Visualizing feature evolution of large-scale software based on problem and modification report data

2004 ◽  
Vol 16 (6) ◽  
pp. 385-403 ◽  
Author(s):  
Michael Fischer ◽  
Harald Gall
Keyword(s):  
Large Scale ◽  
Report Data ◽  
Feature Evolution

scholarly journals Consistency and Variability in Children’s Word Learning Across Languages

Open Mind ◽  
2019 ◽  
Vol 3 ◽  
pp. 52-67 ◽  
Author(s):  
Mika Braginsky ◽  
Daniel Yurovsky ◽  
Virginia A. Marchman ◽  
Michael C. Frank
Keyword(s):  
Word Learning ◽  
Large Scale ◽  
Parent Report ◽  
Corpus Analysis ◽  
Function Words ◽  
Lexical Categories ◽  
Derived Properties ◽  
Report Data ◽  
And Function

Why do children learn some words earlier than others? The order in which words are acquired can provide clues about the mechanisms of word learning. In a large-scale corpus analysis, we use parent-report data from over 32,000 children to estimate the acquisition trajectories of around 400 words in each of 10 languages, predicting them on the basis of independently derived properties of the words’ linguistic environment (from corpora) and meaning (from adult judgments). We examine the consistency and variability of these predictors across languages, by lexical category, and over development. The patterning of predictors across languages is quite similar, suggesting similar processes in operation. In contrast, the patterning of predictors across different lexical categories is distinct, in line with theories that posit different factors at play in the acquisition of content words and function words. By leveraging data at a significantly larger scale than previous work, our analyses identify candidate generalizations about the processes underlying word learning across languages.

scholarly journals A Method of Fault Fix Priority Identification for Open Source Project

2019 ◽  
Vol 8 (4) ◽  
pp. 2396-2400
Keyword(s):  
Random Forest ◽  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Tracking System ◽  
Detection Accuracy ◽  
Open Source Project ◽  
Report Data ◽  
Bug Tracking ◽  
Project Data

Open source software are adopted as embedded systems, server usage because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of open source software progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because the faults corrector cannot handle many faults reports quickly. In this paper, we aim to identify the fix priority of newly registered faults in the bug tracking system by using random forest, and we make an index to detect the faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. The index is derived and identified by using open source project data obtained from bug tracking system. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved comparing with using only specific version data and using logistic regression

scholarly journals What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews

2020 ◽  
Vol 8 (3) ◽  
pp. 153-163 ◽  
Author(s):  
Frank M. Schneider ◽  
Emese Domahidi ◽  
Felix Dietrich
Keyword(s):  
Large Scale ◽  
Topic Model ◽  
Evaluation Criteria ◽  
Online Reviews ◽  
Self Report ◽  
Confirmatory Factor Analyses ◽  
Report Data ◽  
Self Report Data ◽  
Entertainment Products ◽  
Correlated Topic Model

The question of what is important when we evaluate movies is crucial for understanding how lay audiences experience and evaluate entertainment products such as films. In line with this, subjective movie evaluation criteria (SMEC) have been conceptualized as mental representations of important attitudes toward specific film features. Based on exploratory and confirmatory factor analyses of self-report data from online surveys, previous research has found and validated eight dimensions. Given the large-scale evaluative information that is available in online users’ comments in movie databases, it seems likely that what online users write about movies may enrich our knowledge about SMEC. As a first fully exploratory attempt, drawing on an open-source dataset including movie reviews from IMDb, we estimated a correlated topic model to explore the underlying topics of those reviews. In 35,136 online movie reviews, the most prevalent topics tapped into three major categories—Hedonism, Actors’ Performance, and Narrative—and indicated what reviewers mostly wrote about. Although a qualitative analysis of the reviews revealed that users mention certain SMEC, results of the topic model covered only two SMEC: Story Innovation and Light-heartedness. Implications for SMEC and entertainment research are discussed.

Small sleepers, big data: leveraging big data to explore sleep-disordered breathing in infants and young children

SLEEP ◽  
2020 ◽  
Author(s):  
Zarmina Ehsan ◽  
Earl F Glynn ◽  
Mark A Hoffman ◽  
David G Ingram ◽  
Baha Al-Shawwa
Keyword(s):  
Big Data ◽  
Sleep Apnea ◽  
Young Children ◽  
Sleep Disordered Breathing ◽  
Large Scale ◽  
Obstructive Sleep ◽  
Significant Burden ◽  
Report Data ◽  
Infants And Young Children ◽  
Disordered Breathing

Abstract Study Objectives Infants represent an understudied minority in sleep-disordered breathing (SDB) research and yet the disease can have a significant impact on health over the formative years of neurocognitive development that follow. Herein we report data on SDB in this population using a big data approach. Methods Data were abstracted using the Cerner Health Facts database. Demographics, sleep diagnoses, comorbid medication conditions, healthcare utilization, and economic outcomes are reported. Results In a cohort of 68.7 million unique patients, over a 9-year period, there were 9,773 infants and young children with a diagnosis of SDB (obstructive sleep apnea [OSA], nonobstructive sleep apnea, and “other” sleep apnea) who met inclusion criteria, encompassing 17,574 encounters, and a total of 27,290 diagnoses across 62 U.S. health systems, 172 facilities, and 3 patient encounter types (inpatient, clinic, and outpatient). Thirty-nine percent were female. Thirty-nine percent were ≤1 year of age (6,429 infants), 50% were 1–2 years of age, and 11% were 2 years of age. The most common comorbid diagnoses were micrognathia, congenital airway abnormalities, gastroesophageal reflux, chronic tonsillitis/adenoiditis, and anomalies of the respiratory system. Payor mix was dominated by government-funded entities. Conclusions We have used a novel resource, large-scale aggregate, de-identified EHR data, to examine SDB. In this population, SDB is multifactorial, closely linked to comorbid medical conditions and may contribute to a significant burden of healthcare costs. Further research focusing on infants at highest risk for SDB can help target resources and facilitate personalized management.

Gender Differences and Similarities in Patterns of Drug Use and Attitudes of High School Students

1993 ◽  
Vol 23 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Pietro J. Pascale ◽  
William J. Evans
Keyword(s):  
High School ◽  
Gender Differences ◽  
High School Students ◽  
Drug Use ◽  
Large Scale ◽  
Baseline Data ◽  
Self Report ◽  
School Students ◽  
Report Data ◽  
Self Report Data

The purpose of this research was to study the results of a large scale drug survey of high school students in the context of the baseline data provided by four previous surveys. These surveys were conducted at three-year intervals beginning in 1977. The most recent survey contained self report data from 2,000 students representing fifteen schools in northeast Ohio. The instrument yields information on fourteen categories of drugs. Gender differences in drug use, perceived harmfulness of drugs, and age of first experimentation are reported. No gender differences were found in the reasons students give for turning to drugs. Baseline data from the four previous surveys comprised approximately 8,000 respondents.

scholarly journals Triple Aim Is Triply Tough:

2018 ◽  
Vol 50 (3) ◽  
pp. 179-187 ◽  
Author(s):  
Katrina E. Donahue ◽  
Alfred Reid ◽  
Elizabeth G. Baxley ◽  
Charles Carter ◽  
Peter J. Carek ◽  
...  
Keyword(s):  
South Carolina ◽  
Patient Experience ◽  
Teaching Practices ◽  
Large Scale ◽  
Emergency Department Visits ◽  
Triple Aim ◽  
Health Care Improvement ◽  
Report Data ◽  
Care Improvement ◽  
Future Work

Background and Objectives: The I3 POP Collaborative sought to improve health of patients attending North Carolina, South Carolina, and Virginia primary care teaching practices using the triple aim framework of better quality, appropriate utilization, and enhanced patient experience. We examined change in triple aim measures over 3 years, and identified correlates of improvement. Methods: Twenty-nine teaching practices representing 23 residency programs participated. The Institute for Health Care Improvement Breakthrough Series Collaborative model was tailored to focus on at least one triple aim goal and programs submitted data annually on all collaborative measures. Outcome measures included quality (chronic illness, prevention); utilization (hospitalization, emergency department visits, referrals) and patient experience (access, continuity). Participant interviews explored supports and barriers to improvement. Results: Six of 29 practices (21%) were unable to extract measures from their electronic health records (EHR). All of the remaining 23 practices reported improvement in at least one measure, with 11 seeing at least 10% improvement; seven (24%) improved measures in all three triple aim areas, with two experiencing at least 10% improvement. Practices with a greater number of patient visits were more likely to show improved measures (odds ratio [OR] 10.8, 95% confidence interval [CI]: .68-172.2, P=0.03). Practice interviews revealed that engaged leadership and systems supports were more common in higher performing practices. Conclusions: Simultaneous attainment of improvement in all three triple aim goals by teaching practices is difficult. I3 POP practices that were able to pull and report data improved on at least one measure. Future work needs to focus on cultivating leadership and systems supporting large scale improvement.

