Reviewerly/exHarmony huggingface dataset:
Foreign aid and out-of-pocket health expenditure in Sub-Saharan Africa: does institutional development matter

This paper examines whether and to what extent foreign aid and its interaction with the recipient country's institutional quality reduce out of pocket health expenditure in Sub-Saharan Africa (SSA). We also investigate the validity of fungibility hypothesis in SSA's context by examining the impact of aid on public health expenditure. The study employed the fixed-effects instrumental variables panel regressions using a dataset for 45 SSA countries over the period 1995-2015. We find that aid does not robustly affect out of pocket health expenditure. Neither does the effect of aid depend on institutional quality. This outcome contradicts the previous evidences which have repeatedly emphasised the role of institutional quality as the main factor influencing aid effectiveness. Excluding aid from non-Development Assistance Committee (DAC) donors, we find that aid had no effect on public health expenditure. This finding confirms the absence of fungibility hypothesis in SSA context.
A Novel Strategy to Image Decomposition for Wireless Capsule Endoscopy Images

Now a day wireless capsule endoscopy (WCE) is broadly used for detection of gastro internal organ diseases. WCE is produces quite 55000 images but still there is challenging task of it that captured noisy images. Removing noise from images is difficult aspiration for image denoising technique. Therefore, various redundant blur and amounts of remaining noise ought to be analysis to research the particular results of denoising method. In this research article, different methods are used for image denoising and evaluated performance for wireless capsule endoscopy images. The proposed approach is suggested Bidimensional Empirical Mode Decomposition (BEMD) for WCE images. Here evaluate performance of BEMD method and wavelet. Computer simulation proved that proposed technique offer considerable advantage than other method.
An Application of Adaptive Genetic Algorithm Combining Monte Carlo Method

We show the feasibility of this algorithm in finding tool commonality with N=10. Actually, we have experimented on different values of N. The value of each parameter is shown.
Hypothesis Testing

The idea of data likelihood. Using Bayes’s formula to update hypothesis probabilities. Confidence testing and P-values. Pitfalls of P-values. Bayesian inference with compound datasets. Confidence methods with compound datasets: using test statistics. The $$\chi ^2$$ test statistic.
Inequitable Internet

The advent of the Internet was nothing short of revolutionary. Internet storefronts, social media platforms, government institutions, etc. must have a means of authenticating users to process transactions or interactions within a network. Blockchains, if deployed as a central platform of markets, data sharing, and single sign-on identity management must process massive quantities of transactions, safely and securely. The spheres of identity management, data sharing, and market inclusion stand to benefit from similar analyses. The process by which data is gathered, stored, and distributed is convoluted and opaque to the majority of users interacting with the collecting institutions. The sheer scope of data that is created each day is staggering, already difficult to comprehend, and is increasing exponentially year by year. The problems compound when sensitive data is maliciously seized by unknown actors.
Handwriting 99 Multiplication on App Store

The Modified NIST (MNIST) database, consisting of 70,000 handwritten digit images, in partition to 60,000 training patterns and 10,000 testing patterns, serves as a typical benchmark of evaluating performance of handwritten digit classification. After the LeNet CNNs model proposed by LeCun, researchers regarded this example as “Hello, World” in the field of deep learning. This chapter compares traditional approaches with the CNN model. The dataset of training and testing CNN models here is expanded to the Extension-MNIST (EMNIST) database. It will be employed to pre-train a CNN model for recognizing the handwritten digit image and installation on the iOS device. The user of the presented App can directly write digits on the touchscreen, and the smartphone instantly recognizes what were written. The pre-trained model subject to EMNIST database with a test accuracy of 99.4% has been integrated to an iOS App, termed as handwriting 99 multiplication, which has been successfully published on Apple's App Store.
Introduction to Distribution Management

