Nazli FarajiDavar

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Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a(More)
This paper investigates the application of transductive transfer learning methods for action classification. The application scenario is that of off-line video annotation for retrieval. We show that if a classification system can analyze the unlabeled test data in order to adapt its models, a significant performance improvement can be achieved. We applied(More)
Classification methods traditionally work under the assumption that the training and test sets are sampled from similar distributions (domains). However, when such methods are deployed in practise, the conditions in which test data is acquired do not exactly match those of the training set. In this paper, we exploit the fact that it is often possible to(More)
This paper describes a system that can automatically annotate videos and illustrates its application to tennis games. A unified apparatus is proposed, cast in a Bayesian reasoning framework. This is supported by a cognitive memory architecture that allows the system to store raw video data at the lowest cognitive level and its semantic annotation with(More)
The inter-departmental interactions and coordination of resources are two essential components for realising a smart city platform. In this study, we investigated citizens' role in enhancing and facilitating the delivery of services by merging three key aspects of the smart city research field, namely Internet of People, Internet of Things and Web of Data.(More)
This work develops techniques for the sequential detection and location estimation of transient changes in the volatility (standard deviation) of time series data. In particular, we introduce a class of change detection algorithms based on the windowed volatility filter. The first method detects changes by employing a convex combination of two such filters(More)