scholarly journals A data set linking large-scale, individual semantic networks and cognitive performance

2021 ◽  
Author(s):  
Dirk U. Wulff ◽  
Samuel Aeschbach ◽  
Simon De Deyne ◽  
Rui Mata
Keyword(s):  
Cognitive Performance ◽  
Large Scale ◽  
Semantic Networks ◽  
Data Set ◽  
Web Based ◽  
Age Related ◽  
Report Data ◽  
Concurrent Assessment ◽  
Associative Recall ◽  
Shed Light

We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include 10,800 free associations--collected using a dedicated web-based platform over the course of 2-4 weeks--and responses to several cognitive tasks, including verbal fluency, episodic memory, associative recall tasks, from four younger and four older native German speakers. The data are unique in scope and composition and shed light on individual and age-related differences in mental representations and their role in cognitive performance across the lifespan.

scholarly journals Consistency and variability in children's word learning across languages

2017 ◽  
Author(s):  
Mika Braginsky ◽  
Daniel Yurovsky ◽  
Virginia A. Marchman ◽  
Michael C. Frank
Keyword(s):  
Word Learning ◽  
Large Scale ◽  
Parent Report ◽  
Corpus Analysis ◽  
Function Words ◽  
Lexical Categories ◽  
Derived Properties ◽  
Report Data ◽  
And Function

Why do children learn some words earlier than others? The order in which words are acquired can provide clues about the mechanisms of word learning. In a large-scale corpus analysis, we use parent-report data from over 32,000 children to estimate the acquisition trajectories of around 400 words in each of 10 languages, predicting them on the basis of independently-derived properties of the words' linguistic environment (from corpora) and meaning (from adult judgments). We examine the consistency and variability of these predictors across languages, by lexical category, and over development. The patterning of predictors across languages is quite similar, suggesting similar processes in operation. In contrast, the patterning of predictors across different lexical categories is distinct, in line with theories that posit different factors at play in the acquisition of content words and function words. By leveraging data at a significantly larger scale than previous work, our analyses identify candidate generalizations about the processes underlying word learning across languages.

scholarly journals Measuring Child-Rearing Values. A Research Note

2012 ◽  
Vol 10 (1) ◽  
pp. 47-70 ◽  
Author(s):  
Bogdan Voicu
Keyword(s):  
Large Scale ◽  
Analytic Solution ◽  
Original Data ◽  
Child Rearing ◽  
Convenience Sample ◽  
Parental Values ◽  
Individual Level ◽  
Synthetic Indicators ◽  
Report Data ◽  
Q Sort

Abstract Large scale comparative studies, such as the value surveys (EVS and WVS) or the Eurobarometer, include measurements for parental/child-rearing values. This reflects a persistent interest for the topic, which produced salient studies starting with the first half of the twentieth century (Lynd and Lund 1929; Duvall 1946). Various scholars report data on parental values which use versions of the Q-sort methodology (Kohn 1977), ranking variables (Alwin 1990; Lenski 1961), scale indicators (Tulviste et al. 2007). Q-sort methodology remains the most widely employed. One of its versions is included in the value surveys as well. However, it fails to produce comparable indicators in different countries (Rabušic 2011; Xiao 2001) or at different moments in time (Wright and Wright 1976). This paper uses original data, provided by a Romanian convenience sample, to check if using various versions of the EVS/WVS items may lead to better ways to produce synthetic indicators for parental-values at individual level. SEM models are used to show that the best analytic solution would be to use individual items instead of producing summative indexes.

Sign in / Sign up

Export Citation Format

Share Document