Learning to Read Bushman: Automatic Handwriting Recognition for Bushman Languages

The Bleek and Lloyd Collection contains notebooks that document the tradition, language and culture of the Bushman people who lived in South Africa in the late 19th century. Transcriptions of these notebooks would allow for the provision of services such as text-based search and text-to-speech. However, these notebooks are currently only available in the form of digital scans and the manual creation of transcriptions is a costly and time-consuming process. Thus, automatic methods could serve as an alternative approach to creating transcriptions of the text in the notebooks. In order to evaluate the use of automatic methods, a corpus of Bushman texts and their associated transcriptions was created. The creation of this corpus involved: the development of a custom method for encoding the Bushman script, which contains complex diacritics; the creation of a tool for creating and transcribing the texts in the notebooks; and the running of a series of workshops in which the tool was used to create the corpus. The corpus was used to evaluate the use of various techniques for automatically transcribing the texts in the corpus in order to determine which approaches were best suited to the complex Bushman script. These techniques included the use of Support Vector Machines, Artificial Neural Networks and Hidden Markov Models as machine learning algorithms, which were coupled with different descriptive features. The effect of the texts used for training the machine learning algorithms was also investigated as well as the use of a statistical language model. It was found that, for Bushman word recognition, the use of a Support Vector Machine with Histograms of Oriented Gradient features resulted in the best performance and, for Bushman text line recognition, Marti & Bunke features resulted in the best performance when used with Hidden Markov Models. The automatic transcription of the Bushman texts proved to be difficult and the performance of the different recognition systems was largely affected by the complexities of the Bushman script. It was also found that, besides having an influence on determining which techniques may be the most appropriate for automatic handwriting recognition, the texts used in a automatic handwriting recognition system also play a large role in determining whether or not automatic recognition should be attempted at all

Metabolomics and biosensor approaches to the detection of fever associated diseases

Febrile illnesses are still a major cause of mortality and morbidity globally and the failure to detect and correctly diagnose a specific disease associated with fever is partly responsible for this. This thesis aimed to investigate a biosensor-based method for the detection of fever associated diseases and to further explore the molecular mechanisms and possible biomarkers of febrile illnesses by employing a metabolomics-based approach. The biosensor platform is based on a complementary metal oxide semiconductor technology, which has both technological and economic advantages. Due to the small size of the microchip, accurate signal processing becomes challenging and, thus, computational methods were developed and tested for the quantitative detection of antibodies in a solution tested on the biosensor platform. Three methods, one based on a deterministic approach and two others based on machine learning (ML) algorithms, were tested and compared for the detection of a reaction spot intensity using synthetically generated images. Next, in order to develop an immunoassay protocol for the detection of one specific fever associated infectious disease, human African trypanosomiasis (HAT), several steps were taken. First of all, a suitable and sensitive method of detection was selected, i.e. enzyme linked immunosorbent assay (ELISA). Next, four recombinant antigens currently used for the detection of HAT were selected based on previous evidence and developed using molecular cloning techniques in E.coli. These were tested on infected and control humans serum samples obtained from endemic regions of the Democratic Republic of Congo (DRC). Disposable poly-methyl methacrylate (PMMA) slides which were chemically functionalised were used on top of the chip as the immunoassay surface. Titrations for the selected antigens/antibody were tested using an indirect ELISA-like protocol and the best results after fitting a calibration curve were obtained for an antigen concentration of 2.5 µg/ml. The detection of the antibody to the trypanosome antigen invariant surface glycoprotein 65 (ISG65) proved to be successful and the protocol could be replicated for all the other antigens. However, technical challenges and the closure of the laboratory during the Covid-19 pandemic precluded my taking this part of the project to its conclusion. Following this, metabolomics datasets studying disparate febrile infectious illnesses obtained using liquid chromatography coupled to mass spectrometry (LC-MS) were used in order to investigate and detect possible metabolite-based biomarkers common to fever-associated diseases. A warping based method was developed in order to enable integration by alignment of disparate LC-MS metabolomics datasets. Integration was performed by correcting the RT drift between the datasets using fitted Gaussian Process regression models, a supervised ML method, which was followed by direct matching alignment using MZmine2. The correction was performed by using the standard reference mixture (SRM) information. Statistical analysis on the meta-dataset was performed using linear modelling implemented in the limma R-package. Comparison was made between infected and control samples and commonality was established using the fold change values obtained for the individual datasets. Annotation was carried out by matching the compounds against metabomlomics datasets and through mummichog software, which was also used for pathway analysis. The features obtained from this analysis which were putatively annotated were classified into categories (amino acids, sugars, lipids, nucleotides, etc.). Features in common to all datasets were used to make a connection to the previously established molecular basis of fever. Significant changes were identified to several metabolic pathways, with the most notable perturbations being within the kynurenine pathway, a branch of tryptophan metabolism. Also, features specific to each dataset were used to evaluate the accuracy of the fever biomarkers and investigate possible biomarkers for each different fever-associated disease

