Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools

n recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor envi- ronments. The new Industry-4.0 model allows smart factories to become very advanced IT industries, generating an ever- increasing amount of valuable data. As a consequence, the neces- sity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision making process. This paper discusses the latest software technologies needed to collect, manage and elaborate all data generated through innovative IoT architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life-cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step towards the rich landscape of literature for readers approaching this field, and as a global yet detailed overview of the current state-of-the-art in the Industry 4.0 domain for experts. As a case study, we discuss in detail the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Protective Behavior Detection in Chronic Pain Rehabilitation: From Data Preprocessing to Learning Model

Chronic pain (CP) rehabilitation extends beyond physiotherapist-directed clinical sessions and primarily functions in people's everyday lives. Unfortunately, self-directed rehabilitation is difficult because patients need to deal with both their pain and the mental barriers that pain imposes on routine functional activities. Physiotherapists adjust patients' exercise plans and advice in clinical sessions based on the amount of protective behavior (i.e., a sign of anxiety about movement) displayed by the patient. The goal of such modifications is to assist patients in overcoming their fears and maintaining physical functioning. Unfortunately, physiotherapists' support is absent during self-directed rehabilitation or also called self-management that people conduct in their daily life. To be effective, technology for chronic-pain self-management should be able to detect protective behavior to facilitate personalized support. Thereon, this thesis addresses the key challenges of ubiquitous automatic protective behavior detection (PBD). Our investigation takes advantage of an available dataset (EmoPain) containing movement and muscle activity data of healthy people and people with CP engaged in typical everyday activities. To begin, we examine the data augmentation methods and segmentation parameters using various vanilla neural networks in order to enable activity-independent PBD within pre-segmented activity instances. Second, by incorporating temporal and bodily attention mechanisms, we improve PBD performance and support theoretical/clinical understanding of protective behavior that the attention of a person with CP shifts between body parts perceived as risky during feared movements. Third, we use human activity recognition (HAR) to improve continuous PBD in data of various activity types. The approaches proposed above are validated against the ground truth established by majority voting from expert annotators. Unfortunately, using such majority-voted ground truth causes information loss, whereas direct learning from all annotators is vulnerable to noise from disagreements. As the final study, we improve the learning from multiple annotators by leveraging the agreement information for regularization

Clustering of Bulk RNA-Seq Data and Missing Data Methods in Deep Learning

Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters is. In addition, few methods exist for unsupervised clustering of bulk RNA-seq samples, and no method exists that can do so while simultaneously adjusting for between-sample global normalization factors, accounting for potential confounding variables, and selecting cluster-discriminatory genes. In Chapter 2, we present FSCseq (Feature Selection and Clustering of RNA-seq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and employs a quadratic penalty method with a SCAD penalty. The maximization is done by a penalized EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership. The field of deep learning has also boomed in popularity in recent years, fueled initially by its performance in the classification and manipulation of image data, and, more recently, in areas of public health, medicine, and biology. However, the presence of missing data in these latter areas is very common, and involves more complicated mechanisms of missingness than the former. While a rich statistical literature exists regarding the characterization and treatment of missing data in traditional statistical models, it is unclear how such methods may extend to deep learning methods. In Chapter 3, we present NIMIWAE (Non-Ignorably Missing Importance Weighted AutoEncoder), an unsupervised learning algorithm which provides a formal treatment of missing data in the context of Importance Weighted Autoencoders (IWAEs), an unsupervised Bayesian deep learning architecture, in order to perform single and multiple imputation of missing data. We review existing methods that handle up to the missing at random (MAR) missingness, and propose methods to handle the more difficult missing not at random (MNAR) scenario. We show that this extension is critical to ensure the performance of data imputation, as well as downstream coefficient estimation. We utilize simulation examples to illustrate the impact of missingness on such tasks, and compare the performance of several proposed methods in handling missing data. We applied our proposed methods to a large electronic healthcare record dataset, and illustrated its utility through a qualitative look at the downstream fitted models after imputation. Finally, in Chapter 4, we present dlglm (deeply-learned generalized linear model), a supervised learning algorithm that extends the missing data methods from Chapter 3 directly to supervised learning tasks such as classification and regression. We show that dlglm can be trained in the presence of missing data in both the predictors and the response, and under the MCAR, MAR, and MNAR missing data settings. We also demonstrate that the trained dlglm model can directly predict response on partially-observed samples in the prediction or test set, drawing from the learned variational posterior distribution of the missing values conditional on the observed values during model training. We utilize statistical simulation and real-world datasets to show the impact of our method in increasing accuracy of coefficient estimation and predictionunder different mechanisms of missingness.Doctor of Philosoph

Harnessing the Power of Generative Models for Mobile Continuous and Implicit Authentication

Authenticating a user's identity lies at the heart of securing any information system. A trade off exists currently between user experience and the level of security the system abides by. Using Continuous and Implicit Authentication a user's identity can be verified without any active participation, hence increasing the level of security, given the continuous verification aspect, as well as the user experience, given its implicit nature. This thesis studies using mobile devices inertial sensors data to identify unique movements and patterns that identify the owner of the device at all times. We implement, and evaluate approaches proposed in related works as well as novel approaches based on a variety of machine learning models, specifically a new kind of Auto Encoder (AE) named Variational Auto Encoder (VAE), relating to the generative models family. We evaluate numerous machine learning models for the anomaly detection or outlier detection case of spotting a malicious user, or an unauthorised entity currently using the smartphone system. We evaluate the results under conditions similar to other works as well as under conditions typically observed in real-world applications. We find that the shallow VAE is the best performer semi-supervised anomaly detector in our evaluations and hence the most suitable for the design proposed. The thesis concludes with recommendations for the enhancement of the system and the research body dedicated to the domain of Continuous and Implicit Authentication for mobile security