Data Sets

Data augmentation has been broadly applied in training deep-learning models to increase the diversity of data. This study ingestigates the effectiveness of different data augmentation methods for deep-learning based human intention prediction when only limited training data is available. A human participant pitches a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the single-participant data set is further evaluated on a multi-participant data set to assess its generalization ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data augmentation methods that crop or deform images can improve the prediction performance; 2) Random cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50% to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.

Now a days, peoples are increasing most amount of time in social media and because of the popularity of online social media and increasing social networks, online malicious individuals which are called spammers has started spamming on these platforms for potential victims. Spams invite persons to external phishing sites or malware downloads huge security issue online and undermined the user experience. The increasing number of accounts in social networking sites is a serious and major threat to the internet users. To detect and avoid fake identities, it is need to understand the dynamic contamination. In this paper, tried to analyse various types of spamming attacks spam detection techniques, campaigns in Online Social site networks and information about spam detection.

Ecommerce, also known as electronic commerce or internet commerce, refers to the buying and selling of goods or services using the internet, and the transfer of money and data to execute these transactions. The types of E-commerce includes-business-to-business (B2B), business-to-consumer (B2C) or business-to-government (B2G). E-commerce with the help of latest trends can improve communication and doing E-Commerce business is more easier. With more digital innovations in finance, public people were showing more interest in this form of business. In addition to that, financial sector stakeholders and academic are also more attracted by this. The main objective of Cryptocurrencies is to replace centralized systems such as banks, eliminating the need for an intermediary and by this greatly simplify the shopping process. With Ecommerce there is a possibility to reach more people across the globe at less expense than the traditional business. With the introduction of new concept like crytpcurrency, Bitcoin is a highly secured digital currency, used by more people. It simply allows its users to move value around the World Wide Web. Market volatility is the key issue with cryptocurrency payments. Price fluctuations in the value of a cryptocurrency which is being used for payment can lead to net losses (or conversely, net gains) for e-commerce merchants. With so many factors affecting the implementation of cryptocurrency-based payments, it's essential to be able to fail fast while innovating or testing the waters. To accommodate for risks and volatility, consider incremental additions such as rolling out crypto payments support on a web app before making them available via native app capabilities or vice versa.

Data mining is an inter disciplinary field and it finds application everywhere. To solve many different day to life problems, the algorithms could be made use. Since R studio is more comfortable for researcher across the globe, most widely used data mining algorithms for different cases studies are implemented in this paper by using R programming language. Could be implemented with help of R programming. The advanced sensing and computing technologies have enabled the collection of large amount of complex data. Data mining techniques can be used to discover useful patterns that in turn can be used for classifying new data or other purpose. The algorithm for processing large set of data is scalable. Algorithm for processing data with changing pattern must be capable of incrementally learning and updating data patterns as new data become available. Still data mining algorithm such as decision tree support the incremental learning of data with mixed data types, the user is not satisfied with scalability of these algorithms in handling large amount of data. The following algorithms were implemented using R studio with complex data set. There are four algorithms in the project-1) Clustering Algorithm 2) Classification Algorithm 3) Apriori Algorithm 4) Decision Tree Algorithm. It is concluded that R studio produced most efficient result for implementing the above said algorithms.

and 90/10 and 50/10 percentile comparisons) that within-group earnings inequality rather than betweengroup earnings inequality is the main contributor to overall earnings inequality. Simulations in which the relatively disadvantaged gender and/or racial/ethnic group is treated as if it were the relatively advantaged group tend to reduce overall earnings inequality measures only slightly and in some cases have the effect of increasing earnings inequality measures. , seminar participants at numerous venues, and two anonymous referees for useful comments on earlier versions of this research. 8 Women still lag behind men in most indicators of well-being -wage gaps, control over resources, voice, health care (Cunningham et al, 2004) -but there are increasingly noted trends of disadvantage among men, including higher death rates, alcoholism, and suicides than women, as well as particularly in LAC countries a reduction in their rate of higher educational attainment (Jacobsen 2006). 9 Sociologists have theorized that racial or ethnic inequalities are a result of a history of power relations that create an unlevel playing field through setting up a situation wherein endowments, opportunities, and/or expectations differ by demographic gruop . These theories are becoming salient as ethnic and racial groups are