Experience-driven MAR games: Personalising Mobile Augmented Reality games using Player Models

PhD ThesesWe are witnessing an unprecedented growth of Mobile Augmented Reality (MAR) technologies, one of the main research areas being MAR games. While this field is still in its early days, researchers have shown the physical health benefits of playing these type of games. Computational models have been used in traditional (non-AR) digital games to predict player experience (PX). These models give designers insights about PX, and can also be used within games for real-time adaptation or personalised content generation. Following these findings, this thesis investigates the potential of creating models that use movement data and game metrics to predict PX. An initial pilot study is conducted to evaluate the use of movement data and game metrics to predict players’ emotional preferences between different game levels of an exploration-based MAR game. Results indicate that emotional preferences regarding frustration (≈ 93%) and challenge (≈ 93%) can be predicted to a reliable and reasonable degree of accuracy. To determine if these techniques can be applied to serious games for health, an AR exergame is developed for experiments two, three and four of this thesis. The second and third experiments aim to predict key experiential constructs, player competence and immersion, that are important to PX. These experiments further validate the use of movement data and game metrics to model different aspects of PX in MAR games. Results suggest that players’ competence (≈ 73%) and sense of mastery (≈ 81%) can be predicted to a reasonable degree of accuracy. For the final experiment, this mastery model is used to create a dynamic difficulty adaptation (DDA) system. The adaptive exergame is then evaluated against a non-adaptive variant of the same game. Results indicate that the adaptive game makes players feel a higher sense of confidence during gameplay and that the adaptation mechanics are more effective for players who do not engage in regular physical activity. Across the four studies presented, this thesis is the first known research activity that investigates using movement data and game metrics to model PX for DDA in MAR games and makes the following novel contributions: i) movement data and game metrics can be used to predict player’s sense of mastery or competence reliably compared to other aspects of PX tested, ii) mastery-based game adaptation makes players feel greater confidence during game-play, and iii) mastery-based game adaptation is more effective for players who do not engage in physical activity. This work also presents a new methodology for PX prediction in MAR games and a novel adaptation engine driven by player mastery. In summary, this thesis proposes that PX modelling can be successfully applied to MAR games, especially for DDA which results in a highly personalised PX and shows potential as a tool for increasing physical activity

Earthquake Engineering

The book Earthquake Engineering - From Engineering Seismology to Optimal Seismic Design of Engineering Structures contains fifteen chapters written by researchers and experts in the fields of earthquake and structural engineering. This book provides the state-of-the-art on recent progress in the field of seimology, earthquake engineering and structural engineering. The book should be useful to graduate students, researchers and practicing structural engineers. It deals with seismicity, seismic hazard assessment and system oriented emergency response for abrupt earthquake disaster, the nature and the components of strong ground motions and several other interesting topics, such as dam-induced earthquakes, seismic stability of slopes and landslides. The book also tackles the dynamic response of underground pipes to blast loads, the optimal seismic design of RC multi-storey buildings, the finite-element analysis of cable-stayed bridges under strong ground motions and the acute psychiatric trauma intervention due to earthquakes

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Efficient Learning Machines

Computer scienc

Driver drowsiness monitoring using eye movement features derived from electrooculography

