Abstract
This document presents the case study for the ABZ 2023 conference. The case study introduces a safety critical interactive system called AMAN (Arrival MANager), which is a partly-autonomous scheduler of landing sequences of aircraft in airports. This interactive systems interleaves Air Traffic Controllers activities with automation in AMAN. While some AMAN systems are currently deployed in airports, we consider here only a subset of functions which represent a challenge in modelling and verification.

Abstract
The optimal management of distributed energy resources (DERs) and renewable-based generation in multi -energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy sys-tems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic pro-gramming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk -seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The pro-posed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.

Abstract
https://aimc2023.pubpub.org/pub/latent-spaces-tonal-music Variational Autoencoders (VAEs) have proven to be effective models for producing latent representations of cognitive and semantic value. We assess the degree to which VAEs trained on a prototypical tonal music corpus of 371 Bach's chorales define latent spaces representative of the circle of fifths and the hierarchical relation of each key component pitch as drawn in music cognition. In detail, we compare the latent space of different VAE corpus encodings — Piano roll, MIDI, ABC, Tonnetz, DFT of pitch, and pitch class distributions — in providing a pitch space for key relations that align with cognitive distances. We evaluate the model performance of these encodings using objective metrics to capture accuracy, mean square error (MSE), KL- divergence, and computational cost. The ABC encoding performs the best in reconstructing the original data, while the Pitch DFT seems to capture more information from the latent space. Furthermore, an objective evaluation of 12 major or minor transpositions per piece is adopted to quantify the alignment of 1) intra- and inter-segment distances per key and 2) the key distances to cognitive pitch spaces. Our results show that Pitch DFT VAE latent spaces align best with cognitive spaces and provide a common-tone space where overlapping objects within a key are fuzzy clusters, which impose a well-defined order of structural significance or stability — i.e., a tonal hierarchy. Tonal hierarchies of different keys can be used to measure key distances and the relationships of their in-key components at multiple hierarchies (e.g., notes and chords). The implementation of our VAE and the encodings framework are made available online.

Abstract
Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission, the fifth requirement (“Diversity, non-discrimination and fairness”) declares: “In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system’s life cycle. [...] This requirement is closely linked with the principle of fairness”. In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature.

Abstract
Recent societal changes have brought new challenges to contemporary organisations, e.g., how to properly manage the work-family/family-work dyad and, thus, promote adequate task performance. This paper aimed to study the relationship between conflict (work-family and family-work) and task performance, and whether this relationship was moderated by well-being. Thus, the following hypotheses were formulated: (1) conflict (work-family and family-work) is negatively associated with task performance; (2) conflict (work-family and family-work) is negatively associated with well-being; (3) well-being is positively associated with task performance; and (4) well-being moderates the relationship between conflict (work-family and family-work) and task performance. A total of 596 subjects participated in this study, all employed in Portuguese organisations. The results underlined that only family-work conflict was negatively and significantly associated with task performance. Work-family conflict established a negative and significant relationship with well-being. Well-being was positively and significantly associated with performance and moderated the relationship between conflict (work-family and family-work) and task performance. These results show that organisations should provide employees with situations that promote their well-being, especially in Portugal, where a relationship culture exists (rather than task culture, which is predominant in the USA and Canada, for example) which means that additional and considerable time must be dedicated to personal and family matters for people to fit in and be accepted harmoniously.

Abstract
The growing demand for conventional internal combustion engine vehicles can aggravate the current energy and environmental crisis by presenting a higher dependence on fossil fuels and a higher level of greenhouse gases. The transition to electric mobility is a sustainable solution to mitigate the negative impact on the environment and energy security. In recent years, global sales of electric vehicles (EVs) have grown steadily; however, their worldwide market share is still less than 10%. The present study aims to improve and strengthen the knowledge base on consumer behavior toward EV purchases by investigating the antecedents of EV purchase intention, as well as their impact. This paper is based on a systematic literature review where 63 articles published between 1994 and 2021 were analyzed. The antecedents were classified into three main categories: consumer characteristics, EV characteristics, and EV-related policies. A summary model represents the impact information of each of the main antecedents. The descriptive results of the sample are also discussed. Finally, this study indicates directions for future research and recommendations for developing the most effective strategies and policies that will accelerate the transition to a more sustainable future.