Miracle-dz/UniFAR-FTUs huggingface dataset:
Dividend policy and earnings quality in Vietnam

The purpose of this paper is to examine the relationship between dividend policy and earnings quality of Vietnamese listed firms. ,The sample includes firms listed on Vietnam stock exchange during the period between 2010 and 2016. Two measures of earnings quality are the annual firm-specific absolute value of residuals from Dechow and Dichev’s (2002) model and from Dechow and Dichev (2002) as modified by McNichols’s (2002) model. The firms’ dividend policy is captured by dividend paying status. This is a dummy variable that takes the value of 1 if the firm pays dividends and 0 otherwise. In addition, dividend yield and dividend payout ratio, which are continuous variables, are also used in this paper as alternative proxies for dividend policy. ,Using panel data analysis, this paper documents that dividend payers have higher earnings quality than dividend non-payers. Dividends are an indicator of earnings quality. These findings are consistent with prior studies. After controlling for variables that may be related to earnings quality as well as for the year and industry fixed effects, this relation remains unchanged. In addition, this result is also robust after controlling for firm fixed effects. ,This paper offers the empirical evidence on the relation between dividend policy and earnings quality in Vietnam, which is a frontier market.
Historical Memory and Brand Preference

We study how the historical memories of war affect consumer choices of brands from different country-of-origins in the context of the Chinese automotive market. Our research design exploits two natural experiments: (i) the Imperial Japanese Army's Continental Cross-Through Operation in 1944 in Guangxi of China that created local variations in historical memories, and (ii) the China-Japan conflict over the sovereignty disputes in 2012 that activated the memories. We find that, after the 2012 conflict, sales of Japanese cars dropped 8.5% more, while sales of Chinese brands increased 8.9% more, in invaded counties than in neighboring non-invaded counties, despite similar pre-trends. The effects persisted for more than 24 months. The heterogeneous effects show rich managerial implications: the effects are stronger for higher-priced and larger cars, as well as more recognizably Japanese or Chinese brands. Creating local independent brands help foreign brands to take advantage of the memory effect. We also explore the mechanism and find supporting evidence for intergenerational transmission of memories and local protest as mechanisms of the memory effects.
Measuring performance in education: a cross-country analysis

This research aims to identify and analyse the variables that affect the efficiency of public spending in secondary education, in a cross-country perspective. Applying the methods of data envelopment analysis, Malmquist index, and panel data regression, we analysed data from 65 countries that undertook the PISA examination. The results demonstrate that Brazil was the most inefficient in applying its resources over the period and achieved only 84.3% of its potential outputs. Finally, the significant variables in explaining the efficiency were the natural logarithm of the GDP per capita, the curriculum autonomy of schools, financial autonomy, and the level of corruption and the socioeconomic backgrounds of students.
Enhanced Handover Clustering and Forecasting Models Based on Machine Learning and Big Data

In mobile networks, handover (HO) is one of the most important and complex KPIs (Key Performance Indicators), which directly affect to Quality of Service (QoS), Quality of Experience (QoE), and mobility performance. Moreover, its configuration parameters such as handover thresholds and handover neighbor lists are the key factors for implementing network optimization such as load balancing and energy saving. In a study before, the authors proposed clustering and forecasting models using ML algorithms and Time Series models to cluster, forecast, and manage the HO behavior of a huge number of cells. In this study, on the other hand, the authors firstly investigated more network KPIs to analyze their relationship with HO KPIs, and then, proposed new clustering, forecasting, and abnormal detection models that are expected to make them much more comprehensive. Finally, the performances of the proposed models were evaluated by applying them to a real dataset collected from the HO KPIs and other KPIs of more than 6000 cells of a real network during the years, 2016 and 2017. The results showed that the study was successful in identifying the relationship among network KPIs and significantly improving the performance of the HO clustering, forecasting, abnormal detection models. Moreover, the study also introduced the integration of emerging technologies such as machine learning (ML), big data, software-defined network (SDN), and network functions virtualization (NFV) to establish a practical and powerful computing platform for future self-organizing networks (SON).
An Empirical Examination of Sustainable Supply Chain Risk and Integration Practices: A Performance-Based Evidence from Pakistan

