Inference, Dynamics and Interaction Group

The Inference, Dynamics and Interaction group is a research group within the Information, Data and Analytics Section, and brings together three fundamental research areas: modern inference techniques, dynamic systems and control theory and interaction design. These are applied in wide range of situations:

- Systems Biology
- Mobile Interaction
- Cognitive Neuroscience/neuroimaging
- Entertainment systems
- Sensor systems
- Urban Interactions/Smart Cities

The group's strength lies in the unusual combination of theoretical backgrounds from machine learning to HCI, and the focus on building innovative working systems which achieve performance previously thought impossible, using the latest algorithms, sensors and devices. The group's skills in combining software engineering and mathematical inference allows us to attack complex systems problems with large high-dimensional data spaces and so in real-time.

Visit the dedicated Inference, Dynamics and Interaction Group Website to find out more.

Current Research Projects:

  • MoreGrasp – EC Horizon2020 project 2015-2018
  • QuantIC - Quantum Technology Hub in Quantum imaging
  • CoSound A Cognitive Systems Approach to Enriched and Actionable Information from Audio Streams Supported by the Danish Strategic Research Council, Jan. 2012 – Dec. 2016
  • Information Theory approach for measuring & optimising computer-human interaction, Nokia-funded Ph.D. studentship.
  • Stomatal-based systems analysis of water use efficiency, BBSRC funded project, Prof. Michael Blatt (PI), Dr. Simon Rogers (coI) (BB/L001276/1)
  • In-silico integration of primary CML stem cell polyomic datasets to identify kinase-independent networks and novel prognostic biomarkers, Leukaemia and Lymphoma Research funded project, Prof. Tessa Holyoake (PI), Dr. Simon Rogers (coI)
  • Computational inference of biopathway dynamics and structures, EPSRC funded (EP/L020319/1), Prof. Dirk Huemsier (PI), Dr. Simon Rogers (coI), Dr. Maurizio Filippone (coI)
  • Unifying metabolome and proteome informatics, BBSRC funded (BB/L018616/2), Dr. Andrew Dowsey (PI), Dr. Simon Rogers (coI)

Previous Research Projects:

  • Bang & Olufsen funded Ph.D. studentship.
  • Human Emotional Communication in the field of Quality and Rapport, Nokia-funded Ph.D. studentship.
  • EC-COST action IC0601 on Sonic Interaction Design.
  • TOBI: Tools for Brain-Computer Interaction, EC-funded project. Roderick Murray-Smith (Glasgow PI), John Williamson, project coordinator:Prof. José del R. Millán, 2008-2013.
  • Multimodal, Negotiated Interaction in Mobile Scenarios, EPSRC funded project (£638k), Roderick Murray-Smith (PI), with Matt Jones (Swansea), Stephen Brewster, 2007-2010.
  • PASCAL network member, EC-funded network in Pattern Analysis, Statistical Modelling and Computational Learning.
  • Social Interaction: A Cognitive-Neurosciences Approach, ESRC funded project (£3.7 million) , Simon Garrod (PI), 2008-2012.

Academic Staff: 

 

Researchers:  

 

Affiliates:

 

Previous Members:

  • Machine Learning
  • Statistical Pattern Recognition
  • Human Computer Interaction
  • Mobile HCI
  • Brain Computer Interaction
  • Sensor systems
  • Urban Interactions/Smart Cities

This Week’s EventsAll Upcoming EventsPast EventsWebapp

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Past Events

ProbUI: Generalising Touch Target Representations to Enable Declarative Gesture Definition for Probabilistic GUIs (20 April, 2017)

Speaker: Daniel Buschek

We present ProbUI, a mobile touch GUI framework that merges ease of use of declarative gesture definition with the benefits of probabilistic reasoning. It helps developers to handle uncertain input and implement feedback and GUI adaptations. ProbUI replaces today's static target models (bounding boxes) with probabilistic gestures ("bounding behaviours"). It is the first touch GUI framework to unite concepts from three areas of related work: 1) Developers declaratively define touch behaviours for GUI targets. As a key insight, the declarations imply simple probabilistic models (HMMs with 2D Gaussian emissions). 2) ProbUI derives these models automatically to evaluate users' touch sequences. 3) It then infers intended behaviour and target. Developers bind callbacks to gesture progress, completion, and other conditions. We show ProbUI's value by implementing existing and novel widgets, and report developer feedback from a survey and a lab study.

