Science for Everyone

2010 ◽  
pp. 344-354
Author(s):  
Charles A. Wood
Keyword(s):  
Data Streams ◽  
Emerging Technologies ◽  
Data Use ◽  
Real Data ◽  
Scientific Instruments ◽  
Culture Of Learning ◽  
Directed Learning

Recent and emerging technologies offer many opportunities for exploration and learning. These technologies allow learners (of any age) to work with real data, use authentic scientific instruments, explore immersive simulations and act as scientists. The capabilities soon to be available raise questions about the role of schools and do rely on directed learning traditionally supplied by teachers. The prevalence of new tools and data streams can transform society, not just kids, into a culture of learning.

scholarly journals The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

2021 ◽  
Author(s):  
Dariusz Brzezinski ◽  
Leandro L. Minku ◽  
Tomasz Pewinski ◽  
Jerzy Stefanowski ◽  
Artur Szumaczuk
Keyword(s):  
Data Streams ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Local Data ◽  
Real World Data ◽  
Minority Class ◽  
The Impact ◽  
Concept Drifts

AbstractClass imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers’ reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.

The Role of Emerging Technologies in Internet of Things

2018 ◽  
Author(s):  
Mallikarjuna B. ◽  
Chakradhar P. ◽  
Sridhar Reddy Gadila
Keyword(s):  
Internet Of Things ◽  
Emerging Technologies

scholarly journals Job Seekers’ Self-Directed Learning Activities Explained Through the Lens of Regulatory Focus

2021 ◽  
pp. 089484532199164
Author(s):  
Adam M. Kanar ◽  
Dave Bouckenooghe
Keyword(s):  
Job Search ◽  
Regulatory Focus ◽  
Self Efficacy ◽  
Learning Activities ◽  
Job Seekers ◽  
Self Directed Learning ◽  
Significant Relationships ◽  
Directed Learning ◽  
Job Searching

This study aimed to understand the role of regulatory focus for influencing self-directed learning activities during a job search. The authors surveyed 185 job-searching university students at two time points to explore the conditions under which regulatory focus (promotion and prevention foci) impacts self-directed learning activities and the number of employment interviews secured. Both promotion and prevention foci showed significant relationships with self-directed learning activities and number of interviews, and positive and negative affect partially mediated these relationships. The relationships between both regulatory focus strategies and self-directed learning were also contingent on self-efficacy. More specifically, prevention focus and self-directed learning showed a positive relationship for job seekers with high levels of self-efficacy but a negative one for job seekers with low levels of self-efficacy. This research extends the understanding of the role of regulatory focus in the context of self-directed learning during a job search. Implications for research and practice are discussed.

scholarly journals A Stochastic Kinetic Type Reactions Model for COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1221
Author(s):  
Giorgio Sonnino ◽  
Fernando Mora ◽  
Pasquale Nardone
Keyword(s):  
Real Data ◽  
Stochastic Noise ◽  
Mathematical Basis ◽  
Sis Model ◽  
Theoretical Predictions ◽  
Coronavirus Infection ◽  
Massive Distribution ◽  
Second Wave ◽  
Stochastic Sis Model

We propose two stochastic models for the Coronavirus pandemic. The statistical properties of the models, in particular the correlation functions and the probability density functions, were duly computed. Our models take into account the adoption of lockdown measures as well as the crucial role of hospitals and health care institutes. To accomplish this work we adopt a kinetic-type reaction approach where the modelling of the lockdown measures is obtained by introducing a new mathematical basis and the intensity of the stochastic noise is derived by statistical mechanics. We analysed two scenarios: the stochastic SIS-model (Susceptible ⇒ Infectious ⇒ Susceptible) and the stochastic SIS-model integrated with the action of the hospitals; both models take into account the lockdown measures. We show that, for the case of the stochastic SIS-model, once the lockdown measures are removed, the Coronavirus infection will start growing again. However, the combined contributions of lockdown measures with the action of hospitals and health institutes is able to contain and even to dampen the spread of the SARS-CoV-2 epidemic. This result may be used during a period of time when the massive distribution of vaccines in a given population is not yet feasible. We analysed data for USA and France. In the case of USA, we analysed the following situations: USA is subjected to the first wave of infection by Coronavirus and USA is in the second wave of SARS-CoV-2 infection. The agreement between theoretical predictions and real data confirms the validity of our approach.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

The role of current and emerging technologies in meeting Japan’s mid- to long-term carbon reduction goals

2021 ◽  
Vol 304 ◽  
pp. 117669
Author(s):  
Anshuman Chaube ◽  
Andrew Chapman ◽  
Akari Minami ◽  
James Stubbins ◽  
Kathryn D. Huff
Keyword(s):  
Emerging Technologies ◽  
Carbon Reduction

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology an Organizing

2021 ◽  
Author(s):  
Brian T. Pentland ◽  
Youngjin Yoo ◽  
Jan Recker ◽  
Inkyu Kim
Keyword(s):  
Drug Discovery ◽  
Dynamic Simulation ◽  
Emerging Technologies ◽  
Emerging Technology ◽  
Basic Question ◽  
Transformative Change ◽  
Pharmaceutical Drug ◽  
Wide Range ◽  
Lock In

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.

A Fitting Approach to Construct and Measurement Alignment

2017 ◽  
Vol 21 (3) ◽  
pp. 592-632 ◽  
Author(s):  
Margaret M. Luciano ◽  
John E. Mathieu ◽  
Semin Park ◽  
Scott I. Tannenbaum
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Iterative Process ◽  
Emerging Technologies ◽  
Measurement Techniques ◽  
Great Promise ◽  
Big Data Technologies ◽  
Nearly Continuous ◽  
Dynamic Phenomena ◽  
Over Time

Many phenomena of interest to management and psychology scholars are dynamic and change over time. One of the primary impediments to the examination of dynamic phenomena has been challenges associated with collecting data at a sufficient frequency and duration to accurately model such changes. Emerging technologies that produce nearly continuous streams of big data offer great promise to address those challenges; however, they introduce new methodological challenges and construct validity concerns. We seek to integrate the emerging big data technologies into the existing repertoire of measurement techniques and advance an iterative process to enhance their measurement fit. First, we provide an overview of dynamic constructs and temporal frameworks, highlighting their measurement implications. Second, we discuss different data streams and feature emerging technologies that leverage big data as a means to index dynamic constructs. Third, we integrate the previous sections and advance an iterative approach to achieving measurement fit, highlighting factors that make some measurement choices more suitable and viable than others. In so doing, we hope to accelerate the advancement of dynamic theories and methods.

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