The current situation with COVID-19 is changing our courses of action toward ensuring health security. This is particularly crucial in airports, which usually receive more than 300,000 travellers in one single day. In this work, we present an Internet of Things (IoT) network to monitor the status of toilets and improve their maintenance. The system is based on IoT networks with different sensors to control soap levels, room capacity, distances, temperature, and humidity. This information is processed by a multi-agent system that detects possible anomalies and makes decisions accordingly. A case study in a real environment is developed in order to demonstrate the usefulness of the system. The results show that the proposed method can be used to successfully manage and control airport toilets.

In tonal music, musical tension is strongly associated with musical expression, particularly with expectations and emotions. Most listeners are able to perceive musical tension subjectively, yet musical tension is difficult to be measured objectively, as it is connected with musical parameters such as rhythm, dynamics, melody, harmony, and timbre. Musical tension specifically associated with melodic and harmonic motion is called tonal tension. In this article, we are interested in perceived changes of tonal tension over time for chord progressions, dubbed tonal tension profiles. We propose an objective measure capable of capturing tension profile according to different tonal music parameters, namely, tonal distance, dissonance, voice leading, and hierarchical tension. We performed two experiments to validate the proposed model of tonal tension profile and compared against Lerdahl’s model and MorpheuS across 12 chord progressions. Our results show that the considered four tonal parameters contribute differently to the perception of tonal tension. In our model, their relative importance adopts the following weights, summing to unity: dissonance (0.402), hierarchical tension (0.246), tonal distance (0.202), and voice leading (0.193). The assumption that listeners perceive global changes in tonal tension as prototypical profiles is strongly suggested in our results, which outperform the state-of-the-art models.

Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a management tool that includes an Android application that acts on the user’s smartphone by allowing or blocking resources according to the context, in order to address this issue. Back-end web server processes and imposes a protocol according to the conditions that the user selected beforehand. The experimental results show that the proposed solution successfully communicates with the Android Device Administration framework, and the device appropriately reacts to the expected set of permissions imposed according to the user’s profile with low response time and resource usage.

Irish traditional music contains a variety of features that help to understand the cultural idiosyncrasies of Irish people. One of the most prominent aspects is genre, which provides information on the musical structure, the composition’s essence and, in turn, offers insights into the cultural nuances and behaviors inherent to a particular community. Identifying genre in traditional music can be challenging without prior specialized musical knowledge. Hence, the integration of technological tools becomes valuable, offering assistance in discerning and categorizing genres, thereby facilitating a broader appreciation and understanding of traditional musical heritage. In this study, we introduce a novel system designed to classify the genre of Irish music by analyzing a latent representation of the raw audio extracted from synthesized scores. We evaluate prevailing genre classification models for Irish traditional music and highlight the need for specialized approaches to capture its unique characteristics. We demonstrate how this novel latent representation could be effective in the task of classifying Irish traditional music genres. To support this research, we compile an extensive score dataset of different types of Irish traditional music scores from The Session platform and the Irish Traditional Music Archive (ITMA). This new dataset can aid in the research and exploration of Irish music genres. We have also developed a practical tool using XML and ABC data formats to assist the ITMA in efficiently cataloguing newly acquired data.