Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR-images with inherent scale selection

Selection of the best set of scales is problematic when developing signaldriven approaches for pixel-based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class-conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class-conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimised by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class-conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic MR images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal-driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterise spatial properties of labelled images

BIOMOLECULE INSPIRED DATA SCIENCE

BIOMOLECULE INSPIRED DATA SCIENC

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Challenges in machine learning for predicting psychological attributes from smartphone data

Predicting psychological attributes using psychometric approaches is a complex task that involves estimating latent constructs that cannot be directly measured. Psychometrics focuses on the measurement and assessment of psychological attributes, such as personality traits, behavioral patterns, or psychological disorders. Traditionally, personality assessment relied on self-report questionnaires, but advancements in technology have opened up new possibilities for assessment, particularly through the analysis of digital footprints. Smartphone sensor data has become particularly valuable in this context. By analyzing data related to movement, conversation patterns, activities, and interests, it is possible to gather insights that can contribute to predicting psychological attributes. Machine learning techniques are commonly employed to develop predictive models in this field. However, it is essential to ensure that the predictions are meaningful, accepted, and interpretable to gain trust from users. Interpreting machine learning models is crucial in the context of psychometric prediction. Interpreting the models helps identify biases, understand their operations, and determine the variables they rely on. This process enhances the accuracy of the models, establishes trust in their predictions, and promotes fairness in the prediction process. Given the large datasets involved in using smartphone sensor data, the issue of multicollinearity arises, making it challenging to identify which features are truly essential for predicting psychological attributes. To address this challenge, this thesis focuses on grouping similar features and quantifying their importance, aiming to reduce data complexity and highlight the most relevant factors. Additionally, visualizing the impact of these feature groups can provide a deeper understanding in the behavior of the predictive models.Psychometrie bezieht sich auf die Messung psychologischer Merkmale wie Persönlichkeitsmerkmale, Verhaltensmuster oder psychischer Störungen. Üblicherweise werden hierfür Selbstauskunftsfragebögen verwendet, da psychologische Merkmale oft nicht direkt messbar sind. Dank technologischer Fortschritte eröffnen sich jedoch moderne Möglichkeiten, psychologische Merkmale vorherzusagen, insbesondere durch die Analyse digitaler Fußspuren. Besonders relevant sind in diesem Zusammenhang Smartphone-Sensordaten. Durch die Auswertung von Daten zu Bewegungsmustern, Gesprächsverhalten, Aktivitäten und Interessen können Erkenntnisse gewonnen werden, die zur Vorhersage psychologischer Merkmale beitragen können. Hierbei kommen häufig maschinelle Lernverfahren zum Einsatz. Dabei ist es wichtig sicherzustellen, dass die Vorhersagen sinnvoll, akzeptiert und interpretierbar sind. Die Interpretation maschineller Lernverfahren spielt bei der Vorhersage psychologischer Merkmale eine entscheidende Rolle. Sie hilft dabei, die Funktionsweise der Modelle zu verstehen und wichtige Variablen zu identifizieren. Bei der Verwendung von Smartphone-Daten entstehen große Datensätze, was das Problem der Multikollinearität mit sich bringt. Dies erschwert die Bestimmung, welche Merkmale tatsächlich relevant sind, um psychologische Merkmale vorherzusagen. Um dieser Herausforderung zu begegnen, konzentriert sich diese Arbeit darauf, ähnliche Merkmale zu gruppieren und ihre Bedeutung zu quantifizieren. Dadurch kann die Komplexität der Daten reduziert und die relevantesten Faktoren hervorgehoben werden. Darüber hinaus kann die Visualisierung der Effekte dieser Merkmalsgruppen ein besseres Verständnis für das Verhalten der Vorhersagemodelle liefern

Towards Automatic Speech-Language Assessment for Aphasia Rehabilitation

Speech-based technology has the potential to reinforce traditional aphasia therapy through the development of automatic speech-language assessment systems. Such systems can provide clinicians with supplementary information to assist with progress monitoring and treatment planning, and can provide support for on-demand auxiliary treatment. However, current technology cannot support this type of application due to the difficulties associated with aphasic speech processing. The focus of this dissertation is on the development of computational methods that can accurately assess aphasic speech across a range of clinically-relevant dimensions. The first part of the dissertation focuses on novel techniques for assessing aphasic speech intelligibility in constrained contexts. The second part investigates acoustic modeling methods that lead to significant improvement in aphasic speech recognition and allow the system to work with unconstrained speech samples. The final part demonstrates the efficacy of speech recognition-based analysis in automatic paraphasia detection, extraction of clinically-motivated quantitative measures, and estimation of aphasia severity. The methods and results presented in this work will enable robust technologies for accurately recognizing and assessing aphasic speech, and will provide insights into the link between computational methods and clinical understanding of aphasia.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140840/1/ducle_1.pd

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution