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The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action is kept stationary by the environment through time. Nevertheless, in many real-world applications this assumption does not hold and the agent has to face a non-stationary environment, that is, with a changing reward distribution. Thus, we present a new MAB algorithm, named f-Discounted-Sliding-Window Thompson Sampling (f-dsw TS), for non-stationary environments, that is, when the data streaming is affected by concept drift. The f-dsw TS algorithm is based on Thompson Sampling (TS) and exploits a discount factor on the reward history and an arm-related sliding window to contrast concept drift in non-stationary environments. We investigate how to combine these two sources of information, namely the discount factor and the sliding window, by means of an aggregation function f (.). In particular, we proposed a pessimistic (f = min), an optimistic (f = max), as well as an averaged (f = mean) version of the f-dsw TS algorithm. A rich set of numerical experiments is performed to evaluate the f-dsw TS algorithm compared to both stationary and non-stationary state-of-the-art TS baselines. We exploited synthetic environments (both randomly-generated and controlled) to test the MAB algorithms under different types of drift, that is, sudden/abrupt, incremental, gradual and increasing/decreasing drift. Furthermore, we adapt four real-world active learning tasks to our framework—a prediction task on crimes in the city of Baltimore, a classification task on insects species, a recommendation task on local web-news, and a time-series analysis on microbial organisms in the tropical air ecosystem. The f-dsw TS approach emerges as

The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental studies featured coralline algae for their ecological significance in both recent and past Oceans and need to rely on robust taxonomy. Research efforts towards new putative diagnostic tools have recently been focused on cell wall ultrastructure. In this work, we explored a new classification tool for coralline algae, using fine-tuning pretrained Convolutional Neural Networks (CNNs) on SEM images paired to morphological categories, including cell wall ultrastructure. We considered four common Mediterranean species, classified at genus and at the species level (Lithothamnion corallioides, Mesophyllum philippii, Lithophyllum racemus, Lithophyllum pseudoracemus). Our model produced promising results in terms of image classification accuracy given the constraint of a limited dataset and was tested for the identification of two ambiguous samples referred to as L. cf. racemus. Overall, explanatory image analyses suggest a high diagnostic value of calcification patterns, which significantly contributed to class predictions. Thus, CNNs proved to be a valid support to the morphological approach to taxonomy in coralline algae.