track Global COVID-19 Epidemic

This project built a world-wide database of coron-avirus cases, which helps to model the spread of the coronavirus disease (COVID-19), and to identify policy and social factors that impact the spread of COVID-19. Four essential tasks are implemented: 1) build a comprehensive database of coronavirus cases world-wide; 2) visualize the heatmap of confirmed cases for each country, provide detailed spreading trends for each countries and comparison among countries; 3) collect tweets about COVID-19 in real-time and extract people's daily concern flow; 4) integrate breaking news such as first confirmed/death case in each country. This demo will provide decision-makers with accurate data-driven representations in an easy to understand format that enables them to make more timely and cost-effective preparation and response plans.
Wiktionary matcher results for OAEI 2020

This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative (OAEI) 2020. Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as exter-nal background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year. 3
A Visual Approach to Data Fusion in Sensor Networks

—Critical Infrastructures such as public transport are essential for every modern society. This makes them even more attractive and vulnerable for terroristic attacks. Recent attacks all over the world demonstrate a strong need of intelligent solutions to detect attackers in an early stage in order to avoid harm to people and the system. Even though privacy rights are important for society, they can also confuse the process of early detection, as many countries limit the use of personalized analysis systems. We therefore introduce a novel approach for data fusion in sensor networks that processes anonymous data from distance based sensors such as explosive detectors. The aim of our ongoing study is not only to detect the presence of an attacker carrying an explosive at a given time, but also to analyze movement over time in an learning environment. For the graphical representation we apply heat maps on a grid overlaying to the given train station.
Approaches for Quantifying Video Prominence, Narratives, & Discussion: Engagement on COVID-19 Related YouTube Videos

Initial scientific studies suggest the spread of extreme content, “the COVID-19 infodemic,” likely plays a crucial role in news spread about the “the COVID-19 pandemic. ” In this paper, we quantify the evolution of polarization and engagement in YouTube social networks about public-health interventions for COVID-19. Although YouTube is a major information and news source with high engagement with younger populations, the platform is not widely researched in social network analysis. Discussions about coronavirus on social media can influence how individuals interpret news about the disease and affect their compliance with various non-pharmaceutical interventions. We compare coronavirus video content by identifying three subgroups of public-health intervention-related videos: individual interventions, government interventions, and medical interventions, as well as seven video title narratives. The polarization index measures the level of agreement with the video content using votes: likes and dislikes. The engagement index measures the level of user interaction by comparing views, votes, and number of comments. We observe that over time, engagement for the intervention video subgroups has increased whereas the diffusion for other non-intervention videos has decreased, which suggests that information about COVID-19 interventions has become more popular as the pandemic develops. Additionally, YouTube's search ranking algorithm seems to strongly take into account video polarization as videos that remain prominent in the search rankings have polarization scores 37% lower than videos that are removed from the top ten results. Topicality of video content may also play a role as medical treatment-related videos are the least promoted in the search results amongst the video subgroups despite having low video polarization. Engagement is lowest overall on medical intervention videos, which may be due to vaccine and treatment development as a topic being downgraded quickly from YouTube's search results. We recommend further research into YouTube's search result ranking model to better understand YouTube's role in the spread of news and information about coronavirus and other topics. Despite focusing on the COVID-19 pandemic, the methods for analyzing YouTube videos may be applied to other events or crises.
Prevalence of obesity and overweight among children and adolescents in Minia Governorate, Egypt

Basic Science Department, Faculty of Physical Therapy, Ahram Canadian University, Egypt Department of Physical Therapy for pediatrics & pediatric Surgery, Faculty of Physical Therapy, Deraya University, Minia, Egypt Department of Physical therapy for internal medicine, Deraya University, Minia, Egypt Department of physical therapy for cardiovascular/ respiratory disorders, and geriatrics, National Heart Institute, Giza, Egypt Department of Physical Therapy, Faculty of applied medical sciences, Hail University, Hail, Kingdom of Saudi Arabia Department of Basic Science, Faculty of Physical Therapy, Cairo University, Cairo, Egypt
Measur es of Prosody

Over the past few decades, theories of prosody have been developed that have broadened the topic in such a way that the term ‘prosody’ does not merely pertain to low-level realization of suprasegmental features such as f0, duration, and amplitude, but also concerns highlevel prosodic structure (e.g. Beckman 1996; Shattuck-Hufnagel and Turk 1996; Keating 2006; Fletcher 2010; Cho 2016). Prosodic structure is assumed to have multiple functions, such as a delimitative function (e.g. a prosodic boundary marking), a culminative function (e.g. a prominence marking), and functions deriving from the distribution of tones at both lexical and post-lexical levels. It involves dynamic changes of articulation in the laryngeal and supralaryngeal system, often accompanied by prosodic strengthening—that is, hyperarticulation of phonetic segments to enhance paradigmatic contrasts by a more distinct articulation, and sonority expansion to enhance syntagmatic contrasts by increasing periodic energy radiated from the mouth (see Cho 2016 for a review). Under a broad definition of prosody, therefore, prosody research in speech production concerns the interplay between phonetics and prosodic structure (e.g. Mücke et al. 2014, 2017). It embraces issues related to how abstract prosodic structure influences the phonetic implementation by the laryngeal and supralaryngeal systems, and how higher-level prosodic structure may in turn be recoverable from or manifest in the variation in the phonetic realization. For example, a marking of a tonal event in the phonetic substance involves dynamic changes not only in the laryngeal system (regulating the vocal fold vibration to produce f0 contours) but also in the supralaryngeal system (regulating movements of articulators to produce consonants and vowels in the textual string). With the help of articulatory measuring techniques, the way these two systems are coordinated in the spatio-temporal dimension is directly observable, allowing various inferences about the role of the prosodic structure in this coordination to be made. This chapter introduces a number of modern articulatory measuring techniques, along with examples across languages indicating how each technique may be used or has been
CaMR: Towards Connotation-aware Music Retrieval on Social Media with Visual Inputs

