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    The Concept of Identifiability in ML Models
    (Setúbal : SciTePress - Science and Technology Publications, Lda., 2022) von Maltzan, Stephanie; Bastieri, Denis; Wills, Gary; Kacsuk, Péter; Chang, Victor
    Recent research indicates that the machine learning process can be reversed by adversarial attacks. These attacks can be used to derive personal information from the training. The supposedly anonymising machine learning process represents a process of pseudonymisation and is, therefore, subject to technical and organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and precisely distinguish what is meant by personal data and non-personal data and above all determine whether ML models represent pseudonyms from the training data.
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    Digital Transformation of Education Credential Processes and Life Cycles – A Structured Overview on Main Challenges and Research Questions
    ([Wilmington, DE, USA] : IARIA, [2020], 2020) Keck, Ingo R.; Vidal, Maria-Esther; Heller, Lambert; Mikroyannidis, Alexander; Chang, Maiga; White, Stephen
    In this article, we look at the challenges that arise in the use and management of education credentials, and from the switch from analogue, paper-based education credentials to digital education credentials. We propose a general methodology to capture qualitative descriptions and measurable quantitative results that allow to estimate the effectiveness of a digital credential management system in solving these challenges. This methodology is applied to the EU H2020 project QualiChain use case, where five pilots have been selected to study a broad field of digital credential workflows and credential management. Copyright (c) IARIA, 2020
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    Crowdsourcing Scholarly Discourse Annotations
    (New York, NY : ACM, 2021) Oelen, Allard; Stocker, Markus; Auer, Sören
    The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.