Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences, thus might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but can also be helpful for abnormal users selection. This is realized by a variable splitting-based algorithm called iSplit LBI.
The Apple Watch smartwatch has been found to be pretty accurate when it comes to detecting abnormal heart rhythms. A continuing study on the potentials of wearables has identified that the watchOS device has a 97 percent accuracy rate in determining abnormal heart conditions. In the study, titled "Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch," published Wednesday in JAMA Cardiology, it is stated there that the Apple Watch has a high accuracy rate in determining unusual heart rhythms. In fact, Apple's smartwatch is said to perform better than add-on ECG accessory KardiaBand. Raw sensor measurements were transformed into fibrillation risk scores.
No abnormal radiation readings have been detected from North Korea's nuclear test on Sunday, the Nuclear Regulation Authority said. "Generally, radioactive substances are not supposed to be released into the air in case of an underground nuclear test," an NRA official noted, adding that monitoring would be bolstered. In addition to its monitoring posts, the NRA will analyze airborne dust and rainwater every day for the time being to check for changes in its regular readings. Such examinations are usually conducted every one to three months. The NRA is also making use of a diffusion estimate from WSPEEDI, the worldwide version of the System for Prediction of Environmental Emergency Dose Information (SPEEDI) used in Japan.
Guesgen, Hans W. (Massey University) | Whiddett, Dick (Massey University) | Hunter, Inga (Massey University) | Elers, Phoebe (Massey University) | Lockhart, Caroline (Massey University ) | Singh, Amardeep (Massey University) | Marsland, Stephen ( Victoria University of Wellington )
This paper investigates how spatial and temporal context informationcan be used in smart homes to detect abnormal behaviours.We discuss how various formalisms, such as probabilitytheory, the Dempster-Shafer calculus, and fuzzy logic,can be used to capture context information and argue thatfuzzy logic is the most suitable.
Wong, Josiah (University of Central Florida) | Hastings, Lauren (University of Central Florida) | Negy, Kevin (University of Central Florida) | Gonzalez, Avelino J. (University of Central Florida) | Ontañón, Santiago (Drexel University) | Lee, Yi-Ching (George Mason University)
Detection of abnormal behavior is the catalyst for many applications that seek to react to deviations from behavioral expectations. However, this is often difficult to do when direct communication with the performer is impractical. Therefore, we propose to create models of normal human performance and then compare their performance to a human's actual behavior. Any detected deviations can be then used to determine what condition(s) could possibly be influencing the deviant behavior. We build the models of human behavior through machine learning from observation; more specifically, we employ the Genetic Context Learning algorithm to create models of normal car driving behaviors of different humans with and without ADHD (Attention Deficit Hyperactivity Disorder). We use a car simulator for our studies to eliminate risk to our test subjects and to other drivers. Our results show that different driving situations have varying utility in abnormal behavior detection. Learning from Observation was successful in building models to be applied to abnormal behavior detection.