Abstract
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human-drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

Abstract
The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion.

Abstract
Rhythmic similarity, a fundamental task within Music Information Retrieval, has recently been applied in creative music contexts to retrieve musical audio or guide audio-content transformations. However, there is still very little knowledge of the typical rhythmic similarity values between overlapping musical structures per instrument, genre, and time scales, which we denote as rhythmic compatibility. This research provides the first steps towards the understanding of rhythmic compatibility from the systematic analysis of MedleyDB, a large multi-track musical database composed and performed by artists. We apply computational methods to compare database stems using representative rhythmic similarity metrics - Rhythmic Histogram (RH) and Beat Spectrum (BS) - per genre and instrumental families and to understand whether RH and BS are prone to discriminate genres at different time scales. Our results suggest that 1) rhythmic compatibility values lie between [.002,.354] (RH) and [.1,.881] (BS), 2) RH outperforms BS in discriminating genres, and 3) different time scale in RH and BS impose significant differences in rhythmic compatibility. © 2021 ACM.

Abstract
We propose a fiber-based environmental sensor that exploits the reflection-phase-shift tunability provided by the use of layered coatings composed of dielectric slabs spaced by conducting membranes. A transfer-matrix study is done in a simplified theoretical model, for which an enhanced sensitivity of the reflection interference pattern to the output medium is demonstrated, in the typical refractive index range of liquid media. An experimental configuration using a cascaded Fabry-Perot microcavity coated by a graphene oxide/polyethylenimine (GO/PEI) multilayered structure is demonstrated. Its cost-effective chemical production method makes graphene oxide-based hybrid coatings excellent candidates for future real-life sensing devices.

Abstract
Entrepreneurial universities are a significant element of entrepreneurial ecosystems and aspire to foster entrepreneurial initiative through their "third mission". However, while entrepreneurial ecosystems are scrutinized using a contextual approach to detect differences and similarities and how they affect entrepreneurship, little is known about how entrepreneurial universities impact entrepreneurial initiatives in general, considering multiple environments. Drawing on entrepreneurial university and entrepreneurial ecosystem theories, a conceptual framework is proposed that aims to explain the effect of the entrepreneurial university on an entrepreneurial initiative through its three "missions", using an entrepreneurial ecosystem taxonomy. Based on individual data from the Global Entrepreneurship Monitor, this entrepreneurial initiative analyzed 18 European countries in 2017. The results do not generally support the importance of entrepreneurial universities to entrepreneurial initiative. The relevance of entrepreneurial universities increases in more fragile entrepreneurial ecosystems since individuals need support over multiple dimensions. Conversely, the entrepreneurial universities that are embedded in stronger entrepreneurial ecosystems lose relevance and negatively affect the entrepreneurial initiative. Therefore, the value of entrepreneurial universities is reduced when individuals receive greater support from other dimensions. The variations across both groups suggest that the concept of entrepreneurial universities is not a contemporaneous phenomenon; however, their effect is progressively revealed by the maturity of each university's mission. This perspective substantially changes the understanding of entrepreneurial universities as a thwartwise strategy, suggesting that the universities' impact is expanded as their missions gradually evolve. Overall, the study contributes to an understanding of the implications for universities that blindly follow entrepreneurship, neglecting the exogenous environment, namely, the entrepreneurial ecosystem and individual drive.

Abstract