scholarly journals Urban Data in the primary classroom: bringing data literacy to the UK curriculum

2016 ◽  
Vol 12 (3) ◽  
Author(s):  
Annika Wolff ◽  
Jose J Cavero Montaner ◽  
Gerd Kortuem
Keyword(s):  
Real Life ◽  
Aerial Survey ◽  
Data Sets ◽  
Complex Data ◽  
Solar Pv ◽  
Older Learners ◽  
Data Literacy ◽  
The Uk ◽  
The Right ◽  
Urban Data

As data becomes established as part of everyday life, the ability for the average citizen to have some level of data literacy is increasingly important.  This paper describes an approach to teaching data skills in schools using real life, complex, urban data sets collected as part of a smart city project. The approach is founded on the premise that young learners have the ability to work with complex data sets if they are supported in the right way and if the tasks are grounded in a real life context. Narrative principles are used to frame the task, to assist interpretation and tell stories from data and to structure queries of datasets. An inquiry-based methodology organises the activities.  This paper describes the initial trial in a UK primary school in which twelve students aged 9-10 years learnt about home energy consumption and the generation of solar energy from home solar PV, by interpreting existing visualisations of smart meter data and data obtained from aerial survey. Additional trials are scheduled with older learners which will evaluate learners on more challenging data handling tasks. The trials are informing the development of the Urban Data School, a web-based platform designed to support teaching data skills in schools in order to improve data literacy among school leavers.

Solar PV Power Generation Forecasting and O&M Management Applications: A Review

2019 ◽  
Author(s):  
Chen Zhang ◽  
Tao Yang ◽  
Wei Gao ◽  
Yong Wang
Keyword(s):  
Power Generation ◽  
Electricity Market ◽  
Renewable Energy Sources ◽  
Data Sets ◽  
Complex Data ◽  
Paper Machine ◽  
Solar Pv ◽  
Construction Costs ◽  
Complex Data Sets ◽  
Pv Power Generation

Abstract The growing resource shortage and environmental concerns have forced mankind to develop and utilize renewable energy sources. The penetration of solar photovoltaic (PV) power in the electricity market has been increasing over the past few decades due to its low construction costs, zero pollution nature, and enormous support from governments. However, the intermittency and randomness of PV power also cause huge grid fluctuations which limit its integration in the system. An accurate forecasting of solar PV power generation and optimization of operation and maintenance (O&M) management are essential for further development of the solar PV farms. A great number of related researches have been done in recent years. A review of PV power generation forecasting techniques together with their brief applications on the optimization of O&M management is presented in this paper. Machine learning methods are thought to be the most suitable at the present stage because of their ease of implementation and their capability in processing non-linear, complex data sets. Typical forecasting accuracy measures are summarized and further applications of PV power forecasting on the O&M management are also presented.

The British paid labour market1

2019 ◽  
pp. 45-62
Author(s):  
Eva A. Duda-Mikulin
Keyword(s):  
Labour Force ◽  
Qualitative Interviews ◽  
Real Life ◽  
Foreign Born ◽  
Women Migrants ◽  
Economic Migration ◽  
Customer Services ◽  
The Uk ◽  
The Right ◽  
The Eu

Chapter three explores the British paid labour market and more specifically economic migration to the UK and its impact with the message that migrants contribute through taxation and alleviating labour and skills shortages. I discuss existing statistical data on UK’s labour force and its characteristics. This quantitative data is complemented with rich qualitative accounts from recent Polish women migrants to the UK. Different sectors of the economy are explored, in particular agriculture, hospitality, customer services and healthcare. These are said to be most reliant on workforce from the EU. Data on population characteristics is analysed taking into account the fact that it is ageing rapidly as is the rest of Europe. This increases the need for foreign-born labour to take on jobs unpopular with British workers, particularly when the EU labour force is younger and fitter in comparison to UK-born workers. This also suggests that after Brexit the UK is likely to experience issues with staff recruitment and labour shortages in certain areas of the economy. The chapter is supported by extracts from qualitative interviews with women migrants from Poland with the aim to bring in real-life stories from those who took advantage of the right to free movement.

Generalized graphlet kernels for probabilistic inference in sparse graphs

2014 ◽  
Vol 2 (2) ◽  
pp. 254-276 ◽  
Author(s):  
JOSE LUGO-MARTINEZ ◽  
PREDRAG RADIVOJAC
Keyword(s):  
Domain Knowledge ◽  
Edit Distance ◽  
Kernel Method ◽  
Protein Structures ◽  
Real Life ◽  
Kernel Functions ◽  
Data Sets ◽  
Complex Data ◽  
Sparse Graphs ◽  
Real Life Data

