Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Is routine hospital episode data sufficient for identifying individuals with chronic kidney disease? : A comparison study with laboratory data

© The Author(s) 2014. Acknowledgements We thank the Information Services Division, Scotland, who provided the SMR01 data, and NHS Grampian, who provided the biochemistry data. We also thank the University of Aberdeen’s Data Management Team. Funding This work was supported by the Chief Scientists Office for Scotland (grant no. CZH/4/656).

Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – modelling and validation

Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004–2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9±3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

Forensic analysis of buildings affected by mining subsidence based on Differential Interferometry (Part III)

This work presents a forensic analysis of buildings affected by mining subsidence, which is based on deformation data obtained by Differential Interferometry (DInSAR). The proposed test site is La Union village (Murcia, SE Spain) where subsidence was triggered in an industrial area due to the collapse of abandoned underground mining labours occurred in 1998. In the first part of this work the study area was introduced, describing the spatial and temporal evolution of ground subsidence, through the elaboration of a cracks map on the buildings located within the affected area. In the second part, the evolution of the most significant cracks found in the most damaged buildings was monitored using biaxial extensometric units and inclinometers. This article describes the work performed in the third part, where DInSAR processing of satellite radar data, available between 1998 and 2008, has permitted to determine the spatial and temporal evolution of the deformation of all the buildings of the study area in a period when no continuous in situ instrumental data is available. Additionally, the comparison of these results with the forensic data gathered in the 2005–2008 period, reveal that there is a coincidence between damaged buildings, buildings where extensometers register significant movements of cracks, and buildings deformation estimated from radar data. As a result, it has been demonstrated that the integration of DInSAR data into forensic analysis methodologies contributes to improve significantly the assessment of the damages of buildings affected by mining subsidence.

Evaluating the robustness of EmotiBlog for sentiment analysis and opinion mining

Preliminary research demonstrated the EmotiBlog annotated corpus relevance as a Machine Learning resource to detect subjective data. In this paper we compare EmotiBlog with the JRC Quotes corpus in order to check the robustness of its annotation. We concentrate on its coarse-grained labels and carry out a deep Machine Learning experimentation also with the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC Quotes corpus demonstrating the EmotiBlog validity as a resource for the SA task.

A data driven study of relationships between relief and farmland abandonment in a Mediterranean region

The environmental, cultural and socio-economic causes and consequences of farmland abandonment are issues of increasing concern for researchers and policy makers. In previous studies, we proposed a new methodology for selecting the driving factors in farmland abandonment processes. Using Data Mining and GIS, it is possible to select those variables which are more significantly related to abandonment. The aim of this study is to investigate the application of the above mentioned methodology for finding relationships between relief and farmland abandonment in a Mediterranean region (SE Spain).We have taken into account up to 28 different variables in a single analysis, some of them commonly considered in land use change studies (slope, altitude, TWI, etc), but also other novel variables have been evaluated (sky view factor, terrain view factor, etc). The variable selection process provides results in line with the previous knowledge of the study area, describing some processes that are region specific (e.g. abandonment versus intensification of the agricultural activities). The European INSPIRE Directive (2007/2/EC) establishes that the digital elevation models for land surfaces should be available in all member countries, this means that the research described in this work can be extrapolated to any European country to determine whether these variables (slope, altitude, etc) are important in the process of abandonment.

Subsidence activity maps derived from DInSAR data: Orihuela case study

A new methodology is proposed to produce subsidence activity maps based on the geostatistical analysis of persistent scatterer interferometry (PSI) data. PSI displacement measurements are interpolated based on conditional Sequential Gaussian Simulation (SGS) to calculate multiple equiprobable realizations of subsidence. The result from this process is a series of interpolated subsidence values, with an estimation of the spatial variability and a confidence level on the interpolation. These maps complement the PSI displacement map, improving the identification of wide subsiding areas at a regional scale. At a local scale, they can be used to identify buildings susceptible to suffer subsidence related damages. In order to do so, it is necessary to calculate the maximum differential settlement and the maximum angular distortion for each building of the study area. Based on PSI-derived parameters those buildings in which the serviceability limit state has been exceeded, and where in situ forensic analysis should be made, can be automatically identified. This methodology has been tested in the city of Orihuela (SE Spain) for the study of historical buildings damaged during the last two decades by subsidence due to aquifer overexploitation. The qualitative evaluation of the results from the methodology carried out in buildings where damages have been reported shows a success rate of 100%.

