Reviewerly/exHarmony huggingface dataset:
Extraction and Clustering of Adverse Drugs: Using Twitter Data

Social media is a place where one can create, update and share both the Public/Private Messages and Updates through various Platforms to several communities. It plays a vital role in our Traditional and Cultural Values. There are different types of Media Platform in which one can communicate and gets interacted with each other. But many of the people prefer social media like LinkedIn, Facebook, Twitter, Various Creative and Non Creative blogs and Various YouTube channels. There is a huge transformation in the field of Medicine with marvelous up gradation of speed in social media networking for the common public. People make use of these social media platform to seek general information regarding medicine and diseases and its symptoms.
Foreword to the special issue of the workshop on data‐intensive computing in the clouds

The purpose of this special issue is to collect a selection of representative research articles that were primarily presented at the Eighth Workshop on Data-Intensive Computing in the Clouds, held in conjunction with SC'17. In particular, this annual workshop brings together domain scientists, researchers, scholars, vendors, and practitioners from the complementary fields of data science, cloud computing, and high-performance computing, in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions. Data scientists increasingly rely on high-performance computers and cloud infrastructures to analyze high volumes of scientific data, automatically process data, and manage data privacy and performance. As scientific data continues to grow in volume and complexity, computational capabilities also increase at both supercomputing facilities and industry data center. Processing scientific data on emerging hardware with high performance-efficiency and privacy requires a knowledge combination from specific scientific domains and computer system techniques. This special issue presents examples of the successful collaboration from domain scientists, researchers, scholars, vendors and practitioners to address the research challenges on processing scientific data on the infrastructures of high-performance computers and cloud. The scope of this special issue is representative of the multidisciplinary nature of scientific computing in high-performance computing and cloud computing. This special issue addresses the challenges on practical experiences on processing scientific data in different domains and different platforms in high-performance computers and cloud. In particular, Zamani et al.1 show how to automatically integrate large-scale facilities with cyberinfrastructure services for automated data processing. Peng and Plale2 identify the requirements for managing computational analysis among candidate storage solutions. Koulouzis et al.3 describe their experiences that identify the time-critical requirements of environmental scientists making use of computational research support environments and provide a case study whereby their software suite is used to optimize runtime service quality for a data subscription service. Dayarathna and Suzumura4 demonstrate their approach on producing optimized stream query performance, and further compare the solution to naive deployments using two real-world stream processing applications in the domains of health care and search advertising. We encourage the readers to review the aforementioned articles to gain insight into the breadth and depth of problems and innovative solutions in the multidisciplinary field of scientific data in high-performance computing and cloud computing.
LibGuides: Project Planning for the Beginner: Reviewing the Literature

This Sage Research Methods tool is designed for the first time researcher to guide you through your research project. This section takes you through the literature review process, explaining the reasons for reviewing the literature and differentiation between the types of literature available.
Algorithmic Models for MicroBlog Information Mining System

Abstract Based on the “MicroBlog Community Massive Information Mining and Monitoring System”, this paper introduced the algorithm and model construction of microblog information word processing and mining conversion core functions of microblog information subsystem, in order to obtain the information objects needed in each link of the process of microblog information mining, which provides a basis for the realization of monitoring and early warning in the MicroBlog monitoring platform.
Influence of Koha Software on Technical Operations of the Public University Libraries in North Central Nigeria

The Study was designed to find out the Influence of Koha Software on Technical Operations in Public University Libraries in North Central Nigeria. The study was guided by two specific objectives, two research questions and two hypotheses. Descriptive Survey research design was adopted for the study. The Study area was North Central Nigeria. The population comprised of 650 library staff in 12 public university libraries in North central Nigeria. The sample size for the study was 354 library staff from five (5) public university libraries using Koha in North central Nigeria. Purposive sampling technique was used to select five (5) out of the twelve (12) public university libraries in North Central Nigeria using Koha. Method of data collection was a self developed questionnaire titled: Influence of Koha Software on Technical Operations Questionnaire (IKSTOQ). Research questions were answered using descriptive statistics of mean and standard deviation while hypotheses were tested using chi-square at 0.05 level of significant. The findings of the study revealed that Koha software packages has significant negative effect on acquisition while it has significant positive effect on cataloguing and classification operations in public university libraries in North Central Nigeria. The study recommends the use of acquisition module to purchase library materials and subscribe journals among others.
Diseño de un sistema de apoyo para estudiantes foráneos en la Pontificia Universidad Javeriana