AbstractGraph kernels for learning and inference on sparse graphs have been widely studied. However, the problem of designing robust kernel functions that can effectively compare graph neighborhoods in the presence of noisy and complex data remains less explored. Here we propose a novel graph-based kernel method referred to as an edit distance graphlet kernel. The method was designed to add flexibility in capturing similarities between local graph neighborhoods as a means of probabilistically annotating vertices in sparse and labeled graphs. We report experiments on nine real-life data sets from molecular biology and social sciences and provide evidence that the new kernels perform favorably compared to established approaches. However, when both performance accuracy and run time are considered, we suggest that edit distance kernels are best suited for inference on graphs derived from protein structures. Finally, we demonstrate that the new approach facilitates simple and principled ways of integrating domain knowledge into classification and point out that our methodology extends beyond classification; e.g. to applications such as kernel-based clustering of graphs or approximate motif finding. Availability:www.sourceforge.net/projects/graphletkernels/

scholarly journals Using subgroup discovery to analyze the UK traffic data

2004 ◽  
Vol 1 (1) ◽  
pp. 249-264
Author(s):  
Branko Kavšek ◽  
Nada Lavrač
Keyword(s):  
Learning Algorithm ◽  
Real Life ◽  
Rule Learning ◽  
Roc Curves ◽  
Experimental Comparison ◽  
Subgroup Discovery ◽  
Data Sets ◽  
Traffic Data ◽  
Data Set ◽  
The Uk

Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well known UCI data sets. This paper summarizes the modifications needed for the adaptation of the CN2 rule learner to subgroup discovery and presents its application to a real-life data set - the UK traffic data - confirming its appropriateness for subgroup discovery in real-life applications through experimental comparison with the CN2 rule learning algorithm as well as through the evaluation of an expert. Furthermore we make the first step towards the comparison of the new CN2-SD algorithm to another state-of-the-art subgroup discovery algorithm SubgroupMiner by applying both algorithms to a slightly different data set - the UK traffic challenge data set. The results of this application are presented in the form of ROC curves, showing CN2-SD’s potential in finding descriptions (subgroups) for minority classes, while SubgroupMiner found ‘better’ subgroups when trying to describe the majority class given the problem at hand.

The changing landscape of OSH regulation in the UK: Achieving the right balance in policy and practice

2013 ◽  
Author(s):  
David Hollis ◽  
Stavroula Leka ◽  
Aditya Jain ◽  
Nicholas Andreou
Keyword(s):  
Policy And Practice ◽  
The Uk ◽  
The Right

scholarly journals Neural alignment predicts learning outcomes in students taking an introduction to computer science course

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Brain Activity ◽  
Activity Patterns ◽  
Real Life ◽  
Final Exam ◽  
Neural Representations ◽  
Real Life Setting ◽  
The Right ◽  
Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

Animals in Dreams of Children, Adolescents, and Adults: The UK Library Study

2021 ◽  
pp. 027623662096063
Author(s):  
Michael Schredl ◽  
Mark Blagrove
Keyword(s):  
Empirical Research ◽  
Life Experiences ◽  
Real Life ◽  
Media Consumption ◽  
Continuity Hypothesis ◽  
Future Studies ◽  
Adolescents And Adults ◽  
The Uk

Animal dreams have fascinated mankind for ages. Empirical research indicated that children dream more often about animals than adults and dogs, cats, and horses are the most frequent animals that appear within dreams. Moreover, most dreamer-animal interactions are negative. The present study included 4849 participants (6 to 90 yrs. old) reporting 2716 most recent dreams. Overall, 18.30% of these dreams included animals with children reporting more animal dreams that adolescents and adults. The most frequent animals were again dogs, horses, and cats; about 20% of the dream animals were in fact pets of the dreamers. About 30% of the dream animals showed bizarre features, e.g., metamorphosing into humans or other animals, bigger than in real life, or can talk. Taken together, the findings support the continuity hypothesis of dreaming but also the idea that dreams reflect waking-life emotions in a metaphorical and dramatized way. Future studies should focus on eliciting waking-life experiences with animals, e.g., having a pet, animal-related media consumption, and relating these to experiences with animals in dreams.

scholarly journals What do users think of the UK’s three COVID-19 contact-tracing apps? A comparative analysis

2021 ◽  
Vol 28 (1) ◽  
pp. e100320
Author(s):  
Vahid Garousi ◽  
David Cutting
Keyword(s):  
Mobile Apps ◽  
Analysis Data ◽  
End Users ◽  
Contact Tracing ◽  
User Reviews ◽  
The Public ◽  
Engineering Teams ◽  
Technical Issues ◽  
The Uk ◽  
The Right

ObjectivesOur goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: ‘NHS COVID-19’ for England and Wales, ‘StopCOVID NI’ for Northern Ireland and ‘Protect Scotland’ for Scotland. Our two research questions are (1) what are the users’ experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps?MethodsWe assess the popularity of the apps and end users’ perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020).ResultsResults show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working.DiscussionTowards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users’ feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future.ConclusionOur results show that more work is needed by the stakeholders behind the apps (eg, apps’ software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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