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Partial observable update for subjective logic and its application for trust estimation
(Elsevier, 2015)
Subjective Logic (SL) is a type of probabilistic logic, which is suitable for reasoning about situations with
uncertainty and incomplete knowledge. In recent years, SL has drawn a significant amount of attention from the ...
Campaign participation prediction with deep learning
(Elsevier, 2021-08)
Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to ...
Misclassification risk and uncertainty quantification in deep classifiers
(IEEE, 2021)
In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ...
Handling epistemic and aleatory uncertainties in probabilistic circuits
(Springer, 2022-04)
When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may occur, hence the need for Bayesian ...
Explain to me: Towards understanding privacy decisions
(International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023)
Privacy assistants help users manage their privacy online. Their tasks could vary from detecting privacy violations to recommending sharing actions for content that the user intends to share. Recent work on these tasks are ...
Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020)
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
Probabilistic logic programming with beta-distributed random variables
(Association for the Advancement of Artificial Intelligence, 2019-07-17)
We enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms ...
Combining semantic web and IoT to reason with health and safety policies
(IEEE, 2018-01-12)
Monitoring and following health and safety regulations are especially important - but made difficult - in hazardous work environments such as underground mines to prevent work place accidents and illnesses. Even though ...
A knowledge driven policy framework for internet of things
(ScitePress, 2017)
With the proliferation of technology, connected and interconnected devices (henceforth referred to as IoT) are fast becoming a viable option to automate the day-to-day interactions of users with their environment—be it ...
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