A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Smarter learning software: Education and the big data imaginary

Big data and smarter learning software systems are beginning to impact on education, particularly within the schools sector. This paper traces the emergence of a ‘big data imaginary,’ a vision of a desirable future of education that its advocates believe is attainable through the application of big data technologies and practices. Firstly, it identifies a ‘first wave of big data’ in nineteenth-century education exhibitions and its continuities with the visualization of large-scale educational data today. Secondly, it details the emergence of ‘educational data science’ as an exemplar of how ‘second wave big data’ has entered the imagination of many actors within education. Thirdly, it then demonstrates how education is being reimagined in relation to ‘smart cities’ that depend on big data for their functioning, before fourthly detailing the recent appearance of ‘startup schools’ that are being established by Silicon Valley entrepreneurs to run as testbeds of smarter learning software systems. A concluding section discusses how the future of education may be governed by the production and circulation of the ‘data and algorithms of the powerful.

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page

Decomposing the Dynamics of Regional Earnings Disparities in Israel

The literature on regional growth convergence and economic disparities has tended to confound four interwoven measurement phenomena. i) mean reversion (so-called beta convergence) where richer regions move towards the average from above and poorer regions from below. ii) diminishing inequality (so called sigma convergence) where the horizontal or spatial distribution of income becomes more equal. iii) mobility, where the rank of a region in the overall distribution of income changes either upwards or downwards. iv) leveling, where the richer regions become poorer (leveling-down) or the poorer regions become richer (leveling-up). We use a new statistical methodology, which treats these four phenomena on an integrated basis. The methodology is applied to Israeli regional earnings and house price data. We find that whereas earnings are strongly sigma divergent during the 1990s, this trend is offset when regional cost of living differences are taken into consideration. In this event, regional housing markets induce convergence in similar measure to the divergence induced by regional labor earnings.

Platformization of Urban Life: Towards a Technocapitalist Transformation of European Cities

The increasing platformization of urban life needs critical perspectives to examine changing everyday practices and power shifts brought about by the expansion of digital platforms mediating care-services, housing, and mobility. This book addresses new modes of producing urban spaces and societies. It brings both platform researchers and activists from various fields related to critical urban studies and labour activism into dialogue. The contributors engage with the socio-spatial and normative implications of platform-mediated urban everyday life and urban futures, going beyond a rigid techno-dystopian stance in order to include an understanding of platforms as sites of social creativity and exchange

Platformization of Urban Life

The increasing platformization of urban life needs critical perspectives to examine changing everyday practices and power shifts brought about by the expansion of digital platforms mediating care-services, housing, and mobility. This book addresses new modes of producing urban spaces and societies. It brings both platform researchers and activists from various fields related to critical urban studies and labour activism into dialogue. The contributors engage with the socio-spatial and normative implications of platform-mediated urban everyday life and urban futures, going beyond a rigid techno-dystopian stance in order to include an understanding of platforms as sites of social creativity and exchange

Building a Multivariable Linear Regression Model of On-road Traffic for Creation of High Resolution Emission Inventories

Emissions inventories are an important tool, often built by governments, and used to manage emissions. To build an inventory of urban CO2 emissions and other fossil fuel combustion products in the urban atmosphere, an inventory of on-road traffic is required. In particular, a high resolution inventory is necessary to capture the local characteristics of transport emissions. These emissions vary widely due to the local nature of the fleet, fuel, and roads. Here we show a new model of ADT for the Portland, OR metropolitan region. The backbone is traffic counter recordings made by the Portland Bureau of Transportation at 7,767 sites over 21 years (1986-2006), augmented with PORTAL (The Portland Regional Transportation Archive Listing) freeway traffic count data. We constructed a regression model to fill in traffic network gaps using GIS data such as road class and population density. An EPA-supplied emissions factor was used to estimate transportation CO2 emissions, which is compared to several other estimates for the city\u27s CO2 footprint