C Co o r re e I In nd d i ic ca at to o r rs s f fo o r r D De e t te er rm mi in na an nt ts s a a n nd d P Pe e r rf fo o r rm ma an nc ce e o o f f t th he e E El le e c ct tr ri ic ci it ty y S Se ec ct to o r r i in n D De ev ve e l WPS3599 C Co o r re e I In nd di i c ca at to or rs s f fo or r D De e t te e r rm m i in na an nt ts s a an nd d P Pe e r rf fo or rm m a an nc ce e o of f E El le ec ct tr ri i c ci it t y y S Se ec c t to or r R Re ef fo or rm m i in n D D e ev ve el lo op pi in ng g C Co ou un nt tr r i ie e s s 1 Abstract Since the early 1990s, substantial resources and efforts have been spent on implementing market-oriented electricity reforms in developing countries. While there are important sectoral, economic, and social dimensions involved in electricity reform, empirical analysis and evaluation of reforms have been of limited use for testing the economic rationale of reforms and policy advice. This may partly be attributed to a lack of generally accepted and measured indicators for monitoring the progress, impacts, and performance of reforms. In this paper we propose a set of indicators as a first step toward filling this gap and developing a coherent framework for studying electricity reform in developing countries that covers resource and institutional endowments, key reform steps, market structure, performance, and various impacts. JEL Classification: L52, L94, Q48 C Co o r re e I In nd di i c ca at to or rs s f fo or r D De e t te e r rm m i in na an nt ts s a an nd d P Pe e r rf fo or rm m a an nc ce e o of f E El le ec ct tr ri i c ci it t y y S Se ec c t to or r R Re ef fo or rm m i in n D D e ev ve el lo op pi in ng g C Co ou un nt tr r i ie e s s C Co o r re e I In nd di i c ca at to or rs s f fo or r D De e t te e r rm m i in na an nt ts s a an nd d P Pe e r rf fo or rm m a an nc ce e o of f E El le ec ct tr ri i c ci it t y y S Se ec c t to or r R Re ef fo or rm m i in n D D e ev ve el lo op pi in ng g C Co ou un nt tr r i ie e s s 2 Indicators reflect or represent the state of a phenomenon in the form of information. At the same time, indicators do not exist in isolation. Rather, they are often part of larger systems within which they assume their meaning. This system or framework may be presented in the form of more or less formal models. The following definition of an indicator in OECD (1993) is relevant here: "A parameter, or a value derived from parameters, which points to/provides information about/describes the state of a phenomenon/environment/area with a significance extending beyond that directly associated with a parameter value".

Data from several projects show a signifcant relationship between the size of a module and its defect density. Here we address implications of this observation. Does the overall defect density of a software project vary with its module size distribution? Even more interesting is the question-can we exploit this dependence to reduce the total number of defects? We examine the available data sets and propose a model relating module size and defect density. It takes into account defects that arise due to the interconnections among the modules as well as defects that occur due to the complexity of individual modules. Model parameters are estimated using actual data. We then present a key observation that allows use of this model for not just estimation the defect density, but also potentially optimizing a design to minimize defects. This observation, supported by several data sets examined, is that the module sizes often follow exponential distribution. We show how the two models used together provide a way of projecting defect density variation. We also consider the possibility of minimizing the defect density by controlling module size distribution.

Prólogo AgrAdecimientos 12 resumen ejecutivo 14 1. desiguAldAd de oPortunidAdes: Qué es, cómo Puede medirse y Por Qué es imPortAnte 41 1.1. el concepto de la desigualdad de oportunidades 47 1.2. Pasar de los conceptos a la operativización de la igualdad de ...

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19
virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious
characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential
global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical
measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and
socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for
diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased
analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought
to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in
the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized
as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission
estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment
analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly
articles covering COVID-19.We survey and compare research works in these directions that are accompanied by open source
data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will
provide the scientific community with an initiative to start open source extensible and transparent research in the collective
fight against the COVID-19 pandemic.

This work presents an Ant Colony Optimization-based approach to feature selection that works in tandem with an ACO classifier (Ant-Miner) in a wrapper approach to improve the classification accuracy of the Ant-Miner with a small and appropriate feature subset. The objective is to analyze the performance of five ACO algorithms on the feature selection problem and the performance of the proposed FS-ACO/Ant-Miner system when compared to other feature selection for classification algorithms. The experimental results indicate that ...

