scholarly journals Bilingualism affects infant cognition: Insights from new and open data

2020 ◽  
Author(s):  
Sadaf Pour Iliaei ◽  
Hilary Killam ◽  
Rodrigo Dal Ben ◽  
Krista Byers-Heinlein
Keyword(s):  
Cognitive Abilities ◽  
Open Data ◽  
Learning Phase ◽  
Infant Cognition ◽  
Total N ◽  
Bilingual Advantage ◽  
Learned Behavior ◽  
Initial Learning ◽  
Auditory Cue ◽  
Open Datasets

Bilingualism has been hypothesized to shape domain-general cognitive abilities across the lifespan, in what some have called the “bilingual advantage”. Here, we examined the replicability of a seminal study that showed monolingual–bilingual differences in infancy (Kovács & Mehler, 2009a) by collecting new data from 7-month-olds and 20-month-olds and reanalyzing three open datasets from 7–9 month-olds (D’Souza et al., 2020, Experiment 1; Kalashikova et al., 2020, Visual and Auditory conditions). All infants (total N = 181) were tested in an anticipatory eye movement paradigm, where they learned to use a visual and/or auditory cue to anticipate a visual reward presented consistently on one side of a screen during training, and on the opposite side of the screen at test. To correctly anticipate the reward at test, infants had to update their previously learned behavior. Across 3 out of 4 studies, results from a new analytic approach showed that 7–9 month-old bilinguals were better able to update the previously-learned response at test (a “bilingual advantage”), which could be related to bilinguals’ weaker initial learning of the contingency during the learning phase (a “monolingual advantage”). At 20 months, bilinguals performed better at test, though groups showed similar performance during the learning phase. Overall, these results show that bilingualism affects how infants process both new and updated information during learning.

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Sharing Datasets of the COVID-19 Pandemic in the Czech Republic (Preprint)

2021 ◽  
Author(s):  
Martin Komenda ◽  
Jiří Jarkovský ◽  
Daniel Klimeš ◽  
Petr Panoška ◽  
Ondřej Šanca ◽  
...  
Keyword(s):  
Czech Republic ◽  
General Public ◽  
Open Data ◽  
Online News ◽  
Public Authorities ◽  
The Czech Republic ◽  
Aggregated Data ◽  
Open Datasets ◽  
Epidemiological Characteristics ◽  
The Impact

BACKGROUND At the time of the COVID-19 pandemic, the impact of providing access to data plays a crucial role in providing the general public and media with up-to-date information. Open datasets also represent one of the means for evaluation of the pandemic on a global level. OBJECTIVE The primary aim of this paper is to describe the methodical and technical framework for publishing datasets describing basic and advanced epidemiological characteristics related to the COVID-19 epidemic in the Czech Republic, including the use of these datasets in practice. METHODS As a reaction to the epidemic situation, a new portal COVID‑19: Current Situation in the Czech Republic was developed and launched in March 2020 to provide a fully-fledged and trustworthy source of information for the public and media. The portal also contains a section for the publication of (i) public open datasets available for download in CSV and JSON formats and (ii) authorized-access-only section where the authorized persons can (through an online generated token) safely visualize or download regional datasets with aggregated data at the level of the individual municipalities and regions. The data are also provided to the local open data catalogue of the Ministry of Health and to the National Catalogue of Open Data. RESULTS The datasets have been published in various authentication regimes and widely used by general public, scientists, public authorities and decision-makers. The total number of API calls since its launch in March 2020 to 15th December 2020 exceeded 13 million. The datasets have been adopted as an official and guaranteed source for outputs of third parties, including public authorities, non-governmental organizations, scientists and online news portals. CONCLUSIONS Datasets currently published as open data meet the 3-star open data requirements, which makes them machine-readable and facilitates their further usage without restrictions. This is essential for making the data more easily understandable and usable for data consumers. In conjunction with the strategy of the MH in the field of data opening, additional datasets meeting the already implemented standards will be also released, both on COVID-19 related and unrelated topics.

scholarly journals A Fuzzy Logic-Based Approach for Modelling Uncertainty in Open Geospatial Data on Landfill Suitability Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 737
Author(s):  
Neema Nicodemus Lyimo ◽  
Zhenfeng Shao ◽  
Ally Mgelwa Ally ◽  
Nana Yaw Danquah Twumasi ◽  
Orhan Altan ◽  
...  
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Population Distribution ◽  
Open Data ◽  
Open Government ◽  
Suitability Analysis ◽  
Modelling Uncertainty ◽  
Open Government Data ◽  
Government Data ◽  
Open Datasets

Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their usability. This work addresses the imprecisions on suitability layers generated from such data. The proposed method is founded on fuzzy logic theories. The model integrates OGD, OSM data and remote sensing products and generate reliable landfill suitability results. A comparison analysis demonstrates that the proposed method generates more accurate, representative and reliable suitability results than traditional methods. Furthermore, the method has facilitated the introduction of open government data for suitability studies, whose fusion improved estimations of population distribution and land-use mapping than solely relying on free remotely sensed images. The proposed method is applicable for preparing decision maps from open datasets that have undergone similar generalization procedures as the source of their uncertainty. The study provides evidence for the applicability of OGD and other related open data initiatives (ODIs) for land-use suitability studies, especially in developing countries.

