Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Unsupervised Technique In Time Series Data

This Paper Is Concerned About The Difficulty Of Perceiving Inconsistencies In Time Blueprint Information Utilizing Coevals Analysis, Which May Be A Free Method. Charge Cards Cost Billions Of Dollars In Purchaser And Cash-Related Affiliations Consistently. Delinquents Expect To Discover New Plans And Procedures To Proceed With Unlawful Activities. One Approach To Managing Separate Compulsions Is To Investigate Sketchy Changes In Client Lead. We've Executed A Brief Lopsided Classifier Utilizing Bit Technique That Licenses Us To Bundle Information Trapped In To Express Credits By Performing Factorization Of Qualities And Utilizing Likelihood Edge That Is Set To 90 You Depleted With A Viewpoint On Which It Orders Record As Extortion. These Are Exchanges That Leave Requests Unequivocally From Their Partner Pack And Are Hailed As Conceivably Deceptive. Time Approach, The Character Of The Companion Social Events, And In Like Manner The Advantageousness Of Designating Compulsion Banners To Exchanges Are Depicted. Massive Information Has A High Ability To Anticipate The End, Easily Use, Increment Capacity, And Update Dynamically In A Couple Of Fields Like The Money The Board, Shakedown Control And To Improve The Deal. Of Late Extortion Exchanges Ceaselessly Are Developing, So Perceiving Colossal Degree Plans Across Different Exchanges Or Recognizing Strange Direct From A Private Client Can Change The Fake In Online Exchange. Due To These Mastercard Exchange Certificates Total Weighty Contemplations Ought To Be Made And Made A Pass At Genuine Visa Information. Banks Have Utilized Early Bending Cautioned Structures Doubtlessly For Years. The Sub-Point Is To Present, Take A Gander At And Investigate As Of Late Passed On Exposures In Visa Mutilation Disclosure. The Significance Of The Use Of The Methods Explored Here Is Inside The Minimization Of Mastercard Twisting.
Integer-valued Pth-order autoregressive model

The most commonly used time series model is the discrete time series which assumes the variables being tested are continuous and produce continuous values. Whereas in many applications, a discrete time series model is needed to handle discrete variables and produce discrete values as well. The time series model that handles count or non-negative integer data is the Integer-valued Autoregressive model with the pth-order or INAR(p). This model is built with binomial thinning operator which implements probabilistic operations with discrete distribution that are suitable to model count data such as Poisson and Binomial. Model parameters will be estimated using the Yule-Walker method. In this research, we will discuss and describe the characteristics of the INAR(p) model using the binomial thinning operator. The INAR(p) specification follows the Autoregressive model with the pth order, AR(p). Forecasting in INAR(p) uses median forecasting by calculating the conditional probability of each possible non-negative integer value, then selecting a forecast value with a cumulative conditional probability greater than 0.5. The INAR(p) time series model will be applied to the 115 simulated data with non-negative integer values.
Effective Spam Bot Detection Using Glow Worm-Based Generalized Regression Neural Network

In today’s world, social network plays a major role for communicating all over the world. The available popular social networking platforms are Twitter and Facebook. In both the platforms, the users can communicate with any user irrespective of the location and time. But this platform also has a disadvantage which is the spreading of false information and also false communication between the users. The false information spread is due to the fake users and bot users. To overcome this drawback in the social network, several machine learning algorithms were proposed for identifying the malicious users. Hence, in this, the spam users are detected in the Twitter social network platform. To perform the classification of users as original and fake users, the user and social attribute features were extracted. Then, the features are reduced using the optimization approach called glow worm optimization. Then, the reduced features are trained using the generalized regression neural network. The reduction of features is based on minimizing the error rate of the classifier. This approach helps to improve the classification as compared to the deep Q-learning algorithm. The whole process is performed on the social honeypot data set, and it is implemented using MATLAB R2018a version. The proposed approach outperforms the conventional algorithm in terms of accuracy, sensitivity, specificity and F-measure.
Pengembangan Buku Paket IPS Terpadu Materi Keadaan Alam Indoesia Kelas VII di MTs Negeri 1 Surakarta

