Miracle-dz/UniFAR-FTUs huggingface dataset:
Um processo para gerenciamento de arquitetura de software corporativa

Big challenges are still faced by software development organizations. Due the fact that many organizations are coming up in this area, the dispute between them is growing. They are investing more in some programs and projects in order to improve its products, because they help them to surpass the dispute. The improvement has the focus on, among other things, the speed of delivery, customer level acceptance of the product and the constant level of quality in the developed systems, relative to the non functional requirements defined by organization. Moreover, there are indirect factors that are searched by the organization, because they are reflected in the final product quality. One of these indirect factors is software architecture which has a high level of influence on the software product quality. In addition, software architecture has an important role, since its influence at product quality level can be perceived by the customer and the organization employees. Although, the corporations still has difficulties to develop software architectures with the quality level needed to their business. In some cases, the architectural definitions are fully dependent on the software developing team. With the intention to optimize the product quality, this work suggests a software architecture management process, based on Hoshin kanri, that are using organization targets to manage the activities executed in all functional levels. The main intention of this architectural management is to execute a preventive work increasing the rate of success on the development of final product. Another important factor is the alignment of the architectures developed by the corporation. It allows constant architectural evolutions to accomplish goals of the organization. Moreover, this alignment can help interaction between several project teams. This must help the participation of developers in many projects, in order to increase the reuse level of software components and allow more independence from the development team.
Students‘ Awareness and Choice about Vocational Education at Secondary Level

This study focused on investigating awareness about and choice of secondary school students towards vocational education. Objectives of the study were to explore students‘ awareness about vocational education, investigate students‘ choice of selecting vocational education after completing their secondary education, and explore students‘ views on parental desire about their children to join vocational education as a profession. Data was collected from 400 secondary school students of District Haripur by using stratified random sampling technique. A survey questionnaire was used for the study; the reliability index for the questionnaire was 0.79. Percentage, frequency, and t- test have been used to analyze the data. Findings show that a majority of the students (86%) considered vocational education as essential for future job, but did not recommend it for female students. Teachers‘ guidance was considered as the major source of awareness about vocational education. As perceived by the students, more than half of the parents (i.e. 65.5 %) strongly agreed that their children should join vocational education and nearly fifty percent (i.e. 48.1%) of the students showed their willingness to select vocational education in the future. However, no significant difference was found between male and female students regarding their awareness about and choice of vocational education at secondary level.
Coopetition and orchestration in demand-side inter-platform ecosystems: a quantitative analysis *

The increasing relevance of platform ecosystems has spurred a rich literature studying their value creation and value capture dynamics. These are shaped by coopetitive relations between a platform owner that acts as the only orchestrator and its complementors. However, little is known about these dynamics in the case of inter-platform ecosystems. In the latter, nongeneric complementarities between the agents making up the ecosystem are established between platforms, rather than within them. This article proposes an empirical analysis to study value creation, value capture and orchestration in demand-side inter-platform ecosystems. Using recent data from internet traﬃc between the major 246 European digital platforms, we develop a methodology to measure nongeneric complementarities in consumption between them and assess the nature of their competitive relationships. We corroborate that demand-side inter-platform ecosystems are a non-negligible phenomenon: 18% of these platforms show them. Moreover, we identify four types of such ecosystems deﬁned by the coopetitive relationship linking their members: complementors, indirect competitors, direct competitiors and unrelated. Finally, we ﬁnd evidence of some demand-side inter-platform ecosystems being co-orchestrated by multiple platforms and of one being user co-orchestrated.
A Taxonomy of Machine Learning-based Fraud Detection Systems

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse.
Towards Partially Evaluating Symbolic Interpreters for All (Short Paper)

Symbolic execution is a program analysis technique to automatically explore the execution space of programs by treating some inputs symbolically. To efficiently perform symbolic execution, one emerging way is to construct a compiler that translates input programs to symbolic programs without the interpretation overhead. Previous work has explored compiling nondeterministic symbolic execution by partially evaluating a symbolic interpreter. In this paper, we follow a “semantics-first“ approach and investigate compiling concolic execution and backward symbolic execution by multi-stage programming. In particular, we construct variants of staged symbolic interpreters that can be partially evaluated using the Lightweight Modular Staging (LMS) framework. We demonstrate our approach using a simple low-level intermediate representation (IR) and evaluate the prototype implementations for the LLVM IR. Our concolic compiler shows comparable performance to SymCC, a state-of-the-art concolic compiler, and our back-ward symbolic compiler solves tasks that are difficult for forward execution engines. The demonstrated approach shows a unifying methodology that can be applied to compiling diverse flavors of symbolic execution.
ROLE OF MASS MEDIA IN SHAPING PUBLIC OPINION

: The media play a central role in informing the public about what happens in the world, particularly in those areas in which audiences do not possess direct knowledge or experience. This article examines the impact the media has in the construction of public belief and attitudes and its relationship to social change. Drawing on findings from a range of empirical studies, we look at the impact of media coverage in areas such as disability, climate change and economic development. Findings across these areas show the way in which the media shape public debate in terms of setting agendas and focusing public interest on particular subjects. For example, in our work on disability we showed the relationship between negative media coverage of people on disability benefit and a hardening of attitudes towards them. Further, we found that the media also severely limit the information with which audiences understand these issues and that alternative solutions to political problems are effectively removed from public debate. We found other evidence of the way in which media coverage can operate to limit understanding of possibilities of social change. In our study of news reporting of climate change, we traced the way that the media have constructed uncertainty around the issue and how this has led to disengagement in relation to possible changes in personal behaviours. Finally, we discuss the implications for communications and policy and how both the traditional and new media might help in the development of better informed public debate.
PERCUSSION RECEPTIVE AND HIT FREE FOLDER SCHEDULING FOR MARINE AURAL LOCALIZATION

While most of the huddle arrive on the courtesy of submerged localization bit work was done to find out how the anchors behoves lug their files vis-a-vis sensor nodes. Our work makes an idea of bar agitate of binder scheduling repeatedly self-localization in submerged negotiation sensor accompany common nodes instinctively. Concerning wrap scheduling, our assist sends diminish localization time, and behaviour so we make a strife of two crate conductance procedures like collision-free method, anew collision-tolerant study. The collision-tolerant restrain less time for localization when correlated to collision-free one for indistinguishable likelihood of localization. Exclusive of systematic concentration consumed by anchors, the program of collision-tolerant includes specifically wonderful advantages.
Effects of External Social Capital on Hospitality Firm's Performance: The Mediating Role of Competitive Strategies

This study investigates the mediating role of competitive strategies on the relationship between external social capital and firm performance. The data of the study were obtained through surveys responded by the managers of five-star hotels. 300 valid questionnaires were considered in the analysis. Firstly, validity and reliability analyses of the questionnaire were made. Hierarchical moderated regression analysis was used to test the research hypotheses. The findings indicate that external social capital has a positive effect on the firm performance. External social capital has a positive effect on competitive strategies. Furthermore, it was found that competitive strategies are partially mediated between external social capital and firm performance. In brief, hotels should not only learn but also implement these strategies to achieve competitive advantage.
The Spatial Distribution of Elderly Welfare Service in South Korea*

Purpose: This study aims to not only measure the elderly welfare supply index but investigate spatial patterns and determinants of local elderly welfare services in South Korea. Research design, data, and methodology: The index for local elderly welfare services is measured by employing standardized scores for critical variables related to elderly welfare. The present study utilized the GIS technique and Moran’s I index to examine spatial patterns of elderly welfare services. The determinants of local elderly welfare service are analyzed by a series of regression models using R. Results: Spatial imbalance and asymmetric distribution were serious in the supply of elderly welfare service. It was also confirmed that the factors affecting the level of welfare services for the elderly vary depending on the type of service. In particular, the higher the proportion of the elderly population and the social welfare budget, the lower the level of welfare services for the elderly. Conclusions: Given the circumstance of spatial mismatch between supply and demand for elderly welfare services, it is necessary to consider and provide policy tactics about how the economic benefits and welfare budgets generated in the region can contribute to strengthening the welfare service system for the elderly.
A Review of Machine Learning Algorithms for Fraud Detection in Credit Card Transaction

Summary The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.
Special Section on Drug Metabolism and Regulation — Minireview Searching for Constitutive Androstane Receptor Modulators

The constitutive androstane receptor (CAR; NR1I3) has been established as one of the main drug- and xenobiotic-responsive transcrip-tional regulators, collectively called xenosensors. CAR activates the expression of several oxidative, hydrolytic, and conjugative drug-metabolizing enzymes and drug transporters, and therefore, it con-tributes to drug and xenobiotic elimination, drug interactions, and toxicological processes. This minireview introduces mechanisms that modulate CAR activity and focuses on the recent approaches used to search and characterize CAR agonists, inverse agonists, and indirect activators. This minireview is dedicated to Dr. Masahiko Negishi to celebrate his scientific achievements during his long ser-vice at the National Institutes of Health. Discovery and characterization of human constitutive androstane receptor (CAR) modulators is important for drug development, toxicity studies, and in generation of chemical tools to dissect biological functions of CAR. This minireview focuses on the main methods used to search for these compounds and discusses their essential features.
Phonotactically probable word shapes represent attractors in the cultural evolution of sound patterns

Abstract Words are processed more easily when they have canonical phonotactic shapes, i.e., shapes that are frequent both in the lexicon and in usage. We explore whether this cognitively grounded constraint or preference implies testable predictions about the implementation of sound change. Specifically, we hypothesise that words with canonical shapes favour, or ‘select for’, sound changes that (re-)produce words with the same shapes. To test this, we investigate a Middle English sound change known as Open Syllable Lengthening (OSL). OSL lengthened vowels in disyllables such as ME /ma. kə/ make, but more or less only when they became monosyllabic and when their vowels were non-high. We predict that word shapes produced by this implementation pattern should correspond to the shapes that were most common among morphologically simple monosyllables and disyllables at the time when OSL occurred. We test this prediction against Early Middle English corpus data. Our results largely confirm our prediction: monosyllables produced by OSL indeed conformed to the shapes that were most frequent among already existing monosyllables. At the same time, the failure of OSL to affect disyllables (such as body) prevented them from assuming shapes that were far more typical of morphologically complex word forms than of simple ones. This suggests that the actuation and implementation of sound changes may be even more sensitive to lexical probabilities than hitherto suspected. Also, it demonstrates how diachronic data can be used to test hypotheses about constraints on word recognition and processing.
Mobile App Fraud Detection

: Rating fraud in the mobile app industry refers to illegal or dishonest practises that are meant to bump up the mobile app market. The applications on the popularity chart. In fact, it is becoming more and more frequent for software developers to use dubious ways, such as inflating them. Sales or uploading of phone app scores, to commit ranking fraud. While the value of preventing rating fraud has been widespread, it is recognised that there is minimal awareness and study in this field. To this end, we have a systemic view of ranking in this article. Fraud and suggest a rating method for the detection of fraud in smartphone apps. Specifically, first of all, we propose to specifically identify the rating scam the mining of active times, including leading sessions, of mobile applications. These leading sessions can be leveraged for local identification. Anomaly instead of global app anomaly rankings. In addition, we analyse three forms of proof, i.e. a rating based on Proof, evidence-based rating and evidence-based analysis through modelling App ranking, rating and review behaviours by tests of mathematical theories. In addition, we suggest an aggregation-based optimization approach to incorporate all proof of fraud detection. Finally, we test the suggested framework for real-world software data obtained from the iOS App Store over a long period of time. In the tests verify the feasibility of the proposed method and demonstrate the scalability of the detection algorithm as well as
LEARNING FROM HALE An Educational Augmented Reality for an Indigenous Hawaiian Architecture