A stochastic formulation of a dynamical singly constrained spatial interaction model (02 March, 2017)

Speaker: Mark Girolami

One of the challenges of 21st-century science is to model the evolution of complex systems.  One example of practical importance is urban structure, for which the dynamics may be described by a series of non-linear first-order ordinary differential equations.  Whilst this approach provides a reasonable model of spatial interaction as are relevant in areas diverse as public health and urban retail structure, it is somewhat restrictive owing to uncertainties arising in the modelling process. 

We address these shortcomings by developing a dynamical singly constrained spatial interaction model, based on a system of stochastic differential equations.   Our model is ergodic and the invariant distribution encodes our prior knowledge of spatio-temporal interactions.  We proceed by performing inference and prediction in a Bayesian setting, and explore the resulting probability distributions with a position-specific metropolis-adjusted Langevin algorithm. Insights from studies of interactions within the city of London from retail structure are used as illustration

Rethinking eye gaze for human-computer interaction (19 January, 2017)

Speaker: Hans Gellersen

Eye movements are central to most of our interactions. We use our eyes to see and guide our actions and they are a natural interface that is reflective of our goals and interests. At the same time, our eyes afford fast and accurate control for directing our attention, selecting targets for interaction, and expressing intent. Even though our eyes play such a central part to interaction, we rarely think about the movement of our eyes and have limited awareness of the diverse ways in which we use our eyes --- for instance, to examine visual scenes, follow movement, guide our hands, communicate non-verbally, and establish shared attention. 

This talk will reflect on use of eye movement as input in human-computer interaction. Jacob's seminal work showed over 25 years ago that eye gaze is natural for pointing, albeit marred by problems of Midas Touch and limited accuracy. I will discuss new work on eye gaze as input that looks beyond conventional gaze pointing. This includes work on: gaze and touch, where we use gaze to naturally modulate manual input; gaze and motion, where we introduce a new form of gaze input based on the smooth pursuit movement our eyes perform when they follow a moving object; and gaze and games, where we explore social gaze in interaction with avatars and joint attention as multi-user input . 

Hans Gellersen is Professor of Interactive Systems at Lancaster University. Hans' research interest is in sensors and devices for ubiquitous computing and human-computer interaction. He has worked on systems that blend physical and digital interaction, methods that infer context and human activity, and techniques that facilitate spontaneous interaction across devices. In recent work he is focussing on eye movement as a source of context information and modality for interaction. 

Working toward computer generated music traditions (12 January, 2017)

Speaker: Bob Sturm

I will discuss research aimed at making computers intelligent and sensitive enough to working with music data, whether acoustic or symbolic. Invariably, this includes a lot of work in applying machine learning to music collections in order to divine distinguishing and identifiable characteristics of practices that defy strict definition. Many of the resulting machine music listening systems appear to be musically sensitive and intelligent, but their fraudulent ways can be revealed when they are used to create music in the styles they have been taught to identify. Such "evaluation by generation” is a powerful way to gauge the generality of what a machine has learned to do. I will present several examples, focusing in particular on our work applying deep LSTM networks to modelling folk music transcriptions, and ultimately generating new music traditions.

 

References:

https://github.com/IraKorshunova/folk-rnn

https://highnoongmt.wordpress.com/2015/05/22/lisls-stis-recurrent-neural-networks-for-folk-music-generation/ 

https://highnoongmt.wordpress.com/?s=%22Deep+learning+for+assisting+the+process%22&submit=Search

 

https://youtu.be/YMbWwU2JdLg

https://youtu.be/RaO4HpM07hE 

https://soundcloud.com/sturmen-1

SHIP: The Single-handed Interaction Problem in Mobile and Wearable Computing (24 November, 2016)

Speaker: Hui-Shyong Yeo

Screen sizes on devices are becoming smaller (eg. smartwatch and music player) and larger (eg. phablets, tablets) at the same time. Each of these trends can make devices difficult to use with only one hand (eg. fat-finger or reachability problem). This Single-Handed Interaction Problem (SHIP) is not new but it has been evolving along with a growth of larger and smaller interaction surfaces. The problem is exacerbated when the other hand is occupied (encumbered) or not available (missing fingers/limbs). The use of voice command or wrist gestures can be less robust or perceived as awkward in the public. 

This talk will discuss several projects (RadarCat UIST 2016, WatchMI MobileHCI 2016, SWIM and WatchMouse) in which we are working towards achieving/supporting effective single-handed interaction for mobile and wearable computing. The work focusses on novel interaction techniques that are not being explored thoroughly for interaction purposes, using ubiquitous sensors that are widely available such as IMU, optical sensor and radar (eg. Google Soli, soon to be available).

Biography:

Hui-Shyong Yeo is a second year PhD student in SACHI, University of St Andrews, advised by Prof. Aaron Quigley. Before that he worked as a researcher in KAIST for one year. Yeo has a wide range of interest within the field of HCI, including topics such as wearable, gestures, mixed reality and text entry. Currently he is focusing on single-handed interaction for his dissertation topic. He has published in conferences such as CHI, UIST, MobileHCI (honourable mention), SIGGRAPH and journals such as MTAP and JNCA.

Visit his homepage http://hsyeo.com or twitter @hci_research

Demo of Google Soli Radar and Single Handed Smartwatch interaction (24 November, 2016)

Speaker: Hui-Shyong Yeo

This demo session will present the Google Soli Radar and Smartwatch interaction system

Biography:

Hui-Shyong Yeo is a second year PhD student in SACHI, University of St Andrews, advised by Prof. Aaron Quigley. Before that he worked as a researcher in KAIST for one year. Yeo has a wide range of interest within the field of HCI, including topics such as wearable, gestures, mixed reality and text entry. Currently he is focusing on single-handed interaction for his dissertation topic. He has published in conferences such as CHI, UIST, MobileHCI (honourable mention), SIGGRAPH and journals such as MTAP and JNCA.

Visit his homepage http://hsyeo.com or twitter @hci_research

Control Theoretical Models of Pointing (11 November, 2016)

Speaker: Rod Murray-Smith

I will present an empirical comparison of four models from manual control theory on their ability to model targeting 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 report differences in a number of controller performance measures, including Overshoot, Settling time, Peak time, and Rise time. 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.

Improvising minds: Improvisational interaction and cognitive engagement (29 August, 2016)

Speaker: Adam Linson

In this talk, I present my research on improvisation as a general form of adaptive expertise. My interdisciplinary approach takes music as a tractable domain for empirical studies, which I have used to ground theoretical insights from HCI, AI/robotics, psychology, and embodied cognitive science. I will discuss interconnected aspects of digital musical instrument (DMI) interface design a musical robotic AI system, and a music psychology study of sensorimotor influences on perceptual ambiguity. I will also show how I integrate this work with an inference-based model of neural functioning, to underscore implications beyond music. On this basis, I indicate how studies of musical improvisation can shed light on a domain-general capacity: our flexible, context-sensitive responsiveness to rapidly-changing environmental conditions.

 

Recognizing manipulation actions through visual accelerometer tracking, relational histograms, and user adaptation (26 August, 2016)

Speaker: Sebastian Stein

Activity recognition research in computer vision and pervasive computing has made a remarkable trajectory from distinguishing full-body motion patterns to recognizing complex activities. Manipulation activities as occurring in food preparation are particularly challenging to recognize, as they involve many different objects, non-unique task orders and are subject to personal idiosyncrasies. Video data and data from embedded accelerometers provide complementary information, which motivates an investigation of effective methods for fusing these sensor modalities.

