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The advent of the Industrial Internet of Things (IIoT) has led to the availability of huge amounts of data, that can be used to train advanced Machine Learning algorithms to perform tasks such as Anomaly Detection, Fault Classification and Predictive Maintenance. Even though not all pieces of equipment are equipped with sensors yet, usually most of them are already capable of logging warnings and alarms occurring during operation. Turning this data, which is easy to collect, into meaningful information about the health state of machinery can have a disruptive impact on the improvement of efficiency and up-time. The provided dataset consists of a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 2019-02-21 to 2020-06-17. There are 154 distinct alarm codes, whose distribution is highly unbalanced. This data can be used to address the following tasks: 

1. Next alarm forecasting: this problem can be framed as a supervised multi-class classification task, or a binary classification task when a specific alarm code is considered.
2. Predicting alarms occurring in a future time frame: here the goal is to forecast the occurrence of certain alarm types in a future time window. Since many alarms can occur, this is a supervised multi-label classification.
3. Future alarm sequence prediction: here the goal is predicting an ordered sequence of future alarms, in a sequence-to-sequence forecasting scenario.
4. Anomaly Detection: the task is to detect abnormal equipment conditions, based on the pattern of alarms sequence. This task can be either unsupervised, if only the input sequence is considered, or supervised if future alarms are taken into account to assess whether or not there is an anomaly.

 All of the above tasks can also be studied from a continual learning perspective. Indeed, information about the serial code of the specific piece of equipment can be used to train the model; however, a scalable model should also be easy to apply to new machines, without the need of a new training from scratch. The collection and release of this dataset has been supported by the Regione Veneto project PreMANI (MANIFATTURA PREDITTIVA: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione della Qualita e la Manutenzione Intelligente - PREDICTIVE MAINTENANCE: design, development and implementation of Digital Manufacturing solutions for the intelligent quality and maintenance systems).

The Industrial IoT (IIoT) paradigm represents an attractive opportunity for new generation measurement applications, which are increasingly based on efficient and reliable communication systems to allow the extensive availability of continuous data from instruments and/or sensors, thus enabling real-time measurement analysis. Nevertheless, different communication systems and heterogeneous sensors and acquisition systems may be found in an IIoT-enabled measurement application, so that solutions need to be defined to tackle the issue of seamless, effective, and low-latency interoperability. A significant and appropriate solution is the Open Platform Communications (OPC) Unified Architecture (UA) protocol, thanks to its object–oriented structure that allows a complete contextualization of the information. The intrinsic complexity of OPC UA, however, imposes a meaningful performance assessment to evaluate its suitability in the aforementioned context. To this aim, this paper presents the design of a general yet accurate and reproducible measurement setup that will be exploited to assess the performance of the main open source implementations of OPC UA. The final goal of this work is to provide a characterization of the impact of this protocol stack in an IIoT-enabled Measurement System, in particular in terms of both the latency introduced in the measurement process and the power consumption.

The last decade has seen tremendous improvements in technologies for Type 1 Diabetes (T1D) management, in particular the so-called artificial pancreas (AP), a wearable closed-loop device modulating insulin injection based on glucose sensor readings. Unluckily, the AP actuator, an insulin pump, is subject to failures, with potentially serious consequences for subject safety. This calls for the development of advanced monitoring systems, leveraging the unprecedented data availability. This paper tackles for the first time the problem of automatically detecting pump faults with multidimensional data-driven anomaly detection (AD) methodologies. The approach allows to avoid the subtask of identifying a physiological model, typical of model-based approaches. Furthermore, we employ unsupervised methods, removing the need of labeled data for training, hardly available in practice. The adopted data-driven AD methods are local outlier factor, connectivity-based outlier factor, and isolation forest. Moreover, we propose a modification of these methods to cope with the dynamic nature of the underlying problem. The algorithms were tuned and tested on: 1) two-synthetic 100-patients' data set, of one-month data each, generated using the ``UVA/Padova T1D Simulator,'' a large-scale nonlinear computer simulator of T1D subject physiology, largely adopted in AP research and accepted by the American Food and Drug Administration as a replacement of preclinical animal trials for AP and 2) a real 7-patients' data set consisting of one month in free-living conditions. The satisfactory accuracy of the proposed approach paves the way to the embedding of these methodologies in AP systems or their deployment in remote monitoring systems.

Cooperative robotics is a trending topic nowadays as it makes possible a number of tasks that cannot be performed by individual robots, such as heavy payload transportation and agile manipulation. In this work, we address the problem of cooperative transportation by heterogeneous, manipulatorendowed robots. Specifically, we consider a generic number of robotic agents simultaneously grasping an object, which is to be transported to a prescribed set point while avoiding obstacles. The procedure is based on a decentralized leader-follower Model Predictive Control scheme, where a designated leader agent is responsible for generating a trajectory compatible with its dynamics, and the followers must compute a trajectory for their own manipulators that aims at minimizing the internal forces and torques that might be applied to the object by the different grippers. The Model Predictive Control approach appears to be well suited to solve such a problem, because it provides both a control law and a technique to generate trajectories, which can be shared among the agents. The proposed algorithm is implemented using a system comprised of a ground and an aerial robot, both in the robotic Gazebo simulator as well as in experiments with real robots, where the methodological approach is assessed and the controller design is shown to be effective for the cooperative transportation task.