An educational Augmented Reality (AR) application with Head Mount Display (HMD) is developed for the revitalization of the Hales. The proposed application allows a user to have a dynamic learning experience of the Hale by 1) full immersion into an extended reality, 2) enabling the hands-on construction & assembly process with real-time feedback, and 3) visualizing context-specific information and concepts. Through this intact experience, tacit knowledge embedded in the Hawaiian Hale design is delivered. In this paper, the implementation of the proposed application is explained, and the usage of the application is also demonstrated.
Pharmacodynamic Study of QingFei Paidu Decoction in the Treatment of Acute Respiratory Distress Syndrome Caused by Coronavirus Disease-2019 (COVID-19)

Background: The outbreak of the Coronavirus Disease-2019 (COVID-19) has threatened the public health of the world, and may eventually lead to acute respiratory impoverishment syndrome (ARDS). ARDS is a clinical syndrome caused by intrapulmonary or extrapulmonary reasons, which has complex pathogenesis and high mortality rate, it’s also one of the important factors in death from the 2019 novel coronavirus (2019-nCoV) epidemic. It has been reported that traditional Chinese medicine (TCM) can exert a good effect in the process of treatment. The present study aimed to observe the protective effects of TCM formula Qingfei Paidu Decoction (QFPD) on ARDS rats and explore the pharmacodynamic mechanism of the compound. Methods: 24 male rats were randomly divided into 4 groups (n=6), blank control group, model group, QFPD group (18.6g·kg -1 ·d -1 ) and dexamethasone group (2mg·kg -1 ·d -1 ). Blank control group rats were given saline, whereas other groups were injected with oleic acid (OA) and lipopolysaccharide (LPS) successively to establish ARDS model, and observed the behavioral performance of rats after model building. The morphological changes of lung tissue under optical microscope were observed; rat lung index (LI) and lung permeability index (LPI) were measured; blood PH, partial arterial oxygen pressure (PaO 2 , mmHg), partial arterial carbon dioxide pressure (PaCO 2 , mmHg), arterial oxygen saturation (SaO 2 ) were measured by blood gas analyzer; the levels of tumor necrosis factor-α (TNF-α), interleukin (IL-1β, IL-6, IL-8, IL-10), thromboxane B 2 (TXB 2 ), 6-keto-prostaglandin F1α (6-keto-PGF1α), kerbs von lungren 6 antigen (KL-6), C-reactive protein (CRP), and the expression of superoxide dismutase (SOD) were measured via test kit. Results: Compared with the model group, the two
Improving the Prediction of Heart Disease Using Ensemble Learning and Feature Selection

Heart or cardiovascular disease is main cause of mortality. The main objective of developing the proposed model is to increase the accuracy and reliability of predicting the coronary heart disease. This paper attempts in predicting the risk of heart disease more accurately using the techniques of ensemble learning. Moreover, the techniques of feature selection and hyper parameter tuning has been implemented in this work leading to further increase in accuracy. Among the three ensemble techniques, stacking, majority voting and bagging used in this work, the improvement achieved in prediction accuracies is 2.11%, 7.42% and 0.14% respectively. Majority voting has shown the best results in terms of increase in prediction accuracies with an accuracy of 98.38%. Keywords: Heart Disease, Ensemble Learning, Feature selection, Machine Learning
Expectant Management of Heterotopic Pregnancy

Heterotopic pregnancy or the coexistence of an intrauterine and an extrauterine pregnancy occurs in about 1 out of 30,000 pregnancies. Between assisted reproductive techniques and ovulation induction, it is said to occur more commonly with the former. The patient presented in this case is a 29 year old primigravid who underwent ovulation induction with clomiphene citrate and was later on diagnosed to have both an intrauterine and a non-viable extrauterine pregnancy. The management for this kind of pregnancy can be medical, surgical or expectant. The patient in this case was managed expectantly. This case study presents a review of literature on heterotopic pregnancy, and describes how this rare type of pregnancy developed in this index patient, how it is diagnosed and managed, and to showcase how expectant management can be a safe option for cases of heterotopic pregnancy.