Foreign students who enter to study a university degree at the Pontificia Universidad Javeriana are insecure from the moment they are affected by the change of city and lifestyle that this means, this insecurity is due to not knowing the place, nor the people where they will go, added to different variables (family, economic, social and personal) which can generate anxiety, academic demotivation and in some cases university dropout. Wild Boxes in a project that offers support and follow-up to foreign students through 9 kits that will be delivered to the university in the student's first semester and all the objects necessary to support the students in a physical and emotional level and help them in they process of adaptation to the city. Likewise, it also has an application that allows foreign students to get to know each other and carry out various activities inside or outside their homes in the company of others. Throughout the development of the project, different types of checks will be adjusted with the target users, thus obtaining first-hand feedback to make adjustments on the site.
An Investigation of the Weight Space for Version Control of Neural Networks.

Deployed Deep Neural Networks (DNNs) are often trained further to improve in performance. This complicates the tracking of DNN model versions and the synchronization between already deployed models and upstream updates of the same architecture. Software Version Control cannot be applied straight-forwardly to DNNs due to the different nature of software and DNN models. In this paper we investigate if the weight space of DNN models contains a structure, which can be used for the identification of individual DNN models. Our results show that DNN models evolve on unique, smooth trajectories in weight space which we can exploit as feature for DNN version control.
Equivalence of several curves assessing the similarity between probability distributions.

The recent advent of powerful generative models has triggered the renewed development of quantitative measures to assess the proximity of two probability distributions. As the scalar Frechet inception distance remains popular, several methods have explored computing entire curves, which reveal the trade-off between the fidelity and variability of the first distribution with respect to the second one. Several of such variants have been proposed independently and while intuitively similar, their relationship has not yet been made explicit. In an effort to make the emerging picture of generative evaluation more clear, we propose a unification of four curves known respectively as: the precision-recall (PR) curve, the Lorenz curve, the receiver operating characteristic (ROC) curve and a special case of Renyi divergence frontiers.
LibGuides: Election 2020: Home

This guide will assist students with accessing information about election 2020 resources and more.
10 Security Essentials for CIO Success | I.S. Partners

Advances in technology and accompanying cybercrimes move at a lightning pace these days, making it a constant high-speed footrace for your Chief ...
A fast and secure RSA public key cryptosystem

RSA is a well-known public-key cryptosystem. It is the most commonly used and currently most important public-key algorithm which can be used for both encryption and signing. RSA cryptosystem involves exponentiation modulo an integer number n that is the product of two large primes p and q. The security of the system is based on the difficulty of factoring large integers in terms of its key size and the length of the modulus n in bits which is said to be the key size. In this paper, we present a method that increases the speed of RSA cryptosystem. Also an efficient implementation of arithmetic and modular operations are used to increase its speed. The security is also enhanced by using a variable key size space. There exist numerous implementations (hardware or software) of RSA cryptosystem, but most of them are restricted in key size. An important improvement achieved in this paper is that the system is designed flexibly in terms of key size according to user security.
Celebrating Library Anniversaries: Project Management and Opportunities for Archiving

Laupus Health Sciences Library at East Carolina University recently celebrated a fifty-year anniversary. This presentation will outline the planning process including the formation of a taskforce, how activities and items were budgeted, how decisions were made, and lessons that were learned. The presentation will also detail the value of the archives in planning for this event and will provide practical tips for how others may preserve the history of their library and engage their users.
Inland river image dehazing algorithm based on water surface depth prior

This study proposes a single-image restoration algorithm requiring no additional scene information that is suitable for the removal of haze from images of inland waterways. This algorithm uses a water surface depth of focus prior to obtaining a rough atmospheric-light transmission image, and then applies guided filter refinement and sky segmentation based on grey level histograms to estimate the atmospheric light intensity, thereby performing image dehazing automatically. The performance and applicability of our proposed algorithm are verified by the dehazing results obtained using the proposed algorithm for a large sample set of hazy images of inland waterways compared with those obtained using two standard single-image dehazing algorithms in terms of the processed image quality and processing speed. The results confirm the reliability of the water surface depth of focus prior model. Our method is appropriate for inland waterway images and provides better image quality and computational performance than the existing algorithms.
LibGuides: EDU 461: Children's Literature: Post-Test

This guide will help students in EDU 461 perform research and create lesson plans based on children's books.
Facilitating Connections Through the Data Curation Network

