Tomorrow’s Internet-connected cars could be vulnerable to hackers in the way computers are today, warn researchers at the University of Washington (UW) and the University of California, San Diego (UCSD). During a recent test, the researchers were able to remotely control a car’s braking and other functions. “We demonstrate the ability to adversarially control a wide range of automotive functions and completely ignore driver input–including disabling the brakes, selectively braking individual wheels on demand, stopping the engine, and so on,” the researchers write. The researchers were also able to insert malicious software into the car and then erase any evidence of tampering. “Taken together, ubiquitous computer control, distributed internal connectivity, and telematics interfaces increasingly combine to provide an application software platform for external network access,” write the researchers.
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Clear and simple visualizations of biomechanical data could improve rehabilitation after a stroke, accelerate the recovery from joint replacements, and prevent older people from falling, according to United Kingdom researchers. University of Strathclyde professor Philip Rowe is leading an initiative to develop bespoke software for capturing biomechanical data and presenting it in a way that would assist health care professionals in their effort to communicate movement information to patients. Currently, movement information is only available in graphical, tabular, or numerical form. The software would work with Strathclyde’s specialist motion analysis system and portable motion sensors. “By using animation, we can enable patients to visualize a movement, and how it affects their body,” Rowe says.
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Louisiana State University (LSU) professors Supratik Mukhopadhyay and S.S. Iyengar have developed software that can monitor systems and use the data it collects to make decisions on what to do next. The software can sift through massive amounts of data to detect a complex event or pattern. Iyengar says a complex event might involve multiple events that take place over time, with the software comparing the current circumstances to events in the past. Each time the network takes an action, it reevaluates the situation to make the next decision. Iyengar says the software learns quickly, which makes the system as efficient as possible. The researchers’ goal is to embed the software in a computer chip so that customers can take the chip and develop their own interface. They say the system could be adapted for use in many applications, such as military procedures, golf course sprinkler systems, and the electrical power grid.
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The European Union-funded Palette project has developed online tools that have led to new approaches to the science of education. “There are many online collaboration tools, but most of them are oriented towards enterprises, not communities,” says Palette coordinator Christine Vanoirbeek. She says the semantic Web plays a key role in the project, as it is more about intelligent Web-searching, which is based on interlinked concepts. One of the objectives of Palette was to get communities of practice to develop their own ontologies. Many of the Palette tools also exploit the advantages of Web 2.0 social networking technologies or the document-searching capabilities of the semantic Web, such as ways to east document sharing across the Internet by eliminating the need to exchange emails or open a series of applications during a collaboration. “The big challenge in the Palette project was to make these tools interoperable, so that the tools could be combined and information reused,” Vanoirbeek says.
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Companies are increasingly interested in using social networking data to automatically determine users’ behavior patterns, but researchers say more work is needed to define what it takes for two people to be connected. “You don’t get to directly observe relationships, you get to observe communication events,” says Yahoo!’s Jake Hofman. Algorithms can infer different social network patterns based on the interpretation of communications events, making these certain networks more suitable for different circumstances. For example, a network based on relatively infrequent communications might work well for sharing tagged news items, while more frequent communications might work better for networks designed for sharing more intimate information. Incomplete information can throw off attempts to characterize social networks automatically, says Georgia Tech professor Eric Gilbert. Studying the structure of a network in greater detail can compensate for the problem of incomplete information, Gilbert says. Meanwhile, Microsoft’s Munmun De Choudhury says more research is needed to make algorithms able to better understand the nature of social connections.
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