With the ubiquitous network connectivity and the proliferation of mobile devices, people are increasingly consuming digital contents from social media driven music sharing platforms (e.g., YouTube, Soundcloud). In this paper, we study a novel problem of connotation-aware music retrieval that focuses on the connotation which expresses the implicit feeling or emotion beyond the explicit content in artworks. Our goal is to automatically retrieve relevant music on social media based on the connotation of visual inputs (e.g., images, photos) provided by the users. The problem is challenging as it requires the accurate identification of the implicit connotation from both images and music pieces, and the precise matching of the identified connotation across different data modalities. We develop a connotation-aware music retrieval (CaMR) framework to address the above challenges. Evaluation results from a real-world social media dataset demonstrate that the CaMR framework can retrieve music that is highly relevant to the connotation of the input image.
A platform for crowdsourcing corpora for argumentative

One problem that Argument Mining (AM) is facing is the difficulty of obtaining suitable annotated corpora. We propose a web-based platform, BookSafari, that allows crowdsourcing of annotated corpora for relation-based AM from users providing reviews for books and exchanging opinions about these reviews to facilitate argumen-tative dialogue. The annotations amount to pairwise argumentative relations of attack and support between opinions and between opinions and reviews. As a result of the annotations, reviews and opinions form structured debates which can be understood as bipolar argumentation frameworks. The platform also empowers annotations of the same pairs by multiple annotators and can support different measures of inter-annotator agreement and corpora selection.
Outcomes of dorsal plating for selected distal radius fractures

To determine the functional outcome and complications following dorsal plating for unstable fractures of the distal radius. We searched our IRB-approved Distal Radius Fracture Databases and identified all patients who were treated with a dorsally applied plate. Thirtyfour distal radius fractures in 33 patients with a mean age of 50 years and average follow-up of 14 months were treated with a dorsal locking plate from 2007 to 2015. Fifteen and six patients had dorsal shearing fracture pattern and delayed presentation, respectively. There were no loss of reduction, malunion, or nonunion. Average VAS pain score was 2.1/10. Eight patients (23%) required hardware removal, one of which was due to extensor tendon rupture (3%) and five due to extensor tendon irritation (15%). Dorsal locked plating of distal radius fractures with newer low-profile implants is a viable option for particular fractures types, such as the dorsal rim shear type fractures.
The Influence of Rewards in Enhancing Employee Performance Through Psychological Empowerment

In 2015, the non-metallic mineral products industry was ranked the 15th largest export earner for Malaysia. One main product of this industry is ceramic tiles where there are large manufacturers located in Southern Malaysia. However, tile manufacturers are facing a lot of challenges especially from cheaper imported tiles from neighbouring countries as well as from increasing raw material costs. In order for them to survive, manufacturers have to look into ways at controlling their unit labour cost which is the average cost of labour per unit of output produced. In order to do so, employers must strive to build a team of high performing employees. For this reason, this study aims to determine ways in which high performing team of employees can be formed. The findings revealed that rewards positively influence employees’ performance. However with the inclusion of psychological empowerment, the effect on performance was negative indicating that the greater the proportion of psychological empowerment, the lower was the employees’ performance with the presence of rewards. Therefore, employers have to ensure that the right level of psychological empowerment is felt by employees when designing the rewards system as highly rewarded employees find psychological empowerment as an additional burden to them and will hamper
Virtual Resection for Predicting the Outcome of Epilepsy Surgery

Virtual Resection for Predicting the Outcome of Epilepsy Surgery | M. Müller, C. Rummel, K. Schindler, A. Steimer Michael Müller1,2, Christian Rummel2, Kaspar Schindler1 and Andreas Steimer1 1 Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern 2 Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern Virtual Resection for Predicting the Outcome of Epilepsy Surgery
High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs

In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learning-based technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.
DEEP LEARNING APPROACH FOR STRESS DETECTION BASED ON SOCIAL INTERACTIONS

Deep Learning methods are widely used in automation technology in this decade. This used in self driving cars, health care, voice search, automatic text generation , Image Recognition, Image caption generation, advertising, predicting earth quakes, Brain cancer detection, finance, market price forecasting and many more. This paper we are using Deep Reinforcement learning (DRL), Support Vector Machine (SVM) methods for human stress detection based on social Interactions. Deep Reinforcement Learning is the used variety of a neural network which learns by communicates with its environment via interpretation, actions, and rewards. DRL has been successfully used to determine human strategies. DRL is essential as it is among the most general purpose learning techniques that you can use for developing business applications. It also requires significantly less data for training models. Another advantage is that you can train it by using simulation. This completely removes the need for labeled data.
RELATION EXTRACTION USING DEEP LEARNING METHODS - A SURVEY

Relation extraction has an important role in extracting structured information from unstructured raw text. This task is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text. Nowadays, neural networks play an important role in the task of relation extraction. The traditional non deep learning models require feature engineering. Deep Learning models such as Convolutional Neural Networks and Long Short Term Memory networks require less feature engineering than non-deep learning models. Relation Extraction has the potential of employing deep learning models with the creation of huge datasets using distant supervision. This paper surveys the current trend in Relation Extraction using Deep Learning models.