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Register a new userA Method and Analysis to Elicit User-reported Problems in Intelligent Everyday Applications
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Added by
Malin Eiband
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The complex nature of intelligent systems motivates work on supporting users during interaction, for example through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This paper contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps algorithmic decision-making. We enriched this data with users coping and support strategies through a follow-up online survey (N=286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output, rather than processes.
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This reflection paper takes the 25th IUI conference milestone as an opportunity to analyse in detail the understanding of intelligence in the community: Despite the focus on intelligent UIs, it has remained elusive what exactly renders an interactive system or user interface intelligent, also in the fields of HCI and AI at large. We follow a bottom-up approach to analyse the emergent meaning of intelligence in the IUI community: In particular, we apply text analysis to extract all occurrences of intelligent in all IUI proceedings. We manually review these with regard to three main questions: 1) What is deemed intelligent? 2) How (else) is it characterised? and 3) What capabilities are attributed to an intelligent entity? We discuss the communitys emerging implicit perspective on characteristics of intelligence in intelligent user interfaces and conclude with ideas for stating ones own understanding of intelligence more explicitly.
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This paper reports on an in-depth study of electrocardiogram (ECG) biometrics in everyday life. We collected ECG data from 20 people over a week, using a non-medical chest tracker. We evaluated user identification accuracy in several scenarios and observed equal error rates of 9.15% to 21.91%, heavily depending on 1) the number of days used for training, and 2) the number of heartbeats used per identification decision. We conclude that ECG biometrics can work in the wild but are less robust than expected based on the literature, highlighting that previous lab studies obtained highly optimistic results with regard to real life deployments. We explain this with noise due to changing body postures and states as well as interrupted measures. We conclude with implications for future research and the design of ECG biometrics systems for real world deployments, including critical reflections on privacy.
The conjugate gradient (CG) method, a standard and vital way of minimizing the energy of a variational state, is applied to solve several problems in Skyrmion physics. The single-Skyrmion profile optimizing the energy of a two-dimensional chiral magnet is found without relying on specific boundary conditions. The two-dimensional Skyrmion lattice and three-dimensional hedgehog crystal state is recovered with efficiency using the modified CG (p-GD) method. The p-GD method is proposed as a complement to the traditional Monte Carlo annealing method, which still gives better results for the ground state but at the far greater cost in computation time.
With the tremendous progress in sensing and IoT infrastructure, it is foreseeable that IoT systems will soon be available for commercial markets, such as in peoples homes. In this paper, we present a deployment study using sensors attached to household objects to capture the resourcefulness of three individuals. The concept of resourcefulness highlights the ability of humans to repurpose objects spontaneously for a different use case than was initially intended. It is a crucial element for human health and wellbeing, which is of great interest for various aspects of HCI and design research. Traditionally, resourcefulness is captured through ethnographic practice. Ethnography can only provide sparse and often short duration observations of human experience, often relying on participants being aware of and remembering behaviours or thoughts they need to report on. Our hypothesis is that resourcefulness can also be captured through continuously monitoring objects being used in everyday life. We developed a system that can record object movement continuously and deployed them in homes of three elderly people for over two weeks. We explored the use of probabilistic topic models to analyze the collected data and identify common patterns.
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