Reviewerly/exHarmony huggingface dataset:
Investigation of Regression Models of Glycemic Dynamics under Conditions of Measurement Uncertainty

In the course of the research, it was analyzed that there are currently many widespread methods of invasive detection of glycemia. However, such methods can lead to infectious diseases. To minimize the risk of infection, research into the non-invasive method of determining glycemia, which can be used for medical purposes, is gaining popularity. The article describes the most common of these methods currently used.The article deals with the statistical substantiation of procedures for identifying the parameters of the glycemic dynamics model in the uncertainty of the measurement results. The heterogeneity of glycemic dynamics is ensured by the factor stimulating insulin secretion by exogenous glucose (glucose loading).&lt;&gt;The purpose of the article is to substantiate the choice of regression model of change in the level of glycemia with significant restrictions on the volume of primary measurement information, which cause uncertainty of nonlinear changes of the model at normative glucose loads.For the first time, polynomial regression analysis was applied to identify variants of polynomial regression recovery in incomplete measurement experiments for existing methods of type 2 diabetes, as well as models for the identification of diabetic conditions. Implementation of such dependencies can confirm the presence of stochastic relationships between the factors of influence and response of the organism. Three models are identified that should be prioritized in identifying the diabetic condition. It is shown in the paper that the research problems are reduced to the procedure of polynomial regression restoration as a model of glycemic dynamics depending on the number of possible variants. The analysis of the model shows that the main elements that influence the result are the sum of the regressors with a certain coefficient and the sum of the random remainder of the result of the calculation, which satisfies the conditions of interdependence and normality of probability distribution. One basic model, which is most suitable for the problems of recognizing the parametric model of glycemic dynamics in verifying the validity of statistical hypotheses, was also identified. The paper deals with a polynomial model of function by types of parametric uncertainty regarding the functional relationship between the factors of influence and reaction of the organism. The paper presents, as an example, the estimation of polynomial regressions that model the uncertainty of changes in glycemic levels for alternative conditions.
The Strategy to Maintain an Incremental Game for Android Originated from Indonesia’s Om Telolet Om Phenomenon Idea Using The SWOT-MAGIQ Approach

The gaming industry in Indonesia has begun to increase, accompanied by the emergence of many startups.In competition, game developers need ideas to develop game creativity.One of the simplest is adopting from the social phenomenon include Juragan Terminal game from Om Telolet Om.However, the triumph of the game depends as long as the phenomenal social is still trading tropically.Therefore, when social phenomena have faded, the game is no longer in the public spotlight.Developers are challenged to maintain the game application by attracting the attention of users or retaining the number of existing gamers.From the results of the SWOT-MAGIQ analysis, it can conclude that it must consider challenges and bonuses in the game.Therefore, users get satisfaction in playing games.The most important thing is how to maintain the game's quality.Thus, loyal gamers still like it.
LibGuides: Library Emergency Remote Support: General Resources on COVID-19 (Coronavirus)

Library support and information for Fisher students studying remotely and faculty teaching remotely. General Resources on COVID-19 (Coronavirus)
Research Guides: Tips and Tricks for Teaching Remotely: Accessing Databases & E-Resources

This guide is intended to introduce you to material and resources that may help you when teaching a class remotely.
Обобщенный блочный алгоритм Флойда – Уоршелла

One of the most commonly used on practice all-pairs shortest paths algorithms on weighted graphs is Floyd – Warshall algorithm. Blocked version serves as a basis for obtaining effective parallel algorithms to be implemented on multicore central processing units, on computers with distributed memory, on graphics processing units (GPU). Increasing computation granularity in blocked versions of algorithm leads to a more efficient usage of caches and more efficient organization of parallel computations. In this paper we introduce generalization of blocked Floyd – Warshall algorithm. Computing blocks execution order was reorganized in such a way that array elements which participate in communication operations, both reading and writing, are pushed out of fast-access memory less often. This means that in GPU implementation slow global memory is used less often, compared with the original blocked algorithm.
Application of Machine Learning Techniques in Predicting of Breast Cancer Metastases Using Decision Tree Algorithm, in Sokoto Northwestern Nigeria

