Social Data Mining for Crime Intelligence

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems

Initial Condition Estimation in Flux Tube Simulations using Machine Learning

Space weather has become an essential field of study as solar flares, coronal mass ejections, and other phenomena can severely impact Earth's life as we know it. The solar wind is threaded by magnetic flux tubes that extend from the solar atmosphere to distances beyond the solar system boundary. As those flux tubes cross the Earth's orbit, it is essential to understand and predict solar phenomena' effects at 1 AU, but the physical parameters linked to the solar wind formation and acceleration processes are not directly observable. Some existing models, such as MULTI-VP, try to fill this gap by predicting the background solar wind's dynamical and thermal properties from chosen magnetograms and using a coronal field reconstruction method. However, these models take a long time, and their performance increases with good initial guesses regarding the simulation's initial conditions. To address this problem, we propose using varied machine learning techniques to obtain good initial guesses that can accelerate MULTI-VP's computational time

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users