Towards Knowledge Infusion for Robust and Transferable Machine Learning in IoT

Machine learning (ML) applications in Internet of Things (IoT) scenarios face the issue that supervision signals, such as labeled data, are scarce and expensive to obtain. For example, it often requires a human to manually label events in a data stream by observing the same events in the real world. In addition, the performance of trained models usually depends on a specific context: (1) location, (2) time and (3) data quality. This context is not static in reality, making it hard to achieve robust and transferable machine learning for IoT systems in practice. In this paper, we address these challenges with an envisioned method that we name Knowledge Infusion. First, we present two past case studies in which we combined external knowledge with traditional data-driven machine learning in IoT scenarios to ease the supervision effort: (1) a weak-supervision approach for the IoT domain to auto-generate labels based on external knowledge (e.g., domain knowledge) encoded in simple labeling functions. Our evaluation for transport mode classification achieves a micro-F1 score of 80.2%, with only seven labeling functions, on par with a fully supervised model that relies on hand-labeled data. (2) We introduce guiding functions to Reinforcement Learning (RL) to guide the agents' decisions and experience. In initial experiments, our guided reinforcement learning achieves more than three times higher reward in the beginning of its training than an agent with no external knowledge. We use the lessons learned from these experiences to develop our vision of knowledge infusion. In knowledge infusion, we aim to automate the inclusion of knowledge from existing knowledge bases and domain experts to combine it with traditional data-driven machine learning techniques during setup/training phase, but also during the execution phase

Mobile money

The surge in access to mobile phones throughout the developing world has brought with it a wide range of benefits. One of the most noteworthy breakthroughs has been mobile money, enabling users to deposit, transfer, and withdraw funds from a digital account without owning a bank account Mobile money provides a dramatic reduction in transaction costs, as well as improvements in convenience, security, and time taken for the transaction. There is a growing literature that documents the benefits of mobile money, including improvements in the ability to smooth consumption better in the face of health and economic shocks, improving women’s empowerment, and reducing poverty. More recently, there has been a growth in digital financial services that use mobile money as the rails to deliver other products (largely credit). However, such innovations are few and far between with more research needed on their deployment and impact.otherpublishe

Policy Issues in Implementing Smart Cards in Urban Public Transit Systems

Many public transportation institutions have been discarding their magnetic strip payment cards or traditional cash-based fee collection systems in favor of automated fare collection systems with smart card technology. Smart cards look like traditional credit cards or ID cards; however, using RFID technology, they allow for contactless payment and identification. Smart cards are becoming increasingly popular among transit agencies primarily because they are convenient for customers, reduce administrative costs for transit agencies, and have the potential of improving the performance of complex transit systems overall. The increased availability and affordability of contactless cards has also contributed to this trend in adoption

Integrated spatial analysis of volunteered geographic information

Volunteered Geographic Information (VGI) is becoming a pervasive form of data within geographic academic research. VGI offers a relatively new form of data, one with both potential as a sensitive way to collect information about the world, and challenges associated with unknown and heterogeneous data quality. The lack of sampling control, variable expertise in data collection and handling, and limited control over data sources are significant research challenges. In this thesis, data quality of VGI is tackled as a general composite measure based on coverage of the dataset, the evenness in the density of data, and the relative evenness in contributors to a given dataset. A metric is formulated which measures these properties for VGI point pattern data. The utility of the metric for discriminating qualitatively different types of VGI is evaluated for different forms of VGI, based on a relative comparison framework. The metric is used to optimize both the spatial grains and spatial extents of several VGI study areas. General methods are created to support the assessment of data quality of VGI datasets at several spatial scales

Bit Bang 9: Entrepreneurship

This book is the 9th in the Bit Bang series of books produced as multidisciplinary teamwork exercises by doctoral students participating in the course Bit Bang 9: Entrepreneurship at Aalto University during the academic year 2016–2017. Working in teams, the students set out to answer questions related to entrepreneurship and to brainstorm radical scenarios of what the future could hold. This joint publication contains articles produced as teamwork assignments for the course