In this paper, we show that public USB charging stations pose a significant privacy risk to smartphone users even when no data communication is possible between the station and the user's mobile device. We present a side-channel attack that allows a charging station to identify which Webpages are loaded while the smartphone is charging. To evaluate this side-channel, we collected power traces of Alexa top 50 Websites on multiple smartphones under several conditions, including battery charging level, browser cache enabled/disabled, taps on the screen, Wi-Fi/LTE, TLS encryption enabled/disabled, time elapsed between collection of training and testing data, and location of the Website. The results of our evaluation show that the attack is highly successful: in many settings, we were able to achieve over 90% Webpage identification accuracy. On the other hand, our experiments also show that this side-channel is sensitive to some of the aforementioned conditions. For instance, when training and testing traces were collected 70 days apart, accuracies were as low as 2.2%. Although there are studies that show that power-based side-channels can predict browsing activity on laptops, this paper is unique, because it is the first to study this side-channel on smartphones, under smartphone specific constraints. Further, we demonstrate that Websites can be correctly identified within a short time span of 2 × 6 seconds, which is in contrast with prior work, which uses 15-s traces. This is important, because users typically spend less than 15 s on a Webpage.

A series of recent neural network models have begun to address experimental findings showing that group brainstormers are often less productive than the same number of individuals working in isolation. These models also deal with various manipulations that can enhance productivity in both group and individual brainstormers. The present study uses network interactions between nodes representing ideas and categories to reproduce some of the behavioral data about interactive groups instructed to brainstorm on an open-ended real-life problem. In particular, we simulate results showing that brief breaks or incubation periods can enhance productivity in brainstormers who would otherwise be fixated on categories in which they receive suggestions. Future refinements of this model may be used to better predict group participants' search of ideas.

Wind-up has received great attention in recent years due to the fact that rational treatment of chronic pain depends on understanding the physiological mechanisms underlying the various characteristics of chronic pain. Several different models have been proposed for wind-up, none of which has an accurate physiological basis. We present a model that explains several aspects of wind-up by means of exploiting both short-term and long-term synaptic plasticity mechanisms. We believe that applying this idea to transcutaneous electrical nerve stimulation may provide us with better solutions in pain relief.