In today’s emerging environment sustainable supply chain risks play a vital role in firms’ performance more than ever, because risks tend to disrupt sustainable operations, which ultimately reduces a firm’s performance, but these risks can be managed through supply chain integration practices, which leads to higher firms’ performance. Therefore, this paper examines the relationship between sustainable supply chain risks, supply chain integration, and firm’s financial performance. This study employs 296 survey observations along with financial data of published annual statements to estimate the quantitative causal-effects of three dimensions of sustainable supply chain risks on supply chain integration and financial performance. The findings of the study suggest that sustainable internal business process risks, sustainable supply risks, and sustainable demand risks have a negative relationship with supply chain integration. Furthermore, results of the study explored that all the three supply chain integration practices have a positive impact on firms’ financial performance, which suggests that implementing supply chain integration practices reduces the effect of supply chain risks and increases the firm’s performance.
Perspectives on reusing codified project knowledge: a structured literature review

Project documentation represents a valuable source of knowledge in project-based organizations. The practical reality is, however, that the knowledge codified in project documents is hardly re-used in future projects. A central problem in this context is the extensive amount of usually textual material. As a consequence, computer-assisted processes are indispensable in order to analytically manage the constantly growing and evolving databases of available project documents. The goal of this study is to summarize the current research focusing on the computer-assisted reuse of textually codified project knowledge and to define the corresponding state-of-the-art in this this specific field of information systems research. As a result of a literature review, this study structures the body of research contributions and outlines what kinds of computer-assisted techniques are incorporated, what practical application areas these solutions address, and in what business domains they are applied. In particular, this should point out research opportunities and thereby make a contribution to the further development of knowledge management in project environments.
Utilizing image processing techniques for detecting breast abnormalities in thermography images

Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.
Service quality measurement for omni-channel retail: scale development and validation

This study aims to construct a scale for measuring customer-perceived service quality in the context of omni-channel retailing. First, a multidimensional scale is empirically developed for assessing omni-channel retailing service quality. Then, a prediction model of customer satisfaction and loyalty is proposed based on a qualitative analysis of responses from a semi-structured interview. Finally, the prediction model is validated using a closed questionnaire survey with 280 customers to verify the nomological validity of the scale. An eight-dimension scale is developed, including store appearance, personal interaction, efficiency, aesthetic design, privacy/security, integration, personalisation and fulfilment/reliability. The results demonstrate that all dimensions have a significant positive impact on customer satisfaction and loyalty toward omni-channel retailers. Specifically, integration is found to be the most important factor. This study fills the gap in omni-channel retailing domain and provides useful guidance for omni-channel practitioners to improve service quality to satisfy and retain consumers, ultimately achieving sales growth.
Performance of Machine Learning Algorithms and Diversity in Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.
Microprocessor- Oriented Algorithms for Data Communications

The design of a multimicroprocessor system intended for a class of real time processing applications is dis­ cussed. The applications considered require the computa­ tion of a subset of an N-input Fourier transform under the assumption of serial data input. The suitability of two different algorithms and the corresponding parallel archi­ tectures are analyzed and compared. More specifically, we compare the computation of the FFT algorithms on an SJMD machine with the implementation of the DFT algo­ rithm on an MISD machine. Our results indicate that the latter is better suited for the targeted applications. An actual operational MISD computer implemented with offthe-shelf microprocessors intended for one of the targeted applications is described.
Examining destination images from travel blogs: a big data analytical approach using latent Dirichlet allocation

ABSTRACT In the big data era, destination images have played an increasingly important role in tourism development. However, seldom tourism research has utilised big data analytics to examine destination images from travel blogs. Therefore, this study proposes and evaluates a big data analytical approach using latent Dirichlet allocation to extract attributes of online destination images from 140,286 travel blogs about 20 cities in China. Results reveal 14 dimensions with 54 attributes of destination images of the studied cities. Interesting findings are discovered between online destination images and tourism cities. This study also summarises the implications for tourism research and practice.
A new epistemology of evidence-based policy