The Operations Evaluation Department (OED) is an independent unit within the World Bank; it reports directly to the Bank's Board of Executive Directors. OED assesses what works, and what does not; how a borrower plans to run and maintain a project; and the lasting contribution of the Bank to a country's overall development. The goals of evaluation are to learn from experience, to provide an objective basis for assessing the results of the Bank's work, and to provide accountability in the achievement of its objectives. It also improves Bank work by identifying and disseminating the lessons learned from experience and by framing recommendations drawn from evaluation findings. OED Working Papers are an informal series to disseminate the findings of work in progress to encourage the exchange of ideas about development effectiveness through evaluation. The findings, interpretations, and conclusions expressed here are those of the author(s) and do not necessarily reflect the views of the Board of Executive Directors of the World Bank or the governments they represent. The World Bank cannot guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply on the part of the World Bank any judgment of the legal status of any territory or the endorsement or acceptance of such boundaries.

World Bank Economic Review is a professional journal for the dissemination of World Bank-sponsored and outside research that may inform policy analyses and choices. It is directed to an international readership among economists and social scientists in government, business, and international agencies, as well as in universities and development research institutions. The Review emphasizes policy relevance and operational aspects of economics, rather than primarily theoretical and methodological issues. It is intended for readers familiar with economic theory and analysis but not necessarily proficient in advanced mathematical or econometric techniques. Articles will illustrate how professional research can shed light on policy choices. Inconsistency with Bank policy will not be grounds for rejection of an article. Articles will be drawn from work conducted by World Bank staff and consultants and from papers submitted by outside researchers. Before being accepted for publication, all articles will be reviewed by two referees who are not members of the Bank's staff and one World Bank staff member. Articles must also be recommended by a member of the Editorial Board. Non-Bank contributors are requested to submit a proposal of not more than two pages in length to the Editor or a member of the Editorial Board before sending in their paper. Comments or brief notes responding to Review articles are welcome and will be considered for publication to the extent that space permits.

Two training algorithms for self-evolving neural networks are discussed for rule-based data analysis. Efficient classification is achieved with a fewer number of automatically added clusters, and application data is analyzed by interpreting the trained neural network as a fuzzy rule-based system.

Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class has on traditional training criteria in the fitness function. This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data. Using a range of real-world classification problems with class imbalance, we empirically show that these new fitness functions evolve classifiers with good performance on both the minority and majority classes. Our approaches use the original unbalanced training data in the GP learning process, without the need to artificially balance the training examples from the two classes (e.g., via sampling).

The paper reviews the trends in poverty, economic policies and growth in a sample of African countries during the 1990s, drawing on the better household data now available. Overall, experiences have varied: some countries have seen sharp falls in income poverty, while others have witnessed marked increases. In some countries economic growth has in the aggregate been ‘pro-poor’, and in others not. But the aggregate numbers hide systematic distributional effects. Taking both macro and micro perspectives of growth and poverty in Africa, the paper draws four key conclusions. First, economic policy reforms (improving macroeconomic balances and liberalizing markets) appear conducive to reductions in poverty. Second, market connectedness is crucial to enable participation in the gains from economic growth. Some regions and households by virtue of their sheer remoteness have been left behind when growth picks up. Third, education and access to land emerge as key private endowments to help h...

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Prólogo AgrAdecimientos 12 resumen ejecutivo 14 1. desiguAldAd de oPortunidAdes: Qué es, cómo Puede medirse y Por Qué es imPortAnte 41 1.1. el concepto de la desigualdad de oportunidades 47 1.2. Pasar de los conceptos a la operativización de la igualdad de ...

Even with global emissions of greenhouse gases drastically reduced in the coming years, the global annual average temperature is expected to be two degree C above pre-industrial levels by 2050. A two degree C warmer world will experience more intense rainfall and more frequent and more intense droughts, floods, heat waves, and other extreme weather events. Households, communities, and planners need to put in place initiatives that 'reduce the vulnerability of natural and human systems against actual and expected climate change effects'. Without such adaptation, development progress will be threatened, perhaps even reversed. This initial study report, which focuses on the first objective, finds that the cost between 2010 and 2050 of adapting to an approximately two degree C warmer world by 2050 is in the range of $75 billion to $100 billion a year. Four lessons stand out from the study: first, adaptation to a two degree C warmer world will be costly; second, the world cannot ...

