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Machine learning in optics: from solving inverse problems in imaging to high-speed hardware implementations (07 June, 2019)

Speaker: Alejandro Turpin

Advanced computational algorithms such as machine learning and Bayesian inference have left their traditional space within computing science and are impacting multiple areas, such as biomedical imaging, artificial vision, and neuroscience. In this talk I will discuss two different works where machine learning, in particular artificial neural networks, have been used in inverse problems in imaging to overcome the limitations from hardware: imaging through complex media and 3D imaging with single point detectors.

Trained to Fuzz! (13 May, 2019)

Speaker: Martin Sablotny

 Software testing is used to ensure the correct functionality of a program and to discover flaws in the software which can introduce security issues. A prominent software testing technique is so-called fuzz testing. Here, a test case generator creates input data for a program under test and the execution of it is monitored to discover unintended behaviour. However, developing test case generators for fuzz testing is a labour intensive task mainly because it is necessary to study the format specifications and reimplement them before even starting to generate any test cases. In this talk, I’ll outline a novel machine learning based approach which can significantly speed up the development of fuzz testers. First, I’ll show that it is possible to improve an existing fuzzer by utilising generative deep learning methods and provide guidance on how to select good performing model without actually executing any test cases. Secondly, readily available real-world data is used to train a test generator from ground up. Finally, I will outline how deep reinforcement learning can be applied to fuzz testing and teach the fuzzer how to generate test cases which maximises code coverage in a closed-loop manner.

SICSA DVF Masterclass – Predicting multi-view and structured data with kernel methods (10 May, 2019)

Speaker: Prof. Juho Rousu (SICSA DVF)

During the last two decades, kernel methods – including, but not limited to the celebrated support vector machine  – have been extremely succesfull in many walks of life. They continue to be a good alternative to deep neural networks in many real-world applications where data is complex and high-dimensional, and the amount of training data is medium-scale – from hundreds to a few tens of thousands of training examples.

In this masterclass I will focus on how kernel methods can be used for applications where the prediction setup involves heterogeneous or structured data, in particular learning with multiple data sources and predicting structured output.



Bhadra, S., Kaski, S. and Rousu, J., 2017. Multi-view kernel completion. Machine Learning, 106(5), pp.713-739.

Cichonska, A., Pahikkala, T., Szedmak, S., Julkunen, H., Airola, A., Heinonen, M., Aittokallio, T. and Rousu, J., 2018. Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics, 34(13), pp.i509-i518.

Hue, M. and Vert, J.P., 2010, June. On learning with kernels for unordered pairs. In ICML (pp. 463-470).

Marchand, M., Su, H., Morvant, E., Rousu, J. and Shawe-Taylor, J.S., 2014. Multilabel structured output learning with random spanning trees of max-margin markov networks. In Advances in Neural Information Processing Systems (pp. 873-881).

Scholkopf, B. and Smola, A.J., 2001. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.

Shawe-Taylor, J. and Cristianini, N., 2004. Kernel methods for pattern analysis. Cambridge university press.

Su, H., Gionis, A. and Rousu, J., 2014, January. Structured prediction of network response. In International Conference on Machine Learning (pp. 442-450).

Su, H. and Rousu, J., 2015. Multilabel classification through random graph ensembles. Machine Learning, 99(2).

Taskar, B., Guestrin, C. and Koller, D., 2004. Max-margin Markov networks. In Advances in neural information processing systems (pp. 25-32).

Tsochantaridis, I., Joachims, T., Hofmann, T. and Altun, Y., 2005. Large margin methods for structured and interdependent output variables. Journal of machine learning research, 6(Sep), pp.1453-1484.

Small Molecule Identification through Machine Learning: CSI:FingerID and beyond (17 April, 2019)

Speaker: Prof. Juho Rousu (SICSA DVF)

Identification of small molecules from biological samples remains a major bottleneck in understanding the inner workings of biological cells and their environment. Machine learning on data from large public databases of tandem mass spectrometric data has transformed this field in recent years, with tools like CSI:FingerID, and CSI:IOKR demonstrating a step-change improvement in identification rates compared to previous approaches.  In this presentation, I will give an overview of the technology inside these tools and review some recent developments in making use of additional information sources for improving the identification rates, in particular learning to predict the order of molecules eluting from liquid-chromatographic system. 

