28503548
Early Outbreak Detection Using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory
Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.
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.
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.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.
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.
14749213
Verteilte Suche nach digitalen Wasserzeichen in eMule
ZusammenfassungZusammenfassung Filesharing erfreut sich einer immer weiter wachsenden Beliebtheit. Allerdings wird diese Technologie zunehmend zur illegalen Verbreitung von urheberrechtlich geschütztem Material genutzt. Dementsprechend existiert eine Vielzahl von Ansätzen um gegen diese Verbreitung vorzugehen. Eine Alternative zum Einsatz von restriktivem Digital Rights Management (DRM) ist die Einbettung digitaler Wasserzeichen zur Markierung von urheberrechtlich geschütztem Material. Dadurch ist eine etwaige Weitergabe von Medien zu ihrer Quelle zurückverfolgbar. Um diesen Ursprung identifizieren zu können, müssen allerdings zuerst die markierten Medien mit der entsprechenden Filesharing-Software heruntergeladen werden. Um der großen Anzahl an Dateien gerecht zu werden, wird hierzu eine skalierbare, verteilte Lösung zum Download dieser Dateien auf der Basis einer von Schlüsselwörtern gesteuerten Suche vorgestellt. Resultat dieser Arbeit ist ein System, welches unter guter Ausnutzung der zur Verfügung stehenden Ressourcen eine hohe Skalierbarkeit und Fehlertoleranz bietet.
17740711
Hierarchical data access in structured P2P-networks
Hierarchically structured organizations have clear and relatively short communication paths. However, one does not take advantage of this fact in modern peer-to-peer overlays. We show how a command and control order can be imposed on a given peer-to-peer network. This allows fast hierarchical data storage and data access. Maintaining a hierarchical organization structure upon a given distributed hashtable overlay network scales with O(logN), where N is the number of peers in the network. This approach can be used to benefit from hierarchical structure in any communication system, especially in first response scenarios.
10506744
Fusion of Acoustic and Linguistic Features for Emotion Detection
This paper describes a system that deploys acoustic and linguistic information from speech in order to decide whether the utterance contains negative or non-negative meaning. An earlier version of this system was Passed QA to the Interspeech-2009 Emotion Challenge evaluation. The speech data consist of short utterances of the children’s speech, and the proposed system is designed to detect anger in each given chunk. Various frame-based cepstral, prosodic and acoustic features are extracted automatically and classified by means of a support vector machine. An automatic speech recognizer transcribes the utterances and yields a separate classification, based on the degree of emotional salience of the words. The emotionally salient words are computed on word hypotheses, so that un-transcribed training data is sufficient. Late fusion is applied to make a final decision on anger vs. non-anger of the utterance.