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. re...

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Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover data sets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 min by 5 min (~10 km by 10 km) latitude-longitude grid. The resulting land use data sets depict circa the year 2000 the area (harvested) and yield of 175 distinct crops of the world. We aggregate these individual crop maps to produce novel maps of 11 major crop groups, crop net primary production, and four physiologically based crop types: annuals/perennials, herbaceous/shrubs/trees, C3/C4, and leguminous/nonleguminous.

Macrophone is a corpus of approximately 200,000 utterances, recorded over the telephone from a broad sample of about 5,000 American speakers. Sponsored by the Linguistic Data Consortium (LDC), it is the first of a series of similar data sets that will be colected for major languages of the world in a cooperative project called Polyphone. It is designed to provide telephone speech suitable for the development of automatic voice-interactive telephone services. In particular, Maerophone contains training material for applications in transportation, scheduling, ticketing, database access, shopping, and other automated telephone interactions. In addition to being phonetically balanced, the spoken material refers to times, locations, monetary amounts, and interactive operations. The utterances are spoken by respondents into telephone handsets and recorded directly in 8-bit mu-law digital form through a T1 connection to the usual switched telephone network. The entire corpus will be made available by LDC in 1994. The paper describes the design of the linguistic materials in the corpus, and the process of solicitation, collection, transcription, and file preparation for the Macrophone corpus.

Sign language is a mode of communication that use a variety of hand movements and actions to convey a message. Deciphering these motions might be a pattern recognition challenge. People use a range of gestures and behaviours to communicate with one another. This study is a system for a human-computer interface that can identify american sign language gestures and produce textual output that reflects the meaning of the gesture. To identify and learn gestures, the proposed system would employ convolutional neural networks and long short term memory networks. This will help to break down the communication gap.

All rights reserved 1 2 3 4 10 09 08 07 This volume is a product of the staff of the International Bank for Reconstruction and Development / The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

All rights reserved 1 2 3 4 10 09 08 07 This volume is a product of the staff of the International Bank for Reconstruction and Development / The World Bank. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

In many low income African countries, three factors are placing an undue burden on the elderly. First, the burden on the elderly has enormously increased with the increase in mortality of prime age adults due to HIV AIDS pandemic and regional conflicts. Second, the traditional safety net of the extended family has become ineffective and unreliable for the elderly. Third, in a few countries, the elderly are called upon to shoulder the responsibility of the family as they became the principal breadwinners and caregivers for young children. While a number of studies have examined the welfare consequences of these developments on children, few studies have systematically analyzed the poverty situation among the elderly (relative to other groups) in low income countries Africa, and the role of social pensions. This study aims to fill this gap.

Reservados algunos derechos. 1 2 3 4 16 15 14 13 Esta obra ha sido realizada por el personal del Banco Mundial con contribuciones externas. Téngase presente que el Banco Mundial no necesariamente es propietario de todos los componentes del contenido de la obra, por lo que no garantiza que el uso del contenido de ésta no violará derechos de terceros. El riesgo de reclamación derivado de dicha violación correrá por exclusiva cuenta del usuario. Las opiniones, interpretaciones y conclusiones aquí expresadas no son necesariamente reflejo de la opinión del Banco Mundial, de su Directorio Ejecutivo ni de los países representados por este. El Banco Mundial no garantiza la exactitud de los datos que figuran en esta publicación. Las fronteras, los colores, las denominaciones y demás datos que aparecen en los mapas de este documento no implican juicio alguno, por parte del Banco Mundial, sobre la condición jurídica de ninguno de los territorios, ni la aprobación o aceptación de tales fronteras. Nada de lo establecido en el presente documento constituirá o se considerará una limitación o renuncia a los privilegios e inmunidades del Banco Mundial, los cuales se reservan específicamente en su totalidad. Derechos y autorizaciones Esta publicación está disponible bajo la licencia Creative Commons de Reconocimiento 3.0 Genérica (CC BY 3.0) http://creativecommons.org/licenses/by/3.0. Bajo la licencia Creative Commons de Reconocimiento, queda permitido copiar, distribuir, transmitir y adaptar esta obra para fines comerciales en las siguientes condiciones:

