Advances in Computer Sciences最新文献

{"title":"Development of a Mobile Application for Testing the Effect of Vibration Feedback on Putting Performance","authors":"Courtney Semkewyc, Shradha Suresh, G. Hung","doi":"10.31021/acs.20181116","DOIUrl":"https://doi.org/10.31021/acs.20181116","url":null,"abstract":"Copyright: © 2018 Semkewyc C. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 international License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Citation: Semkewyc C, Suresh S, Hung GK. Development of a Mobile Application for Testing the Effect of Vibration Feedback on Putting Performance. Adv Comput Sci. 2018 Nov;1(3):116 Article Information Abstract Many golfers have trouble with short putts. This could be either due to poor putting form, or they suffer psychologically from the yips. To reduce or potentially alleviate these issues, an application (app) was developed to provide vibration feedback for putter misalignment during the putting stroke. This was tested on three experimental subjects along with a control subject who did not receive vibration feedback. Five trials were conducted over a two-month period, where each trial consisted of 60 putt attempts. All experimental subjects showed an improvement in putting form, with both an increase in putts made, and a decrease in triggered vibration over the course of the trials. The average percentage of putts made for all three subjects improved from 25% to 50.6% over the five trials, while the average amount of vibration activation decreased from 35.1% to 32.2%. Conversely, the control subject showed a slight decline in performance through the five trials. The difference between the experimental group and the control was statistically significant (p<0.05). The results suggest that this app can be used by both novice and expert golfers to improve putting form by helping them maintain a straight putter swing path.","PeriodicalId":115827,"journal":{"name":"Advances in Computer Sciences","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Analogues of the Pascal Triangle and Electronic Clouds in Atom","authors":"Alexander Urkin","doi":"10.31021/acs.20181114","DOIUrl":"https://doi.org/10.31021/acs.20181114","url":null,"abstract":"","PeriodicalId":115827,"journal":{"name":"Advances in Computer Sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132239213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-Intensive Statistics: A Promising Interplay between Statistics and Computer Science","authors":"S. Sapra","doi":"10.31021/acs.20181113","DOIUrl":"https://doi.org/10.31021/acs.20181113","url":null,"abstract":"Editorial Statistics and computer science have grown as separate disciplines with little interaction for the past several decades. This however, has changed radically in recent years with the availability of massive and complex datasets in medicine, social media, and physical sciences. The statistical techniques developed for regular datasets simply cannot be scaled to meet the challenges of big data, notably the computational and statistical curses of dimensionality. The dire need to meet the challenges of big data has led to the development of statistical learning, machine learning and deep learning techniques. Rapid improvements in the speed and lower costs of statistical computation in recent years have freed statistical theory from its two serious limitations: the widespread assumption that the data follow the bell-shaped curve and exclusive focus on measures, such as mean, standard deviation, and correlation whose properties could be analyzed mathematically [1]. Computer-intensive statistical techniques have freed practical applications from the constraints of mathematical tractability and today can deal with most problems without the restrictive assumption of Gaussian distribution. These methods can be classified into frequentist and Bayesian methods. The former methods utilize the sample information only while the latter methods utilize both the sample and prior information. Frequentist statistical methods have benefitted enormously from the interaction of statistics with computer science. A very popular computer-intensive method is the bootstrap for estimating the statistical accuracy of a measure, such as correlation in a single sample. The procedure involves generating a very large number of samples with replacement from the original sample. Bootstrap as a measure of statistical accuracy has been shown to be extremely reliable in theoretical research [2,3]. Another widely used computer-intensive method for measuring the accuracy of statistical methods is cross validation. It works non-parametrically without the need for probabilistic modelling and measures the mean-squared-error for the test sample using the training sample to evaluate the performance of various machine learning methods for selecting the best method. Other frequentist statistical methods that rely on a powerful computing environment include jackknife for estimating bias and variance of an estimator, classification and regression trees for prediction, generalized linear models for parametric modelling with continuous, discrete or count response [4], generalized additive models for flexible semi-parametric regression modeling [5], the LASSO method for Cox proportional hazard regression in high dimensional settings [6], and EM algorithm [7] for finding iteratively the maximum likelihood or maximum a posteriori (MAP) estimates of parameters in complex statistical models with latent variables, alternating between performing an expectation (E) step, which evaluates the expec","PeriodicalId":115827,"journal":{"name":"Advances in Computer Sciences","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing E-Learning in Far Western Region of Nepal","authors":"Gajendra Sharma, M. Bhatta","doi":"10.18488/journal.67.2018.51.1.20","DOIUrl":"https://doi.org/10.18488/journal.67.2018.51.1.20","url":null,"abstract":"The rapid developments of internet and communication technologies have materially altered many characteristics and concepts of the learning environment. E-learning is highly beneficial to developing countries and believed to have high potential for governments struggling to meet a growing need of education while facing shortage of expert teachers, shortage of update textbooks and limited teaching materials. The objective of this study is to investigate the vital challenges of implementing e-learning systems in far western region of Nepal. The results of this study provided basic mechanism for improving higher education in developing countries. There are a number of commercial or free e-learning systems in the market. Majority of these e-learning systems provide lot of functions and modules. Some courses are entirely based on e-learning instead of traditional learning model. E-learning system also offers graphs and charts of student's results. This system is based on linear workflow. That means students can see new learning resources and tests only after previous was done. Students can also create their own learning plan by defining dates. System is able to export this plan into general calendar format or remind students via e-mail.","PeriodicalId":115827,"journal":{"name":"Advances in Computer Sciences","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132185583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithm for Medical Nanobots using C++","authors":"Manu Mitra","doi":"10.31021/ACS.20181112","DOIUrl":"https://doi.org/10.31021/ACS.20181112","url":null,"abstract":"Medical nanobots not only repair cells and tissues but also multiple nanobots can help cure various types of diseases such as cancers, infection or to remove infected cells/tissues. To automate Medical Nanobot we need program to detect it and work on it; and there may be the need for manual work to move Medical Nanobot and perform operations. A very basic software attempt is made for Medical Nanobot using C/C++ later same methodology can be used for advanced programming of Medical Nanobot. In this paper flow diagram of medical nanobot for disease detection, removal of infected cells, tissues, repairing the cells, tissues and continuous monitoring is made including various pseudo code is demonstrated such as setting up, driving nanobots for manual and automatic, auto pan/tilt, nano gyro sensors for disease detection, camera configuration, nano servo mechanism, handling interrupts and synchronization of nanobot using C++.","PeriodicalId":115827,"journal":{"name":"Advances in Computer Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121357594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}