In this talk I present a method for multi-modal recognition of manipulation activities that combines accelerometer data and video at multiple stages of the recognition pipeline. A method for accelerometer tracking is introduced that provides for each accelerometer-equipped object a location estimate in the camera view by identifying a point trajectory that matches well the accelerometer data. It is argued that associating accelerometer data with locations in the video provides a key link for modelling interactions between accelerometer-equipped objects and other visual entities in the scene. Estimates of accelerometer locations and their visual displacements are used to extract two new types of features: (i)

Reference Tracklet Statistics characterizes statistical properties of an accelerometer’s visual trajectory, and (ii) RETLETS, a feature representation that encodes relative motion, uses an accelerometer’s visual trajectory as a reference frame for dense tracklets. In comparison to a traditional sensor fusion approach where features are extracted from each sensor-type independently and concatenated for classification, it is shown that by combining RETLETS and Reference Tracklet Statistics with those sensor-specific features performs considerably better. Specifically addressing scenarios in which a recognition

system would be primarily used by a single person (e.g., cognitive situational support), this thesis investigates three methods for adapting activity models to a target user based on user-specific training data. Via randomized control trials it is shown that these methods indeed learn user idiosyncrasies.

Skin Reading: Encoding Text in a 6-Channel Haptic Display (11 August, 2016)

Speaker: Granit Luzhnica

In this talk I will present a study we performed in to investigate the communication of natural language messages using a wearable haptic display. Our research experiments investigated both the design of the haptic display, as well as the methods for communication that use it. First, three wearable configurations are proposed basing on haptic perception fundamentals and evaluated in the first study. To encode symbols, we use an overlapping spatiotemporal stimulation (OST) method, that distributes stimuli spatially and temporally with a minima gap. Second, we propose an encoding for the entire English alphabet and a training method for letters, words and phrases. A second study investigates communication accuracy. It puts four participants through five sessions, for an overall training time of approximately 5 hours per participant. 

Casual Interaction for Smartwatch Feedback and Communication (01 July, 2016)

Speaker: Henning Pohl
Casual interaction strives to enable people to scale back their engagement with interactive systems, while retaining the level of control they desire. In this talk, we will take a look on two recent developments in casual interaction systems. The first p

Casual interaction strives to enable people to scale back their engagement with interactive systems, while retaining the level of control they desire. In this talk, we will take a look on two recent developments in casual interaction systems. The first project to be presented is an indirect visual feedback system for smartwatches. Embedding LEDs into the back of a watch case enabled us to create a form of feedback that is less disruptive than vibration feedback and blends in with the body. We investigated how well such subtle feedback works in an in-the-wild study, which we will take a closer look at in this talk. Where the first project is a more casual form of feedback, the second project tries to support a more casual form of communication: emoji. Over the last years these characters have become more and more popular, yet entering them can take quite some effort. We have developed a novel emoji keyboard around zooming interaction, that makes it easier and faster to enter emoji.

An electroencephalograpy (EEG)-based real-time feedback training system for cognitive brain-machine interface (cBMI) (04 November, 2015)

Speaker: Kyuwan Choi

In this presentation, I will present a new cognitive brain-machine interface (cBMI) using cortical activities in the prefrontal cortex. In the cBMI system, subjects conduct directional imagination which is more intuitive than the existing motor imagery. The subjects control a bar on the monitor freely by extracting the information of direction from the prefrontal cortex, and that the subject’s prefrontal cortex is activated by giving them the movement of the bar as feedback. Furthermore, I will introduce an EEG-based wheelchair system using the cBMI concept. If we use the cBMI, it is possible to build a more intuitive BMI system. It could help improve the cognitive function of healthy people and help activate the area around the damaged area of the patients with prefrontal damage such as patients with dementia, autism, etc. by consistently activating their prefrontal cortex.

Adapting biomechanical simulation for physical ergonomics evaluation of new input methods (28 October, 2015)

Speaker: Myroslav Bachynskyi

Recent advances in sensor technology and computer vision allowed new computer input methods to rapidly emerge. These methods are often considered as more intuitive and easier to learn comparing to the conventional keyboard or mouse, however most of them are poorly assessed with respect to their physical ergonomics and health impact of their usage. The main reasons for this are large input spaces provided by these interfaces, absence of a reliable, cheap and easy-to-apply physical ergonomics assessment method and absence of biomechanics expertize in user interface designers. The goal of my research is to develop a physical ergonomics assessment method, which provides support to interface designers on all stages of the design process for low cost and without specialized knowledge. Our approach is to extend biomechanical simulation tools developed for medical and rehabilitation purposes to adapt them for Human-Computer Interaction setting. The talk gives an overview of problems related to the development of the method and shows answers to some of the fundamental questions.

