Abstract
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Abstract
Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

Abstract
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

Abstract
In an industrial setting, predicting the remaining useful life-time of equipment and systems is crucial for ensuring efficient operation, reducing downtime, and prolonging the life of costly assets. There are state-of-the-art machine learning methods supporting this task. However, in this paper, we argue, that both efficiency and understandability can be improved by the use of explainable AI methods that analyze the importance of features used by the machine learning model. In the paper, we analyze the feature importance before a failure occurs to identify events in which an increase in importance can be observed and based on that indicate attributes with the most influence on the failure. We demonstrate how the analyses of Shap values near the occurrence of failures can help identify the specific features that led to the failure. This in turn can help in identifying the root cause of the problem and developing strategies to prevent future failures. Additionally, it can be used to identify areas where maintenance or replacement is needed to prevent failure and prolong the useful life of a system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Email marketing plays a key role in business communications and is one of the most widely used applications by consumers. The literature review points to several determinants that, when applied, increase the open rate of newsletters. This research evaluates the impact of six determinants of persuasion on the opening rate of a newsletter in the hotel industry. The determinants are the day of sending, the time of sending, subject line personalization, scarcity appeal, curiosity appeal, and authority figure. The chosen methodology focused on real experiments, using a high-end luxury hotel, and the respective customer database. The newsletter was sent to the subscriber list, where one part received the control and the other part received a variant with the test version. Ten A/B tests were conducted for each determinant. The results obtained were not in line with what is indicated in the literature review. Although the literature review yielded results that showed that the application of determinants increased the open rate of newsletters, this study obtained findings to the opposite and did not confirm what was prescribed by the reviewed literature. The results of the A/B tests were conclusive and revealed that the determinants did not increase the open rate of newsletters.