Reviewerly/exHarmony huggingface dataset:
CLOUD BASED ARCHITECTURE FOR SMART EDUCATIONAL SYSTEM USING MODERN TECHNOLOGY

Cloud computing is also a revolutionary in field of computer science or information technology. Its technology will be growing day by day and also change the old or tradition system of the IT. Cloud computing plays important role in our technology sector. People are using it in some way, our educational system is not in line with modern times. Now a days way of teaching method can be change. Students are moving towards modern technology. In our education institute to apologies our student or teachers to take knowledge about cloud computing and information technology. And also know about the data ho to save in cloud. Its our country most of the backward areas do not about the awareness cloud challenging for us to implement to our system to cloud base. In this review paper cloud infrastructure will also change the education field in universities and many institutions so now a days website and portals are link to the cloud. At this time teaching method can be change technology will also be change as compared to the old system used in education. so, we change education sector without cloud infrastructure cannot be possible big challenge for the government to applying technology in our education structure.
Automated Police Report Writing System

To deliver next generation police and law enforcement reporting tools, and setting up intelligence platforms that agencies use to take incoming incident reports, lessen live employee resources and allow these enforcement agencies to reallocate resources to much needed community areas.
LibGuides: Teaching, Learning, and Curriculum: Getting Started

This guide supports the Department of Teaching, Learning, and Curriculum. It contains resources available at and through the James White Library
The Value of Conditionals

This introductory chapter sketches the near-ubiquitous role of conditional statements, questions, and commands in human cognitive life and decision-making. It emphasizes that the standard way of instantiating a known or hypothesized generalization ‘Every F is a G’ is with a conditional, ‘If this is an F, it is a G’; this matters in both everyday life and science. The chapter also sketches how conditionals are passed from one context to another by memory and testimony. These features of our use of conditionals suggest desiderata for the semantics of conditionals. In particular, various kinds of computational complexity and context-sensitivity would tend to unfit conditionals for the cognitive role they are expected to play.
Registration, Orientation and Edge Detection from Digital Mammogram Images

Computer-Aided Diagnosis (CAD) systems require computational algorithms on medical images for the detection of breast cancer.This breast cancer is causing to women is the serious concern and serious diseases that were increasing day to day in death rates for women.This paper focused on the Registration, Orientation and edge detection from digital mammogram images utilizing image processing.The proposed method has been tested on all mammograms from standard mammogram database, and extensive quantitative analysis with the classical methods is done.
Application of fuzzy logic controller to optimize thyroid hormone replacement therapy

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
Comparison of methods for exploiting symmetry in calculating the Fourier spectra of circularly symmetric functions

Four different methods for calculating the radial profile of the Fourier transform of a circularly symmetric function are discussed and evaluated. Applications to optics, in which the circularly symmetric function represents the distribution of field across a circular aperture, are many. The four approaches include: (1) numerical integration of the zero-order Hankel transform equation (using the Gauss–Kronrod method); (2) extraction of the radial profile of a two-dimensional Fourier transform; (3) the projection-transform method that makes use of the projection-slice theorem of Fourier analysis; and (4) the quasifast Hankel transform introduced by Siegman. The results indicate that the projection-transform method is the fastest method and that it has accuracy that is second only to numerical integration.
Measurement protection in prepare-and-measure quantum key distribution

In the distribution of quantum states over a long distance, not only are quantum states corrupted by interactions with an environment but also a measurement setting should be re-aligned such that detection events can be ensured for the resulting states. We here present a method of protecting a measurement in quantum key distribution against the interactions quantum states experience during the transmission, without the verification of a channel. As a result, a receiver does not have to revise the measurement that has been prepared in a noiseless scenario since it would remain ever optimal. The measurement protection is achieved by applications of local unitary transformations before and after the transmission, that leads to a supermap transforming an arbitrary channel to a depolarization one. An experimental demonstration is presented with the polarization encoding on photonic qubits. It is shown that the security bounds for prepare-and-measure protocols can be improved.
LibGuides: How-To Videos: How-To Video Tutorials