Distribution management is all about reaching the product as near to the customer as possible and making the product and service available to the customer. The products must be available to the customer at the right place and at the right time and with an assortment which gives a lot of choice to the customers. The intermediaries in the marketing channel performs a lot of activities for the manufacturer and serves as the go-between of the manufacturer as well as the consumer. The value addition of the distribution system is tremendous and that is why it is now a very important component of the marketing strategy. It is the fourth P in the 4Ps of the marketing mix and has not been given much importance traditionally. However, now the situation has changed primarily because it is very difficult nowadays to differentiate in the market through the use of product, price, and promotions alone. Distribution systems can help the company in differentiating themselves from the competitors. The distribution system understands the end-customers very well and thus guides the company in offering the best product mix to the consumers. The distribution system also expects a lot of support and motivation from the company and when the company is able to do that well, a long-term relationship is formed. This is crucial as the then the commitment level of the system goes up and tends to support the company and the brand in every possible manner. It has been seen time and again that when the long-term relationship forms, the company gets a very positive response and return from the market. The idea here is that the distribution systems should not be treated as a combination of some marketing tactics, but a significant and important part of the company's strategic intent and goals.
Use of Machine Learning in the Pattern Finding

Today is the generation of Machine Learning and Artificial Intelligence. Machine Learning is a field of scientific study and statistical models to predict the answers of never before asked questions. Machine Learning algorithms use a huge quantity of sample data that is further used to generate model. The higher amount and quality of training set lead to higher accuracy in approximate result calculation. ML is the most popular field to research and also helpful in pattern finding, artificial intelligence and data analysis. In this paper we are going to explain the basic concept of Machine Learning with its various types of methods. These methods can be used according to user’s requirement. Machine Learning tasks are divided into various categories . These tasks are accomplished by computer system without being explicitly programmed.
Policy and Attribute Based Deduplication System

Cloud storage is a platform where we can store and access the data through internet. Many Social and business applications are backing up their data in cloud. So, the requirement of cloud storage increases day by day. But most of the files present in the cloud storage are redundant. Removing redundant files in cloud is one way to manage the storage space requirement. For that purpose, we apply File-level data deduplication technique. In this paper, a method for deduplication is proposed which is named as policy and attribute based dedupe system. This system includes access control over files to enhance the security. It also reduces the storage space and increase the security of user data.
A tensor intermediate representation for machine learning systems

With the wide deployment of various machine learning algorithms, highly energy-efficient customized machine learning systems have gained popularity. The machine learning compilers are crucial to machine learning systems. The intermediate representation is the key to programming and compilation environments, and it connects the high-level programming language and the lower-level instruction set architectures. The current state-of-the-art intermediate representations are either oriented to high-level algorithms or classical processors based on scalar processing, but they cannot be effectively implemented on tensor-based machine learning systems. To address this problem, we propose a tensor intermediate representation for machine learning systems to improve programming productivity and performance. Concretely, we define a series of tensor types, tensor operations, and tensor memories and optimize the tensor processing based on these definitions. To validate our proposal, we extend the proposed tensor intermediate representation to the low-level scalar intermediate representation of TVM and perform experiments with Tensor Core on a typical machine learning system. Experimental results show that we explore optimizations that are not discovered in the original intermediate representation and achieve 1.62$\times$$\sim$2.85$\times$ performance improvement. Besides, the tensor intermediate representation improves the efficiency of programming by 5.46$\times$ on average.
A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

In this study, we demonstrate the role of visualization to facilitate forensic analysis goal in interpreting metadata of evidence of interest to answer who, what, why, when, where, and how an incident occurred. Two mobile Instant Messaging (IM) applications (i.e. WhatsApp and Line) were deployed as a case study. Subsequently, a tool – W*W Visualizer – was designed and developed with the aims to analyze and visualize the connection of evidence metadata, text frequency and word count, and display report of analysis activities. The tool is developed by adopting Object-Oriented Software Development Model with Visual Studio platform and C# language were used to develop the system. Our findings show that W*W Visualizer could transform the data of the chat database into a visual form, for example graph, chart and word cloud. The tool also allows the user to perform search feature such as searching based on keyword and timestamp from the IM chat history. It is expected that outcomes from this study would significantly influence digital forensics practitioners in analyzing and interpreting evidence data, and judicial authorities in understanding the presentation of evidence.
An efficient resource deployment method for stream-based stochastic demands in distributed cloud platforms