Detecting Swipe Errors on Touchscreens using Grip Modulation (22 October, 2015)

Speaker: Faizuddin Mohd Noor

We show that when users make errors on mobile devices, they make immediate and distinct physical responses that can be observed with standard sensors. We used three

standard cognitive tasks (Flanker, Stroop and SART) to induce errors from 20 participants. Using simple low-resolution capacitive touch sensors placed around a standard device and a built-in accelerometer, we demonstrate that errors can be predicted using micro-adjustments to hand grip and movement in the period after swiping the touchscreen. In a per-user model, our technique predicted error with a mean AUC of 0.71 in Flanker and 0.60 in Stroop and SART using hand grip, while with the accelerometer the mean AUC in all tasks was ≥ 0.90. Using a pooled, non-user-specific, model, our technique achieved mean AUC of 0.75 in Flanker and 0.80 in Stroop and SART using hand grip and an AUC for all tasks > 0.90 for the accelerometer. When combining these features we achieved an AUC of 0.96 (with false accept and reject rates both below 10%). These results suggest that hand grip and movement provide strong and very low latency evidence for mistakes, and could be a valuable component in interaction error detection and correction systems.

A conceptual model of the future of input devices (14 October, 2015)

Speaker: John Williamson

Turning sensor engineering into advances into human computer interaction is slow, ad hoc and unsystematic. I'll discuss a fundamental approach to input device engineering, and illustrate how machine learning could have the exponentially-accelerating impact in HCI that it has had in other fields.

[caveat: This is a proposal: there are only words, not results!]

Haptic Gaze Interaction - EVENT CANCELLED (05 October, 2015)

Speaker: Poika Isokoski
Eye trackers that can be (somewhat) comfortably worn for long periods are now available. Thus, computing systems can track the gaze vector and it can be used in interactions with mobile and embedded computing systems together with other input and output

Eye trackers that can be (somewhat) comfortably worn for long periods are now available. Thus, computing systems can track the gaze vector and it can be used in interactions with mobile and embedded computing systems together with other input and output modalities. However, interaction techniques for these activities are largely missing. Furthermore, it is unclear how feedback from eye movements should be given to best support user's goals. This talk will give an overview of the results of our recent work in exploring haptic feedback on eye movements and building multimodal interaction techniques that utilize the gaze data. I will also discuss some possible future directions in this line of research.

Challenges in Metabolomics, and some Machine Learning Solutions (30 September, 2015)

Speaker: Simon Rogers

Large scale measurement of the metabolites present in an organism is very challenging, but potentially highly rewarding in the understanding of disease and the development of drugs. In this talk I will describe some of the challenges in analysis of data from Liquid Chromatography - Mass Spectrometry, one of the most popular platforms for metabolomics. I will present Statistical Machine Learning solutions to several of these challenges, including the alignment of spectra across experimental runs, the identification of metabolites within the spectra, and finish with some recent work on using text processing techniques to discover conserved metabolite substructures.

Engaging with Music Retrieval (09 September, 2015)

Speaker: Daniel Boland

Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Using listening histories and work from music psychology, a set of engagement-stratified profiles of listening behaviour are developed. The challenge of designing music retrieval for different levels of user engagement is explored with a system allowing users to denote their level of engagement and thus the specificity of their music queries. The resulting interaction has since been adopted as a component in a commercial music system.

Deep non-parametric learning with Gaussian processes (10 June, 2015)

Speaker: Andreas Damianou
http://staffwww.dcs.sheffield.ac.uk/people/A.Damianou/research/index.html#DeepGPs

This talk will discuss deep Gaussian process models, a recent approach to combining deep probabilistic structures with Bayesian nonparametrics. The obtained deep belief networks are constructed using continuous variables connected with Gaussian process mappings; therefore, the methodology used for training and inference deviates from traditional deep learning paradigms. The first part of the talk will thus outline the associated computational tools, revolving around variational inference. In the second part, we will discuss models obtained as special cases of the deep Gaussian process, namely dynamical / multi-view / dimensionality reduction models and nonparametric autoencoders. The above concepts and algorithms will be demonstrated with examples from computer vision (e.g. high-dimensional video, images) and robotics (motion capture data, humanoid robotics).

