Abstract
To identify the most developed terms in the field of Tourism and virtual reality, a quantitative analysis was developed in February 2020. This analysis was focused on seven hundred and eighty-four (784) publications from the Scopus database, published between 1995 and February 2022 and limited to the following areas: Business Management and Accounting, Computer Science, Social Sciences and Engineering. A bibliometric analysis was performed using the VOSviewer software and a technique of matching terms and co-authoring by authors and countries. Were found 5 clusters for the co-occurrence of terms and 7 clusters for the co-authorship of authors and countries.

Abstract
Objective: We aim to answer four questions. First, with the increasing number of publications, is there a concentration in specific subjects, or on the contrary, a dispersion, amplifying the span of themes related to entrepreneurship? Second, is there a hierarchy of subjects, in the sense that some of them constitute the “core” of entrepreneurship? Third, are they connected with other established research areas? Finally, it is possible to identify papers that are influential, acting as hubs in the cluster’s formation? Method: We developed an original version of the computational procedure proposed by Shibata et al (2008), which allows us to understand the diversity of the different sub-areas of the topic investigated, reducing the need for specialist supervision. Originality/Relevance: We developed and applied a method to capture the formation and evolution of research areas in entrepreneurship literature, via direct citation networks, allowing us to understand the iteration between the different research sub-areas. Results: The dispersion is a feature of entrepreneurship as field research, with a hierarchy between research areas, indicating an emergent organization in the expansion processes. We concluded that research on entrepreneurship consists of specialization, that is, by application in niches. © 2022, Associacao Nacional de Estudos em Empreendedorismo e Gestao de Pequenas Empresas - ANEGEPE. All rights reserved.

Abstract
Currently, humanity faces major challenges, namely in terms of energy production, consumption and management, in which electrical engineering should play a relevant role in the search for sustainable solutions. This training area is essential for industrial and economic development, and is therefore associated with economic competitiveness. However, demand for electrical engineering programs is decreasing, despite continuing to be a professional area with high employability. In view of this, it is important to try to understand the motivations and expectations that lead students to apply for programs in the field of electrical engineering. Thus, this study aimed to understand and analyse the motivations and expectations that led students from a Portuguese Polytechnic higher education institution to choose the field of electrical engineering. To this end, a questionnaire survey was used to collect the students' perceptions about why they chose this area, their expectations about their future professional activity, as well as about the role they attribute to engineering. The results show that students highly value the fact that it is a broadband area, with high employability and a wide variety of potential professional activities. Regarding the professional activity that students would like to pursue, the results stand out in the areas of renewable energies, the automobile industry, and electrical installations. The students' perception of the engineering's role reflects a generalized view that its objective is to improve the living conditions of humanity. However, some answers indicate a restricted perception of a technical character (it serves to make projects, calculations). The results obtained help to understand the profile and expectations of students who choose the field of electrical engineering, potentially contributing for the higher education institutions: i) to promote actions with the aim of enhancing the attractiveness of this area of knowledge; ii) to adapt their programs to the expectations of students and thus reduce academic dropouts or failure.

Abstract
Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.

Abstract
To identify the most developed terms in the field of Tourism and virtual reality, a quantitative analysis was developed in February 2020. This analysis was focused on seven hundred and eighty-four (784) publications from the Scopus database, published between 1995 and February 2022 and limited to the following areas: Business Management and Accounting, Computer Science, Social Sciences and Engineering. A bibliometric analysis was performed using the VOSviewer software and a technique of matching terms and co-authoring by authors and countries. Were found 5 clusters for the co-occurrence of terms and 7 clusters for the co-authorship of authors and countries. © 2022 IEEE Computer Society. All rights reserved.

Abstract
The urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm's performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization's perspective. This is an innovative approach to improve the localization results in a realmobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.