Miracle-dz/UniFAR-FTUs huggingface dataset:
Disaggregated Determinants of Aid: Development Aid Projects in the Philippines

Motivation Across the world, rapid‐onset disasters caused by natural hazards such as storms and floods return with seemingly greater force every year. Many disaster hot spots are particularly vulnerable because of already fragile humanitarian and political situations, some having been affected by armed conflict for decades. These phenomena augment the need for long‐term aid, but do they influence the distribution of aid projects? Purpose This article revisits the long‐standing debate over whether donor interests or recipient needs best predict the distribution of development aid. It disaggregates the analysis down to province level in the Philippines, and includes local hazards when assessing an area’s need for aid. Approach and methods Making use of geocoded data on aid projects, rapid‐onset disasters caused by natural hazards and armed conflict in Philippine provinces between 1996 and 2012, this article provides an exhaustive assessment of the within‐country determinants of World Bank development aid projects. Findings The article finds that need to some extent influences the distribution of aid projects across Philippine provinces but that, in general, domestic political alliances bias projects in favour of the politically dominant group. Previous exposure to conflict is associated with increased likelihood of new projects, but only in dominant group‐majority provinces. While previous disaster exposure is a weak predictor of aid in all provinces, low Human Development Index (HDI) levels significantly predict aid inflow in excluded group‐majority provinces. Policy implications In sum, the article challenges the idea that (international) donors’ priorities override the interests of the recipient/local governmesnt in the distribution of aid projects. It also shows that even if the donor’s explicit policy is disaster mitigation and relief, there is still some way to go before these considerations are effectively incorporated into development aid efforts.
Evolution Dynamic for the Strategy Choice of BCRT under the Theory of Learning in Game

The paper researches the evolution dynamic for the strategy choice of bank credit risk transfer strategy by using learning theory of game and evolutionary economics.Assume that the probability of banks' choosing one strategy depends on their own preferences and group behavior,and consider the bank's entering and exiting behaviors,and establish the evolution dynamic of single population of BCRT and adapt to the learning algorithm,and analyze the influence of the character of the capital function of credit risks on evolution equilibrium.On this basis,we consider bank group internal and spillover effect of bank groups,set up two population evolution dynamic,and prove the proposition of evolution trend and evolution equilibrium of the strategy choice of bank credit risk transfer through the theorems and propositions.Theory analysis and numerical simulation shows that in the long run,the evolution of the choice of BCRT strategy depends on the initial condition and the spillover effect in a certain extent.
Comparative effectiveness of antioxidant melatonin and radioprotectors indralin and phenylephrine in local radiation injuries

: In experiments on mice radioprotective properties of indraline, phenyleprine and melatonin were compared at topical application as an ointment at a place of local exposure of animal hind to a dose of 38.3 Gy of 60Co gamma-quanta. Factor of dose reduction was 1.27-1.32 for indraline (1-10% ointment) and 1.29 for phenyleprine (0.25% ointment). Antioxidants were low efficient at radiation skin burn. At later local radiation injuries, such as hind contracture, the efficiency of indraline was 1.33-1.5, that of phenyleprine was 1.28, and that of melatonine (2 and 5% ointment) was 1.23-1.47.
An Inclusive Growth Dividend: Reframing the Role of Income Transfers in India’s Anti-Poverty Strategy

The CEGA Working Paper Series showcases ongoing and completed research by faculty affiliates of the Center. CEGA W orking Papers employ rigorous evaluation techniques to measure the impact of large-scale social and eco-nomic development programs, and are intended to encourage discussion and feedback from the global development community. Abstract : Theory and evidence point to several advantages of unconditional universal income transfers as a tool not just of anti-poverty policy but also of improving productivity and achieving development goals more broadly. Given recent policy initiatives in India to use income transfers to support farmers (estimated to cost 0.5% of GDP), we propose a modest expansion of this approach to cover all citizens, which will be both fiscally feasible and practically implementable. Specifically, we propose that India implement an Inclusive Growth Dividend (IGD) pegged at 1% of GDP per capita, which reaches all citizens and grows equally for all with the country’s growth. It would thus be a powerful practical and symbolic commitment to universally shared prosperity. We review global evidence on the impact of income transfer schemes and argue that an IGD would be a highly cost-effective way of directly reducing poverty, with limited administrative costs of targeting and risks of inclusion and exclusion errors, lower leakage of benefits, and lower disincentives for work compared to most targeted programs. It would also improve financial inclusion and formal savings, relax borrowing constraints for productive investments, and improve female empowerment. Further, we argue that successfully delivering an IGD would augment state capacity and improve the quality of public expenditure over time, by making income transfers an attainable benchmark against which public expenditure is evaluated. Finally, we make the case that an IGD could be a powerful tool for the 15 th Finance Commission to promote the objectives of equity and efficiency given the vast differences in income levels as well as state capacity among Indian states.
Children’s health and income: evolution of the gradient

