14,712 research outputs found
Discovering activity patterns in office environment using a network of low-resolution visual sensors
Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent
variable modeling problems namely, (1) community detection, in which we learn
the latent communities that the social actors in social networks belong to, and
(2) topic modeling, in which we infer hidden topics of text articles. We
consider decomposition of moment tensors using stochastic gradient descent. We
conduct optimization of multilinear operations in SGD and avoid directly
forming the tensors, to save computational and storage costs. We present
optimized algorithm in two platforms. Our GPU-based implementation exploits the
parallelism of SIMD architectures to allow for maximum speed-up by a careful
optimization of storage and data transfer, whereas our CPU-based implementation
uses efficient sparse matrix computations and is suitable for large sparse
datasets. For the community detection problem, we demonstrate accuracy and
computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic
modeling problem, we also demonstrate good performance on the New York Times
dataset. We compare our results to the state-of-the-art algorithms such as the
variational method, and report a gain of accuracy and a gain of several orders
of magnitude in the execution time.Comment: JMLR 201
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
On-the-fly tracing for data-centric computing : parallelization, workflow and applications
As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements
Knowledge Discovery from CVs: A Topic Modeling Procedure
With a huge number of CVs available online, recruiting via the web has become an integral part of human resource management for companies. Automated text mining methods can be used to analyze large databases containing CVs. We present a topic modeling procedure consisting of five steps with the aim of identifying competences in CVs in an automated manner. Both the procedure and its exemplary application to CVs from IT experts are described in detail. The specific characteristics of CVs are considered in each step for optimal results. The exemplary application suggests that clearly interpretable topics describing fine-grained competences (e.g., Java programming, web design) can be discovered. This information can be used to rapidly assess the contents of a CV, categorize CVs and identify candidates for job offers. Furthermore, a topic-based search technique is evaluated to provide helpful decision support
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