In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.

Memento broke many of the traditional paradigms in the film industry by structuring two parallel narratives, one chronologically going backwards and one going forward. The novel form narrative implemented in Memento forces the audience to constantly reevaluate their knowledge of the plot and they keep learning small details every few minutes of the film. It turns out that replaying a knowledge sequence backwards for small time intervals is an incredibly captivating method of learning. Intuitively, the Memento form of learning seems like perfect for AI agents. Last year, researchers from OpenAI leveraged that learning methodology to created AI agents that learned to play Montezuma's Revenge using a single demonstration.

On the need to strengthen the country's components and industrial materials industries, Choi said the government is committed to pouring 5 trillion won into related R&D up till 2022, with support being given to smart chips, future mobility, quantum technology, biotech and laying the foundation for a hydrogen economy.

Some attacks may still slip "under the radar" though, which is why tools that leverage machine-learning, like User and Entity Behavior Analytics (UEBA), are an important support to your SIEM as they will detect more unusual threats as well as greatly increase the overall fidelity of your security alerts. SIEM and UEBA are further supported by threat hunting tools that enable your hunt teams to track down any other threats that may still be lurking in your system. All three approaches are important to your threat detection and response ecosystem. Micro Focus is a global software company with 40 years of experience in delivering and supporting enterprise software solutions that help customers innovate faster with lower risk. Our portfolio enables our 20,000 customers to build, operate, and secure the applications and IT systems that meet the challenges of change.

A BSTRACT We consider the problem of learning high-level controls over the global structure of sequence generation, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer au-toencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global embedding with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and and melody. Empirically, we demonstrate the effectiveness of our method on a variety of music generation tasks on the MAESTRO dataset and a Y ouTube dataset with 10,000 hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to relevant baselines. As the number of generative applications increase, it becomes increasingly important to consider how users can interact with such systems, particularly when the generative model functions as a tool in their creative process (Engel et al., 2017a; Gillick et al., 2019) To this end, we consider how one can learn high-level controls over the global structure of a generated sample. We focus on symbolic music generation, where Music Transformer (Huang et al., 2019b) is the current state-of-the-art in generating high-quality samples that span over a minute in length. The challenge in controllable sequence generation is that Transformers (V aswani et al., 2017) and their variants excel as language models or in sequence-to-sequence tasks such as translation, but it is less clear as to how they can: (1) learn and (2) incorporate global conditioning information at inference time. This contrasts with traditional generative models for images such as the variational autoencoder (V AE) (Kingma & Welling, 2013) or generative adversarial network (GAN) (Goodfel-low et al., 2014) which can incorporate global conditioning (e.g.

--Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting outcomes of certain events such as election debates. In this work, we investigate whether crowd wisdom is useful in predicting such outcomes and whether their opinions are influenced by the experts in the field. We work in the domain of multi-label classification to perform sentiment classification of tweets and obtain the opinion of the crowd. This learnt sentiment is then used to predict outcomes of events such as: US Presidential Debate winners, Grammy A ward winners, Super Bowl Winners. We find that in most of the cases, the wisdom of the crowd does indeed match with that of the experts, and in cases where they don't (particularly in the case of debates), we see that the crowd's opinion is actually influenced by that of the experts. I NTRODUCTION Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics, discuss current issues, complain, etc. Twitter is one such popular microblogging service where users create status messages (called "tweets"). With over 400 million tweets per day on Twitter, microblog users generate large amount of data, which cover very rich topics ranging from politics, sports to celebrity gossip. Because the user generated content on microblogs covers rich topics and expresses sentiment/opinions of the mass, mining and analyzing this information can prove to be very beneficial both to the industrial and the academic community. Tweet classification has attracted considerable attention because it has become very important to analyze peoples' sentiments and opinions over social networks.

With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality. This paper identifies the characteristics of AI applications that distinguish them from traditional software, and analyses the main difficulties in applying existing testing methods. Based on this analysis, we propose a new method called datamorphic testing and illustrate the method with an example of testing face recognition applications. We also report an experiment with four real industrial application systems of face recognition to validate the proposed approach.