According to international agency for research on cancer, female breast cancer was the leading type of cancer worldwide in terms of the number of new cases (approximately 2.1 million) diagnosed in 2018.Predicting outcome of a disease is a challenging task. Data mining techniques tends to simplify the prediction segment. Automated tools have made it possible to collect large volumes of medical data, which are made available to the medical research groups. This study aimed to apply machine learning algorithms using decision three classifier and descriptive statistics to evaluate the performance of the model in predicting the probability of cancer metastasis in patients that present late.Materials and method: The breast cancer disease dataset has been taken from the department of Radiotherapy and Oncology of Usmanu Danfodiyo University Teaching Hospital, Sokoto state, Nigerian. Dataset has 259 instances and 10 attributes. The experimental results of this study used, decision three classifier in IMB SPSS (version 23) software environment. In the experiment, two classes were used and therefore a 2 × 2 confusion matrix was applied. Class 0=Not Metastasized, Class 1=Metastasized. We applied supervised machine learning approach in which dataset were divided into two classes that is training and testing using 10 fold cross validation.Results: Shows that 259 instance of breast cancer, 218(84.2%) cases were not metastasized while 41(15.8%) cases were metastasized to the other region of the body. The overall accuracy of the model was found to be 87%, with the sensitivity of 88%, specificity 75% and the precision of 98%Conclusion: Based on these findings, the machine learning algorism using decision three classifiers predicted that 87% of the tumor presented at stage IV, indicating that the tumour can spread to the other region of the body.
Discovering correspondences between molecular profiles and morphological features via deep learning (Conference Presentation)

Tumor cell populations in histopathology exhibit enormous heterogeneity in phenotypic traits such as uncontrolled cellular and microvascular proliferation, nuclear atypia, recurrence and therapy response. However, there is a limited quantitative understanding of how the molecular genotype correspond with the morphological phenotype in cancer. In this work, we develop a deep learning algorithm that learns to map molecular profiles to histopathological patterns. In our preliminary results, we are able to generate high-quality, realistic tissue samples, and demonstrate that by attenuating the mutation of status of few genes, we are able to guide the histopathology tissue image synthesis to exhibit different phenotypes.
DESIGN AND DEVELOP MEASUREMENT METHODS TO PREVENT BANDWIDTH ANONYMITY SERVICE ATTACKING IN HETEROGENEOUS WIRELESS NETWORKS

The state of the secure bandwidth measurement purpose needed to design, analysis, implementation and evaluation a new bandwidth measurements algorithm, we called it monitor trainer, for securing in cooperative bandwidth measurement in adversarial networking environment. In Existing Work, the bandwidth inflation by a consensus view i.e opportunistic bandwidth measurements or detect bandwidth frauds via existing methods and techniques. The bandwidth inflation attacks are very insidious. From a user perspective, maintaining a good quality of the transferred video is critical. The model is, however, only valid for single-hop communication. Users Cannot Control Bandwidth Measurement. Opportunistic bandwidth measurement method has been designed to secure P2P bandwidth evaluation systems only. Existing system does not provides bandwidth Controlling Which Leads To Inflation Attacks. The Proposed Work, designing to use an unpredictable measurement for training set, to defeat even the smart adversaries. The proposed new measurements delay algorithm to secure capacity measurements for the first time itself.
Fake News Detection in Facebook Using Machine Learning

This Project comes up with the applications of Random Forest techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) can only get you so far. But these models do not consider the important qualities like word ordering and context. It is very possible that two articles that are similar in their word count will be completely different in their meaning. The data science community has responded by taking actions against the problem. There is a Kaggle competition called as the “Fake News Challenge” and Facebook is employing AI to filter fake news stories out of users’ feeds. Combatting the fake news is a classic text classification project with a straight forward proposition. Is it possible for you to build a model that can differentiate between “Real “news and “Fake” news? So a proposed work on assembling a dataset of both fake and real news and employ a Random Forest classifier in order to create a model to classify an article into fake or real based on its words and phrases. The main objective is to detect the fake news, which is a classic text classification problem with a straight forward proposition. It is needed to build a model that can differentiate between “Real” news and “Fake” news.
The Implementation of Field Trip Technique to Increase The Students' Vocabulary at the Seventh Grade of SMPN 3 Tapung in 2018/2019 Academic Year.

