Abstract
The recent Bluetooth 5.1 specification introduced the use of Angle-of-Arrival (AoA) information which enables the design of novel low-cost indoor positioning systems. Existing approaches rely on multiple fixed gateways equipped with antenna arrays, in order to determine the location of an arbitrary number of simple mobile omni-directional emitters. In this paper, we instead present an approach where mobile receivers are equipped with antenna arrays, and the fixed infrastructure is composed of battery-powered beacons. We implement a simulator to evaluate the solution using a real-world data set of AoA measurements. We evaluated the solution as a function of the number of beacons, their transmission period, and algorithmic parameters of the position estimation. Sub-meter accuracy is achievable using 1 beacon per 15 m(2) and a beacon transmission period of 500 ms.

Abstract
Social networks are becoming larger and more complex as new ways of collecting social interaction data arise (namely from online social networks, mobile devices sensors, ...). These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in modelling and mining these networks: it has not only been applied for exploratory analysis (thus allowing the discovery of interaction patterns), but also for more demanding and elaborated tasks such as community detection and link prediction. In this work, we provide an overview of the methods based on tensor decomposition for the purpose of analysing time-evolving social networks from various perspectives: from community detection, link prediction and anomaly/event detection to network summarization and visualization. In more detail, we discuss the ideas exploited to carry out each social network analysis task as well as its limitations in order to give a complete coverage of the topic.

Abstract
The study presented in this article aims at understanding the relevance of mobility initiatives to the internationalisation efforts of Higher Education Institutions (HEIs). By building upon relevant literature, 17 propositions related to this contribution were identified. Empirical evidence from a concrete case of a European university was then used to evaluate those propositions. Data was collected from individual interviews to 19 outgoing faculty and from focus groups with 32 incoming students, resulting in the identification of the promises and pitfalls of mobility. The study concludes that HEIs must define clear strategies and carefully manage their mobility activities to maximise the potential benefits for internationalisation. Based on this main implication, it presents a set of managerial recommendations that may be relevant for those involved in administering or promoting international mobility programmes at universities, governments or international organisations, and for researchers in higher education.

Abstract
Flipped Classroom approach was implemented in an Automation course with around 100 students. Videos focused on GRAFCET topics were given to students prior to class and problem-based challenges were solved in class by the students in a collaborative way. The teacher’s role was to guide students in their learning process. The goal was to identify students’ behavior regarding this learning approach, and the videos in particular, by using questionnaires. Result analysis shows a positive feedback from students motivating teachers to enlarge this learning approach to other courses. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
The extraction of the beat from musical audio signals represents a foundational task in the field of music information retrieval. While great advances in performance have been achieved due the use of deep neural networks, significant shortcomings still remain. In particular, performance is generally much lower on musical content that differs from that which is contained in existing annotated datasets used for neural network training, as well as in the presence of challenging musical conditions such as rubato. In this paper, we positioned our approach to beat tracking from a real-world perspective where an end-user targets very high accuracy on specific music pieces and for which the current state of the art is not effective. To this end, we explored the use of targeted fine-tuning of a state-of-the-art deep neural network based on a very limited temporal region of annotated beat locations. We demonstrated the success of our approach via improved performance across existing annotated datasets and a new annotation-correction approach for evaluation. Furthermore, we highlighted the ability of content-specific fine-tuning to learn both what is and what is not the beat in challenging musical conditions.

Abstract
Understanding the surrounding 3D scene is of the utmost importance for many robotic applications. The rapid evolution of machine learning techniques has enabled impressive results when depth is extracted from a single image. High-latency networks are required to achieve these performances, rendering them unusable for time-constrained applications. This article introduces a lightweight Convolutional Neural Network (CNN) for depth estimation, NEON, designed for balancing both accuracy and inference times. Instead of solely focusing on visual features, the proposed methodology exploits the Motion-Parallax effect to combine the apparent motion of pixels with texture. This research demonstrates that motion perception provides crucial insight about the magnitude of movement for each pixel, which also encodes cues about depth since large displacements usually occur when objects are closer to the imaging sensor. NEON's performance is compared to relevant networks in terms of Root Mean Squared Error (RMSE), the percentage of correctly predicted pixels (delta(1)) and inference times, using the KITTI dataset. Experiments prove that NEON is significantly more efficient than the current top ranked network, estimating predictions 12 times faster; while achieving an average RMSE of 3.118 m and a delta(1) of 94.5%. Ablation studies demonstrate the relevance of tailoring the network to use motion perception principles in estimating depth from image sequences, considering that the effectiveness and quality of the estimated depth map is similar to more computational demanding state-of-the-art networks. Therefore, this research proposes a network that can be integrated in robotic applications, where computational resources and processing-times are important constraints, enabling tasks such as obstacle avoidance, object recognition and robotic grasping.