Week Ending 09.08.19
RESEARCH WATCH: 09.08.19
Over the past week, 878 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was "Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites" by Elena Maris et al (Jul 2019), which was referenced 199 times, including in the article Incognito Mode Is Over. It’s Time to Get Horny on Main in MEL Magazine. The paper author, Elena Maris (Postdoctoral researcher at Microsoft), was quoted saying "The fact that the mechanism for adult site tracking is so similar to, say, online retail should be a huge red flag. This isn’t picking out a sweater and seeing it follow you across the web. This is so much more specific and deeply personal".
Leading researcher Yoshua Bengio (Université de Montréal) published "Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures", which has 0 shares on Twitter so far.
Over the past week, 87 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was by a team at NYU: "Why Build an Assistant in Minecraft?" by Arthur Szlam et al (Jul 2019), which was referenced 19 times, including in the article Facebook taps Minecraft as training ground for next stage of A.I. in Digital Trends. The paper author, Arthur Szlam (New York University), was quoted saying "The set of things a player could possibly do in the game is enormous; in the most naive sense, it is all possible ways of placing all the possible blocks into as big a world as fits in RAM". The paper got social media traction with 21 shares. The investigators describe a rationale for a research program aimed at building an open `` assistant in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue. A user, @MichaelFNunez, tweeted "“In this work, we have argued for building a virtual assistant situated in the game of Minecraft, in order to study learning from interaction, and especially learning from language interaction,” the researchers say".
Leading researcher Aaron Courville (Université de Montréal) published "No Press Diplomacy: Modeling Multi-Agent Gameplay" The researchers focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players.
The paper shared the most on social media this week is by a team at DeepMind: "Making Efficient Use of Demonstrations to Solve Hard Exploration Problems" by Tom Le Paine et al (Sep 2019) with 248 shares. The authors introduce R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. @odbmsorg (odbms.org) tweeted "They also introduce a suite of 8 tasks that combine these three properties, and show that #R2D3 can solve several of the tasks where other state of the art methods fail to see even a single successful trajectory after tens of billions of steps of exploration".
Over the past week, 198 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
The paper discussed most in the news over the past week was "Progressive Face Super-Resolution via Attention to Facial Landmark" by Deokyun Kim et al (Aug 2019), which was referenced 13 times, including in the article Facebook Leaks Data of 419M Users; NeurIPS 2019 Accepted Papers Announced; AI Passes 8th Grade Science Test in SyncedReview.com. The paper also got the most social media traction with 1034 shares. A Twitter user, @JohnAndrewsX, posted "So if AI can do this today, imagine what happens when implement AI-drivatars in Forza games one day >> True Unbeatable Mode 🤘😈🤘 #ForzaHorizon #ForzaMotorsport".
Leading researcher Jianfeng Gao (Microsoft) published "REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning", which is getting buzz on social media with 24 shares this week.
The paper shared the most on social media this week is "Face-to-Parameter Translation for Game Character Auto-Creation" by Tianyang Shi (NetEase Fuxi AI Lab) et al (Sep 2019) with 93 shares. The researchers propose a method for automatically creating in - game characters of players according to an input face photo. @bensprecher (Ben Sprecher) tweeted "Very cool. And, it's being used: "Our method has been deployed in a new game last year and has now been used by players over 1 million times."".
Over the past week, 26 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites" by Elena Maris et al (Jul 2019), the paper got social media traction with 179 shares. The investigators explore tracking and privacy risks on pornography websites. On Twitter, @citadelo commented "This research focuses on porn-sites users' tracking: "analysis of 22,484 pornography websites indicated that 93% leak user data to a third party." Unfortunately even incognito window does not solve the entire problem. Another reason to use".
This week was active for "Computer Science - Human-Computer Interaction", with 34 new papers.
The paper discussed most in the news over the past week was "Translating Visual Art into Music" by Maximilian Müller-Eberstein et al (Sep 2019), which was referenced 2 times, including in the article SynVAE AI translates visual artwork into melodies in Venturebeat. The paper got social media traction with 22 shares.
This week was very active for "Computer Science - Learning", with 294 new papers.
The paper discussed most in the news over the past week was "KiloGrams: Very Large N-Grams for Malware Classification" by Edward Raff et al (Jul 2019), which was referenced 3 times, including in the article Introducing KiloGram, a New Technique for AI Detection of Malware in DigitalMunition. The paper got social media traction with 71 shares. A user, @rishmishra, tweeted "This is a really cool paper from and et. al. A million years ago I did an internship with him in this lab", while @drhyrum commented "If it doesn't appear online, can send you slides. But for now, how's a dry paper?".
Leading researcher Yoshua Bengio (Université de Montréal) came out with "Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures" @anirudhg9119 tweeted "Great work by my colleague at (with pretty nice visualizations :) )".
The paper shared the most on social media this week is "Ab-Initio Solution of the Many-Electron Schr\odinger Equation with Deep Neural Networks" by David Pfau et al (Sep 2019) with 549 shares. @paulportesi (Paul Portesi ن) tweeted "Thread by "Thrilled to be able to share what I've been working on for the last year - solving the fundamental equations of quantum mecwith deep learning! The Schroedinger equation - basically Newton's laws at the atomic"".
Over the past week, 16 new papers were published in "Computer Science - Multiagent Systems".
Leading researcher Aaron Courville (Université de Montréal) came out with "No Press Diplomacy: Modeling Multi-Agent Gameplay" The authors focus on training an agent that learns to play the No Press version of Diplomacy where there is no dedicated communication channel between players.
Over the past week, 20 new papers were published in "Computer Science - Neural and Evolutionary Computing".
This week was active for "Computer Science - Robotics", with 46 new papers.
The paper discussed most in the news over the past week was "AGDC: Automatic Garbage Detection and Collection" by Siddhant Bansal et al (Aug 2019), which was referenced 3 times, including in the article Indian robot automatically finds and collects litter in GlobalSpec. The paper got social media traction with 25 shares. The investigators propose a system which is very hygienic and cheap that uses Artificial Intelligence algorithms for detection of the garbage. A user, @drmillsrit, tweeted "It's too bad that none of my #GSCI101 students follow me yet. This would be perfect for them", while @arduino said "This AI-powered system automatically detects and collects garbage using a robotic arm".