Agile Autonomous Driving using End-to-End Deep Imitation Learning

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201

Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks (IJCNN). Update of DO

On stochastic imitation dynamics in large-scale networks

We consider a broad class of stochastic imitation dynamics over networks, encompassing several well known learning models such as the replicator dynamics. In the considered models, players have no global information about the game structure: they only know their own current utility and the one of neighbor players contacted through pairwise interactions in a network. In response to this information, players update their state according to some stochastic rules. For potential population games and complete interaction networks, we prove convergence and long-lasting permanence close to the evolutionary stable strategies of the game. These results refine and extend the ones known for deterministic imitation dynamics as they account for new emerging behaviors including meta-stability of the equilibria. Finally, we discuss extensions of our results beyond the fully mixed case, studying imitation dynamics where agents interact on complex communication networks.Comment: Extended version of conference paper accepted at ECC 201

An Autoethnographic Approach to Examining Electronic Retail Development

Autoethnographic approaches to doing research in retailing are rare. Through the researcher reflecting on and analysing her own personal experiences as a fashion retail store proprietor, this study reconstructed the process of her strategic decision making with regard to moving from selling fashion goods via an independent high street store to selling online. The study is concerned with the issues surrounding the adoption of e-commerce. In doing so, the study reviewed the various development models that exist within e-commerce literature, and in particular, examined the extent to which a retailer adoptions an evolutionary and linear approach to developing a web site. Hence the study’s contribution to advances in retailing is in the field of strategic decisions pertaining to electronic retailing. Specifically the aim of the study was to either confirm or adjust the models within e-commerce literature that describe the internet adoption process. Through the adoption of an autoethnographical approach, the study acknowledges that there is a complex interdependency between the researcher and the researched and thereby utilizes subjective experience as an intrinsic part of the research process. This is achieved through offering the retail proprietor’s ‘insider’ perspective based upon both self narratives and self observations. Whilst the author’s acknowledge that the subject of the study needs to be examined in a broader sense, beyond the self generated data presented in the study, they argue that such self introspections can be considered as a basis of useful, albeit non-scientific, knowledge in itself. In this study the intention is to use the data as a means of generating hypotheses which will be tested in a future study by a more traditional research technique. This study is a work in progress