March 2020 poster presented at the Research Data Access and Preservation Summit in Santa Fe, New Mexico.
Virtual and augmented reality – a way to develop university students; spatial ability in organic chemistry

Virtual and augmented reality – a way to develop university students; spatial ability in organic chemistry
Freedom of Expression versus Blasphemy: A Comparative Study of Pakistan and United States’ News Media

This study compares the freedom of expression exercised by news media of the United States and the Pakistan; with reference to the controversial movie trailer "Innocence of Muslims"; released on You-tube by July 1, 2012. Content analysis research design is applied. Our time frame is September 11-30, 2012 and 50 opinion articles from the Washington post, the Los Angeles Times, Dawn and the Express Tribune are our sample. The framing theory is applied; consistency and discord frame category system is adopted. Dominant frames and their changing trends in different quarters of the timeframe are studied. It was found that discord frame was dominant frame on both sides. The vitality of the discord and consistency frame coverage in Pakistani media was higher than United States' media. The US media was consistency oriented whereas PN media was discord oriented. However, overall trend of both media were found leaning towards the settlement.
Quantum gravity and gravitational waves

New Physics in the Era of Precision Cosmology, Castelldefels, ES, June 16 - 21, 2019. -- Presentacion de 16 diapositivas. -- https://www.grc.org/string-theory-and-cosmology-conference/2019/
Adaptive Reference Images for Blood Cells using Variational Autoencoders and Self-Organizing Maps

CellaVision develops automated microscopy for blood analysis. Their products can pre-classify 19 different types of white blood cells and support the medical technologist performing the final classification. CellaVision provides reference cells. The reference cells are a fixed set of cells handpicked by a technologist, chosen to be typical for that specific class. The reference cells are static, i.e. the same for each cell image currently being classified and could be improved. We propose adaptive reference cells. Using a combination of machine learning techniques, we develop a pipeline consisting of an Xception classifier, a Variational Autoencoder (VAE) and a Self-Organizing Map (SOM). This pipeline is used to produce the adaptive reference cells which are specific for each cell image. The medical technologist classifying an image is thus presented with cells that are the most visually similar to that image. The adaptive reference cells are of particular use for cells that are hard to classify.The result consists of adaptive reference cells using both the output from the VAE and the SOM. The adaptive reference cells using the VAE are found superior. Also, cluster visualization using SOMs is presented together with proposed measurements for the robustness of the classifier and accuracy of the SOM.
Automation of Musical Compositions Synthesis Process Based on Neural Networks

This work is devoted to development and approbation of the methods for automated sound generation based on image color spectrum with using the neural networks. The work contains a description of the transition between color and music characteristics, the rationale for choosing and the description of a used neural network. The choice of the neural network implementation technology is described. It also contains the detailed description about the experiments to choose the best neural network parameters.
Perceived credibility of autistic witnesses and the effect of diagnostic information on credibility ratings

BACKGROUND: People with autism spectrum disorder (henceforth, autism) exhibit a number of atypical behaviours that may be relied upon by jurors when making judgements about their credibility as witnesses. The current study aimed to: (1) examine whether autistic witnesses were perceived as less credible than typically developing (TD) witnesses, irrespective of the number of correct details they reported; and (2) determine whether mock jurors’ credibility ratings of autistic witnesses improved if they were aware of their autism diagnoses and were provided with information about autism. // METHOD: One-hundred-and-twenty-five mock jurors rated the credibility of video testimony of 17 autistic and 17 TD witness participants recalling an event. Half of the juror participants were informed that some of the witnesses were autistic and were provided with information about autism; the other half received no information about witnesses’ diagnoses. // RESULTS: Contrary to predictions, autistic witnesses were seen to be as credible as TD witnesses when no information about their diagnosis was provided. However, when jurors were informed that a witness was autistic and were also provided with further information about autism, they were rated as slightly more credible than TD witnesses. Credibility ratings were only predicted by jurors’ prior knowledge/experience of autism when they were explicitly informed of witnesses’ autism diagnoses. // CONCLUSIONS: These results indicate that disclosing one’s autism diagnosis (alongside further information about autism) may result in a positive bias in terms of witnesses’ perceived credibility. Implications for jury instructions and future research directions are discussed.
A Matrix Library

In the previous chapters, we presented an implementation of a neural network made of layers and neurons (i.e., instances of NeuronLayer and Neuron). Although instructive, that implementation does not reflect classical ways of implementing a neural network. A layer can be expressed as a matrix of weights and a vector of biases. This is how most libraries that build neural networks (e.g., TensorFlow and PyTorch) actually operate.
Pengembangan Media Komik untuk Meningkatkan Hasil Belajar Siswa Sekolah Dasar