Intermittent Control in Man and Machine (30 April, 2015)

Speaker: Henrik Gollee

An intermittent controller generates a sequence of (continuous-time) parametrised trajectories whose parameters are adjusted intermittently, based on continuous observation. This concept is related to "ballistic" control and differs from i) discrete-time control in that the control is not constant between samples, and ii) continuous-time control in that the trajectories are reset intermittently.  The Intermittent Control paradigm evolved separately in the physiological and engineering literature. The talk will give details on the experimental verification of intermittency in biological systems and its applications in engineering.

Advantages of intermittent control compared to the continuous paradigm in the context of adaptation and learning will be discussed.

Get A Grip: Predicting User Identity From Back-of-Device Sensing (19 March, 2015)

Speaker: Mohammad Faizuddin Md Noor

We demonstrate that users can be identified using back-of-device handgrip changes during the course of the interaction with mobile phone, using simple, low-resolution capacitive touch sensors placed around a standard device. As a baseline, we replicated the front-of-screen experiments of Touchalytics and compare with our results. We show that classifiers trained using back-of-device could match or exceed the performance of classifiers trained using the Touchalytics approach. Our technique achieved mean AUC, false accept rate and false reject rate of 0.9481, 3.52% and 20.66% for a vertical scrolling reading task and 0.9974, 0.85% and 2.62% for horizontal swiping game task. These results suggest that handgrip provides substantial evidence of user identity, and can be a valuable component of continuous authentication systems.

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Ambulatory Applications (19 March, 2015)

Speaker: Matthieu Duvinage

Disabilities affecting mobility, in particular, often lead to exacerbated isolation and thus fewer communication opportunities, resulting in a limited participation in social life. Additionally, as costs for the health-care system can be huge, rehabilitation-related devices and lower-limb prostheses (or orthoses) have been intensively studied so far. However, although many devices are now available, they rarely integrate the direct will of the patient. Indeed, they basically use motion sensors or the residual muscle activities to track the next move.

Therefore, to integrate a more direct control from the patient, Brain-Computer Interfaces

(BCIs) are here proposed and studied under ambulatory conditions. Basically, a BCI allows you to control any electric device without the need of activating muscles. In this work, the conversion of brain signals into a prosthesis kinematic control is studied following two approaches. First, the subject transmits his desired walking speed to the BCI. Then, this high-level command is converted into a kinematics signal thanks to a Central Pattern Generator (CPG)-based gait model, which is able to produce automatic gait patterns. Our work thus focuses on how BCIs do behave in ambulatory conditions. The second strategy is based on the assumption that the brain is continuously controlling the lower limb. Thus, a direct interpretation, i.e. decoding, from the brain signals is performed. Here, our work consists in determining which part of the brain signals can be used.

Gait analysis from a single ear-worn sensor (17 March, 2015)

Speaker: Delaram Jarchi

Objective assessment of detailed gait patterns is important for clinical applications. One common approach to clinical gait analysis is to use multiple optical or inertial sensors affixed to the patient body for detailed bio-motion and gait analysis. The complexity of sensor placement and issues related to consistent sensor placement have limited these methods only to dedicated laboratory settings, requiring the support of a highly trained technical team. The use of a single sensor for gait assessment has many advantages, particularly in terms of patient compliance, and the possibility of remote monitoring of patients in home environment. In this talk we look into the assessment of a single ear-worn sensor (e-AR sensor) for gait analysis by developing signal processing techniques and using a number of reference platforms inside and outside the gait laboratory. The results are provided considering two clinical applications such as post-surgical follow-up and rehabilitation of orthopaedic patients and investigating the gait changes of the Parkinson's Disease (PD) patients.

Imaging without cameras (05 March, 2015)

Speaker: Matthew Edgar

Conventional cameras rely upon a pixelated sensor to provide spatial resolution. An alternative approach replaces the sensor with a pixelated transmission mask encoded with a series of binary patterns. Combining knowledge of the series of patterns and the associated filtered intensities, measured by single-pixel detectors, allows an image to be deduced through data inversion. At Glasgow we have been extending the concept of a `single-pixel camera' to provide continuous real-time video in excess of 10 Hz, at non-visible wavelengths, using efficient computer algorithms. We have so far demonstrated some applications for our camera such as imaging through smoke, through tinted screens, and detecting gas leaks, whilst performing sub-Nyquist sampling. We are currently investigating the most effective image processing strategies and basis scanning procedures for increasing the image resolution and frame rates for single-pixel video systems.