The increase in vehicle accidents due to the driver drowsiness over the last years highlights the need for developing reliable drowsiness assistant systems by a reference drowsiness measure. Therefore, the thesis at hand is aimed at classifying the driver vigilance state based on eye movements using electrooculography (EOG). In order to give an insight into the states of driving, which lead to critical safety situations, first, driver drowsiness, distraction and different terminologies in this context are described. Afterwards, countermeasures as techniques for keeping a driver awake and consequently preventing car crashes are reviewed. Since countermeasures do not have a long-lasting effect on the driver vigilance, intelligent driver drowsiness detection systems are needed. In the recent past, such systems have been developed on the market, some of which are introduced in this study. As also stated in previous studies, driver state is quantifiable by objective and subjective measures. The objective measures monitor the driver either directly or indirectly. For indirect monitoring of the driver, one uses the driving performance measures such as the lane keeping behavior or steering wheel movements. On the contrary, direct monitoring mainly comprises the driver’s physiological measures such as the brain activities, heart rate and eye movements. In order to assess these objective measures, subjective measures such as self-rating scores are required. This study introduces these measures and discusses the concerns about their interpretation and reliability. The developed drowsiness assistant systems on the market are all based on driving performance measures. These measures presuppose that the vehicle is steered solely by the driver himself. As long as other assistance systems with the concept to keep the vehicle in the middle of the lane are activated, driving performance measures would make wrong decisions about warnings. The reason is what the sensors measure is a combination of the driver’s behavior and the activated assistance system. In fact, the drowsiness warning system cannot determine the contribution of the driver in the driving task. This underscores the need for the direct monitoring of the driver. Previous works have introduced the drop of the alpha spindle rate (ASR) as a drowsiness indicator. This rate is a feature extracted out of the brain activity signals during the direct monitoring the driver. Additionally, ASR was shown to be sensitive to driver distraction, especially a visual one with an counteracting effect. We develop an algorithm based on eye movements to reduce the negative effect of the driver visual distraction on the ASR. This helps to partially improve the association of ASR with the driver drowsiness. Since the focus of this study is on driver eye movements, we introduce the human visual system and describe the idea of what and where to define the visual attention. Further, the structure of the human eye and relevant types of eye movements during driving are defined. We also categorize eye movements into two groups of slow and fast eye movements. We show that blinks, in principle, can belong to both of these groups depending on the driver’s vigilance state. EOG as a tool to measure the driver eye movements allows us to distinguish between drowsiness or distraction-related and driving situation dependent eye movements. Thus, in a pilot study, an experiment under fully controlled conditions is carried out on a proving ground to investigate the relationship between driver eye movements and different real driving scenarios. In this experiment, unwanted head vibrations within EOG signals and the sawtooth pattern (optokinetic nystagmus, OKN) of eyes are realized as situation dependent eye movements. The former occurs due to ground excitation and the latter happens during small radius (50m) curve negotiation. The statistical investigation expresses a significant variation of EOG due to unwanted head vibrations. Moreover, an analytical model is developed to explain the possible relationship of KON and tangent point of the curve. The developed model is validated against the real data on a high curvature track. In order to cover all relevant eye movement patterns during awake and drowsy driving, different experiments are conducted in this work including daytime and nighttime experiments under real road and simulated driving conditions. Based on the measured signals in the experiments, we study different eye movement detection approaches. We, first, investigate the conventional blink detection method based on the median filtering and show its drawback in detecting slow blinks and saccades. Afterwards, an adaptive detection approach is proposed based on the derivative of the EOG signal to simultaneously detect not only the eye blinks, but also other driving-relevant eye movements such as saccades and microsleep events. Moreover, in spite of the fact that drowsiness influences eye movement patterns, the proposed algorithm distinguishes between the often confused driving-related saccades and decreased amplitude blinks of a drowsy driver. The evaluation of results shows that the presented detection algorithm outperforms the common method based on median filtering so that fast eye movements are detected correctly during both awake and drowsy phases. Further, we address the detection of slower eye blinks, which are referred to as typical patterns of the drowsiness, by applying the continuous wavelet transform to EOG signals. In our proposed algorithm, by adjusting parameters of the wavelet transform, fast and slow blinks are