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In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks, Comment: Accepted in AdvML@KDD'22

Part 1: Beyond Computers: Wearables, Humans, and Things - WHAT!; International audience; We introduce the rapid change of the visual art ecosystem, triggered by current science and technology development. ICT enables new multimedia based an interactive art forms, with an increasing variety of stakeholders. We provide examples of audience involvement, of immersion, and of brain-computer interaction as a new paradigm for participation. We point to the use of new material dimensions, as well as to expanding shared creation and cognition. We also point to opportunities to apply this development to accommodate special needs. In order to support the dissemination of these possibilities, we advocate the development of a task-modeling based ontology to describe, analyse, and support the evolving art ecosystem.

We present IntraVox, a novel voice-based interaction system that supports users with complex needs by introducing a highly personalized, human voice command between smart home devices and sensors. Voice-enabled personal assistants (e.g., Amazon Alexa, Google Home) can be used to control smart home devices and increase the quality of life. However, people suffering from conditions such as dementia or learning disabilities and autism (LDA) can encounter verbal and memory problems in communicating with the assistants. IntraVox supports these users by verbally sending smart home control commands, based on the sensor data collected, to a voice-enabled personal assistant using a human voice. Over 12 months, we conducted 7 workshops and 9 interviews with 79 participants from multiple stakeholder categories - people with LDA, older and frail people, social and health care organisations. Results show that our solution has the potential to support people with complex needs and increase independence. Moreover, IntraVox could have the added value of reinforcing the learning of specific commands and making visible the inner workings of a home automation system, by giving users a clear cause-and-effect explanation of why certain events occur.

Algorithms are present in many of our everyday activities. However, there is generally low awareness of their presence among users, and there are various conceptualizations to define them. Additionally, algorithms are often both complex and opaque. These characteristics raise challenges when applying co-design activities to the interaction design of algorithms. We argue that researchers can overcome these challenges by developing sensitizing activities: activities that foreground the presence of algorithms, thus raising algorithmic awareness and a shared understanding, without influencing their initial experiences and expectations. We share how we applied sensitizing activities in two case studies: sensitizing interviews, and diary studies together with two-phase workshops. We share our experiences applying these techniques to overcome the challenges of low algorithmic awareness and multiple algorithmic understandings of participants. Finally, we offer recommendations for researchers and practitioners when applying sensitizing activities in this design context and invite further methodological discussion on this challenging topic. no ISSN ispartof: NordiCHI '20: Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society ispartof: Nordic forum for Human-Computer Interaction (HCI) research NordiCHI location:Tallin date:25 Oct - 29 Oct 2020 status: Published online

Mainstream display technologies have progressed towards higher resolutions and contrast levels. However, for IoT applications, where displays are embedded in the environment, there are requirements to optimize displays for lower power consumption or minimal environmental impact, potentially sacrificing contrast or switching time. We approach this issue by considering the spatial design of UI components. As a first step, we explore switch type UI components (e.g., check box, toggle switch) and evaluate the performance of 4 alternative spatial designs at low contrast levels. As a contribution, we open the issue of spatial UI design for low contrast and demonstrate an approach for its evaluation.

The meSch project addresses the challenges of creating a personally meaningful, sensorily rich, and socially expanded museum visitor experience through tangible and embodied interaction with digital content. It is of paramount importance that cultural heritage professionals are directly involved in the design of those experiences. The meSch approach is to empower cultural heritage professionals with tools that guide them through a do-it-yourself process of creating or adapting digitally augmented experiences for their own museum spaces, therefore reducing the barriers of introducing Internet of Things technology in cultural heritage spaces.