Week Ending 3.29.2020

 

RESEARCH WATCH: 3.29.2020

 
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Over the past week, 57 new papers were published in "Computer Science - Artificial Intelligence".

This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 232 new papers.

Over the past week, 29 new papers were published in "Computer Science - Computers and Society".

  • The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic" by Ramesh Raskar et al (Mar 2020), which was referenced 2 times, including in the article Techie collective to whip together official WHO-backed COVID-19 app within a week to meet 'urgent, global need' in The Register. The paper got social media traction with 56 shares. On Twitter, @TechnicallA posted "best tracking of infected Covid19 - CZECH Republic is number one in mobile phones per population / infected separate / I thing", while @rzanardelli posted "MIT's Private Kit: Safe Paths - Can we slow the spread without giving up individual privacy? Participatory sharing and (privacy-safe) broadcasting are gaining track! this is huge!!!".

  • The paper shared the most on social media this week is "Mapping the Landscape of Artificial Intelligence Applications against COVID-19" by Joseph Bullock (Sasha) et al (Mar 2020) with 63 shares. The researchers present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. @janbeger (Jan Beger) tweeted "#ML and can support the response against #COVID19 in a broad set of domains. In particular, emerging applications in diagnosis, clinical outcome prediction, drug discovery and development, epidemiology, and infodemiology".

Over the past week, 18 new papers were published in "Computer Science - Human-Computer Interaction".

  • The paper discussed most in the news over the past week was "This PIN Can Be Easily Guessed" by Philipp Markert et al (Mar 2020), which was referenced 9 times, including in the article How Secure Are 4- and 6-Digit Mobile Phone PINs in Homeland Security News Wire. The paper author, Maximilian Golla, was quoted saying "Since users only have ten attempts to guess the PIN on the iPhone anyway, the blacklist does not make it any more secure". The paper got social media traction with 33 shares. The investigators provide the first comprehensive study of user - chosen 4- and 6-digit PINs (n=1220) collected on smartphones with participants being explicitly primed for the situation of device unlocking. A user, @stshank, tweeted "That quote was from the preprint of a paper to be published later this year from researchers at Ruhr University Bochum, Max Planck Institute for Security and Privacy, and George Washington University".

This week was very active for "Computer Science - Learning", with 301 new papers.

Over the past week, 16 new papers were published in "Computer Science - Multiagent Systems".

  • The paper discussed most in the news over the past week was by a team at The University of Sydney: "Modelling transmission and control of the COVID-19 pandemic in Australia" by Sheryl L. Chang et al (Mar 2020), which was referenced 40 times, including in the article Confused about what to do about coronavirus? This data says just stay home in ABC Online. The paper author, Mikhail Prokopenko (The University of Sydney), was quoted saying "If we want to control the spread of COVID-19 – rather than letting the disease control us – at least eighty per cent of the Australian population must comply with strict social distancing measures for at least four months". The paper also got the most social media traction with 319 shares. The researchers develop an agent - based model for a fine - grained computational simulation of the ongoing COVID-19 pandemic in Australia. On Twitter, @arthaey observed "This paper models 80-90% social distancing compliance is needed, & only works while we KEEP doing it, until a vaccine: (blue line is 70% compliance, red 80%, yellow 90%; spikes later are when social distancing is lifted)".

Over the past week, 32 new papers were published in "Computer Science - Neural and Evolutionary Computing".

  • The paper discussed most in the news over the past week was by a team at Google: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch" by Esteban Real et al (Mar 2020), which was referenced 3 times, including in the article Addressing Drawbacks Of AutoML With AutoML-Zero in Analytics India Magazine. The paper also got the most social media traction with 1134 shares. On Twitter, @tomvarsavsky observed "One of the most interesting results I've seen in ML in the last 5 years. Evolving programs using a generic search space and generic mutations leads to the discovery of not only SGD and two layer NNs but also rand init, ReLU, Grad Norm. Can someone find a hidden inductive bias?".

This week was active for "Computer Science - Robotics", with 57 new papers.


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