In many real-world applications of Machine Learning it is of paramount importance not only to provide accurate predictions, but also to ensure certain levels of robustness. Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points. Unfortunately, one of the main issues in adversarial training is that robustness w.r.t. gradient-based attackers is always achieved at the cost of prediction accuracy. In this paper, a new algorithm, called Wasserstein Projected Gradient Descent (WPGD), for adversarial training is proposed. WPGD provides a simple way to obtain cost-sensitive robustness, resulting in a finer control of the robustness-accuracy trade-off. Moreover, WPGD solves an optimal transport problem on the output space of the network and it can efficiently discover directions where robustness is required, allowing to control the directional trade-off between accuracy and robustness. The proposed WPGD is validated in this work on image recognition tasks with different benchmark datasets and architectures. Moreover, real world-like datasets are often unbalanced: this paper shows that when dealing with such type of datasets, the performance of adversarial training are mainly affected in term of standard accuracy.

In aerial robotics, path following constitutes a popular
      task requiring a vehicle to pursue a given trajectory.
      Resting upon the fulfillment of a desired time law,
      trajectory tracking techniques often turn out to be
      ineffective in presence of external disturbances, favoring
      the adoption of maneuver regulation strategies wherein the
      desired trajectory is parameterized in terms of the
      path-variable. In this scenario, this work proposes a new
      delay-compensating maneuver regulation controller for fully
      actuated aerial vehicles, whose aim is to guarantee the
      perfect tracking of a given path in the shortest time
      interval. The innovative aspect of such a solution relies
      on the introduction of a recovery term that compensates for
      possible delays in
      the task execution. In addition, the dual-quaternion
      formalism is adopted to model the dynamics of the aerial
      platforms allowing feedback linearize the whole system,
      including both position and attitude, with a single
      controller. The tests conducted in Gazebo physic simulator
      show that the proposed controller outperforms the popular
      trajectory tracking PID regulators.

One of the main challenges in developing Machine Learning-based solutions for Semiconductor Manu-facturing is the high number of machines in the production and their differences, even when consideringchambers of the same machine; this poses a challenge in the scalability of Machine Learning-based so-lutions in this context, since the development of chamber-specific models for all equipment in the fab isunsustainable. In this work, we present a domain adaptation approach for Virtual Metrology (VM), one ofthe most successful Machine Learning-based technology in this context. The approach provides a commonVM model for two identical-in-design chambers whose data follow different distributions. The approach isbased on Domain-Adversarial Neural Networks and it has the merit of exploiting raw trace data, avoidingthe loss of information that typically affects VM modules based on features. The effectiveness of theapproach is demonstrated on real-world Etching.

Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging gender-related information encoded in WEs.

Through the critical lens of construct validity, we validate the proposed measure on synthetic and real collections. Subsequently, we use GSR to compare widely-used Information Retrieval ranking algorithms, including lexical, semantic, and neural models. We check if and how ranking algorithms based on WEs inherit the biases of the underlying embeddings. We also consider the most common debiasing approaches for WEs proposed in the literature and test their impact in terms of GSR and common performance measures. To the best of our knowledge, GSR is the first specifically tailored measure for IR, capable of quantifying representational harms.

This paper presents an innovative mechatronic design of a high-accuracy pointing mechanism for orbital laser communication terminals. The system is based on a dual-stage architecture and is miniaturized to fit nanosatellite-class spacecraft, aiming to enable optical communication on small-size space platforms. The focus is on control design aspects and on the performance assessment of an experimental prototype under emulated external environmental disturbances.

To increase the classification, the rate of prediction based on existing models requires additional technique or in this case optimizing the model. Hyperparameter tuning is an optimization technique that evaluates and adjusts the free parameters that define the behaviour of classifiers. Data sets were classified practical with classifiers like SVM, k-NN, ANN and DA. To further improve the design efficiency, the secondary optimization level called hyperparameter tuning will be further investigated. DA, SVM, k-NN, decision tree (Tree), logistic regression (LR), random forest tree (RF) and neural network (NN) are evaluated. The k-NN provided 96.47% of the test sets with the best reliability in classifications. Bayesian optimization has been used to refine the hyperparameter; hence, standardize Euclidean distance metric with a k value of one is the ideal hyperparameters which could achieve classification performance of 97.16%.

This chapter demonstrates the pipeline from data collection until classifier models that achieve the best possible model in identifying the disparity between hunger states. The pre-processing segment describes the features of the data sets obtained by means of image processing. The method includes the simple moving average (SMA), downsizing factors, dynamic time warping (DTW) and clustering by the k-means method. This is to rationally assign the necessary significant information from the data collected and processed the images captured for demand feeder and fish motion as a synthesis for anticipating the state of fish starvation. The selection of features in this study takes place via the boxplot analysis and the principal component analysis (PCA) on dimensionality reduction. Finally, the validation of the hunger state will be addressed by comparing machine learning (ML) classifiers, namely the discriminant analysis (DA), support vector machine (SVM) and k-nearest neighbour (k-NN). The outcome in this chapter will validate the features from image processing as a tool for identifying the behavioural changes of the fish in school size.

