From the article: "Individuals with dementia often experience a decline in their ability to use language. Language problems have been reported in individuals with dementia caused by Alzheimer’s disease, Parkinson’s disease or degeneration of the fronto-temporal area. Acoustic properties are relatively easy to measure with software, which promises a cost-effective way to analyze larger discourses. We study the usefulness of acoustic features to distinguish the speech of German-speaking controls and patients with dementia caused by (a) Alzheimer’s disease, (b) Parkinson’s disease or (c) PPA/FTD. Previous studies have shown that each of these types affects speech parameters such as prosody, voice quality and fluency (Schulz 2002; Ma, Whitehill, and Cheung 2010; Rusz et al. 2016; Kato et al. 2013; Peintner et al. 2008). Prior work on the characteristics of the speech of individuals with dementia is usually based on samples from clinical tests, such as the Western Aphasia Battery or the Wechsler Logical Memory task. Spontaneous day-to-day speech may be different, because participants may show less of their vocal abilities in casual speech than in specifically designed test scenarios. It is unclear to what extent the previously reported speech characteristics are still detectable in casual conversations by software. The research question in this study is: how useful for classification are acoustic properties measured in spontaneous speech."
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One of the aims of the TALENTS-project is to create (interdisciplinary) learning communities in which engineering professionals, students, teachers, and researchers can learn together and collaborate as equal partners, within the context of authentic challenges, starting from their individual learning goals. To what extent are partners willing to participate in this partnership and under which conditions do they consider it to have added value? We conducted individual interviews with engineering students (N=11), teachers (N=12) and professionals (N=10) about what they require to participate in the learning community, employing epistemic, spatial, instrumental, temporal, and social elements of learning environments. We also inquired which resources participants were willing to invest. Data were summarized on group level in a within-group matrix, following these elements. Next, we employed a cross-group analysis, focusing on commonalities and differences. The most striking results were found in the epistemic, social, and instrumental elements. Respondents have similar needs when it comes to improving dialogue to formulate a challenge. However, professionals prefer to have more influence on formulating this challenge and its output, whereas teachers wish to focus on students’ development. Students wish to co-create with partners and they place importance on matching students with a challenge that aligns with their educational background and personal interest. To create an environment based on equality, students need traditional roles of teachers, clients, and students to be less apparent. Ultimately, almost all respondents are willing to co-operate as equal partners in the learning community because they can see it leads to added value.
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ABSTRACT Purpose: This short paper describes the dashboard design process for online hate speech monitoring for multiple languages and platforms. Methodology/approach: A case study approach was adopted in which the authors followed a research & development project for a multilingual and multiplatform online dashboard monitoring online hate speech. The case under study is the project for the European Observatory of Online Hate (EOOH). Results: We outline the process taken for design and prototype development for which a design thinking approach was followed, including multiple potential user groups of the dashboard. The paper presents this process's outcome and the dashboard's initial use. The identified issues, such as obfuscation of the context or identity of user accounts of social media posts limiting the dashboard's usability while providing a trade-off in privacy protection, may contribute to the discourse on privacy and data protection in (big data) social media analysis for practitioners. Research limitations/implications: The results are from a single case study. Still, they may be relevant for other online hate speech detection and monitoring projects involving big data analysis and human annotation. Practical implications: The study emphasises the need to involve diverse user groups and a multidisciplinary team in developing a dashboard for online hate speech. The context in which potential online hate is disseminated and the network of accounts distributing or interacting with that hate speech seems relevant for analysis by a part of the user groups of the dashboard. International Information Management Association
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