Reviewerly/exHarmony huggingface dataset:
Fraud Detection in Health Insurance Claims using Machine Learning Algorithms

Fraud can be spread broadly and it is extremely costly to the therapeutic protection framework. Unscrupulous protection might be a case created to cover up or twist information that is intended to deliver social insurance edges. Cheats might be of the numerous sorts and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the wellbeing segment. The commitment of this case misrepresentation discovery is Associate in nursing trial study on extortion recognizable proof and exploitative examples. Along these lines, to identify the misrepresentation information handling procedures are utilized. For the most part essential based oddities are implemented exploitation applied math call rules and k-means, rule based mining and affiliation rule bolstered appropriation calculations are applied. Through these abnormalities the extortion in certifiable information is recognized. Be that as it may, there might be a great deal of progress done by exploitation various information handling procedures. In this way the arranged methodology has been assessed basing on the protection information and furthermore the trial results from our methodology are efficient in human services misrepresentation. Other self-advancing misrepresentation location ways can likewise be applied on this protection information.
A New Semantic Similarity Measure Based on Ontology for Movie Rate Prediction

A recommendation algorithm comprises of two important steps: 1) Predicting rates, and 2) Recommendation. Rate prediction is a cumulative function of the similarity score between two movies and rate history of those movies by other users. There are various methods for rate prediction such as weighted sum method, regression, deviation based etc. All these methods rely on finding similar items to the items previously viewed/rated by target user, with assumption that user tends to have similar rating for similar items. Computing the similarities can be done using various similarity measures such as Euclidian Distance, Cosine Similarity, Adjusted Cosine Similarity, Pearson Correlation, Jaccard Similarity etc. All of these well-known approaches calculate similarity score between two movies using simple rating based data. Hence, such similarity measures could not accurately model rating behavior of user. In this paper, we will show that the accuracy in rate prediction can be enhanced by incorporating ontological domain knowledge in similarity computation. This paper introduces a new ontological semantic similarity measure between two movies. For experimental evaluation, the performance of proposed approach is compared with two existing approaches: 1) Adjusted Cosine Similarity (ACS), and 2) Weighted Slope One (WSO) algorithm, in terms of two performance measures: 1) Execution time and 2) Mean Absolute Error (MAE). The open-source Movielens (ml-1m) dataset is used for experimental evaluation. As our results show, the ontological semantic similarity measure enhances the performance of rate prediction as compared to the existing-well known approaches.
A protocol for selecting a rule of combination for fusion of imprecise sensor data

There does not seem to be a day that goes by where there is not news of a new and potentially catastrophic cyber attack infecting computers and networks.In addition, even if it were possible to fully list all possible attacks, cyber sensors might not have the fidelity to correctly identify the type of attack.To further complicate things, it is more likely that these sensors must not only contend with quickly detecting known threats but also attacks which are new and unforeseen.Hence, modelling techniques are needed that will help take into account the possibility that the full possible range of potential threats may not be precisely represented as well model ambiguity and partial information.Dempster-Shafer Theory is one such framework because it not only deals with imprecision but there are also different combination rules which can be used depending on whether it is assumed that the full set of possible outcomes is known or not.Here, a protocol is provided to help the analyst select which combination rule should be used based on the assumptions he or she is making.The importance of these assumptions is illustrated through an example to show how results can vary according to the rule chosen.
A Keyword Based Educational and Non-Educational Website Recognition Tool

Today we all depend upon internet to do our daily activities. For booking hotel, air tickets, finding particular places, travelling, cooking, education, banking, etc. we require internet. To get a specific thing immediately, we require filtering tools. E-learning is a new and rapidly growing media in modern education system, which is totally based upon internet. While surfing on internet students may get distracted from offensive and irrelevant websites. In avoiding such distractions, filters play a vital role. This paper proposes a filter tool which carries out web scraping of text data, data cleaning, Natural language processing and filtering the non-learning sites in real-time. We have collected the text from paragraphs, images and video tags. This extracted textual data is in the form of sentences, which are processed part of speech (POS) by NLP. In NLP we are using WSD method to find the exact meaning of the ambiguous words in that context. This tool creates a knowledge base of student related sites using NLP and SVM classification technique. Word sense disambiguation is used to find the correct senses of those words, in the present sentence, which may have multiple meanings. We have created a keyword database of all learning sites. Lastly, we are classifying the sites in two categories learning and non-learning using Support Vector Machine in this tool
Paradoxes of Business: Towards A Practice Model to Attend to Paradox Tensions in Organizations and Develop the Leaders of Tomorrow

Paradoxes in business i.e. strategy paradoxes, leadership paradoxes, management and organizational paradoxes are inseparable from organizations’ front-end decision making. Existing literature has examined these paradoxes in mainly diverse theory driven perspectives that pointed towards the need to develop a learning cycle to sustain a practical model to help in navigating the business paradoxes. The aim of this paper is to uncover the best possible practical approach that would facilitate the process of navigating the paradoxes in organizations. I review a vast array of paradox and brain plasticity literature and conclude that a change in leadership behaviour towards accepting, embracing, and exercising these paradoxes in a form of a business simulation is crucial. Thus, I propose a learning model to sustain and support this practical model.
Dung beetle inspired local search in artificial bee colony algorithm for unconstrained and constrained numerical optimisation

