Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Cast together: inclusive and unobtrusive mobile interactions with a situated display

We describe our Cast Together prototype that demonstrates inclusive and unobtrusive mobile interactions with a situated display. The prototype consists of a mobile and web application, and a Google Chromecast connected to a situated display. An inclusive and unobtrusive experience is encouraged for co-located persons by sharing notifications on the display, allowing users to decide at a glance if an alert requires further attention, and sharing app launches provides others with insight into private smartphone interactions. Music and photo collections generated from social media profiles can project personalities without active engagement with a personal device. Profiles can be linked to physical objects with NFC tags, and the act of exploring collections can become a visible performance by explicitly scanning objects with the smartphone. Shared information can also be managed implicitly by hiding details when a user steps out of the room, or by reacting to a change of place. A user study with two colleagues in an office setting leads to initial insights with our Cast Together probe

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Empowerment as a metric for Optimization in HCI

We propose a novel metric for optimizing human-computer interfaces, based on the information-theoretic capacity of empowerment, a task-independent universal utility measure. Empowerment measures, for agent-environment systems with stochastic transitions, how much influence, which can be sensed by the agent sensors, an agent has on its environment. It captures the uncertainty in human-machine systems arising from different sources (i.e. noise, delays, errors, etc.) as a single quantity. We suggest the potential empowerment has as an objective optimality criterion in user interface design optimization, contributing to the more solid theoretical foundations of HCI.Peer reviewedFinal Accepted Versio

Impact of smartphone notification display choice in a typing task

External displays have the potential to make smartphone notifications less obtrusive when a user has committed their attention to a primary task. We compare six notification displays, and evaluate the impact that negotiating smartphone interruptions has on a typing task when the number of notifications to ignore and act on are equal. A lab experiment with 30 participants is conducted, and initial results show that desktop pop-ups are preferred significantly more, where they require the fewest actions to read. Managing notifications via the notification bar is least preferred, despite requiring fewer actions to respond. This work is a well-controlled pre-cursor to the application of notification displays in social scenarios. The results motivate the use of external displays to manage attention around smartphone interruptions

Investigating UI displacements in an Adaptive Mobile Homescreen

The authors present a system that adapts application shortcuts (apps) on the homescreen of an Android smartphone, and investigate the effect of UI displacements that are caused by the choice of adaptive model and the order of apps in the homescreen layout. They define UI displacements to be the distance that items move between adaptations, and they use this as a measure of stability. An experiment with 12 participants is performed to evaluate the impact of UI displacements on the homescreen. To make the distribution of apps in the experiment task less contrived, naturally generated data from a pilot study is used. The authors’ results show that selection time is correlated to the magnitude of the previous UI displacement. Additionally, selection time and subjective rating improve significantly when the model is easy to understand and an alphabetical order is used, conditions that increase stability. However, rank order is preferred when the model updates frequently and is less easy to understand. The authors present their approach to adapting apps on the homescreen, and initial insights into UI displacements

PathologyGAN: Learning deep representations of cancer tissue

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H&E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio

Modelling and correcting for the impact of the gait cycle on touch screen typing accuracy

Walking and typing on a smartphone is an extremely common interaction. Previous research has shown that error rates are higher when walking than when stationary. In this paper we analyse the acceleration data logged in an experiment in which users typed whilst walking, and extract the gait phase angle. We find statistically significant relationships between tapping time, error rate and gait phase angle. We then use the gait phase as an additional input to an offset model, and show that this allows more accurate touch interaction for walking users than a model which considers only the recorded tap position

Gesture Typing on Virtual Tabletop: Effect of Input Dimensions on Performance

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 present the best tradeoff between performance and user preference and that users' indirect tracking ability seems to be the overall performance limiting factor