Detailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g., power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate, or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information. Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making. We created a framework to derive a global database of pollutant plants, such as cement, iron, and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data.

Command and control environments ranging from transport control rooms to disaster response have long been of interest to HCI and CSCW as rich sites of interactive technology use embedded in work practice. Drawing on our engagement with disaster response teams, including ethnography of their training work, we unpack the ways in which situational uncertainty is managed while a shared operational 'picture' is constituted through various practices around tabletop work. Our analysis reveals how this picture is collaboratively assembled as a socially shared object and displayed by drawing on digital and physical resources. Accordingly, we provide a range of principles implicated by our study that guide the design of systems augmenting and enriching disaster response work practices. In turn, we propose the Augmented Bird Table to illustrate how our principles can be implemented to support tabletop work.

Social media has led to the democratisation of opinion sharing. A wealth of information about public opinions, current events, and authors' insights into specific topics can be gained by understanding the text written by users. However, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. Crowdsourcing presents an opportunity to interpret the sentiment, or topic, of free-text. However, the subjectivity and bias of human interpreters raise challenges in inferring the semantics expressed by the text. To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements. Our model encodes the relationships between labels and text features in documents, such as tweets, web articles, and blog posts, accounting for the varying reliability of human labellers. It allows inference of annotations that scales to arbitrarily large pools of documents. Our evaluation using two challenging crowdsourcing datasets shows that by efficiently exploiting language models learnt from aggregated crowdsourced labels, we can provide up to 25% improved classifications when only a small portion, less than 4% of documents has been labelled. Compared to the six state-of-the-art methods, we reduce by up to 67% the number of crowd responses required to achieve comparable accuracy. Our method was a joint winner of the CrowdFlower - CrowdScale 2013 Shared Task challenge at the conference on Human Computation and Crowdsourcing (HCOMP 2013).

Minimizing the energy consumed by heating, ventilation, and air conditioning (HVAC) systems of residential buildings without impacting occupants’ comfort has been highlighted as an important artificial intelligence (AI) challenge. Typically, approaches that seek to address this challenge use a model that captures the thermal dynamics within a building, also referred to as a thermal model. Among thermal models, gray-box models are a popular choice for modeling the thermal dynamics of buildings. They combine knowledge of the physical structure of a building with various data-driven inputs and are accurate estimators of the state (internal temperature). However, existing gray-box models require a detailed specification of all the physical elements that can affect the thermal dynamics of a building a priori. This limits their applicability, particularly in residential buildings, where additional dynamics can be induced by human activities such as cooking, which contributes additional heat, or opening of windows, which leads to additional leakage of heat. Since the incidence of these additional dynamics is rarely known, their combined effects cannot readily be accommodated within existing models. To overcome this limitation and improve the general applicability of gray-box models, we introduce a novel model, which we refer to as a latent force thermal model of the thermal dynamics of a building, or LFM-TM. Our model is derived from an existing gray-box thermal model, which is augmented with an extra term referred to as the learned residual. This term is capable of modeling the effect of any a priori unknown additional dynamic, which, if not captured, appears as a structure in a thermal model’s residual (the error induced by the model). More importantly, the learned residual can also capture the effects of physical elements such as a building’s envelope or the lags in a heating system, leading to a significant reduction in complexity compared to existing models. To evaluate the performance of LFM-TM, we apply it to two independent data sources. The first is an established dataset, referred to as the FlexHouse data, which was previously used for evaluating the efficacy of existing gray-box models [Bacher and Madsen 2011]. The second dataset consists of heating data logged within homes located on the University of Southampton campus, which were specifically instrumented to collect data for our thermal modeling experiments. On both datasets, we show that LFM-TM outperforms existing models in its ability to accurately fit the observed data, generate accurate day-ahead internal temperature predictions, and explain a large amount of the variability in the future observations. This, along with the fact that we also use a corresponding efficient sequential inference scheme for LFM-TM, makes it an ideal candidate for model-based predictive control, where having accurate online predictions of internal temperatures is essential for high-quality solutions.

We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.

Novelty, or abnormality, detection aims to identify patterns within data streams that do not conform to expected behaviour. This paper introduces a novelty detection technique using a combination of Gaussian Processes and extreme value theory to identify anomalous behaviour in streaming data. The proposed combination of continuous and count stochastic processes is a principled approach towards dynamic extreme value modeling that accounts for the dynamics in the time series, the streaming nature of its observation as well as its sampling process. The approach is tested on both synthetic and real data, showing itself to be effective in our primary application of maritime vessel track analysis.

We introduce a new sequential algorithm for making robust predictions in the presence of changepoints. Unlike previous approaches, which focus on the problem of detecting and locating changepoints, our algorithm focuses on the problem of making predictions even when such changes might be present. We introduce nonstationary covariance functions to be used in Gaussian process prediction that model such changes, and then proceed to demonstrate how to effectively manage the hyperparameters associated with those covariance functions. We further introduce covariance functions to be used in situations where our observation model undergoes changes, as is the case for sensor faults. By using Bayesian quadrature, we can integrate out the hyperparameters, allowing us to calculate the full marginal predictive distribution. Furthermore, if desired, the posterior distribution over putative changepoint locations can be calculated as a natural byproduct of our prediction algorithm.

Time series prediction is traditionally the domain of the state-based Kalman filter and very general Kalman filter process models, such as the near constant acceleration model (NCAM), have been developed to successfully track moving targets. However, the standard Kalman filter uses Markov process models and, consequently, it is difficult to track processes which include a complex periodic component. Gaussian processes are a generalisation of the Kalman filter and are able to model periodic behaviour efficiently and succinctly. However, no equivalent Gaussian process model for near constant acceleration has been formulated. We develop an equivalent Gaussian process kernel for NCAM to be used for time-series prediction.

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution).

Conventional centralised information fusion and control architectures will be challenged by developments in sensor networks that allow sophisticated autonomous sensors, owned by different stakeholders with individual goals, to interact and share information. Given this, we advocate the use of tools and techniques from computational mechanism design (CMD), a field at the intersection of computer science, game theory and economics, to address the challenges posed by these networks. In particular, CMD allows us to engineer networks with desirable system-wide properties, in which sensors act as rational selfish agents, each attempting to fulfil their own individuals goals through the exchange of observations and information. In this paper, we present our work developing such networks. Specifically, we discuss our development of a generic and principled information valuation metric for sensor networks and we report our experiences applying it within a real world information fusion sensor network scenario

This paper describes a current research project investigating belief revision in intelligent systems by modelling the librarian in interaction with a literature-seeking user. The work is designed both to test a theory of agent behaviour based on belief revision proposed by Galliers, and to evaluate a model of the librarian developed by Belkin, Brooks and Daniels, through computational implementation. Agent communication is seen as motivated by and motivating belief changes, where belief revision is determined by coherence, combining endorsement, connectivity and conservatism. The librarian is viewed as a distributed expert system with many individual specialised functions operating in particular belief domains. The paper describes our first implementation of the belief revision mechanism and of a very primitive librarian, designed to test the basic viability of our ideas and to allow us to explore different forms of the distributed system architecture.