Interactive Visualisation of Big Music Data. (22 August, 2014)

Speaker: Beatrix Vad

Musical content can be described by a variety of features that are measured or inferred through the analysis of audio data. For a large music collection this establishes the possibility to retrieve information about its structure and underlying patterns. Dimensionality reduction techniques can be used to gain insight into such a high-dimensional dataset and to enable visualisation on two-dimensional screens. In this talk we investigate the usability of these techniques with respect to an interactive exploration interface for large music collections based on moods. A method employing Gaussian Processes to extend the visualisation with additional information about its composition is presented and evaluated

Behavioural Biometrics for Mobile Touchscreen Devices (22 August, 2014)

Speaker: Daniel Buschek

Inference in non‐linear dynamical systems – a machine learning perspective, (08 July, 2014)

Speaker: Carl Rasmussen

Inference in discrete-time non-linear dynamical systems is often done using the Extended Kalman Filtering and Smoothing (EKF) algorithm, which provides a Gaussian approximation to the posterior based on local linearisation of the dynamics. In challenging problems, when the non-linearities are significant and the signal to noise ratio is poor, the EKF performs poorly. In this talk we will discuss an alternative algorithm developed in the machine learning community which is based message passing in Factor Graphs and the Expectation Propagation (EP) approximation. We will show this method provides a consistent and accurate Gaussian approximation to the posterior enabling system identification using Expectation Maximisation (EM) even in cases when the EKF fails.

Gaussian Processes for Big Data (03 April, 2014)

Speaker: Dr James Hensman

Gaussian Process (GP) models are widely applicable models of functions, and are used extensively in statistics and machine learning for regression, classification and as components of more complex models. Inference in a Gaussian process model usually costs O(n^3) operations, where n is the number of data. In the Big Data (tm) world, it would initially seem unlikely that GPs might contribute due to this computational requirement.

Parametric models have been successfully applied to Big Data (tm) using the Robbins-Monro gradient method, which allows data to be processed individually or in small batches. In this talk, I'll show how these ideas can be applied to Gaussian Processes. To do this, I'll form a variational bound on the marginal likelihood: we discuss the properties of this bound, including the conditions where we recover exact GP behaviour.

Our methods have allowed GP regression on hundreds of thousands of data, using a standard desktop machine. for more details, see http://auai.org/uai2013/prints/papers/244.pdf .

Machine Learning for Back-of-the-Device Multitouch Typing (17 December, 2013)

Speaker: Daniel Buschek

IDI Seminar: Machine Learning for Back-of-the-Device Multitouch Typing (17 December, 2013)

Speaker: Daniel Buscheck

IDI Seminar: Uncertain Text Entry on Mobile Devices (21 November, 2013)

Speaker: Daryl Weir

Modern mobile devices typically rely on touchscreen keyboards for input. Unfortunately, users often struggle to enter text accurately on virtual keyboards. We undertook a systematic investigation into how to best utilize probabilistic information to improve these keyboards. We incorporate a state-of-the-art touch model that can learn the tap idiosyncrasies of a particular user, and show in an evaluation that character error rate can be reduced by up to 7% over a baseline, and by up to 1.3% over a leading commercial keyboard. We furthermore investigate how users can explicitly control autocorrection via how hard they touch.

IDI Seminar: Predicting Screen Touches From Back-of-Device Grip Changes (14 November, 2013)

Speaker: Faizuddin Mohd Noor

We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm.

IDI Seminar: Around-device devices: utilizing space and objects around the phone (07 October, 2013)

Speaker: Henning Pohl

For many people their phones have become their main everyday tool. While phones can fulfill many different roles, they also require users to (1) make do with affordance not specialized for the specific task, and (2) closely engage with the device itself. In this talk, I propose utilizing the space and objects around the phone to offer better task affordance and to create an opportunity for casual interactions. Around-device devices are a class of interactors, that do not require the user to bring special tangibles, but repurpose items already found in the user’s surroundings. I'll present a survey study, where we determined which places and objects are available to around-device devices. I'll also talk about a prototype implementation of hand interactions and object tracking for future mobiles with built-in depth sensing.

