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The key to a recommendation system is the prediction of users' preferences. Personalized recommendation for many online music applications depends on the prediction of both long-term as well as the short-term preferences. In this paper, we propose a novel personalized next-song recommendation system that jointly consider the long-term and short-term preferences in its design. To depict the long-term...
Digital news will continue to grow up and evolve, it bring up the new issue for modeling digital news data that are stored in the database so that it is easier to understand and be able to take some important information thoroughly. To simplify the information processing in the database it is require a model and a specific method for clustering the news based on proximity and characteristics of the...
Large amount of time series data generated by sensors and Web users is great source of contextual information. Detecting outliers with unusually high values in time series data is crucial for inferring about any events in the real world. In this work, we describe an infinite Poisson mixture model to detect events by identifying outliers in time series of count data. This unsupervised technique estimates...
We propose new algorithms for implementing a software-defined data center (SDDC) to improve the dependability of storage systems without the addition of new hardware. We define the construction of a system that can predict its future resource requirements and act on these predictions to allocate overprovisioned resources to improve reliability. We introduce algorithms for implementing a smart SDDC...
In recent years, vessel traffic and maritime situation awareness become more and more important for countries across the world. AIS data contains much information about vessel motion and reflects traffic characteristics. In this paper, data mining is introduced to discover motion patterns of vessel movements. Firstly, we do statistical analysis for large scale of AIS data. Secondly, we use association...
Crowd sourcing is emerging as a powerful paradigm to solve a wide range of tedious and complex problems in various enterprise applications. It spawns the issue of finding the unknown collaborative and competitive group of solvers. The formation of collaborative team should provide the best solution and treat that solution as a trade secret avoiding data leak between competitive teams due to reward...
A clustering method that combines an advanced statistical distribution with spatial contextual information is proposed for multilook polarimetric synthetic aperture radar (PolSAR) data. It is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the...
Diagnostics and prognostics of health states are important activities in the maintenance process strategy of dynamical systems. Many approaches have been developed for this purpose and we particularly focus on data-driven methods which are increasingly applied due to the availability of various cheap sensors. Most data-driven methods proposed in the literature rely on probability density estimation...
This paper presents a multi-temporal Stochastic Expectation-Maximization (SEM) algorithm with adaptive Markov Random Field (MRF) for analysis of polarimetric SAR (PolSAR) data for urban land cover mapping. The fitness of alternative distributions of multi-look PolSAR data based on Wishart, G0p and Kp assumptions are compared by using the SEM algorithm. The proposed pixel-based SEM algorithm explores...
In this paper, we present a framework for situation modeling and assessment for mobile robot applications. We consider situations as data patterns that characterize unique circumstances for the robot, and represented not only by the data but also its temporal and spacial sequence. Dynamic Markov chains are used to model the situation states and sequence, where stream clustering is used for state matching...
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of over fitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric...
Images with GPS coordinates are a rich source of information about a geographic location. Innovative user services and applications are being built using geotagged images taken from community contributed repositories like Flickr. Only a small subset of the images in these repositories is geotagged, limiting their exploration and effective utilization. We propose to use optional meta-data along with...
This paper describes a system that is being developed for providing services to support elderly residents in assisted living facilities. Passive RFID tags and readers are used as the basic sensor modality to distinguish people and objects. An intelligent multiagent system with a distributed topology enables local decision making without the need for a centralized server or database. Rather than use...
Many applications such as telecommunication and commercial video broadcasting streams, computer systems logs, and web clicks are categorical or mixed-value data streams that exhibit context-dependency. Models that try to capture this context-dependency tend not to be scalable. This paper offers a solution to the scalability problem of these models by providing a method for generating them around relevant...
Recognizing links between offender patterns is one of the most crucial skills of an investigator. Early recognition of similar patterns can lead to focusing resources, improving clearance rates, and ultimately saving lives in terms of digital forensics. In this paper we propose a forensics methodology using Markov chain during a given time interval for tracking and predicting the degree of criminal...
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