It has been a consensus that deploying geographically dispersed stream-based online services into distributed cloud platforms has gained exceptional advantages. Globally visiting services make user requests characterised with dramatic fluctuation, which introduces stochastic demands for various resources. In order to maximise satisfied user requests and guarantee quality-of-service under given expense budget, efficient resource deployment becomes the key to this problem. We propose a stochastic demand oriented resource deployment method with more profits and less time complexity. Experiments using simulated and realistic data indicate that proposed method can outperform existing algorithms by increasing the weighted summation of satisfied demands up to 37%, fit for all scenarios with heterogeneous distributed cloud resources.
Verifiable Privacy-Preserving Neural Network on Encrypted Data

The widespread acceptance of machine learning, particularly of neural networks leads to great success in many areas, such as recommender systems, medical predictions, and recognition.It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers.However, it must be taken into consideration that the data from clients may be exposed to cloud servers.Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes.But these architectures are based on honest but curious cloud servers, which are unable to tell whether cloud servers have completed the computation delegated to the cloud server.This paper proposes a verifiable neural network framework which focuses on solving the problem of data confidentiality and training integrity in machine learning.Specifically, we first leverage homomorphic encryption and extended diagonal packing method to realize a privacy-preserving neural network model efficiently, it enables the user training over encrypted data, thereby protecting the user's private data.Then, considering the problem that malicious cloud servers are likely to return a wrong result for saving cost, we also integrate a training validation modular Proof-of-Learning, a strategy for verifying the correctness of computations performed during training.Moreover, we introduce practical byzantine fault tolerance to complete the verification progress without a verifiable center.Finally, we conduct a series of experiments to evaluate the performance of the proposed framework, the results show that our construction supports the verifiable training of PPNN based on HE without introducing much computational cost.
SOCIO-PEDAGOGICAL SUPPORT FOR THE FORMATION OF THE STUDENT’S SOCIAL COMPETENCE: A TECHNOLOGICAL ASPECT

In this article, the author actualizes the problem of socio-pedagogical support for the formation of social competence of students, which expands their positive social experience, helps the subjects of support to realize their capabilities and assign the value of social competence. The purpose of the article is to substantiate the essence, content, advantages and limitations of the technology of socio-pedagogical support for the formation of social competence of students in additional education. The study of the socio-pedagogical support of formation of social competence of students is conducted under the scientific guidance of the author on the innovative area «Socio-pedagogical support of students in secondary education» of the Federal state budget scientific institution «Institute for the study of childhood, family and education of Russian Academy of education» on the basis of municipal educational institution of additional education of children «Yaroslavl city Palace of pioneers». The article attempts to present a new direction in pedagogical science at the level of identifying and presenting the content, advantages, risks and limitations of socio-pedagogical support as a technology for the formation of social competence of students in the organization of additional education. The author emphasizes the main theoretical positions that reflect the possibilities and features of additional education for the formation of social competence of students in the system of additional education of children; indicates a number of provisions that characterize social and pedagogical support from the point of view of the formation of social competence of students; argues the relevance of the formation of social competence for students. The article presents the main characteristics of socio-pedagogical support, identifies the stages and effective means of technology of sociopedagogical support for the formation of social competence of students in the organization of additional education. The article also outlines the prospects for experimental work on the topic of innovation platform.
Realistic Handwriting Generation using Generative Adversarial Networks (Rnn)

Generating handwritings of different kinds is quite a challenging task, an area in which not much work has been done yet. Though there has been substantial research done in the area of text recognition, the opposite of handwriting generation. Handwriting generation can prove to be extremely useful for children from blind schools where their speech can get converted into text and be used to generate handwritings of different kinds for them. Handwriting generation also has an important role in field of captcha generation. Our study exhibits in what way recurrent neural networks (RNN) of the type Long Short Term Memory (LSTM) could be used in order to create a composite sequence with structure covering a long range. We propose to use that the Generative Adversarial Network algorithm can be used to generate more realistic handwriting styles with better accuracy than other algorithms. Here, we will be trying to predict one point of data at a time. Our approach is shownfor text, where the type of data is discrete. It can also be used for online handwriting, that is real-valued data. It will then be further drawn out to handwriting generation. The created network will be conditioning its predictions based on a sequence of text. We will be using the resulting system to generate highly realistic cursive handwriting in a wide variety of styles. Experiments that have been carried out on online handwriting databases that are public predict that the method that has been proposed can be used to achieve satisfactory performance, the resultant writing samples achieved a high level of similarity with original samples of handwriting.
Constraining Atmospheric and Sedimentary Iron Sources in a Global Ocean Biogeochemistry Model

