13,296 research outputs found

    Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining

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    In many areas of data mining, data is collected from humans beings. In this contribution, we ask the question of how people actually respond to ordinal scales. The main problem observed is that users tend to be volatile in their choices, i.e. complex cognitions do not always lead to the same decisions, but to distributions of possible decision outputs. This human uncertainty may sometimes have quite an impact on common data mining approaches and thus, the question of effective modelling this so called human uncertainty emerges naturally. Our contribution introduces two different approaches for modelling the human uncertainty of user responses. In doing so, we develop techniques in order to measure this uncertainty at the level of user inputs as well as the level of user cognition. With support of comprehensive user experiments and large-scale simulations, we systematically compare both methodologies along with their implications for personalisation approaches. Our findings demonstrate that significant amounts of users do submit something completely different (action) than they really have in mind (cognition). Moreover, we demonstrate that statistically sound evidence with respect to algorithm assessment becomes quite hard to realise, especially when explicit rankings shall be built

    Foreign Direct Investment and the Internationalisation of South African Mining Companies into Africa

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    The paper investigates the factors influencing the internationalisation of mining firms into Africa and the strategies employed. We focus on the FDI of South African mining firms because of the dominance of this country in the extractive resources industry for over a century. A semi-structured interview survey process consisting of written questionnaires and one-on-one interviews that incorporated both structured as well as open-ended questions was used. The structured questionnaire attempted to identify the entry-mode characteristics of the mining firms as well as the importance of the factors influencing the internationalisation of mining firms. The open-ended questionnaire was designed to be probing in nature, in order to identify how mining companies manage the factors deemed present in an operational context. More than 80% of South African mining firms by market capitalisation provided responses to the survey. The research revealed that security of tenure, political stability and the availability of infrastructure were the three most important factors influencing the internationalisation of South African mining firms out of the nine factors tested in the survey. The most widespread strategies used to manage these factors were political lobbying, bargaining and negotiation.Theory of FDI and the MNE (Ownership-Location-Internalization), Mining, Africa, Factor Analysis, Incorporating Country Variables

    Quantifying Facial Age by Posterior of Age Comparisons

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    We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio
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