Learn how to be successful researchers using these short video clips. Learn how to be successful researchers using these short video clips.
G.R. Little Library: Faculty Guide to Integrating Library Resources Into Blackboard Courses: Interlibrary Loan and Distance Education book requests

A step-by-step guide on how to add library resources to your online courses in Blackboard, including online video, direct links to articles and databases, eBooks, and more.
Research Guides: Exploring Waltham: Resources for Research, Teaching, and Learning: Department of Community Service

A collection of resources about the City of Waltham to help students, faculty, and other partners engage with their surrounding community.
LibGuides: Library Events: BBW at CSCC

Want to know what is going on at the Library? Check out all of our past and upcoming events here! How will CSCC be celebrating BBW this year?
Research Guides: NorQuest Library Basics: Downloading

A guide to accessing and using library resources for online students. This page will provide information on how to download various types of online resources.
Faktor Kesuksesan Sistem E-Office Rumah Sakit dalam Upaya Meningkatkan Kepuasan Pengguna

Nowadays, hospital industries are aware of the importance of technological innovation that needs to be managed properly to drive business continuity and success in the future. The development of technological innovation in hospital industries has increased over time. This happens because of the desire to compete with each other continuously by utilizing technological innovation. This study has observed the existing information systems in the hospital, where the business process of the information system focuses on the hospital's internal correspondence process. This information system is called an e-office which is intended for the National Center General Hospital (RSUPN) Dr. Cipto Mangunkusumo. This study aims to determine what factors influence the successful implementation of the e-office system as seen from the evaluation of user satisfaction. Besides that, this study observed what efforts were made by the PT. Vidya Sentra Utama (Sevima) to create a successful and usable e-office system, where e-office is a large and complex system that includes more than 77 divisions with different business processes. Data collection methods used were interviews, observation, and document analysis. This study contributes to Sevima's knowledge of what factors of success need to be measured to determine user satisfaction and what needs to be done by Sevima's agile team to developing information systems and provide references for further research to evaluate an information system.
Educational Communication Strategies In Primary School

The article presents strategies of organizing educational communication in the process of teaching younger students. It is substantiated that the possibility of including this content of the subject’s material as a “subjective text” in the process of communication and the process of cognition of a younger student is fundamentally important. The article presents the results of a stating experiment to identify difficulties in organizing educational communication of younger students in their first year of study. Difficulties were identified with the help of special tasks reflecting the essence of the following communication strategies: generation of speech expression, discourse generation strategy, dialogue/polylogue generation strategy. Diagnostic results showed that when performing tasks aimed at including students in communication strategies, younger students showed the ability to create spontaneous monologue speeches in accordance with characteristics of the communicative situation. The experience of organizing educational communication strategies in primary school when working with educational content at the subject and sub-subject levels is presented, as well as younger students’ new knowledge generating process. It is concluded that the structure of the texts generated by the younger schoolchild allows us to understand individual characteristics of the native speaker, development level of his/her communicative skills. It is revealed that the individual style is reflected better and more clearly in oral, and not in written discourse.
Answer Set Programming with Composed Predicate Names

Mainstream systems for Answer Set Programming implement intelligent grounding to eliminate object variables from the input program, often obtaining a propositional program of reasonable size. However, non-stratified negation may inhibit the simplification of some rule bodies due to the lack of knowledge on the truth of recursive atoms. Frustration is greatest when the program is clearly locally stratified, such as in case of numerical arguments in rule heads obtained by increasing some body arguments; common examples are minimal distances in graphs and time arguments in planning scenarios. This paper suggests to move some arguments in predicate names, so that the declarative semantics of Answer Set Programming is preserved, but non-stratified negation is possibly avoided thanks to symbolic rule instantiation. A proof of concept is given in terms of Jinja templates for arguments with a clear range.
Review on Sign Language Detection Using MachineLearning

