Sequence segmentation is a central problem in the analysis of sequential and time-series data. In this paper we introduce and we study a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. Our problem formulation has applications in segmenting data colle...

In this paper, we address the problem of scene modeling for performing video surveillance. The problem consists of using the trajectories, obtained by observing objects in a scene, to construct a scene model that can be used to distinguish a normal and an acceptable behavior from a atypical one. In this regard, the proposed method is divided into a training phase and a testing phase. During the...

Anomaly detection in IP networks, detection of deviations from what is considered normal, is an important complement to misuse detection based on known attack descriptions. Performing anomaly detection in real-time places hard requirements on the algorithms used. First, to deal with the massive data volumes one needs to have efficient data structures and indexing mechanisms. Secondly, the dynam...

Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they ...

Recent advances in sensor technology are facilitating the deployment of sensors into the environment that can produce measurements at high spatial and/or temporal resolutions. Not only can these data be used to better characterize systems for improved modeling, but they can also be used to produce better understandings of the mechanisms of environmental processes. One such use of these data is ...

Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. To detect abnormal moments in these processes, a de€nition of ‘normal’ must be established. Œis paper proposes a new statistical model for such systems, modeled as dynamic networks, to address this challenge. It assumes that vertices fall into one of k types and that the probabili...

As recent Internet threats are evolving more rapidly than ever before, one of the major challenges in designing an intrusion detection system is to provide early and accurate detection of emerging threats. In this study, a novel framework is developed for fully unsupervised training and online anomaly detection. The framework is designed so that an initial model is constructed and then it gradu...