13,296 research outputs found
Probabilistic Perspectives on Collecting Human Uncertainty in Predictive Data Mining
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
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
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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