Bach, E., Szedmak, S., Brouard, C., Böcker, S. and Rousu, J., 2018. Liquid-chromatography retention order prediction for metabolite identification. Bioinformatics, 34(17), pp.i875-i883.
Brouard, C., Bach, E., Böcker, S. and Rousu, J., 2017, November. Magnitude-preserving ranking for structured outputs. In Asian Conference on Machine Learning (pp. 407-422).
Brouard, C., Shen, H., Dührkop, K., d’Alché-Buc, F., Böcker, S. and Rousu, J., 2016. Fast metabolite identification with input output kernel regression. Bioinformatics, 32(12), pp.i28-i36.
Dührkop, K., Fleischauer, M., Ludwig, M., Aksenov, A.A., Melnik, A.V., Meusel, M., Dorrestein, P.C., Rousu, J. and Böcker, S., 2019. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nature Methods 16, pp- 299-302
Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S., 2015. Searching molecular structure databases with tandem mass spectra using CSI: FingerID. Proceedings of the National Academy of Sciences, 112(41), pp.12580-12585.

Short Bio:
Juho Rousu is a Professor of Computer Science at Aalto University, Finland. Rousu obtained his PhD in 2001 form University of Helsinki, while working at VTT Technical Centre of Finland. In 2003-2005 he was a Marie Curie Fellow at Royal Holloway University of London. In 2005-2011 he held Lecturer and Professor positions at University of Helsinki, before moving to Aalto University in 2012 where he leads a research group on Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO). Rousu’s main research interest is in learning with multiple and structured targets, multiple views and ensembles, with methodological emphasis in regularised learning, kernels and sparsity, as well as efficient convex/non-convex optimisation methods. His applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.

Joint Variational Uncertain Input Gaussian Processes (20 February, 2019)

Speaker: Carl Edward Rasmussen & Adrià Garriga-Alonso

Standard mean-field variational inference in Gaussian Processes with uncertain inputs systematically underestimates posterior uncertainty. In particular, the factorisation assumption employed in the approximating distribution severely limits the framework’s accuracy. We lift this assumption, and show that the resulting scheme gives much more realistic predictive uncertainties, and can be implemented in a sparse and practical way. The algorithm has implications for latent variable models generally, including stacked (Deep) GPs and time series models.

IDI Journal Club: Graph Attention Networks (31 January, 2019)

Speaker: Joshua Mitton

In this journal club meeting, Josh will lead the discussion of the paper “Graph Attention Networks”.


We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of computationally intensive matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).


Quantum inspired image compression. (11 December, 2018)

Speaker: Bruno Sanguinetti

Pushing image sensors and algorithms to the quantum limit (11 December, 2018)

Speaker: Bruno Sanguinetti

Towards data-driven hearing aid solutions (04 October, 2018)

Speaker: Widex staff

Widex will give an informal overview of the company and current challenges in the hearing aid domain. We will discuss challenges related to data collection, machine learning and real-time optimisation with humans in the loop.

Variational Sparse Coding (13 June, 2018)

Speaker: Francesco Tonolini

We propose a new method for sparse coding based on the variational auto-encoder architecture, which allows sparse representations with generally intractable probabilistic models. We assume data to be generated from a sparse distribution prior in the latent space of a generative model and aim to maximise the observed data likelihood with a variational auto-encoding approach. We consider both the Laplace and the spike and slab priors and in each case derive an analytic approximation to the regularisation term in the variational lower bound, making posterior inference as efficient as in the standard variational auto-encoder case. By inducing sparsity in the prior, training results in a recognition function that generates sparse representations of observed data. Such representations can then be used as information-rich inputs to further learning tasks. 

Deep, complex networks for inversion of transmission effects in multimode optical fibres (30 May, 2018)

Speaker: Oisin Moran

We use complex-weighted, deep convolutional networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale, input images generated with coherent light, and measuring only image amplitude  (not amplitude and phase as is typical) at the output of the \SI{10}{\metre} long \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches and subsequent denoising autoencoders. A new {\it unitary regularisation} approach for complex-weighted networks is proposed which performs best in robustly inverting the fibre transmission matrix, which fits well with the physical theory.

