september 2019


The Best Machine Learning Research of September 2019

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While usually you would use world-based algorithms, the team suggests that that method is at fault due to the fact that they treat all possible worlds equally, despite the negative effects some may cause, and that they do not well-utilize the consistency among possible worlds that is there. The team introduces a representative possible world-based consistent clustering algorithm for this type of uncertain data, with results showing better than other state-of-the-art algorithms.


Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2019 - insideBIGDATA

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.


Top 10 most popular robotics stories of September 2019

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What a month it was for robotics. Whether it was Boston Dynamics launching its Spot quadruped robot, Shopify acquiring 6 River Systems or Universal Robots launching its strongest cobot ever, the robotics stories didn't disappoint in September 2019. Here are the Top 10 most popular robotics stories on The Robot Report for September 2019. Subscribe to The Robot Report's free weekly newsletter to stay updated on the latest analysis and news from the robotics industry. Since being acquired by SoftBank, Boston Dynamics promised to bring its robots to market.


Local 'Artificial Intelligence For Business' Course To Be Held In September

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The past 10 years wouldn't have been possible without you. Information technology and software services company Softclick Investments is hosting an Artificial Intelligence (AI) for Business course from the 24th of September to 26 October at Batanai Gardens. The course is supposed to provide "practical, comprehensive training that enables participants to immediately and effectively partake in enterprise AI projects." The courses require no technical background and will be open to all "executives and professionals from all functions across all industries." At the end of the course, participants will earn a certificate.


Artists create more than 100,000 ultra-realistic AI portraits

Daily Mail - Science & tech

A database of more than 100,000 images of people has been created - but none of them are real. A team of artists, AI experts and engineers teamed up on the project to create the ultra-realistic images. It includes different sexes, races and ages and none of the people are real, but could easily be mistaken for a legitimate portrait. The eerie headshots appear perfect in many cases, but there are often minor glitches, including eye sockets on foreheads, funky teeth and weird ears. A team of artists, AI experts and engineers teamed up on the project to create the ultra-realistic images.


Distributed Machine Learning on Mobile Devices: A Survey

arXiv.org Machine Learning

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.


On-Device User Intent Prediction for Context and Sequence Aware Recommendation

arXiv.org Artificial Intelligence

The pursuit of improved accuracy in recommender systems has led to the incorporation of user context. Context-aware recommender systems typically handle large amounts of data which must be uploaded and stored on the cloud, putting the user's personal information at risk. While there have been previous studies on privacy-sensitive and context-aware recommender systems, there has not been a full-fledged system deployed in an isolated mobile environment. We propose a secure and efficient on-device mechanism to predict a user's next intention. The knowledge of the user's real-time intention can help recommender systems to provide more relevant recommendations at the right moment. Our proposed algorithm is both context and sequence aware. We embed user intentions as weighted nodes in an n-dimensional vector space where each dimension represents a specific user context factor. Through a neighborhood searching method followed by a sequence matching algorithm, we search for the most relevant node to make the prediction. An evaluation of our methodology was done on a diverse real-world dataset where it was able to address practical scenarios like behavior drifts and sequential patterns efficiently and robustly. Our system also outperformed most of the state-of-the-art methods when evaluated for a similar problem domain on standard datasets.


#FinServ_2019-09-04_04-30-57.xlsx

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The graph represents a network of 2,183 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 04 September 2019 at 11:32 UTC. The requested start date was Sunday, 01 September 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 8-day, 2-hour, 46-minute period from Friday, 23 August 2019 at 07:01 UTC to Saturday, 31 August 2019 at 09:47 UTC.


futureofwork _2019-09-10_06-11-51.xlsx

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The graph represents a network of 3,602 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 10 September 2019 at 13:13 UTC. The requested start date was Tuesday, 10 September 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 1-day, 19-hour, 17-minute period from Sunday, 08 September 2019 at 04:42 UTC to Tuesday, 10 September 2019 at 00:00 UTC.


Events - data-service-alliance.ch

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Smart Services Summit 2019 (hosted by Swiss Alliance for Data-Intensive Services) Location: Swisscom Business Campus, Turbinenstrasse 30, 8005 Zürich... We are happy to announce the 2nd Data Service Alliance Match-Making Event, to be held on October 2, 2019 from 1:00pm to 3:30pm, at FHNW camp... The General Assembly 2019 of the Data Service Alliance will take place on October 02, 2019 at FHNW campus, Riggenbachstrasse 16 (O... In collaboration with Zazuko Knowledge Graphs are getting a lot of attention in the industry and the public sector. CLEF 2019 is the 10th CLEF conference continuing the popular CLEF campaigns which have run since 2000 contributing to the systematic evaluation of inf... Organised by: University of Zurich, Digital Society InitiativeOn Monday September 16, the Digital Society Initiative of University of Zurich is organi... AI, big data and XR are transforming the industry from its core again & in order to utilize the full potential, international collaboration and ma...