This study aims to (1) Has an integrated social studies student package development model of material in the seventh grade of Indonesia's natural state of MTs Negeri 1 Surakarta, (2) Determine the effectiveness of the development of integrated social studies student textbooks material in the seventh grade of Indonesia's natural state of MTs Negeri 1 Surakarta. The development model uses the Thiagaraja 4D model. The study design used one group pretest-posttest design with 62 respondents. The study was conducted at MTs 1 Surakarta with taking techniques using two classes, namely, the experimental class and the control class. The data analysis technique used is the T-test, there is an increase in the value of student learning outcomes in the experimental class and the control class. The difference in the results of the understanding of the material nature of Indonesia in the control class experienced only a slight increase, namely the average pretest 45 and the average posttest 75 while the difference in the results of student understanding in the experimental class on the material of the Indonesian natural conditions at the pretest and posttest increased by 28 average pretest 57 and increase with posttest 85 average. The value of the control class is smaller than the experimental class, namely the control class average value of 45% pretest and the average posttest 75% while in the experimental class the average value of pretest is 57% and the average value of posttest is 85%. The T-test test shows a significant value (2-tailed) = 000 which means less than 0.05 so that H0 is rejected and H1 is accepted.
Improving Opinion Mining in Social Networks Data

Opinion mining is an important research area in these days because of the huge number of daily posts about people’s opinions on different topics in social networks. Extracting the opinions is a challenging task. Thus, researchers interact with this problem through the use of machine learning algorithms such as support vector machine, Naive Bayes, Random forest, Logistic regressions, and Maximum Entropy. However, the accuracy of such techniques still needs to be improved. In this study, we introduce an approach to improve the accuracy of classifying English Twitter’s tweets into positive and negative. We use distant supervision and machine learning algorithms such as: Naive Bayes, Support Vector Machine (SVM), and Maximum Entropy. Our data set consists of tweets which labeled into positive or negative. We have accuracy of 88% which is better than existing approaches.
PENINGKATAN KEMAMPUAN PENGUKURAN DALAM MATEMATIKA AWAL MELALUI METODE DISCOVERY LEARNING

This study aims to determine the increase measurement in early math skills in early childhood learning through discovery methods. The research was conducted on a group B2 Kindergarten Pertiwi I Kantor Gubernur Padang with of fifteen children. The research was conducted from March to April 2015. This study used action research by Kemmis and Taggart. This study consisted of two cycles, each cycle consisting of 8 sessions. Each cycle has four main activities: planning, action, observation and reflection. Analysis of the data used quantitative and qualitative approaches. Analysis of quantitative data used descriptive statistics that compare the results obtained from the Pre cycle, first cycle and the second cycle. While the analysis of qualitative data used analyzing data from the field notes and interviews during the research by steps of data reduction, data display and data verification . The results showed an increase measurement in Early Mathematics Ability in early childhood B2 group conducted through discovery learning method. In the pre-cycle measurement in Early Mathematics level of Ability percentage yield 45.90%, in measurement in Early Mathematics level of Ability increased to 65.83% after the act of the first cycle and level increased to 89.10% second cycle becomes. Discovery Learning Methods stimulate student observer, classification, measure, guess and infer for increase measurement in Early Mathematics Ability
Lecturers’ Awareness and Perceptions on The Usefulness of Augmented Reality For Instructional Delivery in Nigerian Universities

The introduction of technology into instructional delivery processes in the  institutions of higher learning has taken numerous dimensions in this 21st century. Several concepts have also emanated through the design of interactive instructional contents. Augmented Reality is one of such designs that tend to simplify instructional content for the teaching and learning processes. This study examined lecturers’ awareness  and perceptions  on instructional usefulness of augmented reality. The study adopted a descriptive research design using the survey approach.  Simple random sampling technique was employed . The instrument used is a researcher-designed questionnaire with four sections to collect responses on demography, awareness level and perceptions   of instructional usefulness of augmented reality. The instrument was validated by four experts comprising two each of educational technology and computer science lecturers. The reliability of the instrument was determined through Cronbach Alpha which yielded 0.91. The questionnaire was administered through Google Form and 197 respondents were able to access it. Three research questions were answered and two hypotheses were tested. Mean and t-test were used to answer the research questions and hypotheses respectively. The findings revealed that the majority of lecturers in tertiary institutions in Nigeria possess high level and rate of awareness and perception respectively. Also, most lecturers found augmented reality to be instructional useful. It is then recommended that a positive move should be built on improving the awareness and perception by way of making infrastructure and equipment available in tertiary institutions in Nigeria.
Research Guides: MLA 9th: Books

This Research Guide lists examples of how to cite sources according to the MLA Handbook, 9th edition.
Efficient Greedy Coordinate Descent via Variable Partitioning