Modelling the creative process through black-box optimisation (23 May, 2018)

Speaker: Anders Kirk Uhrenholt

The creative process from getting an idea to having that idea materialise as an image or a piece of music can often be framed as an optimisation task where the artist makes incremental changes until a local optimum is reached. This begs the question whether machine learning has a role to play in automating the tedious part of this process thereby freeing up time and energy for the user to be creative.
In a typical optimisation setting the cost function can be objectively evaluated with some measurable degree of certainty. But what if the target of the optimisation is something inherently subjective such as a person’s perception of sound or image? This is a central question in the intersection between predictive modelling and creative software where the aim is to support the artist throughout the creative process in an intelligent way.
This talk focuses on said problem specifically for the task of tuning a music synthesizer. The task can be framed as optimising a black-boxed system (the synthesizer) with regards to an unknown cost function (the user’s opinion of the synthesised sound). In the proposed approach metric learning is included as part of the optimisation loop to simultaneously learn a mapping from synthesizer configuration to sound while inferring from user feedback what the artist will think of the produced result.


Speaker: Rodrigo Gutierrez-Quintana

R. Gutierrez-Quintana, K.L. Holmes, Z. Hatfield, P. Amengual Batle, J. Brocal, K. Lazzerini, R. José-López. Small Animal Hospital, School of Veterinary Medicine, University of Glasgow, UK.

   An inexpensive and easily available method for objectively identifying and grading pelvic limb ataxia in dogs in the clinical setting is urgently needed. An alternative approach to conventional gait analysis techniques is the use of accelerometers attached to the body. They have the advantages of being low cost and allowing non-restrictive evaluation in a normal environment. 

   The purpose of this prospective study was to perform gait analysis using a lumbar accelerometer in dogs with pelvic limb ataxia and healthy controls; and assess whether the data obtained could be used to differentiate these 2 groups.

   Fifty-three dogs (21 healthy controls and 32 dogs with pelvic limb ataxia) of different size breeds were included. All dogs were walked in a straight line, on a non-slippery surface, at a slow walking pace for 50 meters using a short lead.  Acceleration signals were measured using a wireless tri-axial accelerometer that was secured with an elastic band at the level of the fifth lumbar vertebra. The average and coefficient of variation of the peak-to-peak amplitude was calculated for each acceleration component (x: Cranio-caudal, y: Latero-lateral and z; Dorso-ventral). Mann-Whitney test was used to compare groups (p<0.05).

   A significant difference between affected and control dogs was identified in the coefficient of variation of the x axis (p<0.0001).

   The results of the present study suggest that the coefficient of variation of the cranio-caudal axis could represent an objective measure of pelvic limb ataxia in dogs. Further longitudinal studies in a larger number of cases are indicated.

Approaches to analysis of genomic data (17 January, 2018)

Speaker: Thomas Otto

A huge amount of data in biological sciences are generated in the hope to answer biological questions. This is possible due to the decreased price of high throughput methods. Although many analysis tools exist, there is a need to improve many of them. Further, there are many opportunities to develop new methods by combining existing dataset sets. 

In this talk, I will present some of the datasets and the methods we used/developed to analyse genomic data, including genomic and transcriptional data from malaria. I will also describe anticipated data, such as single cell RNA-Seq or detection of biomarkers. 

Optimal input for low reliability assistive technology (19 October, 2017)

Speaker: John Williamson

Most devices used for human input are reliable, in the sense
that errors are small in proportion to the information which
passes through the interface channel. There are, however, a few
important and relevant human interface channels which have
both very low communication rates and very low reliability.
We present a practical and general method for
optimal human interaction using binary input devices having very
high noise levels where a reliable feedback channel is available. In
particular, we show efficient navigation and selection techniques are
viable even with a
binary channel (symmetric or asymmetric) where reliability
may be below 75%, with provably optimal performance.
This mechanism can automatically adapt to changing channel statistics
with no overhead, and does not need precise calibration. A range
of visualisations are used to implicitly code for these channels in
a way that it is transparent to users. We validate our results
through a considered process of evaluation from theoretical
analysis, automated simulation, live interaction simulators.

