Reviewerly/exHarmony huggingface dataset:
Visualization Practices in Training Pedagogy Students

The world practice of distance learning has updated the educational technologies that are adequate to the challenges of today and can effectively solve the problems of training competitive specialists in the new information society. Among them, visualization is singled out, which improves the quality of perception, understanding and assimilation of educational material and serves as a powerful motivator of the students’ cognitive activity. The study is aimed at characterizing the potential of visualization as a technology for teaching Pedagogy students, which allows using the tools of the digital environment effectively to achieve educational goals. The main research method is the survey of 96 second-year bachelor-degree students of the subject area “Pedagogical Education” of the Faculty of Philology of the North-Eastern Federal University. A quantitative and qualitative analysis of the results of the research on visualization as educational technology has revealed the interest of future teachers in using visual teaching methods and understanding the role of visualization in enhancing the cognitive activity of students. The respondents have demonstrated, on the one hand, knowledge of the basic means of information visualization (88%). Yet, on the other hand, not all of the respondents (55%) can clearly and consciously differentiate the concepts of “online platform”, “social network” and “visual means of transmitting information” (“visual communication”).
Cross-Border Behavior, Team Trust and Corporate Innovation Performance

Team innovation is related to the long-term survival of the organization.In today's open innovation environment, the cross-border activities of the team and its members are increasingly frequent.The topic of cross-border behavior and team innovation performance has attracted the attention of scholars.The purpose of this paper is to explore the relationship between cross-border behavior, team trust, and corporate innovation performance.In this paper, a sample of 78 teams and their superiors are used as samples, and the hierarchical regression method is used for data analysis.This paper introduces team trust into the relationship between cross-border behavior and team innovation performance, constructs and demonstrates the relationship model between "cross-border behavior, team trust and team innovation performance", and finds the boundary conditions of this relationship model.The conclusion that cross-border behavior (coordination and detection) has a positive impact on team innovation performance is achieved through the intermediary role of team trust,the standardized regression coefficients were 0.253 (P < 0.05), 0.345 (P < 0.01) and 0.355 (P < 0.01), respectively,Cross-border behavior enhances team trust and improves team innovation performance.Finding the risk factors in the process of team innovation from a new perspective has certain implications for risk aversion in enterprise innovation management.
The Bipolar Fuzzy Set And Knn Intergumentary System Analytical Study

As of a variety of factors such as climate change, pollution, sanitary requirements, age, level of fitness care, and many others, pores and derm illnesses appear in unique patterns. These illnesses generate a variety of problems for people's health. In this research, we apply the AGEMSD approach to determine which age groups are affected by derm illness and forecast the accuracy using KNN. The accuracy of the result is greater than 93%.
LibGuides: System Wide Holds - Training Guide: View Requests and Holds

The purpose of this guide is to provide staff a reference resource for processing Requests and Holds on NLPL materials.
Meta-Learning an Inference Algorithm for Probabilistic Programs

We present a meta-algorithm for learning a posterior-inference algorithm for restricted probabilistic programs. Our meta-algorithm takes a training set of probabilistic programs that describe models with observations, and attempts to learn an efficient method for inferring the posterior of a similar program. A key feature of our approach is the use of what we call a white-box inference algorithm that extracts information directly from model descriptions themselves, given as programs. Concretely, our white-box inference algorithm is equipped with multiple neural networks, one for each type of atomic command, and computes an approximate posterior of a given probabilistic program by analysing individual atomic commands in the program using these networks. The parameters of the networks are learnt from a training set by our meta-algorithm. We empirically demonstrate that the learnt inference algorithm generalises well to programs that are new in terms of both parameters and model structures, and report cases where our approach achieves greater test-time efficiency than alternative approaches such as HMC. The overall results show the promise as well as remaining challenges of our approach.
COVID-19 TWEETS SENTIMENT ANALYSIS USING MACHINE LEARNING MODELS

Social media platforms are tremendously being used for sharing news, ideas, opinions, and comments about various happenings in the world. Twitter is one such platform where health-related information and news are shared by both the official sources and the citizens. Significant changes in the lifestyle of adults as well as children due to the COVID-19 imposed lockdown have also been a topic of discussion in the public forum. A huge amount of misinformation is also being spread. Understanding people's sentiments and outlook towards the coronavirus will help health agencies take actions like encouraging and providing positive and factual information. This study focuses on identifying and performing sentiment analysis which will help to explore a enormous amounts of tweets related to COVID-19. It classifies and distinguishes clearly between the different emotions of tweets like positive tweets, negative tweets, and neutral tweets. It also supports predicting the sentiment for unknown tweets. The exploratory data analysis performed on the tweets helps to identify the count of tweets according to location, time, and sentiment. In a lockdown, social media being the only source of understanding the public sentiments about the coronavirus, it is important to take into account the publics opinions and reactions and information being circulated in all major forms.
The Effectiveness of Using E-learning at SMA Negeri 4 Gowa

