Recorded examples from around the world of people reading an identical paragraph.
By Steven H. Weinberger and friends at George Mason University
Recorded examples from around the world of people reading an identical paragraph.
By Steven H. Weinberger and friends at George Mason University
By temporarily restoring plasticity to the brain, adults are able learn in ways previously only available to children.
By Rich McCormick at The Verge
“Uncovering Social Relationships through Smartphone Probes”
By: Marco V. Barbera, Alessandro Epasto, Alessandro Mei, Vasile C. Perta, and Julinda Stefa Department of Computer Science, Sapienza University of Rome, Italy,
Using low level wiFi probe-request frames to sense devices and infer characteristics of people carrying those devices. They also use the PNL (preferred network list) to match people based on public SSID’s they have previously accessed.
http://conferences.sigcomm.org/imc/2013/papers/imc148-barberaSP106.pdf
The ever increasing ubiquitousness of WiFi access points, cou- pled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a re- ality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user ex- perience. Most of the times, this is achieved by recurrently broad- casting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., “Home WiFi”, “Campus WiFi”, and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural ques- tion: “Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?”.
In this work we give a positive answer to this question. We or- ganized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. Dur- ing the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the un- derlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociolog- ical aspects of its participants—language, vendor adoption, and so on.
Retrie Mac address, SSID, and signal strength data of devices within range.
By John Libery at Mlive
http://www.mlive.com/entertainment/kalamazoo/index.ssf/2014/01/project_tracking_mobile_device.html
What a thrill to be interviewed for this podcast!
http://artmusictech.libsyn.com/podcast-012-tom-zicarelli
Objects are trapped by nodes of standing waves – from a phased array of speakers.
By Yoichi Ocjiai,Takayuki Hoshi and Jun Rekimoto at the University of Tokyo.
http://www.engadget.com/2014/01/01/ultrasonic-array-moves-objects-in-3d/?ncid=rss_truncated
The awards list is linked to Google Scholar.
By Jeff Huang
http://jeffhuang.com/best_paper_awards.html
An interview of Kang Zhao in Data Science Weekly
(Kang recommends various data-mining tools and methods)
by Tosh Chiang
(photo from the SF Chronicle – by Liz Hafalia)