Mining Districts, Victoria, Australia, ca. 1868 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Victoria mining districts, mining divisions & the gold fields, engraved by William Slight under the direction of R. Brough Smyth ; colored by Arthur Everett, August 1st, 1868. It was published by Dept of Mines ca. 1868. Scale [ca. 1:1,000,000].The image inside the map neatline is georeferenced to the surface of the earth and fit to the coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, administrative boundaries, railroads, gold reefs, mining districts, telegraph lines, shoreline features, and more. Relief shown by hachures. Includes notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Sediment echosounding data files from the Amundsen Sea

The flow of ice streams, which account for most discharge from large ice sheets, is controlled by processes operating at their bed. Data from modern ice stream beds are difficult to obtain, but where ice advanced onto continental shelves during glacial periods extensive areas of the former bed can be imaged using modern swath sonar tools. We present new multibeam swath bathymetry data analyzed alongside sparse pre-existing data from the Amundsen Sea Embayment. The compilation is the most extensive, continuous area of multibeam data coverage yet obtained on the inner continental shelf of Antarctica. The data reveal streamlined subglacial bedforms that define a zone of paleo-ice stream convergence but, in contrast to previous models, do not show a simple down-flow progression of bedform types along paleo-ice stream troughs. We interpret high spatial variability of bedforms as indicating a complex mechanical and hydrodynamic regime at the former ice stream beds, consistent with observations from some modern ice streams. We conclude that care must be taken when using bedforms to infer paleo-ice stream velocities.

Catalog of information on water data : index to water-data activities in coal provinces of the United States /

Maps on lining papers.

An intensive survey of the Vermilion River Basin (Wabash River drainage) 1997 : data collection methods and summary /

"The Illinois Environmental Protection Agency monitors surface waters (i.e. lakes and streams) through a variety of programs. The most extensive is the Ambient Water Quality Monitoring Network (AWQMN) which consists of 203 stream stations statewide sampled on a 6 week cycle since October 1977." -- p. 1.

Availability of the Colchester coal for mining in northern and western Illinois /

This report is the fourth in a series to assess the availability of coal resources for future mining in Illinois.

Data collection methods and summaries for the assessment of stream water quality in the Vermilion River Basin (Wabash River), 2001 /

Cover title.

Surface water pesticide monitoring of Illinois streams : summary of results 1985-1998 /

This report provides a review and summarization of stream pesticide data collected by the Illinois EPA Ambient Water Quality Monitoring Network between October 1985 and December 1998. A list of sampling stations is provided in Appendix A along with data from the seven most detected pesticides which are provided in Appendix B.

A Guide to Surface Features Related to Underground Coal Mining

The purpose of this guide is to assist investigators conducting geologic hazard assessments with the understanding, detection, and characterization of surface features related to subsidence from underground coal mining. Subsidence related to underground coal mining can present serious problems to new and/or existing infrastructure, utilities, and facilities. For example, heavy equipment driving over the ground surface during construction processes may punch into voids created by sinkholes or cracks, resulting in injury to persons and property. Abandoned underground mines also may be full of water, and if punctured, can flood nearby areas. Furthermore, the integrity of rigid structures such as buildings, dams and bridges may be compromised if mining subsidence results in differential movement at the ground surface. Subsidence of the ground surface is a phenomenon associated with the removal of material at depth, and may occur coincident with mining, gradually over time, or sometimes suddenly, long after mining operations have ceased (Gray and Bruhn, 1984). The spatial limits of underground coal mines may extend for great distances beyond the surface operations of a mine, in some cases more than 10 miles for an individual mine. When conducting geologic hazard assessments, several remote investigation methods can be used to observe surface features related to underground mining subsidence. LiDAR-derived DEMs are generally the most useful method available for identifying these features because the bare earth surface can be viewed. However, due to limitations in the availability of LiDAR data, other methods often need to be considered when investigating surface features related to underground coal mining subsidence, such as Google Earth and aerial imagery. Mine maps, when available, can be viewed in tandem with these datasets, potentially improving the confidence of any possible mining subsidence-related features observed remotely. However, maps for both active and abandoned mines may be incomplete or unavailable. Therefore, it is important to be able to recognize possible surface features related to underground mining subsidence. This guide provides examples of surface subsidence features related to the two principal underground coal mining methods used in the United States: longwall mining and room and pillar mining. The depth and type of mining, geologic conditions, hydrologic conditions, and time are all factors that may influence the type of features that manifest at the surface. This guide provides investigators a basic understanding about the size, character and conditions of various surface features that occur as a result of underground mining subsidence.