Argumentation-based design rationale - the sharpest tools in the box

In this paper the three main argumentation-based design rationale methodologies - IBIS, QOC and DRL – will be discussed with illustrations of particular points drawn from a working example. The areas of scope, expressiveness in terms of design space and argumentation representation and the resulting usability by human and computer will be examined. Particular attention is paid to how the development of the artifact is being controlled by the evaluation of intentions and objectives that allow consistent goals throughout the design to be formulated, evaluated and modified. Furthermore, decision making within an argumentative context is highlighted

When users generate music playlists: When words leave off, music begins?

Music systems that generate playlists are gaining increasing popularity, yet ways to select songs to be acceptable to users is still elusive. We present the results of an explorative study that focused on the language of musically untrained end users for playlist choices, in a variety of listening contexts. Our results indicate that there are a number of opportunities for playlist recommendation or retrieval systems, particularly by taking context into account

Co-designing smart home technology with people with dementia or Parkinson's disease

Involving users is crucial to designing technology successfully, especially for vulnerable users in health and social care, yet detailed descriptions and critical reflections on the co-design process, techniques and methods are rare. This paper introduces the PERCEPT (PERrsona-CEntred Participatory Technology) approach for the co-design process and we analyse and discuss the lessons learned for each step in this process. We applied PERCEPT in a project to develop a smart home toolset that will allow a person living with early stage dementia or Parkinson's to plan, monitor and self-manage his or her life and well-being more effectively. We present a set of personas which were co-created with people and applied throughout the project in the co-design process. The approach presented in this paper will enable researchers and designers to better engage with target user groups in co-design and point to considerations to be made at each step for vulnerable users

The use of provenance in information retrieval

The volume of electronic information that users accumulate is steadily rising. A recent study [2] found that there were on average 32,000 pieces of information (e-mails, web pages, documents, etc.) for each user. The problem of organizin

Reflectance measurement of two-dimensional photonic crystal nanocavities with embedded quantum dots

The spectra of two-dimensional photonic crystal slab nanocavities with embedded InAs quantum dots are measured by photoluminescence and reflectance. In comparing the spectra taken by these two different methods, consistency with the nanocavities' resonant wavelengths is found. Furthermore, it is shown that the reflectance method can measure both active and passive cavities. Q-factors of nanocavities, whose resonant wavelengths range from 1280 to 1620 nm, are measured by the reflectance method in cross polarization. Experimentally, Q-factors decrease for longer wavelengths and the intensity, reflected by the nanocavities on resonance, becomes minimal around 1370 nm. The trend of the Q-factors is explained by the change of the slab thickness relative to the resonant wavelength, showing a good agreement between theory and experiment. The trend of reflected intensity by the nanocavities on resonance can be understood as effects that originate from the PC slab and the underlying air cladding thickness. In addition to three dimensional finite-difference time-domain calculations, an analytical model is introduced that is able to reproduce the wavelength dependence of the reflected intensity observed in the experiment.Comment: 24 pages, 7 figures, corrected+full versio

People-Powered Music: Using User-Generated Tags and Structure in Recommendations

Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches

Horses for courses: Making the case for persuasive engagement in smart systems

Current thrusts in explainable AI (XAI) have focused on using interpretability or explanatory debugging as frameworks for developing explanations. We argue that for some systems a different paradigm – persuasive engagement – needs to be adopted, in order to affect trust and user satisfaction. In this paper, we will briefly provide an overview of the current approaches to explain smart systems and their scope of application. We then introduce the theoretical basis for persuasive engagement, and show through a use case how explanations might be generated. We then discuss future work that might shed more light on how to best explain different kinds of smart systems

On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

Approximate Bayesian computation (ABC) has gained popularity over the past few years for the analysis of complex models arising in population genetics, epidemiology and system biology. Sequential Monte Carlo (SMC) approaches have become work-horses in ABC. Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a sequence of distributions that start out from a suitably defined prior and converge towards the unknown posterior. We derive optimality criteria for different kernels, which are based on the Kullback-Leibler divergence between a distribution and the distribution of the perturbed particles. We will show that for many complicated posterior distributions, locally adapted kernels tend to show the best performance. We find that the added moderate cost of adapting kernel functions is easily regained in terms of the higher acceptance rate. We demonstrate the computational efficiency gains in a range of toy examples which illustrate some of the challenges faced in real-world applications of ABC, before turning to two demanding parameter inference problems in molecular biology, which highlight the huge increases in efficiency that can be gained from choice of optimal kernels. We conclude with a general discussion of the rational choice of perturbation kernels in ABC SMC settings