Greedy coordinate descent (GCD) is an efficient optimization algorithm for a wide range of machine learning and data mining applications. GCD could be significantly faster than randomized coordinate descent (RCD) if they have similar per iteration cost. Nevertheless, in some cases, the greedy rule used in GCD cannot be efficiently implemented, leading to huge per iteration cost and making GCD slower than RCD. To alleviate the cost per iteration, the existing solutions rely on maximum inner product search (MIPS) as an approximate greedy rule. But it has been empirically shown that GCD with approximate greedy rule could suffer from slow convergence even with the state-of-the-art MIPS algorithms. We propose a hybrid coordinate descent algorithm with a simple variable partition strategy to tackle the cases when greedy rule cannot be implemented efficiently. The convergence rate and theoretical properties of the new algorithm are presented. The proposed method is shown to be especially useful when the data matrix has a group structure. Numerical experiments with both synthetic and real-world data demonstrate that our new algorithm is competitive against RCD, GCD, approximate GCD with MIPS and their accelerated variants.
LibGuides: EBSCO eBooks: EBSCO eBooks Collection

How to search and use EBSCO eBooks EBSCO eBooks are online versions of print books that our library has either purchased with a perpetual license or via an annual subscription.
GAMBARAN KEMISKINAN DALAM NOVEL YORICK KARYA KIRANA KEJORA (PENDEKATAN SOSIOLOGI SASTRA IAN WATT)

Abstrack: Descriptions of Poverty in Yorick's novel by Kirana Kejora. The purpose of this study is describe the author's social context, literature as a mirror of society, and literary social function contained in Kirana Kejora's Yorick novel. The approach used is Ian Watt's literary sociology approach. The design of this research is descriptive qualitative. Data collection by the technique of reading and recording objects to be studied. The results showed the author's social context creates literary works based on the reality that occurs around him between an author and a Yorick figure. The picture of poverty contained in Yorick's novel is Yorick's inability to attend school and Yorick's character who lives in an old hut with Encum's grandmother. The social function in Yorick's novel teaches the reader about the resilience of a young boy facing an economically disadvantaged life.
COURSE CERTIFICATION AND OPEN BADGES FOR DIGITAL CULTURE EDUCATION

D. Andone 1, A. Ternauciuc1, V. Mihaescu1, H. Koche2, B. Hougaard2, I. Rossau2, R. Vasiu1 1Politehnica University Timisoara (ROMANIA) 2Aalborg University (DENMARK)
Classification Model For Data Stream Mining With Concept Drift

Data stream is the huge amount of non-stop and high-speed data generated in various fields, including financial processes, social media activities, Internet of Things applications, and many others. Such data cannot be processed through traditional data mining algorithms due to several constraints, including limited memory, data speed, and dynamic environment. Concept Drift is known as the main constraint of data stream mining, mainly in the classification task. It refers to the change in the data stream underlying distribution over time. Thus, it results in accuracy deterioration of classification models and wrong predictions. Spam emails, consumer behavior changes, and adversary activates, are examples of Concept Drift. In this thesis, a Concept Drift Detection Model is introduced, Concept Drift Detection Model (CDDM). It monitors the accuracy of the classification model over a sliding window, assuming the decline in accuracy indicates a drift occurrence. A modification over CDDM is proposed and named W-CDDM.Both models have evaluated against two real datasets and four artificial datasets. The experimental results of abrupt drift show that CDDM, W-CDDM outperforms the other models in the dataset of 100K and 1M instances, respectively. Regarding gradual drift, the W-CDDM overtakes the rest in terms of accuracy, run time, and detection delays in the dataset of 100 K instances. While in the dataset of 1M instances, CDDM has the highest accuracy using the NB classifier. Moreover, W-CDDM achieves the highest accuracy on real datasets.
A Mind Genomics Cartography of Insurance for Home Project Contracts: Selling to the ‘Mind’ of the Insurance Prospect

The paper introduces the science and process of Mind Genomics as a process by which to reveal the mind of prospects regarding the factors surrounding insurance, specifically insurance that the home contractor will complete the project satisfactorily. There are a lot of insurance companies in the world, and, in turn, a great deal of advertising, advertising testing, and an entire world of professional consumer researchers supporting the effort to sell the advertising. The objective is to uncover the existence and nature of groups of people sharing the same point of view about what they want in such insurance. The Mind Genomics process works through experimental design, presenting respondents with vignettes, combinations of messages about insurance, totally different combinations for each respondent. The subsequent analysis by regression reveals which messages make the respondent feel comfortable with the project and the insurance versus which messages make the respondent feel insecure and ready to drop the project. The data suggest three mind-sets of people: project-focused, contractor-focused, and legal/finance-focused, respectively, corresponding to different aspects of the contracting relationship. We introduce the PVI, personal viewpoint identifier, to assign a new person to one of these three mind-sets, to aid sales and client service.
Potensi Layanan Perpustakaan Provinsi Papua Dalam Meningkatkan Literasi Warga Papua di Masa Pandemi Covid-19

This article discusses the potential, challenges and opportunities of regional-owned library in increasing citizen literacy during the Covid-19 pandemic. In order to study this issue, literacy and public service theories are used. The main data in this study is secondary data consisting of scientific references, regulations, government documents and statistical references. Limited observations of regional library facilities are used as primary data in this study. The data analysis is conducted qualitatively with an effort to describe and explore the object under study as a whole. This study concludes that regional libraries have an important function in improving the basic literacy competences of Papuans. The Covid-19 pandemic services continue to run in accordance with standard health protocols and e-pusda services continue to run with a limited internet connection. The potential to increase citizen literacy through library functions during the pandemic is still high by integrating education, activism and library services and striving to make regional libraries as hybrid and digital libraries that allow broad access for Papuans even though the majority of them live in geographic areas isolated.
Comunidades Virtuales de Aprendizaje: Una experiencia en el Área de Ingles

The purpose of this study is to address the study of the influence of the virtual learning community implemented by teachers from two educational institutions: the "Gabriela Mistral" General Basic Education School in the city of Cuenca, Ecuador and Tessa International School in Hoboken, New Jersey in the United States in the teaching of a Foreign Language, for the strengthening of oral expression in a second language. The methodology was based on a research with a mixed approach. For the quantitative, the result is that learning improved due to the interaction that was facilitated by the application of the different digital tools, and for the qualitative, the experience has allowed the children to know beyond their normal environment, they have been able to have contact with other cultures, learn new words that now use them and above all has aroused curiosity to know other countries.
Trisula Scout School Extracurricular Website Development

Most schools from all levels in Indonesia already have a website, both public and private schools. Website is a useful formal communication medium for schools to enrich the use of social media. Domain names for schools in Indonesia have also been provided with the.sch.id for easier accessibility and identification. SD Negeri Rambutan 03 Jakarta have a scout extracurricular activity named Trisula, during Covid-19 Pandemic situation, scout weekly activities were shifting from originally face-to-face to an online exercise. This momentum was one of the reasons why the scout website needs to be built. This community service was a qualitative method with an interview approach, discussion and literature review during the pre-development and web-development process. The website has been successfully generated, customized, and implemented using WordPress and has been internally launched last November of 2020
Online Examination Portal

The aforementioned portal of Online Examination Portal is being featured in the document that will include all characteristics and procedures which are required. This document include acute details about objectives of the structure, portal scope restraint, portal action model, primary structure requirements, team burgeoning, probable devise perils, schedule of the inventory, and eventually surveying and divulging mechanisms for the entire portal structure. Online Examination Conducting Portal is very beneficial for Educational Institute's to formulate an entire exam, saving time to check the papers and concoct mark sheets. Online Examination Portal will help the Educational Institute to scrutinize students capabilities and develop their adroitness. But the flaws for Online Examination portal occur it takes excessive time when user formulates the exam for the first time. And to take the exam we prefer the same tally of set- ups as the number of students. The potent use of Online Examination Portal, the coordinator can use this portal to formulate the tests as their necessity, and for getting better results in less time.
LibGuides: NWACC Library Spring 2021 Services – Get Here From There: Library Instruction

This guide is an overview of library services and changes due to COVID 19 and social distancing.
The COVID-19 Contact Tracing App in the UK: Co-production in the Public Sector Which Went Wrong

This study explores how accountability and trust in relationships between governments and citizens impact the outcomes of co-production of digital technologies implemented in emergency situations. The extraordinary circumstances of the COVID-19 pandemic brought governments into the situations of having to make radical decisions and to act under tight time pressures. The paper focuses on the public discourse around contact tracing apps (CTAs), using the case of the United Kingdom as empirical setting. A framework to study accountability and trust in co-production of public services is developed. The study draws on a netnography method to analyze the reactions of the civil society expressed in social media to the introduction of the CTA, together with the consequences. Our findings carve out two pillars related to the discourse around the CTA, one related to technical and one related to social aspects. The analysis identifies specific concerns of citizens belonging to each pillar, highlighting polarization between government rhetoric attempts to justify the CTA’s possibilities of surveillance and direct interventions by mobilizing the arguments for physical safety, and the escalated public anxiety around the ambiguity of the broad range of CTA’s implications. The study also reveals that the main challenges of the CTA’s implementation were a high level of public skepticism and distrust of the UK government and its actions, as well as concerns related to the potential violation of democratic freedoms and the abuse of CTA and data the app was designed to collect.
Minecraft Learning System for Spatial Reasoning in Middle Grades Learners

LaMeres is also the Boeing Professor at MSU where he is responsible for initiatives to improve the professional skills of engineering graduates.LaMeres teaches and conducts research in the area of computer engineering.LaMeres is currently studying the effectiveness of online delivery of engineering content with emphasis on