Abstract
O cenário atual apresenta, cotidianamente, novas ferramentas tecnológicas, que auxiliam e que permitem dar maior significado ao processo educacional. Nesse contexto, destacamos, principalmente, as tecnologias baseadas em Inteligência Artificial (IA), que são capazes de interpretar, estruturar e cruzar dados, gerando predições com as informações analisadas, podendo, ainda, realizar inferências automáticas e interação com os usuários. Diante da existência de poderosos recursos tecnológicos e a massificação de cursos on-line, podemos afirmar que as ferramentas de acompanhamento evolutivo dos participantes, a interação e as metodologias educacionais tornam-se fundamentais no processo de ensino-aprendizagem. Esse artigo tem o objetivo de nortear a especificação de requisitos do modelo tecnológico, que visa ao auxílio no processo de ensino-aprendizado em curso on-line. Essa investigação utilizou-se de entrevistas e inquéritos com gestores e alunos de universidades brasileiras, para identificar como a IA, aliada a outras tecnologias, poderia auxiliar o processo de ensino-aprendizado, além de proporcionar, aos gestores e professores (mediadores pedagógicos), mais informações operacionais, táticas e estratégicas, nas tomadas de decisão acadêmica. Por último, ratificou-se que, nas instituições analisadas, ainda, não existe o uso da IA e, também, há uma carência de tecnologias emergentes, que possibilitariam automatizar atividades operativas realizadas pelos professores.

Abstract
Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying of an authenticated log (such as a blockchain) without requiring any single party to store or verify the entire log, or to trust another party regarding its contents. AAOSLs can help to enable efficient dynamic participation (e.g., in consensus) and reduce storage overhead. I n this paper, we formalize an AAOSL originally described by Maniatis and Baker, and prove its key correctness properties. Our model and proofs are machine checked in Agda. Our proofs apply to a generalization of the original construction and provide confidence that instances of this generalization can be used in practice. Our formalization effort has also yielded some simplifications and optimizations.

Abstract
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).

Abstract
The COVID-19 pandemic situation has pushed many higher education institutions into a fast-paced, and mostly unstructured, emergency remote education process. In such an unprecedented context, it is important to understand how technology is mediating the educational process and how teachers and students are experiencing the change brought by the pandemic. This research aims to understand how the learning was mediated by technology during the early stages of the pandemic and how students and teachers experienced this sudden change. Data were collected following a qualitative research design. Thirty in-depth and semi-structured interviews (20 students and 10 teachers) were obtained and analysed following a thematic analysis approach. Results provide evidence on the adoption of remote education technologies due to the pandemic with impacts on the education process, ICT platforms usage and personal adaptation. The emergency remote education context led to mixed outcomes regarding the education process. Simultaneously, ICT platforms usage was mostly a positive experience and personal adaptation was mostly a negative experience. These results bring new insights for higher education organizations on actions they could take, such as curating the learning experience with standard, institutional-wide platforms, appropriate training for students and teachers, and suitable remote evaluation practices.

Abstract
The increasingly interconnected world is leading to continuous and profound transformations within and among service systems (e.g., firms, industries, societies). While service research studying such transformations is growing, the literature is missing a conceptualization of service system transformation (SST) that accounts for the richness and diversity of the phenomenon. This hinders the development of approaches to intentionally influence SST toward desired paths. Providing an integrated, multidimensional understanding of SST, this paper explores how service design can intentionally influence SST. To do so, the paper contributes by advancing conceptual clarity of SST and delineating three analytical dimensions-scope, endurance, and paradigmatic radicalness-that, in combination, provide a framework for understanding the diversity of the transformations unfolding within and across service systems. Building upon this conceptualization, the paper systematizes how service design approaches can foster SST along these dimensions, setting the ground for service design to further strengthen its transformative potential.

Abstract