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

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

Housing and Urbanization: A Socio-Spatial Analysis of Resettlement Projects in Hồ Chí Minh City

As Hồ Chí Minh City continues to undergo rapid urbanization, especially with the creation of a multitude of new urban zone developments on the periphery of the inner districts, the resettling of people has become common. Families who live within areas that are selected for urban upgrading or, as in other cases for the construction of new miniature cities, must face the realities of relocation. Many issues arise in the complicated process of resettling the displaced, due to complex land-use laws, bureaucratic dissonance, and lack of investment in actual resettlement housing. The authorities of Hồ Chí Minh City have faced palpable challenges in facilitating the many processes of resettlement, from persuading developers to invest in resettlement housing to establishing suitable compensation packages. Confusing legal labyrinths, delays in plan approval, and miscommunications between agencies, results in tangible affects on the highly vulnerable displaced families. Additionally, a serious disconnect arises between planners’ envisioned solution for resettlement housing and the real needs of the resettled, who are usually low-income workers. When the precise needs of displaced families and their prior sources of economic livelihood are disregarded, the general result is unsuitable design and the disordering of previously established socio-spatial networks. Additionally the displaced tend to be sent to occupy less advantageous space, as a result of gentrification, and are spatially repositioned in more excluded, disconnected marginal zones. Past and present resettlement procedures have faltered due especially to a lack of socio-spatial planning, which has resulted in undesirable threats to equitable metropolisation and rising potentials for urban fragmentation

Explaining how to play real-time strategy games

Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior

XAI-Explainable artificial intelligence

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications

Shielding effectiveness of original and modified building materials

This contribution deals with the determination of the shielding effectiveness of building materials used for office, factory and government buildings. Besides the examination of standard materials, measurements were also performed on modified materials, e.g. ferro concrete with enhanced shielding effectiveness due to a changed mixture or structure of the reinforcement. The measurements of original and modified materials were carried out in a fully anechoic room (FAR) according to IEEE 299-1997 from 80 MHz up to 10 GHz