Use Of Random Sampling Research Articles

RMS-FlowNet++: Efficient and Robust Multi-scale Scene Flow Estimation for Large-Scale Point Clouds

The proposed RMS-FlowNet++ is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation that can operate on high-density point clouds. For hierarchical scene flow estimation, existing methods rely on expensive Farthest-Point-Sampling (FPS) to sample the scenes, must find large correspondence sets across the consecutive frames and/or must search for correspondences at a full input resolution. While this can improve the accuracy, it reduces the overall efficiency of these methods and limits their ability to handle large numbers of points due to memory requirements. In contrast to these methods, our architecture is based on an efficient design for hierarchical prediction of multi-scale scene flow. To this end, we develop a special flow embedding block that has two advantages over the current methods: First, a smaller correspondence set is used, and second, the use of Random-Sampling (RS) is possible. In addition, our architecture does not need to search for correspondences at a full input resolution. Exhibiting high accuracy, our RMS-FlowNet++ provides a faster prediction than state-of-the-art methods, avoids high memory requirements and enables efficient scene flow on dense point clouds of more than 250K points at once. Our comprehensive experiments verify the accuracy of RMS-FlowNet++ on the established FlyingThings3D data set with different point cloud densities and validate our design choices. Furthermore, we demonstrate that our model has a competitive ability to generalize to the real-world scenes of the KITTI data set without fine-tuning.

Adjusting for false discoveries in constraint-based differential metabolic flux analysis

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem.To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region.We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback–Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses.

Association of Alu APO Gene Marker with Type 2 Diabetic patients of Dimapur District, Nagaland India

ABSTRACT: With Type 2 Diabetes, the body either produces insufficient insulin or rejects it, which leads to an accumulation of glucose in the blood. Type 2 diabetes affects 90% of the population.. Examining the relationship between Type 2 diabetes patients from the tribal tribes of Dimapur District and the Alu APO gene marker was the main goal of this study. Using the use of random sampling, blood samples were taken from 31 Type 2 Diabetes Mellitus patients at Eden Medical Centre in Dimapur and 31 control samples were collected from healthy individuals . Depending on the patient availability, blood samples were taken from both male and female patients. Within a day of collecting the blood, DNA was extracted. PCR and an allele-specific marker (APO) were used for genotyping. After genotyping, results were recorded by visualizing the PCR products in UV transilluminator. With this investigation, we discovered that the female exhibits a high statistical significance in the DD genotype (0.00*). On the other hand, neither genotype is statistically significant in males.

Association of Alu APO Gene Marker with Type 2 Diabetic patients of Dimapur District, Nagaland India

ABSTRACT: With Type 2 Diabetes, the body either produces insufficient insulin or rejects it, which leads to an accumulation of glucose in the blood. Type 2 diabetes affects 90% of the population.. Examining the relationship between Type 2 diabetes patients from the tribal tribes of Dimapur District and the Alu APO gene marker was the main goal of this study. Using the use of random sampling, blood samples were taken from 31 Type 2 Diabetes Mellitus patients at Eden Medical Centre in Dimapur and 31 control samples were collected from healthy individuals . Depending on the patient availability, blood samples were taken from both male and female patients. Within a day of collecting the blood, DNA was extracted. PCR and an allele-specific marker (APO) were used for genotyping. After genotyping, results were recorded by visualizing the PCR products in UV transilluminator. With this investigation, we discovered that the female exhibits a high statistical significance in the DD genotype (0.00*). On the other hand, neither genotype is statistically significant in males.

A machine learning approach to color space Euclidization

AbstractIn this work, a machine learning methodology is proposed for the issue of color space Euclidization. Given a color difference formula as reference distance law, the Euclidization task consists in finding an injective transformation from the original color space into a real vector space and the corresponding inverse transformation, such that the Euclidean distances in the embedded color space align with the reference color distances. For this, artificial neural networks are devised as function approximators for the color space transformations being sought. Training these neural networks is accomplished through unsupervised learning, making use of random sampling and gradient descent. As key disagreement measure, either the (symmetric) relative isometric disagreement or the standardized residual sum of squares (STRESS) index is considered at a time and incorporated as part of the optimization criterion into the objective function. Comparative evaluation is carried out on well‐established color distance laws, including the CIELAB‐based DE2000 color difference formula. The evaluation results indicate significant performance advantages of the proposed approach over previous contributions.