This research aimed to increase the students’ vocabulary in English lesson by applying Field Trip technique. This research was conducted by using Classroom Action Research (CAR). The objective of this research was to increase students’ vocabulary by applying Field Trip technique at the seventh grade students of SMPN 3 Tapung. The subject of this research was the students of 7-A of SMPN 3 Tapung in 2018/2019 academic year which consists of 32 students. This research was conducted in two cycles which each consists of planning, action, observation, and reflection. The technique of analyzing data of this study was applied by using qualitative and quantitative data. The qualitative data were taken from interview and observation. The quantitative data were taken from the test.Based on quantitative data, the result of this research showed that there was the increasing of students’ vocabulary. In the pre-test, the students who got the score 80 or more were twenty one of thirty two students (65,62%) with the mean score was 80,93. In the post-test I, students who got the score 80 or more were twenty three of thirty two students (71,87%) with the mean score was 86,56. In the post-test II, students who got the score 80 or more were twenty eight of thirty two students (87,5%) with the mean score was 88,28. Based on qualitative data, it was found that the students were active, enthusiastic, and interested in teaching learning process. It can be concluded there was significant increasing of the students’ vocabulary through Field Trip technique at the seventh grade students of SMPN 3 Tapung.
UCC LibGuides: Interlibrary Loan: Borrow from Other Libraries

Interlibrary Loan is a service that gives you access to resources at other libraries. When you've searched Union County College Libraries catalog and databases and we don't have what you need, our free interlibrary borrowing service is your best bet.
Cybersecurity is as nuanced as the medical specialities

Consider the probability of success and the risk of failure if you pick the wrong specialty to treat your practice.
Development of American Sign Language Recognition System Using Convolution Neural Network

Sign language is a form of communication language for helping the deaf-mute person. Sign language is a unique language which uses the hand gestures and also body movement to express an idea. Due to rapid advancement in image processing using AI, many sign language recognition has been widely researched. However, there is still no perfect system that can accurately and convenient for daily user use — the higher cost of the hardware design when using a hardware approach. For the visual-based approach, environmental concern such as lighting sensitivity, camera position, and background of the image to be trained. The objective of this project is to develop the American Sign Language recognition system using a convolutional neural network. This project is the visual-based approach which needs the knowledge of artificial intelligence work with. To overcome those problems, there is an excellent approach must be present for sign language recognition system even though this approach also more on visual-based technique to generate a machine learning algorithm. But this algorithm can work with a webcam or a camera. Due to the rapid development of Information Technology (IT), the development of camera hardware and Commercialization of web cameras also improve. Also, the camera or webcam are affordable and efficiently to get. This project is focused on the visually based approach rather than a hardware approach in sign language recognition. This system can deal with the sign language that performs by the user whether it is a deaf-mute person or an ordinary person. The system can work with the normal webcam in laptop or computer. The dataset will be prepare and the CNN classification algorithm was be design for this project. After that, validate the accuracy in of the system using Convolutional Neural Network (CNN) image classification. This project consists of the knowledge of convolutional neural network (CNN) which will generate in Jupyter Notebook. Jupyter Notebook was created to develop open source software, and it can interact with machine learning using python programming language. There is 24 set of alphabet dataset with 200 images per each alphabet. In American Sign Language (ASL), J and Z require movement. This the reason why 24 set of data instead of using 26 set data. Convolutional Neural Network (CNN) image classification to be used is because CNN is pre-processing compared to other image classification algorithms. The CNN classifier was achieve the accuracy of 95%. It is hoped that this research contributes to reducing the barrier between the deaf-mute people and ordinary people.
LibGuides: An Introduction to DIY Zines: What is a Zine?

This guide will teach users about DIY zines, where to find them, and how to make your own!
A comparative critical discourse analysis of the news coverage of the rape law reform in Sweden and Denmark

This thesis examines the discourse concerning the consent-based rape law reform in Sweden and Denmark. Sweden implemented the consent-based rape law July 2018 and has since been evaluating the law while the current Danish government promised that the law will change to a consent-based law. I focus on eight Danish articles and eight Swedish news articles in which the consent-based rape law reform is discussed. The articles are chosen from the two newspapers with most online visits in each country; Aftonbladet and Ekstra Bladet. In analysing these articles, the study will use elements from Fairclough’s Critical Discourse Analysis and Entman’s framing theory. The study analyses the use of frames in news media while also analysing the use of interactional control, politeness, modality, and word meaning in the chosen news articles. The study specifically focuses on text analysis and an analysis of the social practice in which the orders of discourse, the ideological and political effects of discourse and the social matrix of discourse are studied. I identified the overarching frames in the coverage and found that the interpretation of consent and rape is dynamic and influenced by changing power dynamics. I found that the development in the Swedish legislation and news coverage of the legislation can be seen as a precursor for the Danish development depending on the nature of the law following the publication of this thesis and the political and legal response to the reform. (Less)
My super fast primer on the difference ...

