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The article proposes the use of a smartwatch-based system for measuring the emotions of individuals in a classroom setting with respect to five mood variables: Activation, Tiredness, Pleasance, Quality of Presentation and Understanding. Internal (body) and external (environment) data such as movement, heart rate, noise, temperature and humidity were collected through the built-in sensors of the smartwatch. The system was verified by means of a longitudinal study that has been carried out in a series of workshops and lectures. Through experience-based sampling, participants were polled at periodic time intervals asking them to enter a self-assessment of the aforementioned mood states directly on the smartwatch. The goal was to demonstrate whether sensor data can be used to effectively predict the five moods. By resorting to a machine learning approach our system was able to predict the moods with an accuracy ranging between 89-95 percent for single-output classification, 92-99 percent for the chain classification task and of approximately 93 percent for the multi-output analysis. Our results showed also that body signals are better predictors compared to the external environmental variables. These results demonstrate and verify the potential of smartwatches in collecting and predicting human emotions, enabling dynamic feedback loops to enhance user experience. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modelling approach for the medium term planning of manufacturing systems characterized by coexisting large volume and small volume products, in which changeovers play a critical role in achieving maximum efficiency. Although the model is based upon the large time bucket paradigm, it is capable of representing start-ups by sequencing the lots of items produced in long sequences. Specifically, we consider a capacitated lot-sizing problem with parallel machines, in which the products have a three-level hierarchical structure: end items are aggregated into families, which in turn are aggregated into types. Minor set-ups and major changeovers take place when switching between families within a type or between types, respectively. The resulting mathematical programming problem is decomposed via Lagrangean relaxation, leading to sub-problems that can be solved in polynomial time. The whole approach has been validated by applying it to large size case studies drawn from the textile industry. [ABSTRACT FROM AUTHOR]

The study of the impact of investor sentiment on stock returns has gained increasing momentum in the past few years. It has been widely accepted that public mood is correlated with financial markets. However, only a few studies discussed how the public mood would affect one of the fundamental problems of computational finance: portfolio management. In this study, we use public financial sentiment and historical prices collected from the New York Stock Exchange (NYSE) to train multiple machine learning models for automatic wealth allocation across a set of assets. Unlike previous studies which set as target variable the asset prices in the portfolio, the variable to predict here is represented by the best asset allocation strategy ex post. Experiments performed on five portfolios show that long short-term memory networks are superior to multi-layer perceptron and random forests producing, in the period under analysis, an average increase in the revenue across the portfolios ranging between 5% (without financial mood) and 19% (with financial mood) compared to the equal-weighted portfolio. Results show that our all-in-one and end-to-end approach for automatic portfolio selection outperforms the equal-weighted portfolio. Moreover, when using long short-term memory networks, the employment of sentiment data in addition to lagged data leads to greater returns for all the five portfolios under evaluation. Finally, we find that among the employed machine learning algorithms, long short-term memory networks are better suited for learning the impact of public mood on financial time series. [ABSTRACT FROM AUTHOR]

In the context of classification most efforts have been devoted to deriving accurate prediction models from a set of examples whose class is supposed to be known with certainty. However, there are situations where class labels are affected by an intrinsic vagueness, as in ranking customers for marketing campaigns or credit approval. In this paper we propose a new two-phase fuzzy classification method aimed at generating accurate classification rules when labels are uncertain. In the first phase, an ensemble method is applied in order to derive the value of the class membership function for each example in the dataset. In the second phase, an optimal classification model is obtained by solving a fuzzy variant of discrete support vector machines. Computational tests performed on benchmark and real world marketing and credit risk datasets show the effectiveness of the proposed method when it is compared to alternative classification techniques. Furthermore, the tests reveal that the new fuzzy discrete SVM model is a robust regularization method capable of generating stable classification rules, reducing the variance of the error and smoothing out the noise due to outliers. [ABSTRACT FROM AUTHOR]

A new integrated approach to capacity management in complex manufacturing systems is developed and the resulting framework is applied to a case study in tyre production. A hierarchical multilayered decomposition of the planning process is proposed, in which lower layers provide an increased level of detail and accuracy in capacity representation and analysis. Thus, a large and comprehensive model describing the manufacturing system is subdivided into smaller sub-models via relaxation and decomposition techniques. The Lagrangean multipliers provide a bi-directional link among different layers, reducing the risk of sub-optimization and infeasibility of the aggregate plans. Each of thc sub-models is caster to solve than the original one and involves a different set of decision variables. Moreover, they relate to different levels of the management hierarchy, so that there emerges a strict correspondence between the decomposition scheme and thc decision process underlying capacity management. [ABSTRACT FROM AUTHOR]