ABSTRACT This paper extends previous measurements of the children’s income-health gradient through 2015. We first replicate findings about the steepness of the gradient from 1986 to 2005. We then find that since 2005, the health gradient between children in low-income families and high-income families has flattened, but it still steepens with age. We also show that overall children’s health has improved since 2005.
Eclecticism and the future of the burden-sharing research programme: Why Trump is wrong

Since the birth of the North Atlantic Treaty Organization, the Europeans and the Americans have disagreed about who should share how much of the collective security burden. The input side of alliance burden sharing – that is, how many troops a member state contributes to the alliance – has been the privileged variable, both at the political as well as the academic levels. Other output variables (e.g. numbers of troops deployed to a particular mission) are highly contested. This article offers an analytically eclecticist framework for studying Atlantic burden sharing that allows combining variables on the input and output sides of the alliance burden sharing debate with those that consider it a social practice.
Aggregation of Heterogeneous Anomaly Detectors for Cyber-Physical Systems

Distributed, life-critical systems that bridge the gap between software and hardware are becoming an integral part of our everyday lives. From autonomous cars to smart electrical grids, such cyber-physical systems will soon be omnipresent. With this comes a corresponding increase in our vulnerability to cyber-attacks. Monitoring such systems to detect malicious actions is of critical importance. One method of monitoring cyber-physical systems is anomaly detection: the process of detecting when the target system is deviating from expected normal behavior. Anomaly detection is a vibrant research area with many different viable approaches. The literature suggests many different anomaly detection methods for the diversity and volume of data from cyber-physical systems. We focus on aggregating the result of multiple anomaly detection methods into a final anomalous or non-anomalous verdict. In this thesis, we present Palisade, a distributed data collection, anomaly detection, and aggregation framework for cyber-physical systems. We discuss various methods of anomaly detection and aggregation and include a case study of anomaly aggregation on a cyber-physical treadmill driving demonstrator. We conclude with a discussion of lessons learned from the construction of Palisade, and recommendations for future research.
RANCANG BANGUN APLIKASI PENDETEKSIAN KESAMAAN PADA DOKUMEN TEKS MENGGUNAKAN ALGORITMA ENHANCED CONFIX STRIPPING DAN ALGORITMA WINNOWING

With the development of technology, the increase of acts of plagiarism in the world of education. Many students who did plagiarism in doing their task, thesis etc. Therefore, an application that can detect the similarity between text documents is required. This research used Enhanced Confix Stripping (ECS) Stemmer algorithm for text stemming process and Winnowing algorithm to calculate the similarity between documents. By specifying the gram and window values of the Winnowing algorithm, it made easier for the user to use the application without being confused to determine the value of the gram and the window to produce an accurate equality value. From the test results 5 pairs of chapters 1 thesis of students who categorized together produced a similarity score of about 45-20%. Keywords : Enhanced Confix Stripping ( ECS ) Stemmer, Winnowing, Similarity, Plagiarism. DOI : 10.5281/zenodo.1407866
Reversing the Lens: Assessing the Use of Federal Employee Viewpoint Survey in Public Administration Research

Despite the proliferation of published work using the U.S. Office of Personnel Management’s (OPM) Federal Employee Viewpoint Survey (FEVS) data, the scholarly community to date lacks a review of the practices and value associated with how scholars have used the survey data in their research. We turn a lens at the public administration research that has used the FEVS to this point. We assess the extent to which peer-reviewed studies follow the fundamental criteria of conducting empirical studies using survey data—from accepted guidelines and practices for preinferential evaluations of survey data to the reporting of baseline and advanced standards and practices of analytical methods for measurement and quantitative analysis. Our review provides an overarching appraisement of public management scholarship employing the FEVS, which can strengthen the partnership between OPM and public administration scholars as they jointly continue improving the survey instrument and pursue questions critical to effective governance.
Macroeconomic Variables and Foreign Direct Investment Inflows in Turkey

Foreign direct investment (FDI) is an essential factor in the development of a country. This study aims to examine what factors influence foreign direct investment. By using the vector error correction model, the research shows that there is a long-term causality relationship between exchange rates and inflation with FDI. However, in the short term, there are no variables that affect FDI. Besides, the Granger causality test shows causality in the direction of GDP and FDI, while other variables do not have causality. This research has implications for policymakers to pay attention to macroeconomic variables in increasing the flow of foreign direct investment.
Tourism forecast combination using the stochastic frontier analysis technique

Forecast combination has received a great deal of attention in the tourism domain. In this article, we propose a novel performance-based tourism forecast combination model by applying a multiple-criteria decision-making framework and the stochastic frontier analysis technique to determine combination weights for individual tourism forecast models. Thirteen time-series models are used to generate individual forecast tourism models, and five competing forecast combination models are selected to evaluate the forecast performance. Using the tourism forecast competition data set, we conclude that the proposed combination model significantly and statistically outperforms the five competing combination models in most cases based on multiple performance indicators. Our results show that the proposed model offers a good solution to identify optimal weights for individual tourism forecast models.
CEO Gender and Firm Debt Policy: An Empirical Study in Indonesia

— This research was aimed to investigate the influence of female director and female commissioner in the firm structure capital of nonfinancial companies listed on Indonesia Stock Exchange (IDX) from 2010-2014 with 372 observations. A structure capital of the firms was measured by the leverage ratio that implicated to debt policy. The leverage ratio showed the level of usage in the company as variable dependent. Dividend payout ratio, return on asset, size of director, and size of commissioner served as control variables. Modeling the leverage’s firm using multiple regression, the result of this study indicated that firms with a larger fraction of female director had negative effect on debt policy significantly. Otherwise, female commissioner’s did not significantly affected debt policy. The control variables that significantly affected debt policy were dividend payout ratio and size of director.
Practical Hyperparameter Optimization for Deep Learning

Recently, the bandit-based strategy Hyperband (HB) was shown to yield good hyperparameter settings of deep neural networks faster than vanilla Bayesian optimization (BO). However, for larger budgets, HB is limited by its random search component, and BO works better. We propose to combine the benefits of both approaches to obtain a new practical state-of-the-art hyperparameter optimization method, which we show to consistently outperform both HB and BO on a range of problem types, including feed-forward neural networks, Bayesian neural networks, and deep reinforcement learning. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.
A Comparison of Supervised Learning Algorithms for the Income Classification

The fundamental population data are needed for every country for purposes of planning, development, and improvement. Census data can provide the basic population data of any country. Moreover, they are rich with lots of hidden information that can be used for machine learning and data mining tasks in order to provide services for country's social and economic development. This paper is focused on the applications of data mining and machine learning in census data to classify the annual income. It aims to show a systematic comparison to examine and evaluate three supervised learning classifiers. The classifiers that have been targeted are decision trees, random forests, and artificial neural networks. The main aims are to explore not only the classifiers properties and the impact of the attributes on the evaluation, but also, evaluate their classification performance under certain conditions to understand how the performance of the models changes over different experiments which potentially provide a guidance to help researchers to determine the most suitable classifier in census data.
The State of the World's Mangrove Forests: Past, Present, and Future

Intertidal mangrove forests are a dynamic ecosystem experiencing rapid changes in extent and habitat quality over geological history, today and into the future. Climate and sea level have drastically altered mangrove distribution since their appearance in the geological record ∼75 million years ago (Mya), through to the Holocene. In contrast, contemporary mangrove dynamics are driven primarily by anthropogenic threats, including pollution, overextraction, and conversion to aquaculture and agriculture. Deforestation rates have declined in the past decade, but the future of mangroves is uncertain; new deforestation frontiers are opening, particularly in Southeast Asia and West Africa, despite international conservation policies and ambitious global targets for rehabilitation. In addition, geological and climatic processes such as sea-level rise that were important over geological history will continue to influence global mangrove distribution in the future. Recommendations are given to reframe mangrove conservation, with a view to improving the state of mangroves in the future.
A mobile app for contactless measurement of vital signs through remote Photoplethysmography

The healthcare domain has undergone a huge transformation thanks to the availability of new technologies. In particular, health monitoring systems have entered everyday life without interfering with the daily routine. Mobile phones are increasingly used as health monitoring systems by means of ad-hoc applications. In this work, we propose a mobile app for contactless monitoring of vital signs, such as heart rate and blood oxygen saturation. Differently from the other devices in the literature, it is able to measure vital signs from the analysis of short videos through the use of remote photoplethysmography technology. A client-server architecture has been developed to run the signal and video processing on the server while implementing video acquisition and communication with the user on the smartphone. Experiments have shown the effectiveness of the developed app in accurately measuring vital parameters.
The Effect Of Favoritism On Job Embeddedness In The Hospitality Industry: A Mediation Study Of Organizational Justice

ABSTRACT This study examines the effect of favoritism on job embeddedness and the mediating influence of organizational justice dimensions, underpinned by social identity and social exchange theories. Survey instruments were distributed to non-favoritism beneficiary employees of three- and four-star family-run hotels in North Cyprus. The findings suggest that favoritism directly affects job embeddedness, procedural justice, distributive justice, and interactional justice in a negative way. In addition, the findings display that three dimensions of organizational justice mediate the influence of favoritism on job embeddedness. This study includes discussions of the theoretical and managerial implications of the findings.
Ubiquitous Learning Environment and Technologies: A Review

In recent years, the education landscape is rapidly changing due to the advancement of information and communication technology infrastructure. The paradigm shift in educational landscape has changed the learner’s behaviors from passive to active in their learning process. Ubiquitous learning is a new learning environment that integrates the benefit of e-learning and mobile learning. The technology plays an important role in supporting ubiquitous learning and a proper technology configuration to obtain learning information is a must. The support for those technology needs in ubiquitous learning is coming from computer-based technologies. The aim of the paper is to performed literature review for ubiquitous learning environment and providing specific information about ubiquitous learning concepts and the criteria that needed in the future research. It begins by briefly describing the transformation of learning environment, the concepts of ubiquitous learning, the characteristics of ubiquitous learning, the technical structure of ubiquitous learning and ubiquitous technologies devices in ubiquitous learning environment is also described. Finally, the comparative studies of ubiquitous learning environment framework are compared and discussed to further enhance the understanding of ubiquitous learning environment. The result showed that technology usage is vital within learners and educators in learning process. Therefore, it is important to have a medium that can be used to improve learning process to be more effective and the most important part to take a look for before implementing new technology into academic learning is the readiness of the infrastructure.
Virtual synchronous generator: an overview

The continuous increase in the penetration of renewable energy (RE) based distributed generations (DGs) in the power system network has created a great concern on the stability of the existing grid. Traditional bulk power plants, which are dominated by synchronous machines (SMs) can easily support system instability, due to the inherent rotor inertia and damping characteristic, as well as voltage (reactive power) control ability. Nevertheless, converter based RE has some special characteristics, such as stochastic real and reactive power output, quick active and reactive power response, small output impedance, and little or no inertia and damping property thereby causing frequency and voltage instability in the system. To solve this problem, virtual synchronous generator (VSG) concept was proposed to emulate some of the features of conventional SG through converter control strategy in order to provide additional inertia virtually. Different control schemes for VSG has been proposed in literature. Surprisingly, an overview of these schemes is yet to be efficiently presented. This paper presents an overview of the VSG control schemes. It provides the concepts, the features of the control schemes and the applications of VSG. Finally, the crucial issues regarding VSG control schemes and the necessary improvement that need to be addressed are highlighted. Keywords: Distributed generation, Synchronous generator, Virtual synchronous generator, Power electronicv converter, Energy storage system, Frequency control
THE EXPECTATION HYPOTHESIS AND INTEREST RATE PREDICTABILITYON AFRICAN MARKETS

In this study, we question the credibility ofinformation’s about the future level of the interest rate that are carried out by the term structure of the interest rates on African markets. We considered for this purpose the market rates of Egypt, Ghana, Kenya, Nigeria and the Republic of South Africa. After comparing the rational predictions of Campbell and Shiller (1991) VAR model with those of the market and realizations, we performed a regression of the average of the short rates (current and anticipated) on the long-term rate, and a regression between the forward rate and the difference between 2 times the long rate and the short rate as suggest the expectation hypothesis. The results show that only market predictions in South Africa are relevant for determining the future short-term rate. The prediction of the VAR was proved irrelevant, and this on all the markets considered.
IMPACT OF PROBABILITY TO DEFAULT ON SUGAR SECTOR USING FIRM LEVEL VARIABLES

It is noticed that there are many running sugar mills in Pakistan which are facing problem of liquidity. They are not able to clear their legal liabilities. Banks are unwilling to give loans to the sugar mills because of unprecedented crisis. These crises create probability to default for firms and it is a vast concern to investors / creditors, borrowing firms, and governments. For smooth working of sugar sector, it is very important to know all factors which are creating unprecedented crisis for the sector because in recent times Punjab Government auctioned Chishtia-Sugar-Mills (Sargodha), Abdullah-Sugar-Mills (Okara), Abdullah-II Sugar-Mills (Sargodha), HaseebWaqas-Sugar-Mills (Nankana-Sahib) and Colony-Sugar-Mills (Khanewal) and stocks of these companies Shakar Ganj-I Sugar-Mills (Jhang), ShakarGanjâ€“II Sugar-Mills (Jhang), Hussain-Sugar-Mills (Faisalabad) and Brother- Sugar-Mills (Kasur) were taken in custody because dues of sugarcane farmers were unpaid. Therefore, sugar mills owners are willing to know the importance of probability to default on sugar sector. Because of this, the importance of sugar sector and factors which are causing liquidity for sugar companies were discussed in this research.