My super fast primer on the difference between critical literacy and critical reading: https://archive.jgregorymcverry.com/the-difference-between-critical-literacy-and-critical-reading-in-childrens-literature/ #ecet2
LibGuides: Access to free online resources: Home

During the current coronavirus crisis, many publishers and other providers of content are offering libraries and colleges access to materials. This guide provides links to those offers. The library has not evaluated the content offered.
LibGuides: Continuity of Horn Library Services - Spring 2020 Semester: Announcements & FAQs

Iinformation related to Horn Library's current operations, access to remote services, and other helpful tips
LibGuides: A Guide for Reader's Advisory and Collection Development: Video Reviews

This LibGuide offers an array of websites, webinars, articles, and blogs providing helpful information to help staff members in their interactions with patrons.
Rejection-Cascade of Gaussians: Real-Time Adaptive Background Subtraction Framework

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians (CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4–5 $$\times $$ and 17% average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder (VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.
LibGuides: Virtual Library Services During Emergency Campus Closure: Open Educational Resources

Due to the current COVID19 / Coronavirus-related emergency campus shutdown, the library has put together this special guide to virtual library services.
LibGuides: Virtual Library Services During Emergency Campus Closure: Home

Due to the current COVID19 / Coronavirus-related emergency campus shutdown, the library has put together this special guide to virtual library services.
Staircase patterns in words: Subsequences, subwords, and separation number

We revisit staircases for words and prove several exact as well as asymptotic results for longest left-most staircase subsequences and subwords and staircase separation number. The latter is defined as the number of consecutive maximal staircase subwords packed in a word. We study asymptotic properties of the sequence hr,k(n), the number of n-array words with r separations over alphabet [k] and show that for any r≥0, the growth sequence (hr,k(n))1∕n converges to a characterized limit, independent of r. In addition, we study the asymptotic behavior of the random variable Sk(n), the number of staircase separations in a random word in [k]n and obtain several limit theorems for the distribution of Sk(n), including a law of large numbers, a central limit theorem, and the exact growth rate of the entropy of Sk(n). Finally, we obtain similar results, including growth limits, for longest L-staircase subwords and subsequences.
LibGuides: Virtual Library Services During Emergency Campus Closure: How to Videos

Due to the current COVID19 / Coronavirus-related emergency campus shutdown, the library has put together this special guide to virtual library services.
Automatic Attendance Management System using Face Detection and Face Recognition

Image Processing is a method to perform various operations on an image, to get an enhanced image or to extract some useful information from it. Face recognition in Image Processing is the process of identifying one or more people in images or videos by analyzing and comparing patterns. In schools and colleges, the presence of the student is identified by marking the attendance. Marking the attendance manually is a difficult process. Some of the automated systems are developed to overcome these difficulties but have drawbacks like fake attendance, accuracy, etc. To overcome these drawbacks, there is a need of automated attendance system. Face recognition is one of the biometric methods to improve this system. Being a prime feature of biometric verification, facial recognition is being used enormously in several applications. The main implementation steps used in this type of system are Face Detection and Recognizing the detected face. As face is a unique identity of a person, the issue of fake attendance can be solved. The system uses Haar cascade classifier for face detection, Local Binary Pattern (LBP) for face recognition technique as it is fast, simple and has better success rate. After these, the database updates the attendance of a student as present by comparing with the student database and face database. This smart and automatic attendance system can be an constructive way to maintain the attendance records of the students in the classroom and to reduce fake attendance. This application is represented using Raspberry Pi, OpenCV and PYTHON.
Effects of Interest Rate Deregulation on Agricultural Financing in Nigeria from (1970 to 2014)

The study of effects of interest rate deregulation on agricultural financing in Nigeria (1970-2014). The study also specifically examined the trend in the rate of interest, volume of credit to agricultural sector and agricultural GDP contribution under the period (1970-2014). The study employed descriptive/trend statistics, ordinary least square regression and correlation analysis to achieve its results. Secondary data used for the study were computed from CBN Statistical bulletin 2014. The results show that interest rate has a strong influence in the volume of credit to the agricultural sector and it also has weak influence on the agricultural GDP contribution. It was also observed that interest rate has been fluctuating under the period (1970-2014) that have resulted to low productivity, and unemployment in agricultural sector but makes the financial institutions to be willing to disburse credit to the public. The agricultural GDP contribution has a downward and upward trend during the period which is as a result of the neglect of the government based on the discovery of oil and lastly there was an upward trend in the volume of credit to the agricultural sector, which means funds were sent to the sector by government but because of bureaucratic processes most farmers are unable to access it.