detected simultaneously. However, this approach suffers from a larger false detection rate in comparison to the derivative-based method. As a result, for blink detection in this work, a combination of these two methods is applied. To improve the quality of the collected EOG signals, the discrete wavelet transform is benefited to remove noise and drift. For the noise removal, an adaptive thresholding strategy within the discrete wavelet transform is proposed which avoids sacrificing noise removal for saving blink amplitude or vice versa. In previous research, driver eye blink features (blink frequency, duration, etc.) have shown to be correlated to some extent with drowsiness. Hence, within a level of uncertainty they can contribute to driver drowsiness warning systems. In order to improve such systems, we investigate characteristics of detected blinks with respect to their different origins. We observed that in a real road experiment, blinks occur both spontaneously or due to gaze shifts. Gaze shifts between fixed positions, which occurred due to secondary visuomotor task, induced and modulated the occurrence of blinks. Moreover, the direction of the gaze shifts affected the occurrence of such blinks. Based on the eye movements during another experiment in a driving simulator without a secondary task, we found that the amount of gaze shifts (between various positions) is positively correlated with the probability of the blink occurrence. Therefore, we recommend handling gaze shift-induced blinks (e.g. during visual distraction) differently from those occurring spontaneously in drowsiness warning systems that rely solely on the variation of blink frequency as a driver state indicator. After studying dependencies between blink occurrence and gaze shifts, we extract 19 features out of each detected blink event of 43 subjects collected under both simulated and real driving conditions during 67 hours of both daytime and nighttime driving. This corresponds to the largest number of extracted eye blink features and the largest number of subjects among previous studies. We propose two approaches for aggregating features to improve their association with the slowly evolving drowsiness. In the first approach, we solely investigate parts of the collected data which are best correlated with the subjective self-rating score, i.e. Karolinska Sleepiness Scale. In the second approach, however, the entire data set with the maximum amount of information regarding driver drowsiness is scrutinized. For both approaches, the dependency between single features and drowsiness is studied statistically using correlation coefficients. The results show that the drowsiness dependency to features evolves to a larger extent non-linearly rather than linearly. Moreover, we show that for some features, different trends with respect to drowsiness are possible among different subjects. Consequently, we challenge warning systems which rely only on a single feature for their decision strategy and underscore that they are prone to high false alarm rates. In order to study whether a single feature is suitable for predicting safety-critical events, we study the overall variation of the features for all subjects shortly before the occurrence of the first unintentional lane departure and first unintentional microsleep in comparison to the beginning of the drive. Based on statistical tests, before the lane departure, most of the features change significantly. Therefore, we justify the role of blink features for the early driver drowsiness detection. However, this is not valid for the variation of features before the microsleep. We also focus on all 19 blink-based features together as one set. We assess the driver state by artificial neural network, support vector machine and k-nearest neighbors classifiers for both binary and multi-class cases. There, binary classifiers are trained both subject-independent and subject-dependent to address the generalization aspects of the results for unseen data. For the binary driver state prediction (awake vs. drowsy) using blink features, we have attained an average detection rate of 83% for each classifier separately. For 3-class classification (awake vs. medium vs. drowsy), however, the result was only 67%, possibly due to inaccurate self-rated vigilance states. Moreover, the issue of imbalanced data is addressed using classifier-dependent and classifier-independent approaches. We show that for reliable driver state classification, it is crucial to have events of both awake and drowsy phases in the data set in a balanced manner. The reason is that the proposed solutions in previous researches to deal with imbalanced data sets do not generalize the classifiers, but lead to their overfitting. The drawback of driving simulators in comparison to real driving is also discussed and to this end we perform a data reduction approach as a first remedy. As the second approach, we apply our trained classifiers to unseen drowsy data collected under real driving condition to investigate whether the drowsiness in driving simulators is representative of the drowsiness under real road conditions. With an average detection rate of about 68% for all classifiers, we conclude their similarity. Finally, we discuss feature dimension reduction approaches to determine the applicability of extracted features for in-vehicle warning systems. On this account, filter and wrapper approaches are introduced and compared with each other. Our comparison results show that wrapper approaches outperform the filter-based methods

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed