Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Ethical Leadership on Employees Turnover Intention: An Empirical Study of the Banking Sector in Malaysia

The purpose of this paper is to investigate the influence of ethical leadership in determining the organizations’ individual-type ethical climate (self-interest, friendship, and personal morality ethical climate) in reducing employee turnover intention. It seeks to identify the role of individual-type climate in mediating the association between ethical leadership and employee turnover intention. Moreover, the moderation effect of emotional exhaustion among employees on the relationship between ethical leadership and turnover intention has been researched to establish the ethical degree of leadership. Using a sample of 260 questionnaires from employees working full-time in the banking sector, the results were analyzed in PLS-SEM. The results of the social exchange theory indicated that ethical leadership is vital in shaping the workplace’s individual-type ethical climate and reducing employees’ turnover intention. The findings demonstrate that the relationship between ethical leadership and turnover intention is mediated by an individual-type ethical climate, which means that employees in a positive ethical climate do not wish to leave immediately. Furthermore, emotional exhaustion was found to moderate the association between ethical leadership and employees’ turnover intention under high emotional exhaustion, where low ethical leadership is experienced, reporting higher levels of turnover intention.
A proposal of an ecosystem based on intelligent ICT tools to support the diagnosis and intervention of patients with communication disorders

espanolLa intervencion temprana en personas con discapacidades de comunicacion (PWCD) es una estrategia fundamental que tiene el objetivo de mejorar varias habilidades relacionadas con el habla, el lenguaje y la deglucion. Sin embargo, las herramientas existentes se centran en el diagnostico o el tratamiento de trastornos especificos, y por lo general, no cubren las diferentes necesidades de las personas que proporcionan rehabilitacion, asistencia sanitaria y servicios educativos para los pacientes (medicos, patologos, y los familiares de los pacientes). Por estos motivos, en este trabajo se presenta una vision general de las herramientas existentes para el diagnostico y la intervencion de PWCD, y sobre esta base, se propone un ecosistema que implementa herramientas TIC inteligentes y un sistema experto capaz de generar automaticamente planes de terapia de lenguaje. El sistema experto ha sido evaluado previamente y ha alcanzado resultados alentadores. EnglishThe early intervention on People With Communication Disabilities (PWCD) is a fundamental strategy that has the aim of improving several skills related with speech, language, and swallowing. However, the existent tools are focused on the diagnosis or treatment of specific disorders, and commonly, they do not cover the different needs of people that provides rehabilitation, healthcare and educational services for patients (doctors, pathologists, and patient's relatives). On those grounds, in this paper we present a general overview of existing tools for diagnosis and intervention of PWCD, and on this basis, we propose an ecosystem that implements intelligent ICT tools and an expert system able to automatically generate therapy plans. The expert system has been previously evaluated and has reached encouraging results.
The dynamics of multidimensional poverty in Brazil : a decomposition analysis for the period 2009-2015

: This paper focuses on the methodology by Alkire-Foster to measure multidimensional poverty in Brazil, using data from the National Household Sample Survey (PNAD) for the years 2009 to 2015. A decomposition analysis was performed in order to identify poverty levels according to the locality and the groups that traditionally suffer deprivation and therefore face difficulties in developing their potential. The results revealed the persistence of the highest poverty levels in the North and Northeast of the country, as well as in rural regions. There was an increase in poverty for the indigenous population. In addition, vulnerability, and difficulty in accessing work were the main determinants of poverty for the female population, although men presented the highest level of poverty. For all age groups, a decrease in the level of poverty was observed, however, the elderly and children present the highest rates.
Adoption of No-Tillage System and Brazilian natural areas: What can a Spatial Propensity Score Matching analysis tell us?

Agrotechnology adoption has a direct impact on land use management. In this article we analyze the causal effect of adopting the No-Tillage System (NTS) on natural and cultivated areas in Brazilian municipalities using the Spatial Propensity Score Matching method. We perform econometric models using different treatment definitions. Our statistical results show that the adoption of NTS had a positive effect on the natural and cultivated agricultural areas of the treated municipalities. The evidence from this case study indicates that the use of soil management technology can lead to environmental preservation, confirming Borlaug’s hypothesis. We believe that the recovery of eroded land and the increase in agricultural productivity may partially explain these results.
Arming IDS Researchers with a Robotic Arm Dataset

Industry 4.0 is rapidly transforming traditional manufacturing practices. Smart manufacturing technologies that automate research and development using a combination of robotic arms and domain-specific cyber-physical systems are at the core of this transformation. Unfortunately, dependence on networked communication increases the risk of security attacks, which must be mitigated using either platforms that are secure by design or intrusion detection and prevention systems. We report on an ongoing project to design and develop intrusion detection systems (IDS) for the Hein Lab, a smart manufacturing research lab in the chemical sciences domain. Designing effective IDS requires large datasets and high-quality, domain-specific benchmarks, which are difficult to obtain. To address this gap, we present the Robotic Arm Dataset (RAD), which we collected at the Hein Lab over a three-month period. We also present our non-intrusive tracing framework RATracer, which can be retrofitted onto any existing Python-based automation pipeline, and two sets of preliminary analyses based on the command and power data in RAD.
Environment Design for Biased Decision Makers

We study the environment design problem for biased decision makers. In an environment design problem, an informed principal aims to update the decision making environment to influence the decisions made by the agent. This problem is ubiquitous in various domains, e.g., a social networking platform might want to update its website to encourage more user engagement. In this work, we focus on the scenario in which the agent might exhibit biases in decision making. We relax the common assumption that the agent is rational and aim to incorporate models of biased agents in environment design. We formulate the environment design problem under the Markov decision process (MDP) and incorporate common models of biased agents through introducing general time-discounting functions. We then formalize the environment design problem as constrained optimization problems and propose corresponding algorithms. We conduct both simulations and real human-subject experiments with workers recruited from Amazon Mechanical Turk to evaluate our proposed algorithms.
Effect of natural extracts on the oxidative stability of pork hamburgers during refrigerated storage

: The effect of three natural extracts made from culinary spices with antioxidant activity on the oxidative stability of lipids and proteins, color changes, and sensory quality of cooked pork during 12 days of refrigerated storage was evaluated. Hamburger-type model systems were made with the Longissimus thoracis et lomborum muscle, dorsal fat, salt, water and the corresponding extract. The antioxidant activity of the extracts was determined by the DPPH and ABTS+ methods, while the oxidation of lipids and proteins by TBA-RS and DNPH, respectively. For the color evaluation, the parameters of luminosity (L*) and Hue angle (°h) were used. The sensory analysis was carried out with an untrained panel, which evaluated the attributes of taste, color, smell and texture. The statistical processing of the data obtained on antioxidant activity, lipid and protein oxidation, as well as color, was performed by an analysis of variance. The sensory evaluation was processed with nonparametric statistics. Extract two had the highest antioxidant activity ( P ≤0.05); the three extracts managed to inhibit ( P ≤0.05) lipid oxidation in the hamburgers ( P ≤0.05); however, none of the three extracts managed to inhibit protein oxidation. There were also no differences ( P ≥0.05) with respect to the L* parameter, while the values of °h showed that the three extracts managed to preserve the color of the cooked hamburgers during refrigerated storage. Finally, the sensory evaluation showed that none of the three extracts altered the organoleptic quality of the hamburgers. randomized P ≤0.05), a Tukey mean comparison test was The results of total color difference and sensory analysis were performed using the Kruskal-Wallis Hypothesis test P ≤0.05). Pearson’s correlation coefficients linear The statistical
Learners’ engagement on a social networking platform: An ecological analysis

Language learners are actively engaging in language learning beyond the classroom. However, social networking sites, despite playing a major role in people’s lives, have been found to be rarely incorporated into language learners’ learning ecologies. An understanding of the factors that shape learners’ engagement on instruction-oriented social networking sites could inform platform design and enhance the likelihood of platforms being utilized. This study examined a group of language learners’ engagement on an instruction-oriented social networking site, Lang-8, over time. Using narrative data and learners’ behavioral data on the platform, the study revealed how various ecological resources on and outside the platform interacted with one another to shape the dynamic changes in different dimensions of learners’ engagement on the platform over time. The study also suggested that learners’ engagement on the platform further induced reconstruction of their language learning ecologies, providing additional learning opportunities both on and beyond the platform. The findings highlight the importance of supporting learner engagement on technological platforms in an informal learning context and provide insights into how such support could be achieved through system design.
Socioeconomic Development and Deforestation in the Cerrado Biome, Brazil: Spatial Interactions and Heterogeneity

