Reviewerly/exHarmony huggingface dataset:
A New Population-based MCMC Method

Author(s): Zhang, Di | Advisor(s): Yu, Yaming | Abstract: Spectral analysis in high-energy astrophysics typically requires sampling from difficult posterior distributions, e.g., multimodal distributions, in a highly structured model with complex data collection mechanisms. Markov chain Monte Carlo (MCMC) methods have been widely explored in such tasks. However, traditional MCMC methods suffer from slow convergence incurred by the local trap problem. In this thesis, we propose a general population-based MCMC strategy, which is able to speed up convergence significantly. Specifically, we initialize multiple chains from dispersed starting values and perform a two-step jump with our proposed between-chain jump. The novel between-chain jumping proposal tries to move to the neighborhood of the iterate in another chain, which encourages full exploration of the parameter space. As for the numerical illustration, we apply the proposed method to fit thermal models to Capella data. The results indicate that our method is effective in two aspects. First, it can be applied in Bayesian model selection to decide which mixture model is more appropriate; secondly, it can be served as an exploratory step to identify modes. The strength shown from the second aspect can help determine the importance of those temperature components and decide upon the relative proportion among those modes. In addition, we show that our method is also useful in other applications including Bayes factor computation for Bayesian model selection, variance component estimation in mixed effect models, and sensor network localization.
D'Nimbe Sdn Bhd : wireless weighing scales and braided cable / Muhammad Firdaus Md Khider ... [et al.]

D'Nimbe Sdn. Bhd is a company that focus on selling a multifunction weighing scales for customers and provides a braided cable weighing scale to reduce the risk of the cable to bend or cut that can cause harm to people. Other than that, for the multifunction weighing scale, we provide battery as the main source and solar energy as a backup.Tanah Merah is an area that many people get into business such as open up a shop, fishmongers and supermarket, hence it causes demand of weighing scale as many people get into business rise over the year. While there are currently three businesses like us in Tanah Merah, however our business have our own speciality such as delivery services and multifunction weighing scale. D'Nimbe Sdn. Bhd. marketing strategy is to emphasize the quality and price of our products and services. We offer an affordable price because some people in Kelantan has a very low income and poor family. Thus, we develop marketing strategy that gives attraction to come to our shop.The management of D'Nimbe Sdn. Bhd. consists of 6 workers which is Muhammad Firdaus, Wan Nor Hafizah, 'Ayuni Nadhirah, Muhammad Aiman, Nur Aina ldayu and Nur Hanim Syafiqa. Our workers has extensive experience in finance, businesses, sales and accounting and we will be taking this responsibilities together instead of different duties and portfolio of partners. Besides, we have service and products commitments plan to promote and distribute our product through newspaper, media social and signboard as a platform. So we hopes that our business can grow and compete with other businesses in Malaysia.
LibGuides: A LibGuide for ECE's: Videos

Are you in the Early Childhood Education program at Algonquin College? Check out this guide for information to help you with your program!
Editorial : Journal of Integrated Security and Safety Science

In 2017, the Journal of Integrated Security Science was founded at the Safety and Security Science section at Delft University of Technology, the Netherlands. Now, in 2021, we are happy to announce that we are rebranding the journal by taking it into an adjusted thematic and theoretical avenue: with a new journal name, a revised scope and updated editorial board, we aim for the journal to become an established value within the field of security and safety science, that guarantees high quality standards. In this editorial, we will elaborate upon the implemented changes, and our goals for the future.
Contrapositive Local Class Inference.

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Often, the prediction at a greater-context (e.g., sentences or paragraphs) may be informative for a more localized prediction at a smaller semantic unit (e.g., words or phrases). However, directly inferring the most salient local features from the global prediction may overlook the semantics of this relationship. This work argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes an inference framework that is more accurate and robust to noise. We show how this contraposition framework can be implemented as a transfer function that rewrites a greater-context from one class to another and demonstrate how an appropriate transfer function can be trained from a noisy user-generated corpus. The experimental results validate our insight that the proposed contrapositive framework outperforms the alternative approaches on resource-constrained problem domains.
MENINGKATKAN PRESTASI BELAJAR SISWA PADA PELAJARAN TEMATIK MATERI BERHITUNG PENGURANGAN DENGAN TEKNIK MEMINJAM MENGGUNAKAN METODE PENUGASAN PADA SISWA KELAS II SD NEGERI 3 MENDAWAI TAHUN PEMBELAJARAN 2020/2021

Based on the observations of classroom teachers there are still many students who have not been able to do the problem of calculating deductions. In addition, in numeracy learning, teachers have never made a diagnosis of learning difficulties experienced by students in depth. Students are said to have learning difficulties if they get low grades in the mastery of reduced numeracy materials. The purpose of research in this PTK is to find out: 1.Student learning achievement on thematic lessons of numeracy material reduction with borrowing techniques before and after using the assignment method. 2. The use of assignment methods can improve students' learning achievement in thematic lessons of subtraction numeracy materials with the technique of borrowing second grade SD Negeri 3 Mendawai. This research method was carried out at SDN 3 Mendawai with a research subject of 21 students. The design of each cycle consists of planning, action, observation and reflection. Data collection techniques through observations, interviews, test results and documentation.
Winnowing Knowledge for Multi-choice Question Answering

We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.
Comparative Analysis of PAPS Fitness Factors for Elementary School Students before and after the COVID-19(Gangwon area)

본 연구는 코로나19 상황에서 초등학교 5학년 학생들의 체력요인 비교 연구를 위해 PAPS(체중, 유연성, 순발력, 악력)을 적용한 기준으로 체력 변화를 분석 하였다. 대상자는 초등학교 5학년 학생으로 대조군 80명(남45, 여35)과 실험군 71명(남36명, 여35명)으로 총 151명을 실험 대상자로 선정하였으며 결과 분석을 위해 독립 T-test를 실시하였다. 첫째, 남/여 초등학생의 체중은 코로나19 전보다 3년 후 평균 수치상으로 증가하는 경향을 보였으나 집단 간 유의한 차이는 나타나지 않았다(p>.05). 둘째, 남/여 초등학생의 유연성 비교는 코로나19 전보다 3년 후 평균 수치상으로 증가하는 경향을 보였으나 집단 간 유의한 차이는 나타나지 않았다(p>.05). 셋째, 남/여 초등학생의 순발력 비교는 코로나19 전보다 3년 후 평균 수치상으로 증가하는 경향을 보였으나 집단 간 유의한 차이는 나타나지 않았다(p>.05). 넷째, 남/여 초등학생의 우/좌 악력 비교는 코로나19 전보다 3년 후 평균 수치상으로 증가하는 경향을 보였으며 남학생 악력(좌)에서만 집단 간 유의한 차이가 나타났다(p<.05). 이상의 비교 결과로 볼 때 코로나19 전과 후에 강원지역 초등학생의 체력요인의 감소는 없다고 볼 수 있다. 이러한 결과는 코로나19에 따른 움직임의 감소보다는 초등학생 성장기에 나타난 영양, 환경, 유전, 운동 등의 다양한 측면에서 영향을 받았을 것으로 보여 진다.This study aimed at analyzing and comparing changes in the PAPS fitness factors (weight, flexibility, quick adaptation, grip strength) of fifth grade elementary school students under the COVID-19 pandemic. Measurement and comparison were carried out on a total of 151 fifth grade elementary school students, including 80 students in the control group (45 male students and 35 female students) and 71 students in the experimental group for comparison (36 male students and 35 female students). The independent t-test was carried out for the analysis result. First, the weight of male and female elementary school students increased numerically on average 3 years later in comparison to their weight prior to the COVID-19, but there was no significant difference between the groups (p>.05). Second, the flexibility of male and female elementary school students increased numerically on average 3 years later compared to their flexibility prior to the COVID-19, but there was no significant difference shown between the groups (p>.05). Third, the quick adaptation of male and female elementary school students increased numerically on average 3 years later in comparison to their quick adaptation prior to the COVID-19, but there was no significant difference shown between the groups (p>.05). Fourth, the right and left hand grip strengths of male and female elementary school students increased numerically on average 3 years later compared to their right and left hand grip strengths prior to the COVID-19 and there was a significant difference only shown in the left hand grip strength of male students between the groups (p<.05). According to the above comparison results, it can be seen that there is no decrease in the fitness factors of elementary school students between before and after the COVID-19 Gangwon area. It is These results are likely to have been influenced by various aspects such as nutrition, environment, genetics, and exercise that appeared during the growth period of elementary school students rather than the decrease in movement caused by COVID-19.
Generating Natural Language with Semantic and Syntactic Generalization

Traditional statistical natural language generation (NLG) systems require substantial hand-engineering: many of the components, such as content planners, sentence planners and surface realizers must all be designed and created manually and updated when a new type of utterance is required. Neural natural language generation (NNLG) models, on the other hand, learn to generate text through processing massive amounts of in end-to-end encoder-decoder frameworks, where syntactic properties are learned automatically by the model. While the learning components of NNLG models are mostly accomplished automatically, they do require, however, that the training be collected and labeled, which can often be a laborious process. The advantages of not needing handcrafted templates and syntax-to-semantics dictionaries may thereby be offset by the need to retrain neural models with new as new domains are added, effectively replacing a knowledge bottleneck with a data bottleneck.To overcome the bottleneck, we experiment with methods to leverage existing datasets to allow our NNLG models to generalize to novel meaning representations and sentence planning operations. We explore the generation of artificial and mixing from different sources as a way to augment existing training available to the NNLG. Given our different methods for augmentation, we evaluate whether NNLGs can learn syntactic and semantic generalizations.In this thesis, we developed methods to enable NNLG models to perform three types of generalization. First, we generalized multiple stylistic features to a single supervision token which represented the personality formed by the stylistic features. We then generated output with two different personality supervision tokens to create novel personalities not present in the training data. Second, we performed generalization on sentence planning operations. Sentence planning is the module of NLG models that affects the way individual propositions are combined into sentences, which usually has an effect on the final style of the realization. We generalized specific sentence planning operations, such as sentence scoping and contrastive discourse structuring, with values and attribute combinations beyond what was seen in the original training data. Finally, we combined training from different sources to produce outputs for meaning representations that blend the ontologies from both sources. In all three experiments, we investigated different representations and architectures to enable models to generalize.Our contributions include the development of methods that enable NNLG models to generalize and thereby expand beyond their original training data. This is an important extension of results to date and a significant step in making NNLG models more useful at low cost. We also generated and released multiple datasets which we used to train and test our models.
The Effect of Institutional Factors and their Weight Importance on Economic Growth of Oli-Producing Developing Countries

The main objective of this study is to examine and estimate the weight importance and effects of institutional factors on the economic growth of oil-producing developing countries considering the effect of the institution index on the economic growth of these countries. Investigation of the relative importance of these factors not only contributes to the identification of the effect of institutional factors on the economic growth of mentioned countries but also provides the field for institutional reforms by identifying the relative weight of each factor. Oil countries were identified in this research based on UNCTAD's division (2008). Considering the correlated nature of institutional factors that are influenced by the conditions of the countries, it is not possible to examine the relative importance of the effect of institutional factors on economic growth through regression methods. Accordingly, Johnson's Relative Weight Technique was used through SPSS programming. Estimation results showed that government effectiveness (GE), regulatory quality (RQ), and control of corruption (CoC) were the factors with the highest weight importance among other institutional factors regarding their effects on the economic growth of oil-producing developing countries. Furthermore, the institution index was calculated after identifying weigh the importance of institutional factors in the weighted average of these factors. Then, the effects of institutions on economic growth were estimated based on the dynamic panel data (GMM) and Dawson Model. Results proved the effect of the institution index on economic growth.
PENERAPAN SISTEM AUTENTIKASI QR CODE DAN HILL CIPHER SEBAGAI PENGAMBIL KEPUTUSAN VALIDASI SERTIFIKAT

The conventional certificate documents used today are physical, so there is a possibility that the certificate will be lost or damaged. The authentication process to determine the validation of conventional certificates is difficult because the process can only be done by the certificate maker by making direct contact to the certificate maker regarding the authenticity of the certificate holder. Therefore we need an application that can make it easier to verify ownership of the certificate by using the QR Code. The authentication system on the certificate can still be falsified and made by those who understand how to generate QR Code on the certificate, so that additional security techniques are needed using cryptography. One algorithm that can be used to secure data in the QR Code is the Hill Cipher algorithm. The results of the study can facilitate the validation of certificate ownership as a decision maker so as to minimize the potential for falsification of certificate data.
THE PRESTIGE OF HIGHER EDUCATION IN THE POST-TEEN WORLD: TREND ANALYSIS

The problem of the prestige of higher education in the situation after the coronavirus pandemic is considered. The socio-economic, socio-political, socio-cultural and socio-psychological factors affecting the social attractiveness and significance of higher education are analyzed. The data of various sociological studies devoted to the problems of higher education in modern Russia are summarized. The conclusion is made about the decline in the prestige of higher education for the modern young generation.
Ανάπτυξη ευφυών συστημάτων για την ανάλυση και επεξεργασία σημάτων μαγνητοεγκεφαλογραφίας (SQUID)

Studying the human brain has proven to be a difficult task, due to many aspects related to its nature. Its importance asan organ, its complexity in both anatomical and neurophysiological level, as well as the difficulty presenting due to itbeing encased in an osseous cavity and of course ethical issues, are some of the most common and important ones. Inaddition to the aforementioned, the brain is a unique organ that encloses a higher level of complexity compared to otherorgans as its current known primary purpose is to create the perception of life for any individual as a summary of differentcomponents deriving from processing all the information that is presented to it by its peripheral sensors and to give themsome control to interact with what they eventually perceive as life. In the past, there have been efforts to approach thebrain as an object with electrical activity and draw conclusions about its functionality by analysing EEGs. What has beenscarcely explored is data from MEGs (magnetoencephalography). SQUID (superconducting quantum interference device)is one of the few magnetometers in the world of exceptionally high capacity to sense minimal Magnetic fields of eventhe magnitude of 1 fT (10-15 Tesla), electrical currents of 10-18 Amp and voltage of 10-18 Volt, thus we have chosen toanalyse data harvested by this device due to its high level of accuracy. The purpose of this work was to exploit threedifferent tools of the field of Artificial Intelligence for knowledge extraction from Big Data, such as classificationalgorithms, followed by an approach in which we built an expert system based on CLIPS tool, as well as neural networksapproach, in order to analyse knowledge related to taste obtained by the 122 lead Magnetometer, SQUID, so as to createan optimal smart system that can predict the taste a subject experiences by analysing the changes in their brain’smagnetic signals magnitude and sign, before and after the administration of a gustatory stimulus.
Improving the process of learning numerals in the 6th grade by applying algorithms. Part 2. The norms of grammatical combinabilty of numerals with nouns

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Principles of Improving the Quality of Higher Education in a Pandemic Period

The COVID-19 pandemic has led to the largest disruption to education systems in history, affecting nearly 1.6 billion students in more than 190 countries and all continents. School and other educational closures have affected 94 percent of the world's student population, with 99 percent in low and lower-middle-income countries.
Analisis Sistem Informasi Akuntansi Penjualan Kredit Pada Dealer Motor Honda PT Pratama Metropolis Sejahtera

Accounting plays an important role for every company in monitoring the course of financial flows and sales and can be a reference in lowering the level of risk that will occur in the future. Accounting itself must also be applied into information systems to get effective results and good reports. With the accounting information system owned by PT Pratama Metropolis Sejahtera, the company gets an effective report in knowing the flow of cash and credit sales that are structured. In the journal that will be discussed by this author there are several conclusions that can later be a reference about the importance of accounting information systems in the company.
Modern R workflow and tools for microeconometric data analysis

This chapter presents the latest developments in R and how they can be applied to the microeconometric analysis of data. The focus is on the use of "modern R" for data processing and analysis. In particular, the chapter covers the "ecosystem" of data processing R packages, known as the tidyverse, that can facilitate econometric research workflows. It also presents a range of packages available for data and microeconometric analysis including generalized linear models, binary responses, count responses, multinomial responses, ordered responses, censored responses, truncated responses, fraction and proportion responses, and applications in several fields of microeconometrics, including instrumental variables, panel data, and quantile regression.
Hadoop Framework Ecosystem: Ant Solution to an Elephantic Data

With big problem comes great responsibility is a common phrase that aligns well with the fast growth rate of data with a different variety, velocity, volume, and veracity. A huge bulk of data is being produced daily from multiple origins or sources like black box, Internet of Things, Telecommunication, Social Media, Stock exchange and many more. Hadoop framework process data and provides an extensible platform that allows projects and applications which involves Big data. This paper objective is to present those tools used by the Hadoop framework to provide this extensible platform. This paper also discusses in detail about the comparison of the various tools used by the Hadoop community to provide solutions to the big data elephantic problems.
A Method for Measurement Data Modeling and High-Dimensional Outlier Detection Based on Large Dimensional Matrix

In the research of Large Scale Networked Control Systems, the real-time analysis of the big data from the measurement of thousands of light, machinery, electrical components and sensors is the inevitable requirements of Networked Control Systems. How to analyze outliers from massive and high-speed measurement data among thousands of nodes in the whole network, and how to find outliers from mass data is an important research topic of scientific big data mining. The Curse of Dimensionality makes many existing methods of outlier detection no longer valid for high-dimensional dataset. In this paper, we propose a local outlier detection factor based on weighted subspace and further propose an effective outlier detection method for high-dimensional data. The method firstly recognizes the local neighbor-space of each data point according to its KNN, and then calculate the sparse factor and subspace weighted vector, which can effectively reflect the local outlier factor and outlier-correlated subspace. After that, an effective outlier detection algorithm for high-dimensional dataset is proposed. we conduct extensive experiments to validate the correctness and evaluate the effectiveness of the proposed algorithm on the real-world dataset.
SysML Models: Studying Safety and Security Measures Impact on Performance Using Graph Tainting

Designing safe, secure and efficient embedded systems implies understanding interdependencies between safety, security and performance requirements and mechanisms. In this paper, we introduce a new technique for analyzing the performance impact of safety/security implemented as hardware and software mechanisms and described in SysML models. Our analysis approach extracts a dependency graph from a SysML model. The SysML model is then simulated to obtain a list of simulation transactions. Then, to study the latency between two events of interest, we progressively taint the dependency graph according to simulation transactions and to dependencies between all software and hardware components. The simulation transactions are finally classified according to which vertex taint they correspond, and are displayed according to their timing and related hardware device. Thus a designer can easily spot which components need to be re-modeled in order to meet the performance requirement. A Rail Carriage use case studied in the scope of the H2020 AQUAS project illustrates our approach, in particular how tainting can handle the multiple occurrences of the same event. CCS CONCEPTS • Computer systems organization → Embedded software.