Abstract
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.

Abstract

Abstract
The past years have witnessed a boost in fall detection-related research works, disclosing an extensive number of methodologies built upon similar principles but addressing particular use-cases. These use-cases frequently motivate algorithm fine-tuning, making the modelling stage a time and effort consuming process. This work contributes towards understanding the impact of several of the most frequent requirements for wearable-based fall detection solutions in their performance (usage positions, learning model, rate). We introduce a new machine learning pipeline, trained with a proprietary dataset, with a customisable modelling stage which enabled the assessment of performance over each combination of custom parameters. Finally, we benchmark a model deployed by our framework using the UMAFall dataset, achieving state-of-the-art results with an F1-score of 84.6% for the classification of the entire dataset, which included an unseen usage position (ankle), considering a sampling rate of 10 Hz and a Random Forest classifier.

Abstract
We present RAMBLE(1), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. RAMBLE makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.

Abstract
Deep learning has offered significant performance improvements on several pattern recognition problems. However, the well-known need for large amounts of labeled data limits applicability and performance where those are not available. Hence, this paper proposes an adaptation of the triplet loss for self-learning with entirely unlabeled data, where there is uncertainty in the generated triplets. The methodology was applied to off-the-person electrocardiogram-based biometric authentication and unconstrained face identity verification tasks, including stress experiments designed to simulate more difficult circumstances. Despite the uncertainty related to the use of unlabeled data, the method was mostly capable of avoiding negatively affecting the model's performance. The promising results show the proposed method can be a viable alternative to supervised learning in cases where only unlabeled data are available. The method is especially suitable for training with continuous stream-based datasets such as on person re-identification in video streams and continuous electrocardiogram-based biometrics.

Abstract
In general, games pose interesting and complex problems for the implementation of intelligent agents and are a popular domain in the study of artificial intelligence. In fact, games have been at the center of some of the most well-known achievements in artificial intelligence. From classical board games such as chess, checkers, backgammon and Go, to video games such as Dota 2 and StarCraft II, artificial intelligence research has devised computer programs that can play at the level of a human master and even at a human world champion level. Planning and learning, two well-known and successful paradigms of artificial intelligence, have greatly contributed to these achievements. Although representing distinct approaches, planning and learning try to solve similar problems and share some similarities. They can even complement each other. This has led to research on methodologies to combine the strengths of both approaches to derive better solutions. This paper presents a survey of the multiple methodologies that have been proposed to integrate planning and learning in the context of games. In order to provide a richer contextualization, the paper also presents learning and planning techniques commonly used in games, both in terms of their theoretical foundations and applications.