This research was conducted for students of SMA Negeri 4 Gowa. This study aims to determine 1) the existence of content or learning materials that are relevant to the learning objectives, 2) use appropriate learning methods, 3) use learning media in various formats such as text, visual, video, multimedia, and others, 4) can occur synchronously or asynchronously. The research method used is quantitative research with a population of 585 people and a sample of 88 respondents. Data collection techniques used are observation, questionnaires, interviews, and documentation. The data analysis technique used is a descriptive percentage and distribution analysis. The results showed that the effectiveness of the use of e-learning at SMA Negeri 4 Gowa was classified as effective in terms of indicators of the existence of content or learning materials that were relevant to the learning objectives, using appropriate learning methods, using learning media in various formats such as text, visual, video, multimedia, etc., and can occur synchronously or asynchronously. To achieve effective use of e-learning, efforts are made to increase student interest in learning and also the availability of learning services to support the online learning process in the COVID-19 pandemic era.
Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit ISPA

K-Medoids is an unsupervised algorithm that uses a distance measure to classify data. The distance measure is a method that can help an algorithm classify data based on the similarity of the variables. Several studies have shown that using the right distance measure can improve the performance of the algorithm in clustering. Euclidean and Chebyshev is two of some distance measures that can be used. In 2016, Karawang Health Office stated that 175.891 Karawang citizens were suffering from ISPA. This figure continued to increase in the following year until 2019. The total of Karawang citizens who suffering from ISPA reached 181.945 people. To assist the government in overcoming this problem, a clustering process will be carried out to group the areas where the ISPA is spreading in Karawang District. The area will be divided into three clusters, namely low, medium and high. Comparison of distance measures is carried out to find the best model based on the evaluation of the Davies Bouldin Index (DBI). The use of Euclidean-distance produces a DBI score of 0,088 meanwhile the use of Chebyshev distance resulted in a DBI score of 0,116. The performance of the K-Medoids algorithm with Euclidean-distance is considered to be better than Chebyshev distance because it produces a DBI score that is near to 0.
A Motion Planning Algorithm in a Lollipop Graph

This paper is concerned with problems relevant to motion planning in robotics. Configuration spaces are of practical relevance in designing safe control schemes for robots moving on a track. The topological complexity of configuration space is an integer which can be thought of as the minimum number of continuous instructions required to describe how to move robots between any initial configuration to any final one without collisions. We calculate this number for various examples of robots moving in different tracks represented by graphs. We present and implement an explicit algorithm for two robots to move autonomously and without collisions on a lollipop track.
ASSESSMENT OF BUSINESS EDUCATORS’ EFFECTIVENESS IN USING DEMONSTRATION AND FIELD TRIP TEACHING STRATEGIES IN SECONDARY SCHOOLS IN IKEDURU L.G.A OF IMO STATE

The study focused on Assessment of Business Educators’ Effectiveness in Utilizing Demonstration and Field-trip Teaching Strategies in Secondary Schools in Ikeduru L.G.A of Imo State. Two research questions were posed in line with the two (2) specific objectives to guide the study with one null hypothesis tested at 0.05 level of significance. The population consisted of thirty four (34) business educators in secondary schools in Ikeduru L.G.A. Research Survey design was used and instrument for data collection was the questionnaire containing 22 items related to how effective business educators in secondary schools use demonstration and field-trip teaching strategies in the discharge of their duties. The instrument was validated by experts and the reliability co-efficient established was 0.84 index, using Crombach Alpha Analysis. The data collected were analyzed using Mean and T-test statistics rating. The result revealed that the business educators in secondary schools in Ikeduru Local Government Area were effective in the use of demonstration teaching strategy while they were ineffective in using field trip teaching strategy. Based on the findings, and conclusion made, it was recommended among others that school authorities should emphasize more on excursion as internal academic programme that should be embarked on termly, the government should make available fund and other facilities for practical to the schools as well for the students’ accessibility. Keywords: Business Educators, Demonstration, Field-trip, Strategies
LibGuides: Toolbox for Authors: Writing and Sharing Your Research: Grammar, Vocabulary & Translations