scholarly journals Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Qi Zhu ◽  
Ning Yuan ◽  
Donghai Guan
Keyword(s):  
Machine Learning ◽  
Learning Process ◽  
Human Learning ◽  
Learning Phase ◽  
Machine Learning Algorithms ◽  
Learning Difficulty ◽  
Learning Models ◽  
Negative Effects ◽  
Initial Learning

In recent years, self-paced learning (SPL) has attracted much attention due to its improvement to nonconvex optimization based machine learning algorithms. As a methodology introduced from human learning, SPL dynamically evaluates the learning difficulty of each sample and provides the weighted learning model against the negative effects from hard-learning samples. In this study, we proposed a cognitive driven SPL method, i.e., retrospective robust self-paced learning (R2SPL), which is inspired by the following two issues in human learning process: the misclassified samples are more impressive in upcoming learning, and the model of the follow-up learning process based on large number of samples can be used to reduce the risk of poor generalization in initial learning phase. We simultaneously estimated the degrees of learning-difficulty and misclassified in each step of SPL and proposed a framework to construct multilevel SPL for improving the robustness of the initial learning phase of SPL. The proposed method can be viewed as a multilayer model and the output of the previous layer can guide constructing robust initialization model of the next layer. The experimental results show that the R2SPL outperforms the conventional self-paced learning models in classification task.

scholarly journals Probabilistic Learning in Children With Attention-Deficit/Hyperactivity Disorder

2020 ◽  
pp. 108705472090509 ◽  
Author(s):  
Marjolein Luman ◽  
Tieme W. P. Janssen ◽  
Marleen Bink ◽  
Rosa van Mourik ◽  
Athanasios Maras ◽  
...  
Keyword(s):  
Instrumental Learning ◽  
Learning Task ◽  
Learning Phase ◽  
Learning Problems ◽  
Typically Developing ◽  
Probabilistic Learning ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Initial Learning ◽  
Reward Probability

Objective: The current study examined instrumental learning in ADHD. Method: A total of 58 children with ADHD and 58 typically developing (TD) children performed a probabilistic learning task using three reward probability conditions (100%, 85%, 70% reward). After a learning phase, application of what was learned was assessed in a test phase. Results: Results showed that children with ADHD performed less accurate compared with TD children during the learning phase, particularly in the 100% and 85% reward probability conditions. These findings were accompanied by a blunted learning rate in the first few task trials. Furthermore, children with ADHD showed poorer application of what was learned. Conclusion: To conclude, children with ADHD show initial learning problems, but increased performance in a similar manner as TD children independent of the probability of reward, although they fail to apply their knowledge. Findings are of clinical relevance as the application of knowledge is important to successfully adapt to daily challenges in life.

scholarly journals Childhood Socioeconomic Status and Polygenic Scores for Cognition Have Independent Associations with Cognitive Performance During Middle Childhood

2021 ◽  
Author(s):  
S.E. Paul ◽  
N.M. Elsayed ◽  
R. Bogdan ◽  
S.M.C. Colbert ◽  
A.S. Hatoum ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Executive Function ◽  
Environmental Factors ◽  
Educational Attainment ◽  
Cognitive Performance ◽  
Cognitive Abilities ◽  
European Ancestry ◽  
Equation Modeling ◽  
Median Income ◽  
Total N

ABSTRACTChildhood cognitive abilities are heritable and influenced by malleable environmental factors such as socioeconomic status (SES). As cognition and SES share genetic architecture, it is critical to understand the extent to which SES is associated with cognition beyond genetic propensity to inform the potential benefit of SES-based interventions. Previous investigations conducted in small samples have suggested that SES is linked with cognitive ability independent of polygenic prediction for educational attainment. Here, we extend this work to a large sample (total n = 4,650) of children (ages 9-10) of genomically-confirmed European ancestry. We find that an SES composite (i.e., family income-to-needs, caregiver education, and neighborhood median income) and a polygenic cognition score composite created using genomic structural equation modeling (COG PGS; Educational Attainment, Intelligence, and Executive Function) are associated with cognitive performance indices (i.e., general ability, executive function, learning/memory, fluid intelligence) that are largely independent of one another. SES x COG PGS interactions are not associated with cognition. These findings provide further evidence for the significant role of modifiable environmental factors in the development of cognitive abilities in youth.

scholarly journals Care Deficiencies and Super-Organization of American Nursing Homes in Hospital Referral Region

2021 ◽  
Vol 8 ◽  
Author(s):  
Tyler Pittman
Keyword(s):  
Nursing Home ◽  
Nursing Homes ◽  
Home Ownership ◽  
Open Data ◽  
Nursing Home Residents ◽  
The United States ◽  
Care Quality ◽  
Hospital Referral Region ◽  
Hospital Referral ◽  
Open Datasets

