We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constraints. To classify emotions, we propose a multi-view skeleton graph convolution-based model that works on a commodity camera mounted onto a moving robot. Our emotion recognition is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. It achieves a mean average emotion prediction precision of 82.47% on the Emotion-Gait benchmark dataset. We outperform current state-of-art algorithms for emotion recognition from 3D gaits. We highlight its benefits in terms of navigation in indoor scenes using a Clearpath Jackal robot.

We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns the training image pairs into similar lighting condition with predictive brightness transformation parameters. Besides, we model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy and provides a learned weighting function for the photometric residuals in direct (feature-less) visual odometry. Evaluation results show that the proposed network outperforms state-of-the-art self-supervised depth estimation networks. D3VO tightly incorporates the predicted depth, pose and uncertainty into a direct visual odometry method to boost both the front-end tracking as well as the back-end non-linear optimization. We evaluate D3VO in terms of monocular visual odometry on both the KITTI odometry benchmark and the EuRoC MAV dataset. The results show that D3VO outperforms state-of-the-art traditional monocular VO methods by a large margin. It also achieves comparable results to state-of-the-art stereo/LiDAR odometry on KITTI and to the state-of-the-art visual-inertial odometry on EuRoC MAV, while using only a single camera.

Emergency vehicle (EV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A key contributor to EV service delay is the lack of communication and cooperation between vehicles blocking EVs. In this paper, we study the improvement of EV service using vehicle-to-vehicle connectivity. We consider the establishment of dynamic queue jumper lanes (DQJLs) based on real-time coordination of connected vehicles. We develop a novel stochastic dynamic programming formulation for the DQJL problem, which explicitly account for the uncertainty of drivers' reaction to approaching EVs. We propose a deep neural network-based approximate dynamic programming (ADP) algorithm that efficiently computes the optimal coordination instructions. We also validate our approach on a micro-simulation testbed using Simulation On Urban Mobility (SUMO).

Regular Decision Processes (RDPs) are a recently introduced model that extends MDPs with non-Markovian dynamics and rewards. The non-Markovian behavior is restricted to depend on regular properties of the history. These can be specified using regular expressions or formulas in linear dynamic logic over finite traces. Fully specified RDPs can be solved by compiling them into an appropriate MDP. Learning RDPs from data is a challenging problem that has yet to be addressed, on which we focus in this paper. Our approach rests on a new representation for RDPs using Mealy Machines that emit a distribution and an expected reward for each state-action pair. Building on this representation, we combine automata learning techniques with history clustering to learn such a Mealy machine and solve it by adapting MCTS to it. We empirically evaluate this approach, demonstrating its feasibility.

This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples form a generative adversarial network. We propose employing Bayesian differential privacy as the means to achieve a rigorous theoretical guarantee while providing a better privacy-utility trade-off. We demonstrate experimentally that our approach produces higher-fidelity samples, compared to prior work, allowing to (1) detect more subtle data errors and biases, and (2) reduce the need for real data labelling by achieving high accuracy when training directly on artificial samples.

Artificial Moral Agents (AMA's) is a field in computer science with the purpose of creating autonomous machines that can make moral decisions akin to how humans do. Researchers have proposed theoretical means of creating such machines, while philosophers have made arguments as to how these machines ought to behave, or whether they should even exist. Of the currently theorised AMA's, all research and design has been done with either none or at most one specified normative ethical theory as basis. This is problematic because it narrows down the AMA's functional ability and versatility which in turn causes moral outcomes that a limited number of people agree with (thereby undermining an AMA's ability to be moral in a human sense). As solution we design a three-layer model for general normative ethical theories that can be used to serialise the ethical views of people and businesses for an AMA to use during reasoning. Four specific ethical norms (Kantianism, divine command theory, utilitarianism, and egoism) were modelled and evaluated as proof of concept for normative modelling. Furthermore, all models were serialised to XML/XSD as proof of support for computerisation.

Fully supervised learning demands tight supervision of large amounts of data, a supervision that can be quite costly to acquire and constrains the scope of applications. To overcome this bottleneck, the machine learning community is seeking to incorporate weaker sources of information in the learning framework. In this paper, we address those limitations through partial labelling: e.g., giving only partial ordering when learning user preferences over items, or providing the label "flower" for a picture of Arum Lilies 1, instead of spending a consequent amount of time to find the exact taxonomy. Partial labelling has been studied in the context of classification Cour et al. (2011); Nguyen and Caruana (2008), multilabelling Yu et al. (2014), ranking Hüllermeier et al. (2008); Korba et al. (2018), as well as segmentation Verbeek and Triggs (2008); Papandreou et al. (2015), however a generic framework is still missing. Such a framework is a crucial step towards understanding how to learn from weaker sources of information, and widening the spectrum of machine learning beyond rigid applications of supervised learning. Some interesting directions are provided by Cid-Sueiro et al. (2014); van Rooyen and Williamson (2017), to recover the information lost in a corrupt acquisition of labels. Yet, they assume that the corruption process is known, which is a strong requirement that we want to relax. In this paper, we make the following contributions: - We provide a principled framework to solve the problem of learning with partial labelling, via structured prediction. This approach naturally leads to a variational framework built on the infimum loss.

Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence. Performance of an evolutionary algorithm depends not only on its operation strategy design, but also on its hyper-parameters. Hyper-parameters can be categorized in two dimensions as structural/numerical and time-invariant/time-variant. Particularly, structural hyper-parameters in existing studies are usually tuned in advance for time-invariant parameters, or with hand-crafted scheduling for time-invariant parameters. In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the structural hyper-parameter which controls computational resource allocation in the CEC 2018 winner algorithm by Q-learning. Experimental results show favorably against the winner algorithm on the CEC 2018 test functions.

Neural networks for structured data like graphs have been studied extensively in recent years. To date, the bulk of research activity has focused mainly on static graphs. However, most real-world networks are dynamic since their topology tends to change over time. Predicting the evolution of dynamic graphs is a task of high significance in the area of graph mining. Despite its practical importance, the task has not been explored in depth so far, mainly due to its challenging nature. In this paper, we propose a model that predicts the evolution of dynamic graphs. Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs. Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology. We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets. Results demonstrate the effectiveness of the proposed model.

Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in text generation systems, namely knowledge-enhanced text generation. The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into generation process. This survey gives a comprehensive review of knowledge-enhanced text generation systems, summarizes research progress to solving these challenges and proposes some open issues and research directions.