In a remarkably short period, ‘evidence-based policy’ (EBP), and the associated discourses of ‘what works’, have risen to prominence as a set of organising principles for public policy decision-making. Critics of EBP frequently point to its implicit positivist assumptions by highlighting the socially constructed nature of evidence. However, the effectiveness of this critique is limited by the imprecise and often pejorative use of the term ‘positivism’. This article therefore seeks to offer a more precise account of the underlying assumptions of EBP. To do so, it draws on an epistemological position known as process reliabilism, which analyses the justification of a belief by assessing whether it has been reached by means of an epistemically reliable decision-making process or processes. Through this framework, the article advocates a new approach to EBP which is framed around the principle of avoiding error, rather than that of seeking truth.
3D Scene Modeling from Dense Video Light Fields

Light field imaging offers unprecedented opportunities for advanced scene analysis and modelling, with potential applications in various domains such as augmented reality, 3D robotics, and microscopy. This paper illustrates the potential of dense video light fields for 3D scene modeling. We first recall the principles of plenoptic cameras and present a downloadable test dataset captured with a Raytrix 2.0 plenoptic camera. Then we describe a scene depth estimation algorithm from a sparse set of light field views with occlusion handling via a low rank matrix completion method. Finally, the obtained disparity maps are used to construct 3D point clouds.
Does Statecraft Improve Student Learning Outcomes? A Controlled Comparison

Abstract This paper presents results from a comparative analysis of two sections of Introduction to International Politics, one of which used a traditional research paper as a supplemental assignment and one that used the Statecraft online simulation. Both sections were taught during the same semester and used common lecture notes, PowerPoint slides, exam reviews, and exams. The only difference was the nature of the supplemental assignment. The paper finds that the best predictor of student performance on exams is a student’s GPA prior to taking the class. At the same time, the evidence suggests that time dedicated to the online simulation in class may have led to worse performance on at least one exam. Finally, the paper finds that students did enjoy the Statecraft simulation and generally believed that it was preferable to a traditional research paper in spite of its representing additional work relative to the more traditional research paper.
Utilization of function point method for measuring software project complexity

The purpose of this study is to know the complexity of a project so that the time and cost of project work in accordance with the needs and the company can complete the project on time. The complexity of a project can be defined as something consisting of so many interrelated sections that can be operated in the context of difference and interdependence. The method used to achieve research objectives using the Function Point Method. Function point method is a method used to estimate the complexity of a software project, providing project volume estimates in the form of development resources required before the project is undertaken. This estimate provides an important basis for providing estimates of the resources required by software companies to prepare tender proposals and project plans. One of the problems encountered in software project development is that the project experiences delays in its completion due to errors in estimating the complexity of the project undertaken and impacting the time and cost of the project. Function point method can prevent or reduce the error of project cost plan. By using the Function Point method, the complexity of software projects can be known so that the time and cost of project work in accordance with the needs and the company can complete the project on time.
HUMAN DEVELOPMENT, UNEMPLOYMENT AND INDONESIAN MIGRANT WORKERS

Three objectives are proposed by this study. The first objective is to examine the long-run relationship among human development, unemployment and the Indonesian Migrant Workers (IMWs). This is followed by examining the causality between the human development and unemployment respectively and the IMWs in the second objective. The extent to which the human development and unemployment determine the IMWs is examined in the third objective. The study is based on time series data and utilizes a Vector Autoregressive (VAR) framework. The findings show that the human development, unemployment and the IMWs are co-integrated. The human development and unemployment respectively causes the IMWs in the short run and in the long run. Human development has a negative significant effect on the IMWs while unemployment has a positive significant effect on the IMWs.
Programa Drenurbs/Nascentes e Fundos de Vale: potencialidades e desafios da gestão sócio-ambiental do território de Belo Horizonte a partir de suas águas

