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Expressing data as linear functions of a small number of unknown variables is a useful approach employed by several classical data analysis methods, e.g., factor analysis, principal component analysis, or latent semantic indexing. These models represent the data using the product of two factors. In practice, one important concern is how to link the learned factors to relevant quantities in the context...
Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore natural to consider tractable approximations to the likelihood function. Composite likelihoods offer a...
Backoff smoothing and topic modeling are crucial issues in n-gram language model. This paper presents a Bayesian non-parametric learning approach to tackle these two issues. We develop a topic-based language model where the numbers of topics and n-grams are automatically determined from data. To cope with this model selection problem, we introduce the nonparametric priors for topics and backoff n-grams...
Recently, as the number of smart phone users increases rapidly, various applications using GPS become widespread. Because GPS sensor has drawback that consumes too much energy, we have to decrease the unnecessary usage of GPS receiver at indoors to reduce the energy consumption. Most of previous works focused on how to reduce the frequency or the period to use GPS. In this paper, we propose a method...
Prevention and management of damage scenarios require adequate situation awareness to make timely, coordinated, and proactive decisions possible. The stakeholders must be able to access and to comprehend relevant information quickly and with justifiable effort. The resulting challenges for intelligence, surveillance, and reconnaissance (ISR) lie not only in the new and further development of individual...
We present a new probabilistic approach to modeling social interactions, that seamlessly integrates community discovery and role assignment for a deeper understanding of connectivity patterns in social networks. The devised approach is an unsupervised learning technique based on a Bayesian hierarchical model of social interactions. This model specifies an intuitive generative process, in which pairs...
In applications of Web data integration, we frequently need to identify whether data objects in different data sources represent the same entity in the real world. This problem is known as entity resolution. In this paper, we propose a generic framework for entity resolution for relational data sets, called BARM, consisting of the Blocker, Attribute matchers and the Record Matcher. BARM is convenient...
In Cloud computing, when dealing with the interaction between providers and consumers, one of the main challenges is to build effective mechanisms for SLA negotiation. Such protocols must deal with different policies and objectives of the competing parties. This paper introduces and discusses protocols for SLA negotiation with multiple providers based on the ascending English auction, without requiring...
One of the main goals of all online social communities is to promote a stable, or perhaps, growing membership built around topics of like interest. Yet, communities are not impermeable to the potentially damaging effects resulting from those few participants that choose to behave in a manner that is counter to established norms of behavior. Typical moderators in online social communities are the ones...
This work discusses the problem of sparse signal recovery when there is correlation among the values of nonzero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algorithms based on the sparse Bayesian learning are presented and the benefits of incorporating...
Many automotive systems use linear approaches to track and predict other traffic participants. While this may be appropriate on highways, linear predictions do not work properly on curved roads or lane crossings. This contribution introduces a generic way for including environmental knowledge — such as the lane trajectory ahead — to anticipate yaw rate and acceleration of other traffic participants...
Despite a considerable amount of previous work on bottom-up saliency modeling for predicting human fixations over static and dynamic stimuli, few studies have thus far attempted to model top-down and task-driven influences of visual attention. Here, taking advantage of the sequential nature of real-world tasks, we propose a unified Bayesian approach for modeling task-driven visual attention. Several...
In this paper we propose a probabilistic model to parameterize human interactive behaviour from human motion. To Support the model taxonomy, we use Laban Movement Analysis (LMA), proposed by Rudolph Laban [11], to characterize human non-verbal communication. In interpersonal communication, body motion carries a lot of meaningful information, useful to analyse group dynamic behaviors in a wide range...
With the increase of published Web services, it has become a great challenge to recommend service consumers the best services with regard to the quality of services (QoS). Collaborative filtering is often employed to predict the QoS of a specific service to a certain consumer. However, in existing collaborative filtering based service recommendation approaches, the context under which consumers submit...
Many applications on smart phones have been developed for a user's convenience. They are using various sensors such as accelerometer, proximity, light and orientation sensors for context-awareness. Energy-efficient battery management systems become more important to support the sensors on a mobile phone because recent applications consume a large amount of energy for diverse functionalities. This...
Since smart phones have become popular, intelligent adaptive services were more highly demanded. The intelligent synthetic character on a mobile phone is one of the promising services. It is difficult to develop an intelligent synthetic character on the mobile environment due to its dynamism and complexity. This paper proposes a method to generate a synthetic character's behaviors using contexts inferred...
Empirical results in business research derived from multiple linear regression models are often susceptible to issues of dimensionality and multicollinearity. We extend the current research practices for addressing multicollinearity by introducing an original method, Bayesian Dominance Hierarchy (BDH) to determine the relative importance of predictors in a multiple regression context.
The performance of channel estimation is often assessed by deriving the proper Cramér-Rao Bound (CRB). However, in the blind case a special treatment is required due to the singularity of the Fisher Information Matrix (FIM). Usually a constraint is introduced to overcome the blind ambiguity and ensuing singularity. Hence, a constrained CRB has been derived in the literature since a long time ago....
We describe an adaptive context tree weighting (ACTW) algorithm, as an extension to the standard context tree weighting (CTW) algorithm. Unlike the standard CTW algorithm, which weights all observations equally regardless of the depth, ACTW gives increasing weight to more recent observations, aiming to improve performance in cases where the input sequence is from a non-stationary distribution. Data...
Single-microphone speech enhancement algorithms that employ trained codebooks of parametric representations of speech spectra have been shown to be successful in the suppression of non-stationary noise, e.g., in mobile phones. In this paper, we introduce the concept of a context-dependent codebook, and look at two aspects of context: dependency on the particular speaker using the mobile device, and...
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