Leveraging from Ontologies in machine learning (05 October, 2017)

Speaker: David Stirling

This presentation considers a number of successful cases that have significantly benefited from the inclusion of an ontology framework. Firstly, a human bespoke ontology describing cyclic temporal control states has enabled successful multi-objective control (an intelligent autopilot) of a simulated aircraft. Secondly, an empirically learnt ontology was derived to identifying several industrial process modalities, which were exploited to reveal underlying causal factors for a set of undesirable modes (states) of high heat loads in a Blast Furnace. The first case reviews a novel approach for learning and building computational models of human skills that are typically utilized in complex control situations. Such skills are often internalized as sub-cognitive and automatic responses, such as those routinely used in driving a car. Previously, a degree of success in modelling these was reported via behavioural cloning. However, skills obtained by this technique, often exhibit a lack of generality and robustness when applied to different control tasks. This is now mitigated in the alternative presented approach, by segmenting and compressing a universal set of reaction plans with symbolic induction methods. This approach is termed, Compressed Heuristic Universal Reaction Planners or CHURPs. The substantially improved robustness and control performance arises from synergistic interactions and collaborations between the different CHURPs entities including, surrogate control and goal sharing. In the latter case, an abstracted ontology containing nine major heat load modalities, was initially learnt as a 38 state Gaussian Mixture Model from several years of Blast Furnace heat load data, and subsequently utilized to diagnose the casual influences determining these states. Such methodologies are now being pursued in a number of kinematic rehabilitation motion studies, as well as oncology and radiotherapy aspects of cancer care.


Dr Stirling obtained his BEng degree from the Tasmanian College of Advanced Education (1976), an MSc (Digital Techniques) in from Heriot-Watt University, Scotland UK (1980), and his PhD from the University of Sydney (1995). He has worked for over 20 years in wide range of industries, including as a Principal Research Scientist with BHP Steel. More recently he joined the University of Wollongong as a Senior Lecturer. David has developed a wide range of expertise in data analysis and knowledge management with skills in problem solving, statistical methods, visualization, pattern recognition, data fusion and reduction. He has applied machine learning and data mining techniques in specialized classifier designs for noisy multivariate data to medical research, exploration geo-science, and financial markets, as well as to industrial primary operations.



Gesture Typing on Virtual Tabletop: Effect of Input Dimensions on Performance (28 September, 2017)

Speaker: Antoine Loriette

The association of tabletop interaction with gesture typing presents interaction potential for situationally or physically impaired users. In this work, we use depth cameras to create touch surfaces on regular tabletops. We describe our prototype system and report on a supervised learning approach to fingertips touch classification. We follow with a gesture typing study that compares our system with a control tablet scenario and explore the influence of input size and aspect ratio of the virtual surface on the text input performance. We show that novice users perform with the same error rate at half the input rate with our system as compared to the control condition, that an input size between A5 and A4 ensures the best tradeoff between performance and user preference and that users’ indirect tracking ability seems to be the overall performance limiting factor. 

A Theory of How People Make Decisions Through Interaction (14 September, 2017)

Speaker: Andrew Howes

In this talk I will discuss current thinking concerning how people make decisions through interaction. The talk offers evidence for the adaptive, embodied and context sensitive nature of human decision making. It also offers a computational theory, inspired by machine learning, of how the constraints imposed by the human visual system, and by the the visualisation design, lead to emergent strategies for interaction. These strategies focus user attention on certain kinds of information and ignore others; they determine apparent risk preferences and, ultimately, the quality of decisions made.

Amplifying Human Abilities: Digital Technologies to Enhance Perception and Cognition (12 September, 2017)

Speaker: Albrecht Schmidt

Historically the use and development of tools is strongly linked to human evolution and intelligence. The last 10.000 years show a stunning progress in physical tools that have transformed what people can do and how people live. Currently, we are at the beginning of an even more fundamental transformation: the use of digital tools to amplify the mind. Digital technologies provide us with entirely new opportunities to enhance the perceptual and cognitive abilities of humans. Many ideas, ranging from mobile access to search engines, to wearable devices for lifelogging and augmented realty application give as first indications of this transition. In our research we create novel digital technologies that systematically explore how to enhance human cognition and perception. Our experimental approach is to: first, understand the users in their context as well as the potential for enhancement. Second, we create innovative interventions that provide functionality that amplifies human capabilities. And third, we empirically evaluate and quantify the enhancement that is gained by these developments. It is exciting to see how ultimately these new ubiquitous computing technologies have the potential for overcoming fundamental limitations in human perception and cognition.

Data-Efficient Learning for Autonomous Robots (23 August, 2017)

Speaker: Marc Deisenroth

Fully autonomous systems and robots have been a vision for many decades, but we are still far from practical realization. One of the fundamental challenges in fully autonomous systems and robots is learning from data directly without relying on any kind of intricate human knowledge. This requires data-driven statistical methods for modeling, predicting, and decision making, while taking uncertainty into account, e.g., due to measurement noise, sparse data or stochasticity in the environment. In my talk I will focus on machine learning methods for controlling autonomous robots, which pose an additional practical challenge: Data-efficiency, i.e., we need to be able to learn controllers in a few experiments since performing millions of experiments with robots is time consuming and wears out the hardware. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, pre-shaped policies, or the underlying dynamics. In the first part of the talk, I follow a different approach and speed up learning by efficiently extracting information from sparse data. In particular, I propose to learn a probabilistic, non-parametric Gaussian process dynamics model.By explicitly incorporating model uncertainty in long-term planning and controller learning my approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art reinforcement learning our model-based policy search method achieves an unprecedented speed of learning, which makes is most promising for application to real systems. I demonstrate its applicability to autonomous learning from scratch on real robot and control tasks. In the second part of my talk, I will discuss an alternative method for learning controllers for bipedal locomotion based on Bayesian Optimization, where it is hard to learn models of the underlying dynamics due to ground contacts. Using Bayesian optimization, we sidestep this modeling issue and directly optimize the controller parameters without the need of modeling the robot’s dynamics.


Spatial Smoothing in Mass Spectrometry Imaging (08 May, 2017)

Speaker: Arijus Pleska

In this paper, we target a data modelling approach used in computational metabolomics; to be specic, we assess whether spatial smoothing improves the topic term and noise identification. By assessing mass spectrometry imaging data, we design an enhancement for latent Dirichlet allocation-based topic models. For both data pre-processing and topic model design, we survey relevant research. Further, we present the proposed methodology in detail providing the prelimi- naries and guiding through the performed topic model en hancements. To assess the performance, we evaluate the spatial smoothing application on a number

Investigation of users’ affective and physiological traits in a multi-modal interaction context (04 May, 2017)

Speaker: Iulia Popescu

In this talk, I will present my Level 5 (MSci) project which explored how users react and what they feel when they are exposed to different types of stimuli (visual, auditory). This study aimed to understand how short-term stressors impact individuals’ behaviour when they need to complete a task in a multi-modal interaction context (e.g. searching for a flight using graphical and spoken dialogue interfaces). Additionally, I will give an overview about the data set which has been delivered as part of this project and how it can be used for further research.

Real-time Mobile Object Removal using Google Project Tango (04 May, 2017)

Speaker: Rhys Simpson

Visually removing objects from a video feed is difficult to perform in real-time, as existing solutions rely on expensive patch lookups and specific environment conditions to produce meaningful results. Results are also guessed from the image surrounding the object, usually making them physically inaccurate and visually displeasing. Recent advances in hardware and software are pushing businesses to make large investments into Augmented Reality, including furniture catalogue applications, which could greatly benefit if existing objects could be visually removed from the video feed in real-time. This paper demonstrates a novel approach where instead of painting frames in an entirely 2D context, a 3D room mesh is captured, tracked and selectively rendered to paint geometry that was behind the object over it. The object’s mask, and filled textures covering the planes the object was in contact with are also sourced and tracked from this mesh. Our approach works for a broad range of objects in typical indoors scenes, where target objects are separate and against large wall and floor planes. We show that our algorithm produces much better results per frame than object removal using traditional 2D inpainting, at an interactive framerate, and demonstrate that temporal incoherence between subsequent video frames is also eliminated.

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.



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).


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 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


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 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

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 ™ world, it would initially seem unlikely that GPs might contribute due to this computational requirement.

Parametric models have been successfully applied to Big Data ™ 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 .

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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