Passados 111 anos de sua fundacao, percebem-se ha algum tempo em Beio Horizonte os equivocos da opcao pelo tratamento dos fundos de vale da cidade a partir das solucoes de canalizacao associadas as avenidas sanitarias. Paises desenvolvidos verificaram a relacao custo-beneficio negativa da opcao pela canalizacao e abandonaram esse tipo de solucao por volta dos anos de 1970, em funcao de novos modelos de gestao de aguas urbanas. Esses modelos buscam solucoes de drenagem com enfoque ambiental procurando-se manter o curso d'agua em leito natural, preservando, despoluindo, protegendo as areas de inundacao e os ecossistemas aquaticos. Experiencias nesse sentido se multiplicam nos ultimos dez anos no mundo inteiro e em diversas cidades brasileiras, incluindo Belo Horizonte, onde se materializam atraves do Programa de Recuperacao Ambiental dos Fundos de Vales e Corregos em Leito Natural do Municipio de Belo Horizonte Drenurbs/Nascentes. A busca pela consolidacao de uma nova concepcao de intervencao para as aguas circulantes no meio urbano atraves de principios que venham a favorecer a adocao de alternativas capazes de preservar a condicao natural dos leitos de escoamento dos cursos d'agua e a grande meta deste Programa. Uma analise pertinente a ser feita em relacao ao modo como a admistracao municipal tem executado a gestao socio-ambiental do territorio de Belo Horizonte surge entao da avaliacao do plano e da pratica deste Programa no momento em que estao concluidas as suas tres primeiras obras. Desta analise podem ser percebidas alguamas assimetrias entre planejamento e pratica, uma serie de sucessos em muitos resultados, bem como diversas limitacoes, desafios, conflitos e tambem possibilidades para o Drenurbs/Nascentes. Deste modo, este trabalho busca avaliar enquanto instrumento de operacionalizacao da legislacao ambiental e introdutor de inovacoes no tratamento das questoes socio-ambientais do municipio de Belo Horizonte. O proposito de inserir os cursos d'agua na paisagem conduz a um novo olhar para o urbano. Um olhar que deve incluir a gestao ambiental no planejamento e nas intervencoes urbanas ao mesmo tempo em que consorcie saneamento ambienta! e inclusao social nas politicas publicas. Incorporacao de fato da gestao ambiental no planejamento urbano e na gestao da cidade; insercao dos cursos d'agua e dos fundos de vales na paisagem urbana e no cotidiano dos cidadaos e melhoria das condicoes de vida da populacao, este e o desafio apresentado a cidade de Belo Horizonte atraves do Programa Drenurbs/Nascentes.
Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data

When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too inefficient for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.
Piecemeal or combined? Assessing greenhouse gas mitigation spillovers in US forest and agriculture policy portfolios

ABSTRACT Forest and agricultural sector response to comprehensive climate policy is well represented in the literature. Less analysis has been devoted to piecemeal solutions. We use the Forest and Agriculture Sector Optimization Model with Greenhouse Gases (FASOMGHG) to project the individual and combined effect of three existing U.S. Department of Agriculture programmes with potential to increase greenhouse gas (GHG) mitigation. We find that a combined policy scenario may achieve greater mitigation than individual constituent programmes, suggesting the possibility of complementary spillover effects in some periods. Mitigation varies over time, however, and some periods experience net emissions as markets and management practices respond to initial policy shocks. The regional distribution of GHG mitigation also varies between policy scenario. Differences in the magnitude and imputed cost of mitigation under each scenario, generating negative values for some programmes and time periods, reinforces the need to evaluate portfolio design to cost-effectively achieve near-term GHG mitigation. Key policy insights Increased near-term GHG mitigation in the forest and agriculture sectors in the US may be possible by expanding or refocusing the emphasis of existing programmes. Implementing several such forest and agricultural programmes simultaneously may lead to greater GHG mitigation than when implemented separately, indicating the possibility of positive spillover effects. Programmes targeted to agricultural management may hold outsized potential to achieve near-term GHG mitigation; Policies aimed at influencing land use conversion appear to be more vulnerable to reversion and subject to larger inter-annual swings. The staged implementation of programmes could also be useful, helping to encourage increased mitigation (or the retention of already achieved mitigation) over time as markets re-equilibrate to initial shocks. Though the particular scenarios assessed here are unique to the US, our findings may be applicable to other locations outside the US where land management is influenced by individual market actors and there is competition between forest and agricultural land uses.