The research aims to develop and test the validity, practicality, as well as the effectiveness of learning media, that is comic media on Social Science subject. Observation model used is the research and development model (R&D) of Borg and Gall. The result of this research shows that comic media is reasonable, practical and effective. The using of comic media gives influence to the learning result of student. It can be seen through the result of t-test trial which shows that tcount is 2.55 > from ttable 2.903. The using of comic media also gets good response from the students, that is 90.5%. Therefore, it can be concluded that comic media is reasonable, practical and effective to improve the learning result on Social Science subject of third grade students in Elementary School.Keyword : Comic media learning, development, learning result , Social Studies
Supervised Bayesian Statistical Learning to Identify Prognostic Risk Factor Patterns from Population Data.

Current methods for building risk models assume averaged uniform effects across populations. They use weighted sums of individual risk factors from regression models with only a few interactions, such as age. This does not allow risk factor effects to vary in different morbidity contexts. This study modified a supervised Bayesian statistical learning method of topic modelling, allowing individual factors to have different effects depending on a patient's other comorbidity. This study used topic modelling to assess more than 71,000 unique risk factors in a population cohort of 1.4 million adults within routine data. The model learnt prognostically important risk factor patterns that predicted 5 year survival, and the resulting model achieved excellent calibration and discrimination with a C statistic of 0.9 in a held out validation cohort. The model explained 92% of the observed variation in 5 year survival in the population. This paper validates using survival supervised Bayesian topic modelling within large routine electronic population health data to identify prognostically important risk factor patterns.
NVU Libraries Research Guides: Sciences: Searching NVU Libraries

This guide provides materials for various types of sciences--biology, chemistry, earth science, math, and more.
Using the Energy Distribution as a Benchmarking Among Models for Adiabatic Quantum Processing in the Protein Folding Problem

Abstract Determining the tertiary complexity of proteins using ab-initio algorithms is a hard problem. Adiabatic Quantum Computing allows to construct simple Ising-like Hamiltonians in order to elucidate the aminoacid interactions minimizing the conformation energy of the protein. Exploring the energy distribution of the Hamiltonian allows to compare the efficiency of different models there proposed. This article compare the efficiency of two algorithms through the conformation energy of a protein stating a benchmark through the energy distributions of the conformation states.
Feature Selection in Classification of Blood Sugar Disease Using Particle Swarm Optimization (PSO) on C4.5 Algorithm

Diabetes Mellitus (DM) is a disease caused by blood sugar level increased were higher than the maximum limit. Food consumed tends to contain uncontrolled sugar which could cause the drastic increase of blood sugar level. It is necessary to efforts, to increasing the public awareness to controlling blood sugar and the risks of increasing blood sugar level so as to determine of preventive and early detection measures One of used of data mining technique is information technology in the health sector which used a lot as a decision maker to predicting and diagnosing a several disease. This research aims to optimizing the features on classification of the data mining with the C4.5 algorithm using Particle Swarm Optimization (PSO) to detect the blood sugar level in patient. The dataset used is the effect of physical activity to the Blood Sugar Level at H. Abdul Manan Simatupang Kisaran Regional Public Hospital. The amount of dataset used is 42 record with 10 attributes. The result of this research obtained that the Particle Swarm Optimization (PSO) may increasing the accuracy performance of C4.5 from 86% to 95%. Whereas the evaluation result of the AUC Value increasing from 0,917 to 0,950. From those 10 attributes which are then selection with using PSO into 7 attributes used to determine the prediction of sugar level. Therefore the Algorithm C4.5 using the Particle Swarm Optimization (PSO) may provide the best solution to the accuracy of detection blood sugar levels.
Efficient determination of equivalence for encrypted data

Secure computation of equivalence has fundamental application in many different areas, including healthcare. We study this problem in the context of matching an individual's identity to link medical records across systems under the socialist millionaires’ problem: Two millionaires wish to determine if their fortunes are equal without disclosing their net worth (Boudot, et al. 2001). In Theorem 2, we show that when a "greater than" algorithm is carried out on a totally ordered set it is easy to achieve secure matching without additional rounds of communication. We present this efficient solution to assess equivalence using a set intersection algorithm designed for “greater than” computation and demonstrate its effectiveness on equivalence of arbitrary data values, as well as demonstrate how it meets regulatory criteria for risk of disclosure.