KEMAMPUAN MATEMATIS MAHASISWA PGSD PADA MATA KULIAH RME MATERI JENIS BILANGAN

Mathematical abilities need to be possessed on the grounds of being able to equip life in solving problems regularly. The RME course bridges students to study mathematics in everyday life. The research was conducted to determine the mathematical thinking abilities of PGSD students in the RME course on number types. The type of research carried out was descriptive qualitative with the use of random sampling to determine 3 research subjects (high, medium and low mathematical abilities). The stages that were passed were the lecturer making questions, assessment criteria, conducting tests, scoring and assessment ending with data reduction assisted by MS Excel. The data obtained was that there were 16.67% students in the high mathematical ability category, 75% in the medium category and 8.33% in low mathematical ability. MS excel calculations show that the average value of students' mathematical ability is 52.78, so it is in the medium category. Students with high mathematical abilities are very good at understanding the material on odd numbers as well as irrational numbers, both on prime numbers and need guidance on prime numbers. Understanding of prime numbers is very good, good for even numbers as well as odd numbers but needs improvement in irrational numbers for students with moderate mathematical abilities. Students with low mathematical abilities have a good understanding of prime numbers, understand enough material for odd and even numbers and have no clue about irrational numbers.

Factors That Influence the Use of Accounting Information Systems

Quality of the company's annual financial reports does not only depend on the company's personal factors but also on effectiveness of the use of systems. From economic point of view, people of Sukawati are more involved in trading.Therefore, the existence of a village lending institution is necessary to support the business capital. Village Bank is a village-owned financial institution that performs financial functions and manages the funds belonging to the village in the form of savings and loans. It sets out the goals for investigating the factors influence the effectiveness of using accounting systems. Research approach uses a sampling approach that involves multiple linear regression evaluations and the use of random samples of interest. These survey patterns are staff using accounting data gadgets in credit status organizations in the Skawati district village. The results indicate that individual skills, user involvement, internal control, and organizational culture have positif effect to use Accounting Information Systems.

Factors Influencing Freshmen’s Acceptance of Online Learning: A Study at Hanoi University of Industry

The sudden outbreak of Covid-19 has challenged Vietnamese educational systems, forcing educational institutions across the nation, especially universities and colleges, to shift to an online mode of teaching and learning. What has concerned Vietnamese educators and policymakers is the quality of these online teaching and learning activities. Blayone (2018), Cigdem and Ozturk (2016) as well as Wei and Chou (2020) shared the belief that the acceptance of students decided the success of online teaching and learning. However, only a little research has been done so far on Vietnamese students' acceptance of online learning. Therefore, this quantitative study focuses on determining factors influencing freshmen's acceptance of online learning at Hanoi University of Industry, thus identifying their acceptance level of online learning. Data was collected by a survey with the use of random sampling and the participation of language-majored students from the Hanoi University of Industry. The collected data was quantitatively processed with the employment of SPSS. The results indicate that freshmen’s acceptance of online learning is affected by four main factors, including performance expectancy, effort expectancy, attitude, and technological competencies. With respect to the research findings, several implications are suggested for effective online learning and teaching in Vietnam.

Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies

The purpose of this study is to compare the predictive power of different machine learning models to reproduce the credit ratings of Moody's assigned to machine-building companies. The study closes several gaps found in the literature related to the choice of explanatory variables and the formation of a sample of data for modeling. The task to be solved is highly relevant. There is a growing need for high-precision and low-cost models for reproducing the credit ratings of machine-building companies (internal credit ratings). This is due to the ongoing growth of credit risks of companies in the industry, as well as the limited number of assigned public ratings to these companies from international rating agencies due to the high cost of rating process. The study compares the predictive power of three machine learning models: ordered logistic regression, random forest, and gradient boosting. The sample of companies includes 109 enterprises of the machine-building industry from 18 countries for the period from 2005 to 2016. The financial indicators of companies that correspond to the industry methodology of Moody's and the macroeconomic indicators of the home countries of the companies are used as explanatory variables. The results show that among models studied the artificial intelligence models have the greatest predictive ability. The random forest model showed a prediction accuracy of 50%, the gradient boosting model showed accuracy of 47%. Their predictive power is almost twice as high as the accuracy of ordered logistic regression (25%). In addition, the article tested two different ways of forming a sample: randomly and taking into account the time factor. The result showed that the use of random sampling increases the predictive power of the models. The inclusion of macroeconomic variables into the models does not improve their predictive power. The explanation is that rating agencies follow a "through the cycle" rating approach to ensure the stability of ratings. The results of the study may be useful for researchers who are engaged in assessing the accuracy of empirical methods for modeling credit ratings, as well as practitioners in banking industry who directly use such models to assess the creditworthiness of machine-building companies.