Super-organization has been associated with worse care quality in nursing homes. Previous research on the chain ownership of American nursing homes excluded government facilities in public-private partnerships, and focused on corporate entities. This longitudinal study proposes a novel method of demarcating the latent ownership networks of for-profit, government and non-profit nursing homes in the United States through use of open data and social network analysis. Facility characteristics and care quality measures were analyzed from an ecological cohort of 9,001 American nursing homes that had a registered organization for owner, and were reimbursed through Medicare or Medicaid. Information was obtained from the Nursing Home Compare open datasets at five semi-annual processing dates from March 2016 to March 2018. Ownership networks of American nursing homes were constructed using the exact legal name of registered organizations. As hospital discharge is a routine admission source of nursing home residents, hospital referral region was actualized to demarcate focal area. Utilizing Bayesian hierarchical models, the association between nursing home super-organization in hospital referral region (inferred by degree-based centrality and Herfindahl-Hirschman Index) to scope of cited care deficiencies (denoted by Total Weighted Health Survey Score) was explored. The percentage of nursing homes having super-organization increased from 56.8 to 56.9% over the 2-year period. During this interval, the mean size of nursing home ownership group in hospital referral region increased from 3.11 to 3.23 facilities. Overall, super-organization in hospital referral region was not associated with care deficiencies in American nursing homes. However, being part of an ownership group with more facilities was beneficial for care quality among nursing homes with super-organization.

scholarly journals Are inconsistent bilingual-monolingual cognitive differences the result of population miscategorization?

2021 ◽  
Author(s):  
Kelly Kendro
Keyword(s):  
Working Memory ◽  
Cognitive Control ◽  
Recent Work ◽  
Cognitive Abilities ◽  
Cognitive Tasks ◽  
Psycholinguistic Research ◽  
Cognitive Differences ◽  
Heritage Speakers ◽  
Bilingual Advantage ◽  
Multiple Tasks

Decades of psycholinguistic research have attempted to determine whether a “bilingualadvantage” exists for cognitive abilities (e.g., Bialystok & Craik, 2010; Cummins, 1977; for a metaanalysis,see Grundy & Timmer, 2017). More recent work has shifted away from investigating thebroader matter of cognition and working memory to focus on specific domains within those capacities(e.g., Linck et al., 2014). Despite countless studies, however, the existence of thisphenomenon remains unconfirmed. This paper examines evidence for and against the presence of a“bilingual advantage” in multiple tasks of working memory and cognitive control, reviewing studies thatcompare monolingual and bilingual performance. It further looks at populations that are understudied inrelation to these questions (i.e., L2 learners and heritage speakers). Finally, the topic of L2 attrition isdiscussed, alongside a novel perspective that may explain the inconsistencies in bilingual-monolingualcomparisons of performance on cognitive tasks.

scholarly journals Anomaly analysis on an open DNS dataset

2018 ◽  
Author(s):  
Benjamin Aziz ◽  
Nikolaos Menychtas ◽  
Ammar Al-Bazi
Keyword(s):  
Quantitative Methods ◽  
Moving Average ◽  
National Laboratory ◽  
Open Data ◽  
Los Alamos National Laboratory ◽  
Anomaly Analysis ◽  
Open Datasets ◽  
Local Averages ◽  
Quantitative And Qualitative Methods ◽  
Weighted Moving Average

The increasing availability of open data and the demand to understand better the nature of anomalies and the causes underlying them in modern systems is encouraging researchers to analyse open datasets in various ways. These include both quantitative and qualitative methods. We show here how quantitative methods, such as timeline, local averages and exponentially weighted moving average analyses, led in this work to the discovery of three anomalies in a large open DNS dataset published by the Los Alamos National Laboratory.

Using OpenStreetMap as a Source of Religious Mapping Data in an OER Human Geography Textbook

2020 ◽  
pp. 112-139
Author(s):  
David Dorrell
Keyword(s):  
Open Data ◽  
Global Scale ◽  
Mapping Data ◽  
Good Data ◽  
Data Production ◽  
Small Scales ◽  
Data Source ◽  
Open Datasets ◽  
Religious Landscape ◽  
Geography Textbook

OpenStreetMap (OSM) is a global scale geographic data source produced by individual volunteers and organizations. It is emblematic of two emerging themes in geographical technology: crowdsourcing and open data. One of the main problems with mapping religion at small scales is the lack of good data for representing the religious landscape. OSM provides data on the location of religious structures in the landscape. The dataset contains hundreds of thousands of religious structures that can be mapped, queried, and analyzed. Students often do not understand how mapping data are produced and how the data are refined and manipulated. In this study, students are asked to interact with maps made from the data as well as the data itself and provide their feedback. Open datasets can be used to make useful teaching maps, but they can also be used to help students see the inner workings of data production.

Sign in / Sign up

Export Citation Format

Share Document