Tom Mitchell, head of Carnegie Mellon University’s Machine Learning Department, says that advances in machine learning could bring about a transformation in psychology and neuroscience. Mitchell says that his group has trained an algorithm to study functional magnetic resonance imaging scans of a person’s brain activity and determine what object they are thinking about. “We can look inside your brain when you see the color red, and we can look inside my brain when I see the color red, and we can ask, ‘Is it or is it not the same pattern of neural activity?’ ” he notes. Mitchell speculates that people could conceivably be networked to exchange information so that one person can tell what the other is thinking. He observes that a number of researchers are developing brain-computer interfaces that can enable the decoding of a person’s thoughts. This could be particularly useful for “locked in” patients who are speech- and mobility-disabled.
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Vienna University of Technology researchers have developed the “deanonymization” attack as a way to reveal the identity of Internet users based on their interactions in social networks. The attack uses social networking groups as well as traditional browser history-stealing tactics to single out specific users. The researchers focused on Germany’s Xing business social network and Facebook and matched stolen browsing histories with social network group members to identify users. “It is the combination of history stealing and group information that is novel,” says Vienna University post-doctoral researcher Gilbert Wondracek. Criminals could use the deanonymization method for targeted attacks, which only requires that the victim visit a malicious Web site that contains the attack code. There is no fix for the attack, but users can turn off their browsing history or use a private-browsing mode to minimize the risk.
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University of Illinois at Urbana-Champaign professor Carole Palmer says data curation is an important part of supporting and advancing research. “This is especially important in data-intensive science, where the power of discovery lies in applying computational approaches to large, aggregated data sets,” Palmer says. She says researchers need to plan around data-management requirements from the beginning of their projects, and to think in terms of a data set’s lifecycle. The biggest hurdles to overcome in collecting, curating, and managing data over a long period of time involve cost and labor. Even with the Internet and search engines, data stored online does not last nearly as long as data preserved in print. “We’re just beginning to do the research needed to guide how we build large-scale, multidisciplinary data repositories and collect and manage data in ways that add value and promote sharing and integration across laboratories, institutions, and disciplines,” Palmer says.
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