This paper explores the capabilities of text-based age and gender prediction geared towards the application of detecting harmful content and conduct on social media. More specifically, we focus on the use case of detecting sexual predators who try to "groom" children online and possibly provide false age and gender information in their user profiles. We perform age and gender classification experiments on a dataset of nearly 380,000 Dutch chat posts from a social network. We evaluate and compare binary age classifiers trained to separate younger and older authors according to different age boundaries and find that macro-averaged Fscores increase when the age boundary is raised. Furthermore, we show that use-case applicable performance levels can be achieved for the classification of minors versus adults, thereby providing a useful component in a cybersecurity monitoring tool for social network moderators.

We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface. The system is trained on an individual user's utterances and the corresponding semantic frames representing controls. During training, no prior information on the alignment between utterance segments and frame slots and values is available. In addition, semantic frames in the training data can contain information that is not expressed in the utterances. To tackle this weakly supervised classification task, we propose a framework based on Hidden Markov Models (HMMs). Structural modifications, resulting in a hierarchical HMM, and an extension called expression sharing are introduced to minimize the amount of training time and effort required for the user. The dataset used for the present study is patcor, which contains commands uttered in the context of a vocally guided card game, Patience. Experiments were carried out on orthographic and phonetic transcriptions of commands, segmented on different levels of n-gram granularity. The experimental results show positive effects of all the studied system extensions, with some effect differences between the different input representations. Moreover, evaluation experiments on held-out data with the optimal system configuration show that the extended system is able to achieve high accuracies with relatively small amounts of training data.

This paper gives an overview of research within the ALADIN project, which aims to develop an assistive vocal interface for people with a physical impairment. In contrast to existing approaches, the vocal interface is trained by the end-user himself, which means it can be used with any vocabulary and grammar, and that it is maximally adapted to the -possibly dysarthric -speech of the user. This paper describes the overall learning framework, the user-centred design and evaluation aspects, database collection and approaches taken to combat problems such as noise and erroneous input.

This paper introduces research within the ALADIN project, which aims to develop an assistive vocal interface for people with a physical impairment. In contrast to existing approaches, the vocal interface is self-learning which means it can be used with any language, dialect, vocabulary and grammar. The paper describes the overall learning framework, and the two components that will provide vocabulary learning and grammar induction. In addition, the paper describes encouraging results of early implementations of these vocabulary and grammar learning components, applied to recorded sessions of a vocally guided card game, patience.

Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat. Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus' spread. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles' relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.1 https://covid19.who.int/