TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities may manifest in the form of offensive speech, consequently making political discussions more aggressive and polarizing than they already are. Such environments may result in harassment and self-censorship from its targets. In this paper, we present preliminary results from a large-scale temporal measurement aimed at quantifying offensiveness in online political discussions. To enable our measurements, we develop and evaluate an offensive speech classifier. We then use this classifier to quantify and compare offensiveness in the political and general contexts. We perform our study using a database of over 168M Reddit comments made by over 7M pseudonyms between January 2015 and January 2017 – a period covering several divisive political events including the 2016 US presidential elections.

SOLUTION: Measuring Offensive Speech in Online Political Discourse

PROBLEM: Machine learning is increasingly used in security-critical applications, such as autonomous driving, face recognition and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to different types of attacks. This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods. Concurrently, a different line of research has tackled a very similar problem: In digital watermarking information are embedded in a signal in the presence of an adversary. As a consequence, this research field has also extensively studied techniques for attacking and defending watermarking methods. The two research communities have worked in parallel so far, unnoticeably developing similar attack and defense strategies. This paper is a first effort to bring these communities together. To this end, we present a unified notation of black-box attacks against machine learning and watermarking that reveals the similarity of both settings. To demonstrate the efficacy of this unified view, we apply concepts from watermarking to machine learning and vice versa. We show that countermeasures from watermarking can mitigate recent model-extraction attacks and, similarly, that techniques for hardening machine learning can fend off oracle attacks against watermarks. Our work provides a conceptual link between two research fields and thereby opens novel directions for improving the security of both, machine learning and digital watermarking.

SOLUTION: Fraternal Twins: Unifying Attacks on Machine Learning and Digital Watermarking

PROBLEM: We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused across the corpus. We also find that Vec2Topic works equally well for non-user generated content, such as papers, reports, etc., and for small corpora such as a single-document.

SOLUTION:
Topic Modeling Using Distributed Word Embeddings