Sign language is the most popular and effective way for communication among hard hearing andnormal people. Normal people find little difficulty in understanding and interpreting the meaning of sign languageexpressed by the hearing impaired, it is inevitable to have an interpreter for the translation of sign language. Thereare very fewer technologies that help to connect this social group to the world. Understanding sign language isthe primary enablers in helping hard of hearing people with the rest of society. American Sign Language detectionis a process in which computer analyses the American Sign Language gestures and then convert them into humanreadable text. Those people who face difficulty in speaking and in hearing can easily communicate with the useof this Sign language detection software. Nowadays many types of research are going on to make this processeasy and accurate. In this paper, an effort has been made to highlight the work and comparative study of that workdone by researchers in American Sign Language.
Accelerating neural networks embedded on a microcomputer trained on KITTI

Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks, however they require expensive and powerful hardware to be implemented. The hardware requirement hinders wide embedded use in education and industry, slowing down the potential increase of safety on the road. In pursue of reduced inference time on GPUs, research has focused to reduce the computational demand of neural networks. However the accelerations on microcomputers are not reported, potentially leading to different results. To contribute to increased insight of the applicability of previous research on microcomputers, this thesis compares accelerations on a microcomputer and GPU. This is pursued by using the pretrained neural network Squeezedet, trained on the KITTI road user dataset. The neural network is pruned in 3 different ways: by reducing the number of filters, by reducing the size of filter kernels along a layer and by reducing the amount of layers. The accelerations are measured on a Raspberry Pi 3b+ and a Tesla K80 GPU. The process of embedding the neural network on the Raspberry Pi is described in great detail, to promote further research and educational use. Acceleration of the network is up to 12 % better on a Raspberry Pi than a GPU, when pruning filters from the network. When pruning full layers, or decreasing filter size the accelerations are up to 4% worse than on a GPU. This means when working with a microcomputer, the choice of pruning method can not be based on reported accelerations on GPUs. Therefore the recommendation is to do more research on accelerating neural networks for microcomputers. This will lead to wider use in industry and education and eventually safer roads.
Audio CAPTCHA with a Few Cocktails: It’s So Noisy I Can’t Hear You (Transcript of Discussion)

Everyone, my name is Benjamin, and from being a Postmaster, which, probably, most of you aren’t, at the moment, to a topic that, I guess, most of you dealed in the past, CAPTCHAs, and our topic is, audio CAPTCHAs, with a few cocktails, it’s so noisy I can’t hear you.
G.R. Little Library: Faculty Guide to Integrating Library Resources Into Blackboard Courses: How to get in touch with your librarians

A step-by-step guide on how to add library resources to your online courses in Blackboard, including online video, direct links to articles and databases, eBooks, and more. Contact methods for getting assistance from librarians remotely
Neuromorphic photonics for real-time applications

This presentation will discuss demonstrations of real-time operation of photonic neural networks going beyond machine learning applications. It contains a review of how a wavelength-based neuron can be implemented using silicon photonics. We discuss three application examples: principal component analysis for blind source separation, fiber nonlinearity compensation in long-haul communication systems and ODE system solving in real-time.
COMPARISON OF HOME LOAN SCHEME OF DIFFERENT BANKS

A modern, powerful advertising system can be complicated. They are social, political, and economic and work in very difficult situations. They do not match any goals to be mixed. The role of war and direct combat objectives, as well as the risks and uncertainties, due to the company's results and implementation, management, coordination, and control of the unique programs attain this interest. Manage and live in harmony and help organize. This is a complicated issue with change being negated, as the need to achieve certain goals.This information asset manager effectively markets decision making. Information on the market value of an MBA education through the manufacturer, distributor and connected to the people. After completing the second and third semesters, he will teach the project by the department's students.The goal of this project is for students to understand real business and professional life. In the report, I think it is very important for the management of these activities
Python Software Suite of Geometric-Oblivious FMM

This guided research focuses on optimizing pre-existing python codes of GOFMM and incorporatingnew functionality into the embedded balanced tree data structure by GOFMM. The original codes relyheavily on nested loops, conditional checks and index tracking, making it difficult to apply GOFMM tonew applications. The revised version implements a set-oriented interface on setting up the GOFMMdata structure. Particularly, we formalize all sampling methods in set. As a result, the codes are muchshorter, understandable and efficient as it involves basic mathematical operations on a fundamental datastructure. Furthermore, we utilize scipy.linalg.interpolative package to simplify analysis in skeleton,such as interpolative decomposition and QR factorization. The resultant codes run much faster and areuser-friendly in implementing related applications.With our new GOFMM interface, we devised two examples that demonstrate high usability ofGOFMM in statistical plotting and image recognition. Our first example takes in 2D position dataand classifies points based on their density. By exploiting relative fast search and data structure inGOFMM, this user case accelerates underlying matrix-vector multiplication. In comparison to its rawdata plot, our plot uses Gaussian kernel density estimation (Gaussian KDE) and displays a high level ofaccuracy. Our second example pipes over one thousand 8 × 8 images into GOFMM, utilizing its tree-likedata structure for fast search and low storage. Then, we implement Gaussian KDE to learn over trainingdataset so that the updating model can accurately classify testing data. The result shows high accuracyof our model classification. With 30 training images, the accuracy of classifying 44 images is 43/44 .
inABout: a journey towards compassion : post-abortion collective compassion methodology

This work proposes a methodology for unstigmatising abortion. inABoutis a set of workshop scenarios and a design practice which is being developed in collaboration with a network of organisations, ...
Improving Internet Connection Issues of WFH Agents | AVOXI

2020 sure has turned the world upside down for people and organizations worldwide. The coronavirus pandemic has sent hundreds-of-thousands of ...
LibGuides: Adult Summer Reading - 2020: Online Book Displays

Why should kids have all the fun? Read some great books this summer and join us for a chance to win prizes, including our Grand Prize: a Kindle!
Shaping Our Mental Model of Security (Transcript of Discussion)

This talk is not on a completed piece of research, it is a work in progress. One of our biggest challenges at the moment is how to improve cyber security and people are at the heart of it. A lot has been written about safety and human error. There’s also been, for a long time, this thought that humans are the weakest link in security. If we could just take the human out of the loop, we would have safer systems, or more secure systems. Whether we are better off with or without the human in the loop, we cannot avoid people being part of security. And one way to keep people secure is to teach them something, and this is where the mental model comes in.
COVID-19 and higher education: Changing adoption rates of online learning

The landscape of higher education is dramatically changing as a result of COVID-19. No other incident has had as great of an impact on the implementation of online learning as the pandemic. Prior to 2020, approximately 15% of learners in higher education took courses online. In March 2020, 100% of students in higher education were taking courses online. The COVID-19 pandemic changed online learning from being a convenient form of learning to an essential form of learning. Instead of focusing solely on a method of keeping students on track to course completion and graduation, online learning became a method of keeping students and faculty safe while continuing with their respective responsibilities within the higher educational system. Components of online learning, while traditionally not designed nor structured for the entire higher educational system, is proving adaptive to massive groups. The purpose of this paper is to examine the changing adoption rate of online learning in higher education during COVID-19 and the present and future challenges of online learning.
Time series Forecasting Incorporating Exogenous Information

The discussions on social media platforms reflect the views of masses on different ongoing long term events. The demand for different people-oriented services observes upsurge during the period of ...
Method for the Recovery of Indexed Images in Databases from Visual Content

The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) []). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning.
Toxic Message Classification Using Convolution And Gru Network

Now-a-days, derogatory comments are often made by one another, not only in offline environments but also immensely in social networking websites and online communities. Thus an identification model which reads any piece of text appearing in any platform is classified and detects the type of toxicity like obscenity, threats, insults and identity-based hatred. Existing system works on the LSTM type of RNN model. Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. In order to improve the efficiency and accuracy of the existing system, we propose Concurrent GRU (ensemble of CNN and GRU models). The motivation for our project is to build a model that can detect toxic comments and find bias with respect to the mention of select identities. The data pre-processing consists of text tokenization and normalization which varies slightly when processing features for the model.
Research Guides: NorQuest Library Basics: Logging In

A guide to accessing and using library resources for online students. This page provides information on your online library account and how to log in to access resources off-campus.