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Book chapterCocarascu O, Toni F, 2020,
Deploying Machine Learning Classifiers for Argumentative Relations “in the Wild”
, Argumentation Library, Pages: 269-285Argument Mining (AM) aims at automatically identifying arguments and components of arguments in text, as well as at determining the relations between these arguments, on various annotated corpora using machine learning techniques (Lippi & Torroni, 2016).
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Journal articleZambelli M, Cully A, Demiris Y, 2020,
Multimodal representation models for prediction and control from partial information
, Robotics and Autonomous Systems, Vol: 123, ISSN: 0921-8890Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from different sensor modalities is difficult, because the learning model must be able to handle diverse types of signals, and learn a coherent representation even when parts of the sensor inputs are missing. In this paper, a multimodal variational autoencoder is proposed to enable an iCub humanoid robot to learn representations of its sensorimotor capabilities from different sensor modalities. The proposed model is able to (1) reconstruct missing sensory modalities, (2) predict the sensorimotor state of self and the visual trajectories of other agents actions, and (3) control the agent to imitate an observed visual trajectory. Also, the proposed multimodal variational autoencoder can capture the kinematic redundancy of the robot motion through the learned probability distribution. Training multimodal models is not trivial due to the combinatorial complexity given by the possibility of missing modalities. We propose a strategy to train multimodal models, which successfully achieves improved performance of different reconstruction models. Finally, extensive experiments have been carried out using an iCub humanoid robot, showing high performance in multiple reconstruction, prediction and imitation tasks.
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Conference paperArcucci R, Mottet L, Casas CAQ, et al., 2020,
Adaptive Domain Decomposition for Effective Data Assimilation
, 25th International Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 583-595, ISSN: 0302-9743 -
Conference paperNadler P, Arcucci R, Guo Y, 2020,
An Econophysical Analysis of the Blockchain Ecosystem
, 2nd International Conference on Mathematical Research for Blockchain Economy, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 27-42, ISSN: 2198-7246 -
Conference paperJha R, Belardinelli F, Toni F, 2020,
Formal verification of debates in argumentation theory.
, Publisher: ACM, Pages: 940-947 -
Book chapterArcucci R, Moutiq L, Guo Y-K, 2020,
Neural Assimilation
, Editors: Krzhizhanovskaya, Zavodszky, Lees, Dongarra, Sloot, Brissos, Teixeira, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 155-168, ISBN: 978-3-030-50432-8- Author Web Link
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- Citations: 7
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Conference paperArcucci R, Casas CQ, Xiao D, et al., 2020,
A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)
, Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 189-198, ISSN: 0927-5452 -
Conference paperNadler P, Arcucci R, Guo Y-K, 2020,
A Scalable Approach to Econometric Inference
, Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 59-68, ISSN: 0927-5452- Author Web Link
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- Citations: 1
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Conference paperCocarascu O, Cabrio E, Villata S, et al., 2020,
Dataset Independent Baselines for Relation Prediction in Argument Mining.
, Publisher: IOS Press, Pages: 45-52 -
Conference paperLiu S, Davison A, Johns E, 2019,
Self-supervised generalisation with meta auxiliary learning
, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Publisher: Neural Information Processing Systems Foundation, Inc.Learning with auxiliary tasks can improve the ability of a primary task to generalise.However, this comes at the cost of manually labelling auxiliary data. We propose anew method which automatically learns appropriate labels for an auxiliary task,such that any supervised learning task can be improved without requiring access toany further data. The approach is to train two neural networks: a label-generationnetwork to predict the auxiliary labels, and a multi-task network to train theprimary task alongside the auxiliary task. The loss for the label-generation networkincorporates the loss of the multi-task network, and so this interaction between thetwo networks can be seen as a form of meta learning with a double gradient. Weshow that our proposed method, Meta AuXiliary Learning (MAXL), outperformssingle-task learning on 7 image datasets, without requiring any additional data.We also show that MAXL outperforms several other baselines for generatingauxiliary labels, and is even competitive when compared with human-definedauxiliary labels. The self-supervised nature of our method leads to a promisingnew direction towards automated generalisation. Source code can be found athttps://github.com/lorenmt/maxl.