Fuza, Systems rely on membership fundons to represent input values for problem presentatin and eventual problem solution. These can be generated in diierent ways, one of which is obtaining an tzapert to &me the functions. This method is not ahvays cost eflective or available, so automatic membership fundion &$nition i s edremely desirable. Many methods for conSaucting membership functions based on knowledge engineering have been &velop& Revwus work has shown that statidid methods can be used to generate these membership functions. The quality of the resulz, however, is very application diipendent. This study foc.ses on a method of automa& membership fundon generation that relies on the use of f w relations This paper describes the implementation of one such metho4 and d e s its appkkation to several data sets, inchding the i d e n @ ! n of vowel sounds in spoken English.

Phylogenetic relationships in the Gentianales with focus on Loganiaceae sensu lato are evaluated using parsimony analyses of nucleotide sequence data from the plastid genes rbcL and ndhF. Inter-and intrafamilial relationships in the Gentianales, which consist of the families Apocynaceae (including Asclepiadaceae), Gelsemiaceae, Gentianaceae, Loganiaceae, and Rubiaceae, are studied and receive increased support from the combination of rbcL and ndhF data, which indicate that the family Rubiaceae forms the sister group to the successively nested Gentianaceae, Apocynaceae, and Loganiaceae, all of which are well supported. The family Gelsemiaceae forms a distinct, supported group sister to Apocynaceae. The Loganiaceae sensu stricto form a strongly supported group consisting of 13 genera:

Introduction White [1967] proposed compressing text by "replacing [a] repeated string by a reference to [an] earlier occurrence". Ziv and Lempel [1977, 1978] implemented this idea by cleverly representing strings that occur in a relatively small sliding window. We extend the basic idea to represent long common strings that may appear far apart in the input text. On typical English text, our method provides little compression; there are few long common strings to be exploited. Some files, though, do contain repeated long strings. Baker [1995] documented significant repetition in the code of large software systems. In a mathematical subroutine library, we found many blocks of code repeated across functions of type float, double, complex, and double complex; our method combined with a standard compression algorithm reduced the file to half the size given by the standard algorithm alone. Corpora of real documents, such as correspondence, news articles, or netnews often contain...

Mali is one of the poorest countries in the world, with a gross domestic product (GDP) per capita of about $ 691 in 2010. Mali's steady per capita GDP growth was accompanied with a significant decline in the poverty rate, from 55.6 percent in 2001 to 43.6 percent in 2009-10. Mali's rapid population growth still has led to an increase of the overall number of people living in poverty, and is constraining progress in terms of per capita health and education outcomes. High fertility rates and gender imbalances are key drivers of Mali's poverty dynamics. The present report compiles three different notes, emphasizing the interrelation between poverty, demographics and gender imbalances. The first note discusses the evolution of poverty in Mali since 2001. The second note reviews recent demographic trends and related policies. The third note reports on the latest available data on females in business and employment within the formal sector. The compilation of these three notes...

Jere R. Absolute values for (-statistics are given in parentheses beneath point estimates. a. Probabilities in square brackets. b. Standard-errors in square brackets. c. Censored normal regression (with 8 right-censored and 2,617 uncensored observations for age when started school and with 2,173 right-censored and 442 uncensored observations for last completed grade). The R 2 is a pseudo R 1 .

Phylogenetic relationships in the Gentianales with focus on Loganiaceae sensu lato are evaluated using parsimony analyses of nucleotide sequence data from the plastid genes rbcL and ndhF. Inter-and intrafamilial relationships in the Gentianales, which consist of the families Apocynaceae (including Asclepiadaceae), Gelsemiaceae, Gentianaceae, Loganiaceae, and Rubiaceae, are studied and receive increased support from the combination of rbcL and ndhF data, which indicate that the family Rubiaceae forms the sister group to the successively nested Gentianaceae, Apocynaceae, and Loganiaceae, all of which are well supported. The family Gelsemiaceae forms a distinct, supported group sister to Apocynaceae. The Loganiaceae sensu stricto form a strongly supported group consisting of 13 genera: