Abstract
Archives are faced with great challenges due to the vast amounts of data they have to curate. New data models are required, and work is underway. The International Council on Archives is creating the RiC-CM (Records in Context), and there is a long line of work in museums with the CIDOC-CRM (CIDOC Conceptual Reference Model). Both models are based on ontologies to represent cultural heritage data and link them to other information. The Portuguese National Archives hold a collection with over 3.5 million metadata records, described with the ISAD(G) standard. The archives are designing a new linked data model and a technological platform with applications for archive contributors, archivists, and the public. The current work extends CIDOC-CRM into ArchOnto, an ontology-based model for archives. The model defines the relevant archival entities and properties and will be used to migrate existing records. ArchOnto accommodates the existing ISAD(G) information and takes into account its implementation with current technologies. The model is evaluated with records from representative fonds. After the test on these samples, the model is ready to be populated with the semi-automatic transformation of the ISAD records. The evaluation of the model and the population strategies will proceed with experiments involving professional and lay users. © 2020, Springer Nature Switzerland AG.

Abstract
High academic failure rates in computer programming are significant transitioning from initial to advanced stages. In online higher education, challenges are greater since students' autonomy requires greater skills for self-regulation and co-regulation of learning. The SimProgramming approach develops these skills and is being adapted to e-learning for this transitioning phase. In this paper, we describe the dynamics and outcomes of student participation and task development in a first iteration of the adapted e-SimProgramming approach, which took place during a 2nd year-2nd semester course for the Informatics Engineering program at Universidade Aberta in the 2018/2019 academic year. We identified pedagogical and technical challenges, requiring changes for subsequent attempts of adopting SimProgramming for online education contexts: target audience and teaching context aspects; self and co-regulation of learning dimensions of e-learning courses; pedagogical design recommendations; and requirements for software tools for learning management.

Abstract
In this paper we measure the accuracy of password strength meters (PSMs) using password guessing resistance against off-the-shelf guessing attacks. We consider 13 PSMs, 5 different attack tools, and a random selection of 60,000 passwords extracted from three different datasets of real-world password leaks. Our results show that a significant percentage of passwords classified as strong were cracked, thus suggesting that current password strength estimation methods can be improved. © 2020 IEEE.

Abstract
Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge base construction, question answering and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods on two datasets, namely ACE 2005 and TAC KBP 2015.

Abstract
Payments using cryptocurrencies may require that the user is able to provide proof of ownership and proof of provenance for a specific transaction. In this paper an innovative web based solution is proposed as a framework that issues reports, on request, pertaining proof of ownership and proof of provenance. The proposed framework provides proof of ownership by using micro-payments and, when used recursively, it can produce provenance reports up to a defined granularity level of transactions. A proof of concept prototype of the proposed framework was implemented and its operation and output is presented and explained. Some limitations and future work directions are also identified. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Blood banks face inventory management problems associated to demand uncertainty and high inventory levels. An efficient blood inventory management is related to the use of simple, transparent and easy-to-understand procedures by blood banks’ employees. However, the literature about good practices in blood bank inventory management is scarce, reinforcing new developments need on this subject to ensure a good availability of blood products and reducing wastage. This research presents a blood inventory management system implemented in software, DOAR, able to meet demand while minimizing blood bags wastage. DOAR is simple, user-friendly and able to optimize blood inventory and donations. The purpose of the software is to provide a link between the demand by blood components and collected blood bags.