Fintech Europe, Plug and Play's fintech-focused innovation platform based out of Frankfurt, Germany, announced today the eight startups selected for its sixth batch. The platform has grown its partner base to 13 Financial Institutions since its inception in May 2018. Together with Deutsche Bank, TechQuartier, BNP Paribas, Nets Group, Nexi, UniCredit, Aareal Bank, Abanca, Danske Bank, DZ Bank, Elo, UBI Banca, and Raiffeisen Bank International, the program seeks to support innovation in the world of Financial Services. After screening applications from all over the world and intensive weeks of reviewing preselected startups with the partners, the final group of eight companies have been accepted into Fintech Europe. The program aims at facilitating pilots, POCs, and business development opportunities for the participating startups and financial institutions.

According to the film's VFX supervisor, Pablo Helman, who stated that 1,750 shots were required for two and a half hours of shooting, the carefully placed on-set lighting captured the actor's facial performances from different angles, while at the same time shining infrared light on the actors' faces without being seen on the production camera. Thus, the system was able to analyze the lighting and texture information and created a machinable geometry network for each frame. Working with multiple cameras is indispensable in the process. While shooting, they work with a three-camera rig with a central camera, which also has a director's camera. The other two cameras are there to record data.

This paper presents a neural network model to generate virtual violinist's 3-D skeleton movements from music audio. Improved from the conventional recurrent neural network models for generating 2-D skeleton data in previous works, the proposed model incorporates an encoder-decoder architecture, as well as the self-attention mechanism to model the complicated dynamics in body movement sequences. To facilitate the optimization of self-attention model, beat tracking is applied to determine effective sizes and boundaries of the training examples. The decoder is accompanied with a refining network and a bowing attack inference mechanism to emphasize the right-hand behavior and bowing attack timing. Both objective and subjective evaluations reveal that the proposed model outperforms the state-of-the-art methods. To the best of our knowledge, this work represents the first attempt to generate 3-D violinists' body movements considering key features in musical body movement.

We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from a popular link prediction benchmark by showing that CoDEx covers more diverse and interpretable content, and contains fewer relation patterns that can be covered by trivial frequency-based rules. Data, code, and pretrained models are available at https://github.com/tsafavi/codex.

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"(Da San Martino et al., 2020). We participated in Task "Technique Classification" (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependentminority-class classifiers. A special classifier, which employs dynamically adapted Least Common Subsequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.

Virtual musicians have become a remarkable phenomenon in the contemporary multimedia arts. However, most of the virtual musicians nowadays have not been endowed with abilities to create their own behaviors, or to perform music with human musicians. In this paper, we firstly create a virtual violinist, who can collaborate with a human pianist to perform chamber music automatically without any intervention. The system incorporates the techniques from various fields, including real-time music tracking, pose estimation, and body movement generation. In our system, the virtual musician's behavior is generated based on the given music audio alone, and such a system results in a low-cost, efficient and scalable way to produce human and virtual musicians' co-performance. The proposed system has been validated in public concerts. Objective quality assessment approaches and possible ways to systematically improve the system are also discussed.

Technology is developing today at a pace that's never been seen before. New advancements and breakthroughs happen far more readily than at any time in the past. One of the most talked-about areas of cutting-edge tech is that of artificial intelligence (AI). AI is driving the digital transformation of organizations in all manner of niches. So wide-ranging are the applications of AI, that you've probably already interacted with an example of the tech today.

(Nasdaq: VERI), the creator of the world’s first operating system for artificial intelligence, aiWARE™, today announced the findings of a new research …

In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add a speech-to-text capability to their applications. Since then, we added support for more languages, enabling customers globally to transcribe audio recordings in 31 languages, including 6 in real-time. A popular use case for Amazon Transcribe is transcribing customer calls. This allows companies to analyze the transcribed text using natural language processing techniques to detect sentiment or to identify the most common call causes. If you operate in a country with multiple official languages or across multiple regions, your audio files can contain different languages.

Today, we are coming close to experience the fourth one too -- the Artificial Intelligence revolution. Even though we now think we've seen it all, the AI revolution is going to make a change in the society just like the previous three did. Naturally, the revolution is going to affect all the aspects of our lives, including the business sphere. Since it's inevitable that AI is about to transform our lives in the near future, the best thing we can do is learn how to use it to work for us and help our business grow. We are all familiar with technology gadgets such as computers, drones, cameras, and even robots that can perform a specific action.