Estimates of iron inputs to the ocean from atmospheric deposition and sedimentary release have uncertainties that range over an order of magnitude.In this study, we use GEOTRACES dissolved iron observations to constrain atmospheric and sedimentary iron sources in a global ocean biogeochemistry model (UVic-Kiel).We test model simulations with global inputs ranging between 1.4-3.4Gmol yr -1 for atmospheric deposition and 14.5-117 Gmol yr - 1 for sedimentary release.Note that for each model experiment, iron scavenging was altered to maintain the global iron inventory at the observed level.Our model simulations with the highest tested inputs for atmospheric deposition (3.4 Gmol yr -1 ) and sedimentary release (117 Gmol yr -1 ) are able to best reproduce the global dissolved iron observations.In particular, model simulations with high source inputs better resolve strong gradients of high iron concentrations near sources and depleted concentrations farther away due to enhanced scavenging.Our model-data analysis suggests a global marine iron cycle with rates of atmospheric deposition, sedimentary release, and scavenging that are on the highend of current uncertainty ranges.
Strategic Data and Cyber Security Management in the Arab World

The chapter discusses strategic technological advancements such as automation, digitalization, digital transformation, data science, data analytics, real-time data acquisition, artificial intelligence, and predictive intelligence, and how all these aspects are affecting the lives of people in the Arab world. In particular, the study highlights the sheer importance for Arab governments and business organization to answer the emerging cyber challenge and how Arab businesses and people can utilize these technology advancements in a safe and secure way.
EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
MANAGING OBJECT ORIENTED SOFTWARE UNDERSTANDABILITY: A DESIGN PERSPECTIVE

Estimating understandability of object oriented software early in the development process; particularly at design phase greatly reduce the overall development cost and effort.To design and deliver quality products inside time and financial plan understandability plays a very important role.This paper shows the need and significance of understandability at design phase and build up a multivariate linear model "Understandability Estimation Model" for Object-Oriented Design.Developed model estimates the understandability of class diagrams in respect of their internal design properties.In this research paper an attempt has been made to propose an understandability estimation framework as a first contribution.A relation between object oriented design properties and understandability has been established as a second contribution.In third contribution, despite the fact that, in order to estimate class diagram's understandability the researcher further developed multivariate models.In conclusion the proposed Understandability models have been authenticated via experimental test. .
The Expenditures on and Efficiency of Corporate Social Responsibility Activities: Evidence from Targeted Poverty Alleviation Projects.

Chinese firms are required to disclose their expenditures on targeted poverty alleviation (TPA) projects and the number of people being lifted out of poverty as a result.This unique setting provides both the input and output of a specific corporate social responsibility (CSR) activity, allowing us to examine the determinants of firms' CSR spending and its efficiency.We find that political pressure from the government is the main driver behind Chinese firms' TPA spending.TPA spending also increases with firms' visibility and resources.Firms under greater political pressure manage their TPA projects less efficiently, lifting fewer people out of poverty with greater expenditure.Firms that are more profitable than their industry peers run their TPA projects more efficiently.
Genetic programming

tutorial Open Access Share on Genetic programming: a tutorial introduction Authors: Una-May O'Reilly CSAIL, MIT CSAIL, MITView Profile , Erik Hemberg CSAIL, MIT CSAIL, MITView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 512–525https://doi.org/10.1145/3377929.3389882Published:08 July 2020Publication History 0citation168DownloadsMetricsTotal Citations0Total Downloads168Last 12 Months35Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Trends of Educational Informatics in Latin America

Educational Informatics is a multidisciplinary research area that uses Information and Communication Technology (ICT) in education. This article reports a wide overview about the new perspective of educational informatics that has been implemented in Latin America. This general view is the result of the different talks presented during the XIII Conference of Educational Informatics, organized by the Ibero-American Network of Educational Informatics RIBIE (Red Iberoamericana de Informática Educativa) in Colombia. These talks were proposed by international panelists from various parts of Latin America in the lines of ICT for Education and Peace, Thinking and Artificial Intelligence, Emergent Scenarios in Education and Mobiles Technologies and Apps for Entrepreneurship and Learning.