Abstract
The expansion of augmented reality across society, its availability in mobile platforms and the novelty character it embodies by appearing in a growing number of areas, have raised new questions related to people's predisposition to use this technology in their daily life. Acceptance models, which have been updated following technological breakthroughs and society changes, are known to be great tools for predicting the intention to use a new technological system. This paper proposes a new acceptance model aiming to ascertain the intention to use augmented reality technology in heritage sites-the Augmented Reality Acceptance Model (ARAM). ARAM relies on the use of the Unified Theory of Acceptance and Use of Technology model (UTAUT) model's constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, to which the new and adapted constructs of trust expectancy, technological innovation, computer anxiety and hedonic motivation are added. This model was validated with data gathered from 528 participants. Results confirm ARAM as a reliable tool to determine the acceptance of augmented reality technology for usage in cultural heritage sites. The direct impact of performance expectancy, facilitating conditions and hedonic motivation is validated as having a positive influence on behavioural intention. Trust expectancy and technological innovation are demonstrated to have a positive influence on performance expectancy whereas hedonic motivation is negatively influenced by effort expectancy and by computer anxiety. The research, thus, supports ARAM as a suitable model to ascertain the behavioural intention to use augmented reality in new areas of activity.

Abstract
To address metadata with researchers it is important to use models that include familiar domain concepts. In the Social Sciences, the DDI is a well-accepted source of such domain concepts. To create FAIR data and metadata, we need to establish a compact set of DDI elements that fit the requirements in projects and are likely to be adopted by researchers inexperienced with metadata creation. Over time, we have engaged in interviews and data description sessions with research groups in the Social Sciences, identifying a manageable DDI subset. A recent Clinical Psychology project, TOGETHER, dealing with risk assessment for hereditary cancer, considered the inclusion of a DDI subset for the production of metadata that are timely and interoperable with data publication initiatives in the same domain. Taking a DDI subset identified by the data curators, we make a preliminary assessment of its use as a realistic effort on the part of researchers, taking into consideration the metadata created in two data description sessions, the effort involved, and overall metadata quality. A follow-up questionnaire was used to assess the perspectives of researchers regarding data description.

Abstract

Abstract
As atividades assíncronas no ensino online requerem um acompanhamento regular por parte dos docentes. Esse acompanhamento acompanha duas dimensões: a de grupo, atendendo às dinâmicas globais da turma; e a individual, atendendo às ações de cada estudante. Estando habitualmente estruturadas em tópicos com duração limitada no tempo, a preponderância destas dimensões altera-se nas necessidades de dedicação e intervenção docente. Neste trabalho apresenta-se uma estratégia de apoio à planificação e desenvolvimento da atividade docente, que decompõe essas atividades de acompanhamento e intervenção em cinco marcos temporais. Pretende esta estratégia permitir ao docente identificar os aspetos mais prementes para recolha, de forma praticável, de elementos que possam informar a sua decisão de intervenção pedagógica.

Abstract
Vulnerability scanning tools are essential in detecting systems weaknesses caused by vulnerabilities in their components or wrong configurations. Corporations may use these tools to assess a system in advance and fix its vulnerabilities, thus preventing or mitigating the impact of real attacks. A set of these tools are organized by plugins, each intended to check a specific vulnerability, such as the case of the Tsunami Security Scanner tool released in 2020 by Google. Multiple plugins for this tool were proposed in a community-based approach and thus, it is important for the users and research community to have these plugins in a framework consistently categorized across multiple sources and types. This paper proposes a comprehensive taxonomy for all the 61 plugins available, hierarchically sorted into 2 main categories, 4 categories, 4 subcategories, and 7 types. An analysis and a discussion on statistics by categories and types over time are also provided. The analysis shows that, so far, there are 4 main contributors, being Google, Community, Facebook, and Govtech. The Google source is still the top contributor counting 39 out of 61 plugins and the highest number of plugins available are in the RCE subcategory. The plugins available are mainly focused on critical and high vulnerabilities.

Abstract
We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage backends that lead to performance, dependability, and correctness issues. DIO eases the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing four production-level applications. Results show that DIO enables diagnosing inefficient I/O patterns that lead to poor application performance, unexpected and redundant I/O calls caused by high-level libraries, resource contention in multithreaded I/O that leads to high tail latency, and erroneous file accesses that cause data loss. Moreover, through a detailed evaluation, we show that, when comparing DIO's inline diagnosis pipeline with a similar state-of-the-art solution, our system captures up to 28x more events while keeping tracing performance overhead between 14% and 51%.