In recent times, swarm intelligence (SI) centred strategies are proving their efficacy in the arena of engineering optimisation problems. Artificial bee colony (ABC) algorithm is one of the efficient SI centred technique. To intensify exploitation concept of ABC, a local search strategy inspired by dung beetle orientation and foraging activities is developed and amalgamated with ABC. This developed local search strategy is termed as dung beetle local search (DBLS) strategy. The developed amended algorithm is titled as dung beetle inspired ABC (DBABC) algorithm. The developed DBABC is analysed using 32 unconstrained benchmark optimisation problems, 18 constrained benchmark optimisation problems, and 3 engineering design problems. The obtained outcomes validate the competitiveness of the proposed approach.
CEC eff. as &amp;quot;integrative proxy&amp;quot; potentially helps to improve inventories, not mechanistic models

Thank you!We realize that we have to clarify the purpose of our exercise.Our intention was not to describe a mechanistic relationship.We rather propose to explore if CEC eff.can act as an extensively available "integrative proxy" to substitute for those variables probably controlling SOC stabilization but which are difficult or impossible to quantify for large areas.If successful, this integrative proxy could allow to produce large scale SOC inventories, but if CEC eff.could help to improve mechanistic soil models remains an open question (this will be specified in lines 41-24, and 285-387).
Electoral result transmission model based on RSA and AES algorithm

In this paper a new Electoral Result Transmission Model (ERTM) is developed based on Advanced Encryption Standard (AES) and RSA algorithm.The model provide mechanisms for encrypting electoral text results and scanned image files before they are submitted to third party networks.The model enables returning officers to submit encrypted digital files to IEBC Servers and only authenticated IEBC officials at National Tallying Centre (NTC) will have rights to decrypt and to verify that the results are exactly as sent by authorized returning officers.The model aims at preventing against active and passive attacks on electoral results during transmission process. .
Building the Security Function Point Method for Web Application Vulnerability Remediation

The web application vulnerability remediation activities are important in terms of actual risk management in corporate security activities. However, traditional software development resource estimation methods do not discuss resource estimation for software vulnerability remediation in terms of security. Moreover, it is difficult to estimate the exact web vulnerability remediation resources using correction factors. In these backgrounds this study aims to establish a resource estimation methodology for web application vulnerability remediation in terms of security from the perspective of dynamic analysis, contributing to foundation building for the systematic management of web application vulnerability remediation among information security organizations and related practitioners. For the new model development, this study used 64 application data of the experimental company to derive the security function point method and 6 web vulnerability assessment project data from the same company to verify the methodology.Hence a web application vulnerability remediation standard was established, and a new security web vulnerability remediation resource estimation technique, “Security Function Point Method (SFPM),” was proposed through data collection based on the standard.It covers the de facto global web application vulnerability framework named OWASP Top 10(2017) and several Korea’s standards fromthe practical field. Thus, it is possible tocalculate the web application vulnerability remediation resourcesin a better way.
Analyzing and Visualizing Projects and their Relations in Software Ecosystems

The ninth IEEE Working Conference on Software Visualization (VISSOFT 2021) builds upon the success of the previous eight editions of VISSOFT, which in turn followed after six editions of the IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT) and five editions of the ACM Sympo0sium on Software Visualization (SOFTVIS). In 2021, VISSOFT will take place and be co-located with ICSME.
Human activity recognition based on interaction modelling

Human activity recognition aims at recognising and interpreting the activities of humans automatically from videos. Among the activities of humans, identifying the interactions between human within minimal computation time and reduced misclassification rate is a cumbersome task. Hence, an interaction-based human activity recognition system is proposed in this paper that utilises silhouette features to identify and classify the interactions between humans. The main issues that affect the performance of activity recognition are sudden illumination changes, detection of static human, data discrimination, data variance, crowding problem, and computational complexity. To accomplish the preceding issues, three new algorithms named weight-based updating Gaussian mixture model (wu-GMM), spatial dissemination-based contour silhouettes (SDCS), and weighted constrained dynamic time warping (WCDTW) are proposed. Experiments are conducted with the gaming dataset and Kinect interaction dataset to show that the proposed system recognises the interactions with reduced misclassification rate and minimal processing time compared to the existing system.
Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

Clustering is a difficult and widely studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g., Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally predefined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved similarity functions can be used to perform clustering using a graph-based representation. The results of a variety of experiments across a range of large, high-dimensional datasets show that the proposed approach can achieve higher and more consistent performance than the benchmark methods. We further extend the proposed approach to automatically produce multiple complementary similarity functions by using a multi-tree approach, which gives further performance improvements. We also analyse the interpretability and structure of the automatically evolved similarity functions to provide insight into how and why they are superior to standard distance metrics.
Security Advantages of Cloud ERP and its Databases in Higher Education

