Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation. This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.

Two years ago IBM set new performance records on conversational telephone speech (CTS) transcription, by benchmarking its deep neural network based speech recognition systems on the Switchboard and Callhome corpora, two popular publicly available data sets for automatic speech recognition [1]. Here we show that this impressive performance holds on other audio genres. Similar to the CTS benchmarks, the industry has for many years evaluated system performances on multimedia audio signals with broadcast news (BN) captioning. We have now achieved a new industry record of 6.5% and 5.9% on two BN benchmarks: RT04 and DEV04F [2]. Both these test sets have been released in the past by the Linguistic Data Consortium (LDC) [3].

I have an alarm set for 3 a.m. on Sunday morning, which is the earliest that we can start making restaurant reservations for our trip. There's even a spreadsheet with all the restaurants we want to visit, as meticulously researched from videos and handy guides from places like Disney Food Blog and WDW Prep School. So I was more than receptive when someone recommended that I check out TouringPlans.com-- a site that uses complex algorithms to help you plan the perfect vacation at Disney World, Disneyland, or a handful of other theme parks like Universal Studios Orlando. It's a premium service, with subscriptions priced starting at $16 per year. It has seven full-time employees on the staff, and 12 part-time employees, says Testa, as well as a business in publishing "Unofficial Guides" to Disney World, Disneyland, and other popular tourist destinations.

When the self-driving delivery vans finally arrive, it's going to be a challenge getting packages from the vehicle to the doorstep. That's where Ford's partnership with Digit – a bipedal robot from Agility Robotics – comes in handy. The main problem is that watching a two-legged robot like Digit can be creepy and disconcerting, especially when it awkwardly walks on its creature-like legs and then bends over – or when it sneakily unfolds itself from the back of the delivery van. Ford plans to use Digit to carry packages up to 40 pounds when humans aren't around to help grab deliveries from the back of a truck or van. Digit can walk up and down stairs, navigate around obstacles in its path, and stay balanced even after getting bumped. The humanoid can also fold up, so it stows away in the back of the delivery van ready to unfold and carry packages once the van arrives.

The BBC's chart mentions a margin of error It uses data generated by artificial intelligence (AI) -- specifically, machine learning -- and it's a good example of some of the challenges that journalists are increasingly going to face as they come to deal with more and more algorithmically-generated data. Information and decisions generated by AI are qualitatively different from the sort of data you might find in an official report, but journalists may fall back on treating data as inherently factual. Here, then, are some of the ways the article dealt with that -- and what else we can do as journalists to adapt. The story draws on data from an external organisation, Ceretai, which "uses machine learning to analyse diversity in popular culture." The organisation claims to have created an algorithm which "has learned to identify the difference between male and female voices in video and provides the speaking time lengths in seconds and percentages per gender."

Cognitive systems include applications with capabilities to enhance human decisions. These systems take advantage of cognitive computing capabilities of vast data-processing power, adding of channels for data collection, and environmental setting to deliver practical business insights. In addition, they use analytics to process data collected and deliver useful insights. The banking segment was estimated to account for more than 17% value share towards the end of 2017 and is expected to expand at a high CAGR of 13.5% than other segments of the global cognitive systems spending market from 2017 to 2025. In 2018, the global Cognitive Systems market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025.

Besides the benefits for the community of people with cognitive disabilities, it can also become a useful educational tool in the foreign language classroom, where students with a limited knowledge of a target language can rely on it to adapt complex literary works.

Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems using few-shot learning and meta learning. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. To accomplish this, we combine the scenario-specific learning with a model-agnostic sequential meta-learning and unify them into an integrated end-to-end framework, namely Scenario-specific Sequential Meta learner (or s^2 meta). By doing so, our meta-learner produces a generic initial model through aggregating contextual information from a variety of prediction tasks while effectively adapting to specific tasks by leveraging learning-to-learn knowledge. Extensive experiments on various real-world datasets demonstrate that our proposed model can achieve significant gains over the state-of-the-arts for cold-start problems in online recommendation. Deployment is at the Guess You Like session, the front page of the Mobile Taobao.

This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget.

It's that time of year again when Apple gives folks a sneak peek at new features for the iPhone and iPad. The company does it every June at the Worldwide Developer's Conference (WWDC) a forum to hype up app makers on new tools they could use in their apps. We'll be in attendance Monday morning in San Jose, as usual, to keep you up on the latest on what's expected to be called iOS 13, the software that runs the iPhone, iPad and iPod Touch. The event starts at 10 a.m. PT and will be live-streamed at Apple.com.