Looking for tips and resources for writing? Explore these suggestions from the WBG Library staff. (Where noted, licensed resources and Intranet pages are available for WBG or IMF staff only. Some suggestions may require additional licensing)
Auto-photo creator-a system for image generation and text imposing using voice information

This work is being carried out for development of an Auto-photo-creator- a system that would extend hands for physically challenged people. Initially, we have used “Attentional generative adversarial networks (Attn: GAN)” with the update of dense net architecture for text-to-image conversion, along with a Deep Attentional Multimodal Similarity Model (DAMSM) to calculate the matching loss and thereby produce high quality images. The updated dense net architecture was able to reduce the loss by 1.62% and could retrieve faster results by 768 seconds per iteration than the existed inception architecture of CNN. We have further extended our work by making a system that takes voice or text as input where necessary conversions were applied on the input to covert the voice to text and finally the images will be generated based on the text. The underlying process is carried with the support of Artificial Intelligence (AI), Machine Learning (ML) and Natural Language processing (NLP). The proposed work will prove to be helpful for people with hearing impairment and visual loss.
Big Data Analytics: A Tutorial of Some Clustering Techniques

Data Clustering or unsupervised classification is one of the main research areas in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. The most widely used in hard partitioning algorithm is the K-means and its variations and extensions such as the K-Medoid. Other algorithms use overlapping techniques where an object may belong to one or more clusters. Partitioning algorithms that overlap include the commonly used Fuzzy K-means and its variations. Other more recent algorithms reviewed in this paper are the Overlapping K-Means (OKM), Weighted OKM (WOKM) the Overlapping Partitioning Cluster (OPC) and the Multi-Cluster Overlapping K-means Extension (MCOKE). This tutorial focuses on the above-mentioned partitioning algorithms. We hope this paper can be beneficial to students, educational institutions, and any other curious mind trying to learn and understand the k-means clustering algorithm.
Pencegahan penyalahgunaan narkotika di pemerintah Kota Prabumulih (evaluasi penerapan Instruksi Presiden Nomor 2 Tahun 2020)

This study aims to evaluate and identify the factors that influence Presidential Instruction Number 2 of 2020. The research used a qualitative approach with formative evaluation techniques, taking place in Prabumulih from November 2020 to January 2021. The results showed that the implementation of Inpres 2/2020 was not optimal because the Prabumulih government had only taken 10% of its actions as a P4GN Facilitator in Prabumulih. There are four inhibiting factors : the necessary size of the Presidential Instruction 2/2020, human resources at the Prabumulih City BNN, minimal budget for the P4GN Program, Implementers who have not carried out socialization actions, changes in budget policy priorities, and increased criminal activity due to economic factors. It is necessary to optimize the collaboration between the City BNN and Prabumulih City Government and strengthen social capital by involving the community.
Influencing Towards Stable Multi-Agent Interactions

Learning in multi-agent environments is difficult due to the non-stationarityintroduced by an opponent's or partner's changing behaviors. Instead ofreactively adapting to the other agent's (opponent or partner) behavior, wepropose an algorithm to proactively influence the other agent's strategy tostabilize -- which can restrain the non-stationarity caused by the other agent.We learn a low-dimensional latent representation of the other agent's strategyand the dynamics of how the latent strategy evolves with respect to our robot'sbehavior. With this learned dynamics model, we can define an unsupervisedstability reward to train our robot to deliberately influence the other agentto stabilize towards a single strategy. We demonstrate the effectiveness ofstabilizing in improving efficiency of maximizing the task reward in a varietyof simulated environments, including autonomous driving, emergentcommunication, and robotic manipulation. We show qualitative results on ourwebsite: https://sites.google.com/view/stable-marl/.
Robust Speckle Interferometers using Subspace Analysis

A computational approach to circumventing the interferometric stability requirements of speckle interferometers is presented. Analysis and representative results detailing its use in NLoS imaging and 3D imaging are included.
A new generic method to improve machine learning applications in official statistics

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.
LibGuides: VR technology for History of Art students: Five elements of SERP

A libguide dedicated to History of Art students to offer the option of immersive learning through VR technology. The five elements of a search engine results page
DIGITAL EMPLOYABILITY SKILLS REQUIRED BY FUTURE SECRETARIES FOR OPTIMUM PRODUCTIVITY IN BUSINESS ORGANIZATIONS

The advances in Information and Communication Technology combined with lifestyle trends, points to a future workforce that is more productive and more capable than ever before. Employers of labour are presently on the lookout for future secretaries who possess digital employability skills for optimum productivity in business organizations. This paper therefore, is a literature review aimed at identifying digital employability skills required from future secretaries for optimum productivity in business organizations. Digital employability skills required by future secretaries for optimum productivity include: electronic record management, application of photo based social media, the use of video based social media among others. The researchers recommend that: the curriculum used for the training of secretaries should be reviewed to include skills in electronic records management procedures, training institutions should be adequately funded to enable them install modern ICT equipment for effective training and proper acquisition of social media application skills, short training programmes should be organized for secretaries at intervals to enable them keep abreast of new technologies in the office among others. Keywords: Employability, Secretaries, Skills, Productivity
RESPON PESERTA DIDIK TERHADAP PENERAPAN MODEL PEMBELAJARAN LANGSUNG PADA MATERIUSAHA DAN ENERGI

The purpose of this study is to describe the results of the application, the completeness of the indicators of student learning outcomes, the completeness of student learning outcomes and the responses of students to learning activities by applying the learning model of subject matter and energy to class VIII E students in the odd semester of SMP Negeri 6 Kupang academic year 2020 / 2021. This type of research is descriptive research. The subjects in the study were students of class VIII E SMP Negeri 6 Kupang according to 20 people and teachers (researchers). The data techniques used were observation, tests and questionnaires, while the instruments used in this study were direct learning management sheets, learning outcomes tests and student response form sheets. The research result analysis technique used is quantitative statistical analysis which includes the calculation of the average score, proportion and proportion. Based on the results of quantitative descriptive statistical analysis, the research data in general shows that the application of the direct learning model of subject matter and energy in class VIII E students in the odd semester of SMP Negeri 6 Kupang in the 2020 / 2021academic year, which allegedly 20 people is optimal.
Challenges in Contact Tracing and Sampling in Three Districts of Western Rajasthan during the COVID-19 Pandemic

Introduction : Contact tracing commenced in districts of Western Rajasthan in March 2020 to control the spread of Covid -19.It was an ongoing process involving isolation and sampling of contacts.Multi department Quick and Rapid response teams (Q/RRT) worked continuously as the pandemic passed through different stages.The objectives of the study was to compare the challenges faced during contact tracing and document good practices in place. Methods:A cross -sectional study was done among contact tracers using a semi structured questionnaire in Google forms in three districts of Pali, Jodhpur and Barmer.Keeping power of study at 80%, 5% error and 95% confidence interval, assuming 50% prevalence of effective contact tracing and 10 % nonresponse total 400 , i.e 133 participants from each district.Results: Major challenges faced by tracers were resistance to contact sampling (64%)lack of training (49%), and random sampling workload (47%).Overall 36% of contacts had downloaded the Aarogya setu app but among them only 68% were using the app.Trainings , workload distribution and intersectoral coordination was best in Jodhpur, Barmer and Pali respectively . Conclusions:Trainings helped in better field management of resistance to sampling among contacts.Excessive workload of lab technicians lead to errors in reporting.Transport for sampling of contacts was a challenge in remote areas.The Aarogya setu app was not used by all contacts.Regular feedback meetings by local administration helped in effective contact tracing
Lattice Reformulation Cuts

Here we consider the question whether the lattice reformulation of a linear integer program can be used to produce effective cutting planes.In particular, we aim at deriving split cuts that cut off more of the integrality gap than Gomory mixed-integer (GMI) inequalities generated from LP-tableaus, while being less computationally demanding than generating the split closure.We consider integer programs (IPs) in the form max\{ \bfitc \bfitx | \bfitA \bfitx = \bfitb , \bfitx \in \BbbZ n + \} , where the reformulation takes the form max\{ \bfitc \bfitx 0 + \bfitc \bfitQ \bfitmu | \bfitQ \bfitmu \geq - \bfitx 0 , \bfitmu \in \BbbZ n - m \} , where \bfitQ is an n \times (n -m) integer matrix.Working on an optimal LP-tableau in the \bfitmu -space allows us to generate n -m GMIs in addition to the m GMIs associated with the optimal tableau in the \bfitx space.These provide new cuts that can be seen as GMIs associated to n -m nonelementary split directions associated with the reformulation matrix \bfitQ .On the other hand it turns out that the corner polyhedra associated to an LP basis and the GMI or split closures are the same whether working in the \bfitx or \bfitmu spaces.Our theoretical derivations are accompanied by an illustrative computational study.The computations show that the effectiveness of the cuts generated by this approach depends on the quality of the reformulation obtained by the reduced basis algorithm used to generate \bfitQ and that it is worthwhile to generate several rounds of such cuts.However, the effectiveness of the cuts deteriorates as the number of constraints is increased.