This paper investigates the relationship between socioeconomic development and deforestation in the Cerrado biome, mainly focusing on the current Brazilian agricultural frontier of Matopiba. More specifically, we search for spatial interactions, heterogeneity, and spatial clusters among the municipalities using the exploratory analysis of spatial data (ESDA). Due to the many possible socioeconomic variables related to deforestation decisions, we create a Socioeconomic Development Index (SDI) that synthesizes all possible influences using factorial analysis from multivariate statistics. We also grouped cluster analysis municipalities with dissimilar deforestation and economic development patterns. The results show that the biome’s economic development and deforestation are spatially concentrated and heterogeneous and are negatively associated for the majority of Cerrado municipalities both in the spatial and cluster analysis, indicating that socioeconomic development may be an inhibitor of deforestation.
Efficient and Accurate Methods for Computing the Gravitational Field of Irregular-Shaped Bodies

Small bodies (e.g. asteroids and moons) are one of the most important targets for physical exploration of space. For space missions, it is a central aspect to test the guidance, navigation, and control algorithms. This is typically ensured through physically - based simulations of the space mission in a virtual testbed, because of its time and cost-efficiency. Information about the gravitational field is crucial for the orbit and especially for planning a landing maneuver. Most small bodies have an irregular shape, which contributes to a complex gravitational field. In this study, we present and compare three different methods to model the gravitational field of small bodies and apply them to three test cases that we describe in detail. Our first method is based on the polyhedral method that provides a closed-form analytical solution of the gravity field for (assumed) homogeneous density. The idea behind the second method is to represent the small body's mass by a polydisperse sphere packing. This allows us an easy and efficient computation through parallelization on the GPU (Graphics Processing Unit). The third method models the internal mass distribution of the body as a set of solid elements in spherical coordinates. The body is divided into longitudes and latitudes and the radius is divided into subsections. The used size of the volume elements is chosen to ensure high accuracy in representing the shape of the body. All three methods are also applicable on the surface of the body, making it interesting in the context of surface gravimetry. We evaluate the three methods using two ideal shapes (sphere and cube) and one real shape model (Martian moon Phobos). We compare the gravitational acceleration at their surface and measure the relative error of the models concerning the analyt-ical solutions. We also look at the computational cost of each method. Our proposed methods indicate that each of them is suitable for modeling asteroids with different characteristics. We provide reliable gravitation data for purposes such as space-craft orbit analysis and evaluation of the small body's surface domain.
Preceding anti-spike IgG levels predicted risk and severity of COVID-19 during the Omicron-dominant wave in Santa Fe city, Argentina

Abstract The SARS-CoV-2 Omicron variant has increased infectivity and immune escape compared with previous variants, and caused the surge of massive COVID-19 waves globally. Despite a vast majority (~90%) of the population of Santa Fe city, Argentina had been vaccinated and/or had been infected by SARS-CoV-2 when Omicron emerged, the epidemic wave that followed its arrival was by far the largest one experienced in the city. A serosurvey conducted prior to the arrival of Omicron allowed to assess the acquired humoral defences preceding the wave and to conduct a longitudinal study to provide individual-level real-world data linking antibody levels and protection against COVID-19 during the wave. A very large proportion of 1455 sampled individuals had immunological memory against COVID-19 at the arrival of Omicron (almost 90%), and about half (48.9%) had high anti-spike immunoglobulin G levels (>200 UI/ml). However, the antibody titres varied greatly among the participants, and such variability depended mainly on the vaccine platform received, on having had COVID-19 previously and on the number of days elapsed since last antigen exposure (vaccine shot or natural infection). A follow-up of 514 participants provided real-world evidence of antibody-mediated protection against COVID-19 during a period of high risk of exposure to an immune-escaping highly transmissible variant. Pre-wave antibody titres were strongly negatively associated with COVID-19 incidence and severity of symptoms during the wave. Also, receiving a vaccine shot during the follow-up period reduced the COVID-19 risk drastically (15-fold). These results highlight the importance of maintaining high defences through vaccination at times of high risk of exposure to immune-escaping variants.
Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis

Background: The global public health and socioeconomic impacts of coronavirus disease 2019 (COVID-19) have been substantial, making herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rate in several months, although the vaccine confidence in Japan is the lowest worldwide. Objective: We attempted to find the reasons for the rapid coronavirus disease 2019 (COVID-19) vaccination in Japan under the lowest vaccine confidence in the world by Twitter analysis. Materials and methods: We downloaded COVID-19 related Japanese tweets from a large-scale public COVID-19 Twitter chatter dataset within the timeline of February 1, 2021 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister's Office of Japan. After preprocessing, we applied unigram and bigram token analysis, then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. Then we identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions. Results: We selected 190,697 vaccine-related tweets after filtering. By n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top six unigrams, tweets with both keywords "reserve" and "venue" showed the largest r = 0.912 (P < 0.001) with the daily vaccination cases. In sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as "infect" and "vaccine confidence". The expectation of the number of tweets generated from topic "infect" was larger than "vaccine confidence. " Conclusion: Our work indicated that awareness of the danger of COVID-19 might increase the willingness to get vaccinated; With sufficient vaccine supply, effective vaccine reservation information delivery may be an important factor for people to get vaccinated; We didn't find evidence for increased vaccine confidence in Japan during the period in our research. We recommend policymakers share fair and prompt information about infectious diseases and vaccination, and make efforts to smoother delivery of vaccine-reservation information.
The Effect of Firms' Information Exposure on Safeguarding Employee Health: Evidence from COVID‐19

We show that information exposure through international business networks is an advantage that enables ﬁrms to take proactive measures that beneﬁt employees and potentially the local community. Speciﬁcally, in the early days of COVID-19, ﬁrms with business networks with China and Italy are more likely to be aware of the severity of COVID-19, and can proactively implement work-from-home (“WFH”) policies to safeguard their employees. Using Safegraph foot traﬃc data, we ﬁnd a higher stay-at-home ratio before local governments impose a lockdown in zip codes where ﬁrms have a larger information advantage. These areas are also associated with a lower spread of COVID-19. Our main ﬁndings are more pronounced when local governments face constraints and when ﬁrms have a higher WFH capability or pro-stakeholder incentives. Collectively, we present evidence on the role of ﬁrms with information exposure in alleviating the negative eﬀects of a public health crisis in the absence of government intervention. This table shows the sample sensitivity tests on our analysis of the eﬀect of information exposure on the stay-at-home ratio before local policy intervention. This sensitivity analysis looks at samples at diﬀerent population and ﬁrm cutoﬀs. We provide a detailed description of the variables in Appendix A1. All regressions include zip-code and county-day ﬁxed eﬀects. The table reports ordinary least squares (OLS) coeﬃcient estimates and (in parentheses) t -statistics based on robust standard errors clustered by state. ***, **, and * denote statistical signiﬁcance at the 1%, 5%, and 10% levels, respectively.
Extending VKG Systems with RDF-star Support

This thesis concerns an extension to Virtual Knowledge Graph (VKG) systems, a state of the art technique of data integration. These systems perform data integration through the mapping of relational data sources to a virtual knowledge graph. The graph is often expressed using the W3C standard of Resource Description Framework (RDF), in this case the graph contains data in the form of triples, also known as facts. RDF-star is a proposed extension to RDF that enables the annotation of RDF triples. It allows for a further layer of contextual data that until now has been diﬃcult to implement using standard RDF. As RDF-star is a current ﬁeld of research with few publications to its name this thesis contains a description of the proposal. We investigate how to extend VKG systems with RDF-star support in order to enhance their support for contextual data. To this end we identify the mapping as the VKG component most crucial to extend. This is the component that speciﬁes how to generate the virtual knowledge graph from the data contained in the data sources. In order to extend the mapping we propose an extension to the W3C standard mapping language R2RML that we call R2RML-star. This allows for generating RDF-star data from relational data sources, enhancing the system’s virtual knowledge graph with all the beneﬁts of RDF-star in regard to contextual data handling. We detail all the ways a VKG system must be extended in order to handle our proposed extension. This includes support for querying the generated RDF-star data, which is done through the use of SPARQL-star, an extension of the W3C standard query language for RDF data, SPARQL. As a proof-of-concept we implement our proposed changes in the state of the art VKG system Ontop. We describe in detail the challenges this posed and how we overcame them, hoping to serve as inspiration for further development in this ﬁeld.
LITE: a low-cost practical inter-operable GPU TEE

There is a strong need for GPU trusted execution environments (TEEs) as GPU is increasingly used in the cloud environment. However, current proposals either ignore memory security (i.e., not encrypting memory) or impose a separate memory encryption domain from the host TEE, causing a very substantial slowdown for communicating data from/to the host. In this paper, we propose a flexible GPU memory encryption design called LITE that relies on software memory encryption aided by small architecture support. LITE's flexibility allows GPU TEE to be co-designed with CPU to create a unified encryption domain. We show that GPU applications can be adapted to the use of LITE encryption APIs without major changes. Through various optimizations, we show that software memory encryption in LITE can produce negligible performance overheads (1.1%) for regular benchmarks and still-acceptable overheads (56%) for irregular benchmarks.