This case study is part of a series of in-depth reports on religiously motivated violent radicalisation - and resilience to it - in 12 countries. The series examines periods in which religious radicalisation and violence has escalated and analyses relevant policy and political discourses surrounding them. While seeking to identify factors that drove radicalisation and violence in each country, the case studies also critically assess programmes of prevention and resilience-building, identifying good practices. This series was produced by GREASE, an EU-funded research project investigating religious diversity, secularism and religiously inspired radicalisation. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 770640.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Exploring and quantifying the effect of weather on sales ASDA, one of the biggest supermarket chains in the UK, is interested in how weather affects sales of certain product groups. Understanding the weather-sales relationship would allow ASDA to manage the supply-chain system and distribution in a timely and efficient manner, particularly reducing the risk of food waste for fresh products such as meat while having products in stock as needed. The analysis of the weather-sales relationship builds on a huge dataset which includes daily sales data for 151 product groups in over 600 stores across the UK over 3 years, and daily weather conditions at each store, including mean daily humidity, wind speed, rainfall, snowfall and minimum and maximum daily temperature. The Alan Turing Institute is the UK's national institute for data science and artificial intelligence, with headquarters at the British Library. Data Study Groups are intensive five day collaborative hackathons hosted at the The Alan Turing Institute, which bring together organisations from industry, government, and the third sector, with multi-disciplinary researchers from academia. ASDA, the Data Study Group Challenge Owner, provided the real-world challenge and the data to be tackled by a group of researchers led by two Principal Investigators and a Facilitator. This report is the culmination of that process and is the result of their joint co-authorship.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. NCSC: Capturing the purpose of websites This challenge was provided by the National Cyber Security Centre (NCSC), which is tasked with protecting the UK public sector, and aims at improving existing internet search technologies. As the internet grows, there is a need in industry, government and academia to better facilitate website recommendation, semantic search, and domain discovery, as well as to improve the security of the web. For instance, it is not possible to easily find all UK public sector domains with a simple search; nor it is possible for commercial organisations to easily generate a list of potential competitors or suppliers/customers from current search engines. An exciting potential approach to enable the NCSC and other organisations to leverage the latest machine learning algorithms on these challenges is to automatically learn a purposeful compact vector representation of every website or domain on the Web.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Smart monitoring for conservation areas WWF (World Wide Fund for Nature) monitors over 250,000 protected areas (e.g. national parks and nature reserves) and thousands of other sites and critical habitats. These sites are the foundation of global natural assets and are central to the preservation of biodiversity and human well-being. Unfortunately, they face increasing pressures from human development. In this challenge, we explore various data science techniques to automatically detect news articles that report emerging threats to key protected areas. We describe a system that identifies such news stories near real-time. This is vital to enable the wider machinery of WWF and the conservation community to engage with governments, companies, shareholders, insurers, and others to help halt the degradation or destruction of key habitats.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Automating the evaluation of local government planning applications It is estimated that England requires up to 345,000 new homes to be built each year in order to keep up with population growth. Although housebuilding has increased since reaching a low point after the financial crisis, the annual net supply of new homes would need to increase by more than 40% to reach this number. This lack of new homes contributes to higher mortgages, higher rents, less social housing and wider deprivation. One of the contributing factors is that the current planning application system remains a complex and inefficient task. Every construction project in the UK, including building or extending a house, fitting new windows on a listed building, or chopping down a tree, requires the submission of complicated planning forms and technical drawings. Each one of these documents needs to be manually validated and approved by a planning officer. Over 3.5 million applications are submitted to councils each year. On average, owing to local government budget cuts of 40% over the last 10 years, it takes three weeks for a council to start looking at a planning application. This creates large backlogs and a lack of information for application submitters, leading to additional calls and emails to chase progress which further increase workload on planning officers. At the same time, over a third (1.2 million) of the applications submitted annually are rejected, often owing to the complexity of submitting a correct application and the manual burden in processing them at councils. The overall objective of this work is to move towards the automated detection of common errors in planning applications using ML/AI approaches.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Spend Network: Automated matching of businesses to government contract opportunities This report presents the output of a week-long collaboration between Spend Network, and lead academics from the University of Manchester and the University of Oxford that attended the Data Study Group at The Alan Turing Institute. Spend Network is a platform that aims to enable efficient public procure-ment. The goal of this work was to match suppliers to tenders. Our approach consists of building vector representations of suppli-ers and tenders and identifying their compat-ibility with the distance between the respec-tive vectors. In building their representations, we make use of both their textual descriptions and the knowledge of previously awarded con-tracts. We find previous contracts informative of future procurement decisions. Our best re-sults use Correlated Topic Models (Blei et al., 2007) for extracting representations of textual descriptions.