IDI Seminar: Extracting meaning from audio – a machine learning approach (03 October, 2013)

Speaker: Jan Larsen

Interdependence and Predictability of Human Mobility and Social Interactions (23 May, 2013)

Speaker: Mirco Musolesi

The study of the interdependence of human movement and social ties of individuals is one of the most interesting research areas in computational social science. Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. One of the open problems is how to improve the prediction exploiting additional available information. In particular, one of the key questions is how to characterise and exploit the correlation between movements of friends and acquaintances to increase the accuracy of the forecasting algorithms.

In this talk I will discuss the results of our analysis of the Nokia Mobile Data Challenge dataset showing that, by means of multivariate nonlinear predictors, it is possible to exploit mobility data of friends in order to improve user movement forecasting. This can be seen as a process of discovering correlation patterns in networks of linked social and geographic data. I will also show how mutual information can be used to quantify this correlation; I will demonstrate how to use this quantity to select individuals with correlated mobility patterns in order to improve movement prediction. Finally, I will show how the exploitation of data related to friends improves dramatically the prediction with respect to the case of information of people that do not have social ties with the user.

Flexible models for high-dimensional probability distributions (04 April, 2013)

Speaker: Iain Murray

Statistical modelling often involves representing high-dimensional probability distributions. The textbook baseline methods, such as mixture models (non-parametric Bayesian or not), often don’t use data efficiently. Whereas the machine learning literature has proposed methods, such as Gaussian process density models and undirected neural network models, that are often too computationally expensive to use. Using a few case-studies, I will argue for increased use of flexible autoregressive models as a strong baseline for general use.

Pre-interaction Identification By Dynamic Grip Classification (28 February, 2013)

Speaker: Faizuddin Mohd Noor

We present a novel authentication method to identify users at they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences.

Evaluating Bad Query Abandonment in an Iterative SMS-Based FAQ Retrieval System (14 February, 2013)

Speaker: Edwin Thuma

We investigate how many iterations users are willing to tolerate in an iterative Frequently Asked Question (FAQ) system that provides information on HIV/AIDS. This is part of work in progress that aims to develop an automated Frequently Asked Question system that can be used to provide answers on HIV/AIDS related queries to users in Botswana. Our system engages the user in the question answering process by following an iterative interaction approach in order to avoid giving inappropriate answers to the user. Our findings provide us with an indication of how long users are willing to engage with the system. We subsequently use this to develop a novel evaluation metric to use in future developments of the system. As an additional finding, we show that the previous search experience of the users has a significant effect on their future behaviour.

IDI Seminar (29 November, 2012)

Speaker: Konstantinos Georgatzis
Efficient Optimisation for Data Visualisation as an Information Retrieval Task

Visualisation of multivariate data sets is often done by mapping data onto a low-dimensional display with nonlinear dimensionality reduction (NLDR) methods. We have introduced a formalism where NLDR for visualisation is treated as an information retrieval task, and a novel NLDR method called the Neighbor Retrieval

Visualiser (NeRV) which outperforms previous methods. The remaining concern is that NeRV has quadratic computational complexity with respect to the number of data. We introduce an efficient learning algorithm for NeRV where relationships between data are approximated through mixture modeling, yielding efficient computation with near-linear computational complexity with respect to the number of data. The method is much faster to optimise as the number of data grows, and it maintains good visualisation performance.

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Roderick Murray-Smith

Professor Roderick Murray-Smith

Professor (Computing Science)

Research interests: Mobile Human Computer Interaction; Machine Learning; Brain Computer Interaction; Dynamic Systems; Probabilistic Inference

Simon Rogers

Dr Simon Rogers

Senior Lecturer (Computing Science)

Dr John Williamson

Lecturer (School of Computing Science)

Dr Ke Yuan

Lecturer in Computing Science (Machine Learning in Computational Biology) (Computing Science)

Dr Bjorn Jensen

Lecturer in Computing Science( Applied Machine LEarning) (Computing Science)