Title: Control Theoretical Models of Pointing

Speaker: Rod Murray-Smith, University of Glasgow
http://www.dcs.gla.ac.uk/~rod/

Abstract: I will talk about two topics:

1. (Joint work with Jörg Müller & Antti Oulasvirta) I will present an empirical comparison of four models from manual control theory on their ability to model targetting behaviour by human users using a mouse: McRuer’s Crossover, Costello’s Surge, second-order lag (2OL), and the Bang-bang model. Such dynamic models are generative, estimating not only movement time, but also pointer position, velocity, and acceleration on a moment-to-moment basis. We describe an experimental framework for acquiring pointing actions and automatically fitting the parameters of mathematical models to the empirical data. We present the use of time-series, phase space and Hooke plot visualisations of the experimental data, to gain insight into human pointing dynamics. We find that the identified control models can generate a range of dynamic behaviours that captures aspects of human pointing behaviour to varying degrees. Conditions with a low index of difficulty (ID) showed poorer fit because their unconstrained nature leads naturally to more dynamic variability. We report on characteristics of human surge behaviour in pointing. We describe trade-offs among the models. We conclude that control theory offers a promising complement to Fitts’ law based approaches in HCI, with models providing representations and predictions of human pointing dynamics which can improve our understanding of pointing and inform design.

2. Casual control. How and why we can design systems to work at a range of levels of engagement.

Biography: Roderick Murray-Smith is a Professor of Computing Science at Glasgow University, in the “Inference, Dynamics and Interaction” research group and the Head of the Information, Data and Analysis Section. He works in the overlap between machine learning, interaction design and control theory. In recent years his research has included multimodal sensor-based interaction with mobile devices, mobile spatial interaction, Brain-Computer interaction and nonparametric machine learning. Prior to this he held positions at the Hamilton Institute, NUIM, Technical University of Denmark, M.I.T., and Daimler-Benz Research, Berlin, and was the Director of SICSA, the Scottish Informatics and Computing Science Alliance. He works closely with the mobile phone industry, having worked together with Nokia, Samsung, FT/Orange, Microsoft and Bang & Olufsen. He was a member of Nokia’s Scientific Advisory Board and is a member of the Scientific Advisory Board for the Finnish Centre of Excellence in Computational Inference Research. He has co-authored three edited volumes, 22 journal papers, 16 book chapters, and 88 conference papers.

Title: Designing Real-time Interactions Using Machine Learning

Abstract: Supervised learning algorithms can be understood not only as a set of techniques for building accurate models of data, but also as design tools that can enable rapid prototyping, iterative refinement, and embodied engagement— all activities that are crucial in the design of new musical instruments and other embodied interactions. Realising the creative potential of these algorithms requires a rethinking of the interfaces through which people provide data and build models, providing for tight interaction-feedback loops and efficient mechanisms for people to steer and explore algorithm behaviours.

In this talk, I will discuss my research on better enabling composers, musicians, and developers to employ supervised learning in the design of new real-time systems. I will show a live demo of tools that I have created for this purpose, centering around the Wekinator software toolkit for interactive machine learning. I’ll discuss some of the outcomes from 7 years of creating machine learning-based tools and observing people using these tools in creative contexts. These outcomes include a better understanding how machine learning can be used as a tool for design by end users and developers, and how using machine learning as a design tool differs from more conventional application contexts.

Biography: Dr. Rebecca Fiebrink is a Lecturer at Goldsmiths, University of London. Her research focuses on designing new ways for humans to interact with computers in creative practice, including on the use of machine learning as a creative tool. Fiebrink is the developer of the Wekinator system for real-time interactive machine learning (with a new version just released in 2015!), a co-creator of the Digital Fauvel platform for interactive musicology, and a Co-I on the £1.6M Horizon 2020-funded RAPID-MIX project on Real-time Adaptive Prototyping for Industrial Design of Multimodal Expressive Technology. She is the creator of a MOOC titled “Machine Learning for Artists and Musicians,” which launched in 2016 on the Kadenze platform. She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule, where she helped to build the #1 iTunes app “I am T-Pain.” She holds a PhD in Computer Science from Princeton University.

Title: Data and Dasein – A Phenomenology of Human-Data
Relations.

Abstract: In contemporary society, data representation
is an important and essential part of many aspects of our daily lives.
In this talk Trevor will present how his doctoral research has
contributed to our understanding on how people experience data and what
role representational modality plays in the process of perception and
interpretation. This research is grounded in phenomenology – he aligns
his theoretical exploration to ideas and concepts from philosophical
phenomenology, while also respecting the essence of a phenomenological
approach in his choice and application of methods. Alongside offering a
rich description of people’s experience of data representation, the key
contributions of his research transcend four areas: theory, methods,
design, and empirical findings. From a theoretical perspective, besides
describing a phenomenology of human-data relations, he has defined, for
the first time, multisensory data representation and established a
design space for the study of this class of representation. In relation
to methodologies, he will describe how he deployed two elicitation
methods to investigate different aspects of data experience. He blends
the Repertory Grid technique with a focus group session and shows how
this adaption can be used to elicit rich design relevant insight. He
will also introduce the Elicitation Interview technique as a method for
gathering detailed and precise accounts of human experience.
Furthermore, he will describe how this technique can be used to elicit
accounts of experience with data. In his talk Trevor will present the
findings of a series of empirical studies, these show, for instance, how
certain representational modalities cause us to have heightened
awareness of our body, some are more difficult to interpret than others,
some rely heavily on instinct and each of them solicit us to reference
external events during the process of Interpretation.

Biography: Trevor Hogan is a Lecturer of Interaction
Design at the Cork Institute of Technology, Ireland. The aim of his
research is to describe and better understand how embodiment influences
and augments people’s experience of data representations. His work is
strongly interdisciplinary and may be situated in the field of
interactive design, but at the intersection of tangible computing,
human-computer interaction, information visualization and psychology. At
CIT Trevor leads the Human-Data Interaction Group, a multidisciplinary
research team, whose aim is explore novel ways of representing data –
through and beyond the visual modality. This group is also focused on
exploring methods and approaches that broaden the evaluation criteria of
data representation – beyond traditional measurements, such as
efficiency and effectiveness, towards novel aspects such as experience,
use qualities, hedonics, affect, empathy, and enchantment.