138,329 research outputs found
Node discovery in a networked organization
In this paper, I present a method to solve a node discovery problem in a
networked organization. Covert nodes refer to the nodes which are not
observable directly. They affect social interactions, but do not appear in the
surveillance logs which record the participants of the social interactions.
Discovering the covert nodes is defined as identifying the suspicious logs
where the covert nodes would appear if the covert nodes became overt. A
mathematical model is developed for the maximal likelihood estimation of the
network behind the social interactions and for the identification of the
suspicious logs. Precision, recall, and F measure characteristics are
demonstrated with the dataset generated from a real organization and the
computationally synthesized datasets. The performance is close to the
theoretical limit for any covert nodes in the networks of any topologies and
sizes if the ratio of the number of observation to the number of possible
communication patterns is large
Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
The continuing development of Semantic Web technologies and the increasing
user adoption in the recent years have accelerated the progress incorporating
explicit semantics with data on the Web. With the rapidly growing RDF (Resource
Description Framework) data on the Semantic Web, processing large semantic
graph data have become more challenging. Constructing a summary graph structure
from the raw RDF can help obtain semantic type relations and reduce the
computational complexity for graph processing purposes. In this paper, we
addressed the problem of graph summarization in RDF graphs, and we proposed an
approach for building summary graph structures automatically from RDF graph
data. Moreover, we introduced a measure to help discover optimum class
dissimilarity thresholds and an effective method to discover the type classes
automatically. In future work, we plan to investigate further improvement
options on the scalability of the proposed method
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