Workers Perception of Welfare Facilities:A Comparative Study of Sugar Mills of Punjab

Purpose: The study aims to examine and compare the workers’ perception towards various labour welfare facilities provided in the co-operative and private sugar mills of Punjab state of India. The study also examines the association of perception scores for welfare facilities with the personal characteristics of workers. Methodology: Data was collected from 490 workers (280 from co-operative sugar mills and 210 from private sugar mills) with the help of a structured questionnaire. The sample was drawn using referral sampling method. Findings: Results showed that out of 29 welfare facilities examined in this study, facilities like canteen, medical, sitting, drinking water, housing and cycle sheds are found satisfactory in both the types of sugar mills. The study also found that workers’ perception scores on welfare facilities were associated with most of their personal characteristics. Implications: The study emphasizes on provision of need based valuable welfare facilities which can enhance the efficiency of workers that will ultimately lead to increased individual and organizational productivity. Limitations: The main limitation is the size of sample which restricts the generalization of results in Punjab state and secondly, the use of random sampling might have improved the results of the study.

Impact of Social Anxiety and Humor Styles on Mental Health

The present study is a “cross-sectional survey research”; aimed toward monitoring the impact of Social Anxiety and Humor Styles on Mental Health. For the study, 500 subjects (205 males, 295 females) were approached. The sample was obtained through the use of random sampling. “The age range” of the applicants was as of 18 to 31 years. The Liebowitz Social Anxiety Scale by Michael Liebowitz was used to measure the Social Anxiety among the participants, the “four Humor Styles (Affiliative, Self-enhancing, Aggressive and Self-defeating)” be measured by means of Humor Style Questionnaire by Dr. Rod A. Martin, and the Mental Health Inventory (MHI-5) by Veit and Ware was used to measure the Mental Health of the research participants. Results of the study revealed that Social Anxiety and Humor Styles have a significant impact on Mental Health. The study further monitored how men and women differ in terms of Social Anxiety, Humor Styles and Mental Health. The findings suggested that no gender differences exist in Social Anxiety and Mental Health. As far as the four Humor Styles are concerned, no gender difference was found in “Aggressive and Self-enhancing Humor Styles”; while Affiliative and “Self-defeating Humor Styles” varied in terms of gender. The findings of the study suggested that make use of the “Adaptive Humor Styles Affiliative and Self-enhancing” lessens the Social Anxiety and causes amelioration in the Mental Health; while the use of “Mal-adaptive Humor Styles (Aggressive and Self-defeating)” exacerbates the Social Anxiety and causes deterioration in the Mental Health.

Random sampling for effective spectrum sensing in cognitive radio time slotted environment

In the context of wireless communication, Cognitive Radio has been proposed to ensure an optimal use of the spectrum. It divides the communication transceivers into two categories. Primary User has the priority to use the spectrum band while secondary user is an opportunistic user that can transmit on that band whenever it is available. In order to determine whether a concerned band is occupied or vacant, spectrum sensing functionality is needed together with an appropriate algorithm able to analyze the produced data in order to understand whether a primary user is transmitting or not. In this paper, we contribute by investigating a narrowband spectrum sensing based on two proposed parameters obtained randomly sampling the signal and a Multilayer Perceptron neural network (MLP) classifier. We show that the use of random sampling is more effective than the use of classical uniform sampling. We analyzed the activity status in the GSM channel by using a synthetically generated signal: the signal has been sampled using both uniform and random sampling, than it has been reconstructed and finally classified with the aim of a neural network based classifier. The performances in terms of Receiver Operating Characteristic curves for different Signal to Noise Ratio are presented.

Early Identification and Prevention of Postnatal Complications among the Postnatal Mothers by Using the ‘Postnatal Care Bundle’

Background: Postnatal period initiates immediately after the birth of a baby. The terms puerperium period is commonly used to refer to the first 6 weeks after delivery of baby. A postnatal care bundle used during this period and it will help to significantly improve patient outcomes.
 Objective: To develop, test and pilot of Postnatal Care Bundle (PNCB) and to assess conventional postnatal care among the postnatal mothers and to evaluate the effectivity of Postnatal Care Bundle (PNCB) for early identification and prevention of postpartum complications among the postnatal mothers and to assess the satisfaction level of staff nurses after implementation of Postnatal Care Bundle and conventional postnatal care for early identification and prevention of postnatal complications among the postnatal mothers.
 Methodology: The interventional analytical and true experimental research design approach will be used and the sample use for the postnatal mothers including the full term normal delivery, caesarean section will be and the random sampling use and sample size is 185 in each group i.e. control and experimental group. One group will be received conventional postnatal care and other group will be on postnatal care bundle. The outcome includes that postnatal care bundle help to early identification and prevention of postnatal complications among the postnatal mothers.
 Conclusion: PNCB will be effective in quality care of postnatal period.

Diverse Sexual Behaviors in Undergraduate Students: Findings From a Campus Probability Survey

Probability-based surveys of college students typically assess sexual behaviors such as oral, vaginal, and anal sex. Little is known about the broader range of sexual behaviors in which students engage. In a random sample survey of undergraduate students, we aimed to: (1) describe how recently participants had engaged in solo and partnered sexual behaviors, (2) examine how frequently participants enacted certain rough sex sexual behaviors (e.g., light spanking, hard spanking, choking, slapping, and others), (3) assess participants' frequency of experiencing certain rough sex behaviors, (4) describe participants' frequency of threesome/group sex, (5) assess the characteristics of participants' experiences with choking during sex; and (6) examine choking and face slapping in regard to consent. A confidential, online cross-sectional survey of 4,989 randomly sampled undergraduate students at a large U.S. university. Participants reported having engaged in a broad range of solo and partnered sexual activities, including rough sex behaviors. The most prevalent general sexual behaviors were solo masturbation (88.6%), oral sex (79.4% received, 78.4% performed), penile-vaginal intercourse (73.5%), and partnered masturbation (71.1%). Anal intercourse was the least prevalent of these behaviors (16.8% received, 25.3% performed). Among those with any partnered sexual experience, 43.0% had choked a partner, 47.3% had been choked, 59.1% had been lightly spanked and 12.1% had been slapped on the face during sex. College health clinicians and educators need to be aware of the diverse and evolving range of solo and partnered sexual behaviors reported by students. In addition to counseling students about pregnancy and sexually transmitted infection risk, clinicians might assess patients' engagement in diverse sexual behaviors, such as choking/strangulation during sex, given the risk for serious outcomes including death. Strengths of our research include the large sample size, use of random sampling, high response rate for college populations, broad range of behaviors assessed, and novel data on choking during sex. Among our limitations, we did not assess to what extent the experiences were wanted, pleasurable, or appealing to participants. Except for in relation to choking and slapping, we also did not assess issues of consent. Participants reported engaging in diverse sexual behaviors, some of which have important clinical implications, are understudied, and warrant further research.

Effects of Empowering Leadership on Employee Voice Behavior: The Mediating Role of Psychological Empowerment

Over the past two decades many organizations are moving towards decentralization approach and hence putting in much effort to ensure that employees acquire the necessary leadership skills. Many organizations have adopted managerial attempts that aim to strengthen and empower leaders to boost organizational performance. Several educational researchers have exploited the relationship that exist between empowering leadership, psychological empowerment. The purpose of this research was to analyze the effects that empowering leadership can have on employee voice behavior, with the mediating role of psychological empowerment. The technique used to gather responses from 151 respondents was mainly quantitative research approach with questionnaires as a tool. The study employed random sampling to involve respondents in the study. Multiple operations such as correlation matrix, regression analysis and bootstrap approach were conducted to analyze data collected from the questionnaire. The findings showed a positive correlation between the variables measured. All hypotheses tested revealed a significant positive relationship between empowering leadership and voice behavior, psychological empowerment and voice behavior, and the mediating role of psychological empowerment. This study is useful in the academic, industrial and organizational sector. Organizations can use it to employ empowering leadership and measure the change in the behavior of employees, attitude, motivation, and empowerment. In terms of academia, this research opens more doors to further investigate the relationship between these variables. The limitations of the study include purely quantitative means of analysis and the use of random sampling. Future researchers can get more insights by employing a qualitative technique and adding more variables to further define the relationship and get better insights.

