The thermal camera can capture keyboard surface temperature change after a human's touch. This phenomenon may be used to steal users' passwords physically. In this paper, based on the study of thermal dynamics of keyboards, we design a password break system using an infrared thermal camera. First, we build a signal model to describe the dynamic process of temperature change on the keyboard using Newton's law of cooling. Next, we develop a maximum likelihood parameter estimation algorithm to estimate the keystroke time instants. Then, by maximizing the probability of key order arrangement, a novel password breaking algorithm is developed. Our algorithm is tested using simulated data as well as real-world data. Experiment results show that our algorithm is effective for physical password breaking using thermal characteristics. Based on our results, we discuss strategies for password protection at the end.
Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer's stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes, and different audio recorder positions.
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In hospitals, doctors and nurses keep vigilant watch over patients' vital signs and blood tests to catch the first symptoms of sepsis. In this life-threatening condition, the body responds to an infection with widespread inflammation that can lead to organ failure. Cases can progress rapidly to severe sepsis and then to septic shock, which has a mortality rate of almost 50percent in the United States.
But even the most vigilant humans get tired, make mistakes, and miss subtle patterns. That's why several hospitals are experimenting with artificially intelligent sepsis detectors. Researchers say these pilot projects are the first real examples of AI being integrated into hospital operations, with data flowing from electronic medical records and alerts being incorporated into physicians' workflows.
This month, Duke University Hospital, in Durham, N.C., is officially launching Sepsis Watch, an AI-based system that identifies incipient sepsis cases and raises the alarm. The hospital is deploying it initially in the emergency department, and will then extend it to the general hospital floor and the intensive care unit. “The most important thing is to catch cases early, before they get to the ICU,” says Suresh Balu, director of the Duke Institute for Health Innovation and one of the project leads.
Sepsis Watch was trained via deep learning to identify cases based on dozens of variables, including vital signs, lab test results, and medical histories; its training data consisted of 50,000 patient records including more than 32 million data points. In operation, it pulls information from patients’ medical records every 5 minutes to evaluate their conditions, offering intensive real-time analysis that human doctors can't provide. If the AI system determines that a patient meets its criteria for someone with the early signs of sepsis, it alerts the nurses on the hospital's rapid response team.
If these AI systems do improve care, plenty of hospitals will be eager to adopt the technology, says Duke's Sendak. Beginning in July 2018, the U.S. government's
Hospital Compare website began publishing data about hospitals' records on providing early and appropriate treatment for sepsis. “The national average is about 50 percent,” Sendak says. “A lot of places struggle with this problem.”
If you want to know more about the news, you can find more from the paper entitled HOSPITALS FIGHT SEPSIS WITH AI published in IEEE Spectrum in Nov. 2018.
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