Machine Learning-based Anomaly Detection (AD) approaches are efficient tools to monitor complexprocesses. One of the advantages of such approaches is that they provide a unique anomaly indicator,a quantitative index that captures the degree of ’outlierness’ of the process at hand considering possiblyhundreds or more variables at the same time, the typical scenario in semiconductor manufacturing. Oneof the drawback of such approaches is that Root Cause Analysis is not guided by the system itself. Inthis work, we show the effectiveness of a method, called DIFFI, to equip Isolation Forest, one of themost popular AD algorithms, with interpretability traits that can help corrective actions and knowledgeunderstanding. Such approach is validated on real world semiconductor manufacturing data related to aChemical Vapor Deposition process.

This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.

This chapter highlights the findings of the developmental monitoring systems for swimming pattern or motion analysis with regard to feeding behaviour. A benchmark for examining the framework on how scientists control fish in animal variable function factors was gathered and referred to gauge the adequate design in constructing a viable device. The validation of image processing and automated demand feeder to determine the results will also be considered, as a validation aspect between the system of tracking and the behaviour of the Lates calcarifer where the pixel intensity will be extracted as the features. The results of this chapter will enable the reader on the development of an integrated feeder scheme that consolidates surveillance scheme to identify the feeding behaviour and relation towards the specific growth rate (SGR).

Solutions for the quality control of metallic surfaces are proposed. Specifically, we study a deflectometric apparatus based on coaxial structured light and the related algorithmic procedure, which is able to detect the faulty surface of a sample captured by a video sequence. First, by considering the metallic surface a dynamic scene illuminated under different light conditions, we develop the descriptor residuals of linear evolution of light (RLEL) that extracts the defectiveness information starting from the movement of the object without explicitly considering the physical characteristics of the light structure. Then, leveraging on RLEL, we present a hybrid learning (HL) technique capable of overcoming the data-driven approach used in classic deep learning (DL). By exploiting a multisteps training process, we combine the model-based descriptor RLEL and a classical data-driven convolutional neural network (CNN) to obtain an unconventional gray-box CNN, which exceeds the performance of popular DL solutions such as 3-D-inception and 3-D-residual DL networks. Remarkably, HL also shows its validity in comparing the performance of the same network structures trained not in a hybrid way, namely without the injection of the model-based information given by RLEL.

We study the solution of a time-varying optimization problem which is observed, that is, it is known, only intermittently. We propose three approaches based on the prediction-correction scheme for solving this problem by exploiting splitting methods. We present convergence results in mean to a bounded asymptotical error, and showcase them in a numerical example featuring a regression problem.

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.

In knowledge-based systems, besides obtaining good output prediction accuracy, it is crucial to understand the subset of input variables that have most influence on the output, with the goal of gaining deeper insight into the underlying process. These requirements call for logistic model estimation techniques that provide a sparse solution, i.e., where coefficients associated with non-important variables are set to zero. In this work we compare the performance of two methods: the first one is based on the well known Least Absolute Shrinkage and Selection Operator (LASSO) which involves regularization with an ?1 norm; the second one is the Relevance Vector Machine (RVM) which is based on a Bayesian implementation of the linear logistic model. The two methods are extensively compared in this paper, on real and simulated datasets. Results show that, in general, the two approaches are comparable in terms of prediction performance. RVM outperforms the LASSO both in term of structure recovery (estimation of the correct non-zero model coefficients) and prediction accuracy when the dimensionality of the data tends to increase. However, LASSO shows comparable performance to RVM when the dimensionality of the data is much higher than number of samples that is p>>n 

The identification of relevant parameters that could describe the state of fish hunger is vital for ensuring the appropriate allocation of food to the fish. The establishment of these relevant parameters is non-trivial, particularly when developing an automated demand feeder system. The present inquiry is being undertaken to determine the hunger state of Lates calcarifer. For data collection, a video analysis system is used, and the video was taken all day, where the fish was fed by an automatic feeding system. Sixteen characteristics of the raw data set have been extracted through feature engineering for 0.5 min, 1.0 min, 1.5 min and 2.0 min, respectively, in accordance with the mean, peak, minimum and variability of each of the different time window scales. Furthermore, the features extracted have been evaluated through principal component analysis (PCA) both for dimension reduction and PCA with varimax rotation. The details were then categorized using support vector machine (SVM), K-NN and random forest tree (RF) classifiers. The best identification accuracy was shown with eight described features in the varimax-based PCA. The forecast results based on the K-NN model built on selected data characteristics showed a level of 96.5% indicating that the characteristics analysed were crucial to classifying the actions of hunger among fisheries.