Estimating the size of a hidden finite set: Large-sample behavior of estimators

A finite set is "hidden" if its elements are not directly enumerable or if its size cannot be ascertained via a deterministic query. In public health, epidemiology, demography, ecology and intelligence analysis, researchers have developed a wide variety of indirect statistical approaches, under different models for sampling and observation, for estimating the size of a hidden set. Some methods make use of random sampling with known or estimable sampling probabilities, and others make structural assumptions about relationships (e.g. ordering or network information) between the elements that comprise the hidden set. In this review, we describe models and methods for learning about the size of a hidden finite set, with special attention to asymptotic properties of estimators. We study the properties of these methods under two asymptotic regimes, "infill" in which the number of fixed-size samples increases, but the population size remains constant, and "outfill" in which the sample size and population size grow together. Statistical properties under these two regimes can be dramatically different.

Probabilistic verification of fairness properties via concentration

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.

E-CRM on the Relationships Quality and Its Consequences: Evidence from Iranian Private Banks

The importance and urgency of the subject Low rates of e-services and research in the department of Electronical Customer Relationship Management (E-CRM) in banking system of the country, particularly is from the perspective of clients, the research investigated E-CRM on the relationships quality and it’s consequences: evidence from Iranian the private banks, Therefore a questionnaire adapted from George et all and Abdallah scale be determined in the statistical community between private’s bank electronic customers of 388 people, after collecting information and data, the questionnaire designed by Likert scale and obtained by use of random sampling, and evaluated and analyzed by structural equations model method and LIZREL software. Data results show that E-CRM had a positive and meaningful effect on the relationships quality and the consequences of customers that include 7 component such as: communication channels, the overall quality of communication, confidence, satisfaction, loyalty, maintenance and the tendency to compromise.The results of research was confirmed the impact and importance of E-CRM on the quality dimensions and relationships consequences with customers like The satisfaction, loyalty and maintenance. Keywords: Customer Relationship Management , Electronical Customer, Relationships Quality. DOI : 10.7176/EJBM/11-25-01 Publication date :September 30 th 2019

E-CRM on the Relationships Quality and Its Consequences: Evidence from Iranian Private Banks

The importance and urgency of the subject Low rates of e-services and research in the department of Electronical Customer Relationship Management (E-CRM) in banking system of the country, particularly is from the perspective of clients, the research investigated E-CRM on the relationships quality and it’s consequences: evidence from Iranian the private banks, Therefore a questionnaire adapted from George et all and Abdallah scale be determined in the statistical community between private’s bank electronic customers of 388 people, after collecting information and data, the questionnaire designed by Likert scale and obtained by use of random sampling, and evaluated and analyzed by structural equations model method and LIZREL software. Data results show that E-CRM had a positive and meaningful effect on the relationships quality and the consequences of customers that include 7 component such as: communication channels, the overall quality of communication, confidence, satisfaction, loyalty, maintenance and the tendency to compromise.The results of research was confirmed the impact and importance of E-CRM on the quality dimensions and relationships consequences with customers like The satisfaction, loyalty and maintenance. Keywords: Customer Relationship Management , Electronical Customer, Relationships Quality. DOI : 10.7176/JIEA/9-5-01 Publication date : August 31 st 2019

THE OBSTACLES FACTORS OF GEOGRAPHY INFORMATION SYSTEMS AND REMOTE SENSING PRACTICUM IN A HIGH SCHOOL LEVEL IN WONOGIRI DISTRICT

Practicum Geographical information systems and remote sensing have become one of the basic competencies in the realm of skills in the geography learning process at the high school level The purpose of this study is to identify problems or obstacles faced by geography teachers in carrying out geographic information systems and remote sensing practicum activities in high schools in Wonogiri Regency this is a qualitative descriptive study with the use of random sampling. Data collection uses a questionnaire about the implementation of geographic information system practice and remote sensing to the teacher. This study uses descriptive statistical analysis techniques to analyze the factors that become obstacles in the application of geographic information system practice and remote sensing in high school. The result is the obstacles factor of geographic information system and remote sensing practicum in in high schools in Wonogiri is (1). Hardware devices that have not been installed software specifically for geography information systems and remote sensing The teacher has a low ability in the geography and remote sensing information system practicum skills and does not coordinate with the teacher or laboratory assistant for Information and Communication Technology (ICT) subjects (3) Insufficient time allocation for practicum and absence of geographic information system and remote sensing practice tests as evaluations