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Journal articleRakicevic N, Kormushev P, 2019,
Active learning via informed search in movement parameter space for efficient robot task learning and transfer
, Autonomous Robots, Vol: 43, Pages: 1917-1935, ISSN: 0929-5593Learning complex physical tasks via trial-and-error is still challenging for high-degree-of-freedom robots. Greatest challenges are devising a suitable objective function that defines the task, and the high sample complexity of learning the task. We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function. The task model is specific to a task and maps the parameter space, defining a trial, to the trial outcome space. The exploration component enables efficient search in the trial-parameter space to generate the subsequent most informative trials, by simultaneously exploiting all the information gained from previous trials and reducing the task model’s overall uncertainty. We analyse the performance of our framework in a simulation environment and further validate it on a challenging bimanual-robot puck-passing task. Results show that the robot successfully acquires the necessary skills after only 100 trials without any prior information about the task or target positions. Decoupling the framework’s components also enables efficient skill transfer to new environments which is validated experimentally.
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Conference paperNadler P, Arcucci R, Guo YK, 2019,
Data assimilation for parameter estimation in economic modelling
, Pages: 649-656We propose a data assimilation approach for latent parameter estimation in economic models. We describe a dynamic model of an economic system with latent state variables describing the relationship of economic entities over time as well as a stochastic volatility component. We show and discuss the model's relationship with data assimilation and how it is derived. We apply it to conduct a multivariate analysis of the cryptocurrency ecosystem. Combining these approaches opens a new dimension of analysis to economic modelling. Economics, Multivariate Analysis, Dynamical System, Bitcoin, Data Assimilation.
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Conference paperLim EM, Molina Solana M, Pain C, et al., 2019,
Hybrid data assimilation: An ensemble-variational approach
, Pages: 633-640Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.
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Journal articleAristodemou E, Arcucci R, Mottet L, et al., 2019,
Enhancing CFD-LES air pollution prediction accuracy using data assimilation
, Building and Environment, Vol: 165, ISSN: 0007-3628It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude.
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Journal articlePeach R, Yaliraki S, Lefevre D, et al., 2019,
Data-driven unsupervised clustering of online learner behaviour 
, npj Science of Learning, Vol: 4, ISSN: 2056-7936The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here we introduce a mathematical framework for the analysis of time series of online learner engagement, which allows the identification of clusters of learners with similar online temporal behaviour directly from the raw data without prescribing a priori subjective reference behaviours. The method uses a dynamic time warping kernel to create a pairwise similarity between time series of learner actions, and combines it with an unsupervised multiscale graph clustering algorithm to identify groups of learners with similar temporal behaviour. To showcase our approach, we analyse task completion data from a cohort of learners taking an online post-graduate degree at Imperial Business School. Our analysis reveals clusters of learners with statistically distinct patterns of engagement, from distributed to massed learning, with different levels of regularity, adherence to pre-planned course structure and task completion. The approach also reveals outlier learners with highly sporadic behaviour. A posteriori comparison against student performance shows that, whereas high performing learners are spread across clusters with diverse temporal engagement, low performers are located significantly in the massed learning cluster, and our unsupervised clustering identifies low performers more accurately than common machine learning classification methods trained on temporal statistics of the data. Finally, we test the applicability of the method by analysing two additional datasets: a different cohort of the same course, and time series of different format from another university.
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Conference paperLertvittayakumjorn P, Toni F, 2019,
Human-grounded evaluations of explanation methods for text classification
, 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Publisher: ACL Anthology, Pages: 5195-5205Due to the black-box nature of deep learning models, methods for explaining the models’ results are crucial to gain trust from humans and support collaboration between AIsand humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2)justifying model predictions, and (3) helping humans investigate uncertain predictions.The results highlight dissimilar qualities of thevarious explanation methods we consider andshow the degree to which these methods couldserve for each purpose.
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Conference paperFalck F, Doshi S, Smuts N, et al., 2019,
Human-centered manipulation and navigation with robot DE NIRO
Social assistance robots in health and elderly care have the potential tosupport and ease human lives. Given the macrosocial trends of aging andlong-lived populations, robotics-based care research mainly focused on helpingthe elderly live independently. In this paper, we introduce Robot DE NIRO, aresearch platform that aims to support the supporter (the caregiver) and alsooffers direct human-robot interaction for the care recipient. Augmented byseveral sensors, DE NIRO is capable of complex manipulation tasks. It reliablyinteracts with humans and can autonomously and swiftly navigate throughdynamically changing environments. We describe preliminary experiments in ademonstrative scenario and discuss DE NIRO's design and capabilities. We putparticular emphases on safe, human-centered interaction procedures implementedin both hardware and software, including collision avoidance in manipulationand navigation as well as an intuitive perception stack through speech and facerecognition.
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Journal articleČyras K, Birch D, Guo Y, et al., 2019,
Explanations by arbitrated argumentative dispute
, Expert Systems with Applications, Vol: 127, Pages: 141-156, ISSN: 0957-4174Explaining outputs determined algorithmically by machines is one of the most pressing and studied problems in Artificial Intelligence (AI) nowadays, but the equally pressing problem of using AI to explain outputs determined by humans is less studied. In this paper we advance a novel methodology integrating case-based reasoning and computational argumentation from AI to explain outcomes, determined by humans or by machines, indifferently, for cases characterised by discrete (static) features and/or (dynamic) stages. At the heart of our methodology lies the concept of arbitrated argumentative disputesbetween two fictitious disputants arguing, respectively, for or against a case's output in need of explanation, and where this case acts as an arbiter. Specifically, in explaining the outcome of a case in question, the disputants put forward as arguments relevant cases favouring their respective positions, with arguments/cases conflicting due to their features, stages and outcomes, and the applicability of arguments/cases arbitrated by the features and stages of the case in question. We in addition use arbitrated dispute trees to identify the excess features that help the winning disputant to win the dispute and thus complement the explanation. We evaluate our novel methodology theoretically, proving desirable properties thereof, and empirically, in the context of primary legislation in the United Kingdom (UK), concerning the passage of Bills that may or may not become laws. High-level factors underpinning a Bill's passage are its content-agnostic features such as type, number of sponsors, ballot order, as well as the UK Parliament's rules of conduct. Given high numbers of proposed legislation (hundreds of Bills a year), it is hard even for legal experts to explain on a large scale why certain Bills pass or not. We show how our methodology can address this problem by automatically providing high-level explanations of why Bills pass or not, based on the given Bills and the
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Conference paperLaw M, Russo A, Bertino E, et al., 2019,
Representing and learning grammars in answer set programming
, AAAI-19: Thirty-third AAAI Conference on Artificial Intelligence, Publisher: Association for the Advancement of Artificial Intelligence, Pages: 2919-2928In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.
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Conference paperTavakoli A, Levdik V, Islam R, et al., 2019,
Exploring Restart Distributions
, Montréal, Canada, The Fourth Multidisciplinary Conference on Reinforcement Learning and Decision Making, Publisher: arXivWe consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those corresponding to the agent's past observations, we help exploration by promoting faster state-space coverage via restarting the agent from a more diverse set of initial states, as well as allowing it to restart in states associated with significant past experiences. This approach is compatible with both on-policy and off-policy methods. However, a caveat is that altering the distribution of initial states could change the optimal policies when searching within a restricted class of policies. To reduce this unsought learning bias, we evaluate our approach in deep reinforcement learning which benefits from the high representational capacity of deep neural networks. We instantiate three variants of our approach, each inspired by an idea in the context of experience replay. Using these variants, we show that performance gains can be achieved, especially in hard exploration problems.
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