LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density function is discontinuous with respect to, while further providing runtime checks for boundary crossings. This enables the introduction of new inference engines that are able to exploit gradient information, while remaining efficient for models which are not everywhere differentiable. We demonstrate this ability by incorporating a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this language has a density with measure zero discontinuities to maintain the validity of the inference engine.Comment: Published in the proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS

Persuasive Technology for Learning in Business Context

"Persuasive Design is a relatively new concept which employs general principles of persuasion that can be implemented in persuasive technology. This concept has been introduced by BJ Fogg in 1998, who since then has further extended it to use computers for changing attitudes and behaviour. Such principles can be applied very well in learning and teaching: in traditional human-led learning, teachers always have employed persuasion as one of the elements of teaching. Persuasive technology moves these principles into the digital domain, by focusing on technology that inherently stimulates learners to learn more quickly and effectively. This is very relevant for the area of Business Management in several aspects: Consumer Behavior, Communications, Human Resource, Marketing & Advertising, Organisational Behavior & Leadership. The persuasive principles identified by BJ Fogg are: reduction, tunnelling, tailoring, suggestion, self-monitoring, surveillance, conditioning, simulation, social signals. Also relevant is the concept of KAIROS, which means the just-in-time, at the right place provision of information/stimulus. In the EuroPLOT project (2010-2013) we have developed persuasive learning objects and tools (PLOTs) in which we have applied persuasive designs and principles. In this context, we have developed a pedagogical framework for active engagement, based on persuasive design in which the principles of persuasive learning have been formalised in a 6-step guide for persuasive learning. These principles have been embedded in two tools – PLOTmaker and PLOTLearner – which have been developed for creating persuasive learning objects. The tools provide specific capability for implementing persuasive principles at the very beginning of the design of learning objects. The feasibility of employing persuasive learning concepts with these tools has been investigated in four different case studies with groups of teachers and learners from realms with distinctly different teaching and learning practices: Business Computing, language learning, museum learning, and chemical substance handling. These case studies have involved the following learner target groups: school children, university students, tertiary students, vocational learners and adult learners. With regards to the learning context, they address archive-based learning, industrial training, and academic teaching. Alltogether, these case studies include participants from Sweden, Africa (Madagascar), Denmark, Czech Republic, and UK. One of the outcomes of this investigation was that one cannot apply a common set of persuasive designs that would be valid for general use in all situations: on the contrary, the persuasive principles are very specific to learning contexts and therefore must be specifically tailored for each situation. Two of these case studies have a direct relevance to education in the realm of Business Management: Business Computing and language learning (for International Business). In this paper we will present the first results from the evaluation of persuasive technology driven learning in these two relevant areas.

Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society. Extended results from our previous workshop: arXiv:1812.0081