Abstract
Although selection menus are widely used for interaction, their use on 3D virtual reality applications needs to be objectively assessed. The focus of this study is to evaluate a traditional panel and a radial menu in two distinct virtual environment placements (i.e. fixed on the wall and following the users' hands). Fifty-one participants used two different menus of the four possible combinations. To evaluate the menus' effectiveness and efficiency, we measured usability (System Usability Scale Questionnaire), user satisfaction (After-Scenario Questionnaire), time to finish the tasks (in seconds) and the number of unnecessary steps (errors) performed by the users. Overall results showed a clear preference for the traditional panel menu type and the fixed wall placement of the menu. We conclude that all menu types perform well, despite different user preferences, and that fixing the menu to the wall gives users a better overview of both the menu and the virtual environment, improving their ability to perceive their actions on the menu.

Abstract
We propose the use of Temporal Convolutional Networks for audio-based beat tracking. By contrasting our convolutional approach with the current state-of-the-art recurrent approach using Bidirectional Long Short-Term Memory, we demonstrate three highly promising attributes of TCNs for music analysis, namely: i) they achieve state-of-the-art performance on a wide range of existing beat tracking datasets, ii) they are well suited to parallelisation and thus can be trained efficiently even on very large training data; and iii) they require a small number of weights. © 2019 IEEE

Abstract
To be competitive, companies must constantly innovate, and having efficient and well-managed supply chains is undoubtedly an important success factor. In the case of spare parts manufacturing, supply chain management is a very complex and arduous task. Quite often, spare parts have to be produced for products that have been on the market for very long, with the need to keep a large and varied stocks to ensure supply service level. With an increasing investment in the development and applications, the Additive Manufacturing (AM) technology can yield significant benefits to spare parts manufacturing. AM allows the production of parts with a high level of customization, without the need for setups, and helps to decrease costs, inventory levels and lead time. This new reality creates numerous challenges, forcing the design reformulation of traditional supply chains, and leading to an allocation of the production of certain types of parts downstream. This paper proposes a simulation model to address the use of the 3D printing technology on the supply chain of an elevator maintenance service provider. The simulation model allows the assessment of new supply chain designs, measuring their performance, thus avoiding the need of experimenting new solutions in the real system. © 2019, IEOM Society International.

Abstract
We propose a multi-task learning approach for simultaneous tempo estimation and beat tracking of musical audio. The system shows state-of-the-art performance for both tasks on a wide range of data, but has another fundamental advantage: due to its multi-task nature, it is not only able to exploit the mutual information of both tasks by learning a common, shared representation, but can also improve one by learning only from the other. The multi-task learning is achieved by globally aggregating the skip connections of a beat tracking system built around temporal convolutional networks, and feeding them into a tempo classification layer. The benefit of this approach is investigated by the inclusion of training data for which tempo-only annotations are available, and which is shown to provide improvements in beat tracking accuracy.

Abstract
This paper proposes a new methodology for fair remuneration of consumers participation in demand response events. With the increasing penetration of renewable energy sources with a high variability; the flexibility from the consumers' side becomes a crucial asset in power and energy systems. However, determining how to effectively remunerate consumers flexibility in a fair way is a challenging task. Current models tend to apply over-simplistic and non-realistic approaches which do not incentivize the participation of the required players. This paper proposes a novel methodology to remunerate consumers flexibility, in a fair way. The proposed model considers different aggregators, which manage the demand response requests within their coalition. After player provide their flexibility, the remuneration is calculated based on the flexibility amount provided by the players, the previous participation in demand response programs, the localization of the players, the type of consumer, the effort put in the provided flexibility amount, and the contribution to the stability of the coalition structure using the Shapley value. Results show that by assigning different weights to the distinct factors that compose the calculation formulation, players remuneration can be adapted to the needs and goals of both the players and the aggregators.

Abstract
NaSSIE - Navigation and Sensoring Skills in Engineering is a platform developed with the intent of facilitating the acquisition of some skills by Engineering Students, which is a core part of the process of controlling a mobile robot. In this paper, the chosen hardware and consequent physical construction of the prototype as well as vehicle’s associated software will be presented. As a use case, this platform was tested during the Robotic Day 2017 in Czech Republic. Preliminary results will also be presented of this year’s preparation for the Micromouse competition. © 2019, Springer Nature Switzerland AG.