14362967
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
14972044
Service-Oriented Adaptation in Ubiquitous Computing Environments
Ubiquitous computing environments may offer dynamically discoverable services that can be utilized by adaptive applications to improve their functionality and quality of service. We have developed a general software development framework for context-aware adaptive applications. In this paper we present our novel modeling concepts that extend an existing component-based modeling approach by service-oriented adaptation concepts. With our new extensions a dynamically discovered service may replace an application component if it increases the overall utility of the application. We discuss open questions and point to potential solution approaches.
6820231
A tangible AR desktop environment
Abstract We present an AR desktop environment which integrates the standard 2D computer desktop into an augmented 3D space. The underlying physical space is given by the standard office desk used in everyday work. Instead of sitting only in front of a computer screen, the user wears a video-through head mounted displays and interacts with the environment in both tangible and virtual ways. In this augmented environment, standard 2D application windows are attached to physical clipboards and can be freely positioned in the working space by simply moving the clipboard. Additionally, 3D content can be brought into and manipulated within the same space. To support interaction with 3D content we have experimented with a circular platform, on which a 3D model can be placed, and which the user can turn in a natural tangible way. By fusing familiar desktop utensils, two-dimensional computer desktop applications, and three-dimensional models, we provide a seamless transition from the traditional 2D computer desktop to a 3D augmented working environment. The paper describes the concept and implementation of the system and illustrates some interfaces and interaction techniques used in the context of CAD engineering.
2959784
Computational protein biomarker prediction: a case study for prostate cancer
BackgroundRecent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.ResultsThorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably.ConclusionsModern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.
20326889
Towards a Comprehensive Teamwork Model for Highly Dynamic Domains
Cooperative behaviour of agents within highly dynamic and nondeterministic domains is an active field of research. In particular establishing responsive teamwork, where agents are able to react to dynamic changes in the environment while facing unreliable communication and sensory noise, is an open problem. Unexpectedly changing situations force agents to react and adapt under tight time-constraints. Hence they often cannot communicate or agree upon their decisions before acting upon them. We present a novel model for cooperative behaviour geared towards such domains. In our approach, the agents estimate each other’s decision and correct these estimations once they receive contradictory information. We aim at a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and clear operational semantics for the new model. We show experimentally that the resulting behaviour stabilises towards teamwork and can achieve a cooperative goal.
61094772
Video to the rescue
Automatic person identity verification based on biometrics is a challenging problem, and has received much attention during recent years due to its many applications in on-line transaction processing, law enforcement, and security applications. However, most identity verification systems are primarily based on voice biometrics, and hence are more vulnerable to acoustic noise and channel distortions, in addition to train/test mismatch conditions. In this paper, we show how we can use video information to improve the performance of identity verification systems. The approach based on multimodal fusion of voice and face information from speaking face video allows robust identity verification performance. In addition, depending on the type of features and fusion technique used, it is also possible to perform liveness checks, allowing the system to detect fraudulent attacks on the system.