An EduCon is a Lead Management cum Customer Relationship Management Software for Educational Consultants. It is cloud-based software hosted on Linux environment. Developed on Php with database on MySQL and Mongo DB, it is heavy-duty software which can handle 500 instances at once with 1 lakh+ active records. EduCon has a strong communication system with bulk SMS and bulk Email server integration with Text Guru and Mail Chimp respectively. This communication system is used to contact leads and send notification and transactional messages. This communication system is also used to take after service feedback from leads.
Infectious disease surveillance update

On Nov 27, Bolivia health officials confirmed the third case of measles virus occurring this year in the Santa Cruz region; measles cases were reported this year for the first time after 20 years. The most recent case was a 16-month-old who had attended a regional health centre with symptoms of fever and dehydration; after differential testing, the diagnosis of measles was confirmed. Three further suspected cases of measles have been identified following investigations. The first two cases of measles had been reported in April 2020, the first case was a pregnant woman who was a doctor in the Santa Cruz region and the second case was a child believed to have infected the doctor. Case finding activities are ongoing to identify possible cases and children who may not be fully vaccinated.
The Impact of Inflation on the Financing of Private Sector Activities through Banks: A Case Study of Developing Countries

The Impact of Inflation on the Financing of Private Sector Activities through Banks: A Case Study of Developing Countries
Correction

This article refers to:Social Environment Simulation in VR Elicits a Distinct Reaction in Subjects with Different Levels of Anxiety and Somatoform Dissociation
No. 51943. International Bank for Reconstruction and Development and Ethiopia

Global Environment Facility Grant Agreement (Sustainable Land Management Project II) between the Federal Democratic Republic of Ethiopia and the International Bank for Reconstruction and Development (acting as an implementing Agency of the Global Environment Facility) (with schedules, appendix and Standard Conditions for Grants made by the World Bank out of various funds, dated 15 February 2012). Addis Ababa, 10 December 2013
Science education for doctoral students: MODEST approach and experience

&amp;lt;p&amp;gt;MODEST (Modernization of Doctoral Education in Science and Improvement Teaching Methodologies) is a capacity building project funded by the Erasmus+ programme.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project is coordinated by the University of Latvia. There are three other EU partners (from Finland, Poland and the United Kingdom) and a total of ten partners from three partner countries (Russia, Belarus and Armenia).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project aims to improve the structure and content of doctoral education and the internal capacities of services that manage doctoral studies in accordance with the modern European practices, to facilitate a successful adherence with Bologna process reforms and its instruments, to improve and increase the quality of international and national mobility of doctoral students of Armenia, Belarus and Russia, and to establish a sustainable professional network providing the use of participatory approaches and ICT-based methodologies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During the past year, almost one hundred members of academic and administrative personnel as well as doctoral students have contributed to creating a total of 14 new courses mainly in transferable skills: Research methodology and research design; Project writing, project management, and funding sources; International research writing and presentation skills; Research ethics, Intellectual property rights and personal data protection; 3I - Interdisciplinarity, interculturality, internationalization in research; Organization of doctoral training; Educational/constructive alignment, design and implementation of courses for doctoral studies; Digital literacy; Data analysis and expert systems; Virtual environment; Commercialization of research, managerial skills; Personal development; Complexity; and Sustainable development and global challenges of 21st century. Each course has specific target group(s) such as PhD students, university teachers, doctoral programme managers, or administrative staff.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Summary of each developed course &amp;amp;#8211; aims, learning outcomes, content (including course blocks on lectures, seminars, homeworks, etc.), planned learning activities and teaching methods, assessment methods and criteria, and other relevant &amp;amp;#8211; will be presented. The developed courses will be an integral part of the Doctoral Training Centers for PhD students to be established in the MODEST partner universities in Armenia, Belarus and Russia.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The MODEST project serves as a great example of transfer of good practices in higher education, especially on doctoral level, but it has also created new connections for educational and scientific collaboration. From the PEEX perspective, MODEST is an important initiative strengthening connections between European universities and institutions in Russia, Belarus and Armenia. The project will continue until 2022. More detailed information is available at www.emodest.eu.&amp;lt;/p&amp;gt;
Service selection in a marketplace: a multi-perspective solution

Most of the current research work on web service selection only considered the selection problem from the perspective of one party – service consumers. A service marketplace serves many parties including service consumers and providers. Thus, it is important to consider multiple parties. In this thesis, we propose a service selection model considering the benefits of multiple parties: consumers, providers and the marketplace. The model ranks services based on not only how much these services satisfy the user requirements but also how much the requests can be distributed to different providers and the revenue gain in the marketplace. We design different objective functions, then combine into a QoS-Plus-PF objective function. The results show that proposed model could achieve a high degree of satisfaction of user requests (i.e., 0.61% to 5.26% worse than the optimal score), and meanwhile have the capability of promoting more diversified set of services (i.e., 48.95% promotion percentage).
Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

© 1997-2012 IEEE. Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically handcrafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, the existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows the promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations but also achieves the best prediction among the peer performance predictors.
Measures Of Location, Spread, And Skewness

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.