Josh is Creative Producer at PureMotion, an innovative video production company based in the UK. Always keen to drill down into the detail of what he's faced with, Josh is happiest when turning clients' visions into eye-catching, great-looking videos.

In the early hours of the morning, a distraught teen posts on social media about wanting to hurt herself. Her friends and family are sleeping, but an algorithm answers, providing links to 24/7 help. Artificial intelligence already sorts what you see on social media, but increasingly, it's being harnessed to monitor and respond to mental health crises. Canada is at the cutting edge of the development. The federal government recently tapped an Ottawa-based AI company to screen social media posts for warning signs of suicide.

As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception. In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

In recent years, deep learning poses a deep technical revolution in almost every field and attracts great attentions from industry and academia. Especially, the convolutional neural network (CNN), one representative model of deep learning, achieves great successes in computer vision and natural language processing. However, simply or blindly applying CNN to the other fields results in lower training effects or makes it quite difficult to adjust the model parameters. In this poster, we propose a general methodology named V-CNN by introducing data visualizing for CNN. V-CNN introduces a data visualization model prior to CNN modeling to make sure the data after processing is fit for the features of images as well as CNN modeling. We apply V-CNN to the network intrusion detection problem based on a famous practical dataset: AWID. Simulation results confirm V-CNN significantly outperforms other studies and the recall rate of each invasion category is more than 99.8%.

As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an agreed upon test set to benchmark malware detection systems on pure classification performance. Instead we tackle the problem by creating a new testing methodology, where we evaluate the change in performance on a set of known benign & malicious files as adversarial modifications are performed. The change in performance combined with the evasion techniques then quantifies a system's robustness against that approach. Through these experiments we are able to show in a quantifiable way how purely ML based systems can be more robust than AV products at detecting malware that attempts evasion through modification, but may be slower to adapt in the face of significantly novel attacks.

Autoregressive feedback is considered a necessity for successful unconditional text generation using stochastic sequence models. However, such feedback is known to introduce systematic biases into the training and it obscures a principle of generation: committing to global information and forgetting local nuances. We show that a non-autoregressive deep state space model with a clear separation of global and local uncertainty can be build from only two ingredients: An independent noise source and a deterministic transition function. Recent advances on flow-based variational inference allow training an evidence lower-bound without resorting to annealing, auxiliary losses or similar measures. The result is a highly interpretable generative model on par with a comparable auto-regressive model on the task of word generation.

Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scenarios with natural sparsity patterns as in structured prediction applications, this factor can be reduced to the expected number of active features over input-output pairs. We give a general proof that applies sparse SZO optimization to Lipschitz-continuous, nonconvex, stochastic objectives, and present an experimental evaluation on linear bandit structured prediction tasks with sparse word-based feature representations that confirm our theoretical results.

Density estimation is an old and central problem in statistics and machine learning. There exists only few approaches to cast this problem in a differential privacy framework and to our knowledge, while all provide proofs of security, very little is still known about the approximation guarantees of the \textit{unknown density} by the private one learned. In this paper, we exploit the tools of boosting to show that, provided we have access to a weak learner in the original boosting sense, there exists a way to learn a \textit{private density} out of \textit{classifiers}, which can guarantee an approximation of the true density that degrades gracefully as the privacy budget $\epsilon$ decreases. There are three key formal features of our results: (i) our approximation bound is, as we show, near optimal for our technique at hand and (ii) the privacy guarantee holds \textit{even when} we remove the famed adjacency condition of inputs in differential privacy, thereby leading to a stronger privacy guarantee we relate to as \textit{integral privacy}. Finally, (iii) we provide for the first time approximation guarantees for the capture of fat regions of the density, a problem which is receiving a lot of attention in the generative adversarial networks literature with the mode capture problem. Experimental results against a state of the art implementation of private kernel density estimation display that our technique consistently obtains improved results, managing in particular to get similar outputs for a privacy budget $\epsilon$ which is however orders of magnitude smaller.

Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, in an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on bar graphs and pie charts. We introduce a novel architecture that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the recently released FigureQA dataset, which provides images and accompanying questions, for bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline and traditional CNN-LSTM models when evaluated on this dataset. Our model also has a considerably faster training time of approximately 2 days on 1 GPU compared to the Relation Networks baseline which requires around two weeks to train on 4 GPUs.

Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to it simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.