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Get Bristol moving: tackling air pollution in Bristol city centre Air quality is an increasing public health concern in UK cities, and Bristol is no exception breaching annual targets for nitrogen dioxide. In Bristol, five people die each week as a result of poor air quality. Like most UK cities, in Bristol the main cause of air pollution is traffic. To reduce the negative impacts of air pollution, and meet increasingly stringent European Union pollutant standards, Bristol is currently consulting on a Clean Air Strategy. A range of policies is proposed including increased public transport, clean air zones and road closures. The city council currently collects a wealth of data (about air quality, traffic flows and weather) for analyzing the distribution of air quality and the relationship with various drivers. Such understanding has the potential to feed into the design and evaluation of air quality policies. However, the council has limited capacity for analyzing and interpreting such large and complex datasets. This project mines this data to understand both the spatial and temporal distribution of air quality. the driving factors of air pollution in different parts of the city, with a particular focus upon traffic.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Leveraging LiDAR and Street View data for road feature detection with OSNI The Ordnance Survey of Northern Ireland (OSNI) mission is to provide high quality geospatial data. Historically this has been for 2D mapping, but modern survey techniques and increasing user requirements have shifted focus toward 3D data. Since 2019, OSNI has operated a vehicle mounted Mobile Mapping System (Leica Pegasus:Two Ultimate Mobile Mapping System) across Northern Ireland capturing 3D Point Cloud data and spherical street view imagery. The range of potential applications is significant, including urban planning, asset identification and management, automating identification of road sign changes for navigation and transport network datasets, identifying feature locations such as scenic views, drainage, potholes and road surface quality, street furniture maintenance, 5G network planning and managing autonomous vehicles. While availability and accessibility of this kind of raw data is improving, there are significant technical challenges in deriving insights from the richness of this dataset. To address these challenges this project seeks to explore the potential of OSNI's highly detailed Light Detection and Ranging (LiDAR) and imagery data via Machine Learning (ML) and data science methods, with a focus on developing pipelines to visualise, classify and identify road features like drainage which could potentially help various government authorities better monitor road infrastructure. There are many other potential applications for the sort of data OSNI collects, and we hope some of the pipelines and visualisations explored below can aid broader applicability. Below are the results from each of the streams of work conducted.
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. The assessment of surfaces for potential contamination by biological (e.g. anthrax pathogen Bacillus anthracis) and chemical (e.g. nerve agents such as VX) hazards is relevant for a range of military and civilian applications. To this end, Dstl and the Defence and Security Accelerator (DASA) are providing a dataset collected using a range of different sensor modalities that have measured various surfaces contaminated with surrogate bacteria, hazardous chemicals and relevant control materials. Both un-mixing and identification of the contaminant contribution from that of the underlying surface is non-trivial. Participants were invited to explore how data science and machine learning techniques can be applied to recognise and discriminate between the various contaminants based on data from individual sensors or fusion of multiple data sources, and how models can be applied to characterise contamination on new surfaces without re-training.
"August 1, 1961." ; "The Report of the U.S. Study Team to Thailand represents the views of a group of consultants and government officials."--Preface. ; Mode of access: Internet.
A monthly publication devoted to the interests of masonry in Gallup, New Mexico. Edited by the Study Club Committee. O.F. Peck, Chairman. R.J.A Hart, C.E. Uhland, W.M. Boggess, and T.A. Furhman. Mrs. Ruth Finely, News Editor.BIOGRAPHICAL NOTE The Day Family were anglo Indian traders, on the Navajo Reservation in eastern Arizona. The collection includes the personal and business papers of Sam Day, Sr. (1845-1925) surveyor, Indian trader, legislator and United States Indian Commissioner; Anna Day, Sam Sr.'s wife (1872-1932); and of their children, Charles L. Day (1879-1918), Samuel Day, Jr. (1889-1944), United States deputy Marshall.
Background Despite an overall reduction in suicide, educational disparities in suicide have not decreased over the last decade. The mechanisms behind educational disparities in suicide, however, remain unclear: low educational status may increase the risk of suicide ("causation") or low educational status and suicide may share confounders. This paper assesses whether educational disparities in suicide (EDS) are more likely to be due to causation. Method The DEMETRIQ study collected and harmonized register-based data on mortality follow-up from forty population censuses from twelve European populations. More than 102,000 suicides were registered over 392 million person-years. Three analyses were carried out. First, we applied an instrumental variable approach that exploits changes in the legislation on compulsory educational age to instrument educational status. Second, we analyzed EDS by age under the hypothesis that increasing EDS over the life cycle supports causation. Finally, we compared EDS in men and women under the assumption that greater EDS in women would support causation. Findings The instrumental variable analysis showed no evidence for causation between higher education and suicide, for men or women. The life-cycle analysis showed that the decrease of educational inequalities in suicide between the baseline 1991 period and the 2001 follow-up period was more pronounced and statistically significant in the first three younger age groups. The gender analysis indicated that EDS were systematic and greater in men than in women: the rate ratio of suicide for men with low level of education (RR = 2.51; 95% CI:2.44–2.58) was higher than the rate ratio in women (RR = 1.32; 95CI%:1.26–1.38). Interpretation Overall, there was little support for the causation hypothesis, suggesting that the association between education and suicide is confounded. Educational inequalities in suicide should be addressed in early life by early targeting of groups who struggle to complete their education and display higher risk of mental disorder or of mental health vulnerabilities. ; publishersversion ; published
