13,987 research outputs found
Structured Review of the Evidence for Effects of Code Duplication on Software Quality
This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Coalitional Games for Distributed Collaborative Spectrum Sensing in Cognitive Radio Networks
Collaborative spectrum sensing among secondary users (SUs) in cognitive
networks is shown to yield a significant performance improvement. However,
there exists an inherent trade off between the gains in terms of probability of
detection of the primary user (PU) and the costs in terms of false alarm
probability. In this paper, we study the impact of this trade off on the
topology and the dynamics of a network of SUs seeking to reduce the
interference on the PU through collaborative sensing. Moreover, while existing
literature mainly focused on centralized solutions for collaborative sensing,
we propose distributed collaboration strategies through game theory. We model
the problem as a non-transferable coalitional game, and propose a distributed
algorithm for coalition formation through simple merge and split rules. Through
the proposed algorithm, SUs can autonomously collaborate and self-organize into
disjoint independent coalitions, while maximizing their detection probability
taking into account the cooperation costs (in terms of false alarm). We study
the stability of the resulting network structure, and show that a maximum
number of SUs per formed coalition exists for the proposed utility model.
Simulation results show that the proposed algorithm allows a reduction of up to
86.6% of the average missing probability per SU (probability of missing the
detection of the PU) relative to the non-cooperative case, while maintaining a
certain false alarm level. In addition, through simulations, we compare the
performance of the proposed distributed solution with respect to an optimal
centralized solution that minimizes the average missing probability per SU.
Finally, the results also show how the proposed algorithm autonomously adapts
the network topology to environmental changes such as mobility.Comment: in proceedings of IEEE INFOCOM 200
Supervised Land Use Inference from Mobility Patterns
This paper addresses the relationship between land use and mobility patterns. Since each particular zone directly feeds the global mobility once acting as origin of trips and others as destination, both roles are simultaneously used for predicting land uses. Specifically this investigation uses mobility data derived from mobile phones, a technology that emerges as a useful, quick data source on people's daily mobility, collected during two weeks over the urban area of Málaga (Spain). This allows exploring the relevance of integrating weekday-weekend trip information to better determine the category of land use. First, this work classifies patterns on trips originated and terminated in each zone into groups by means of a clustering approach. Based on identifiable relationships between activity and times when travel peaks appear, a preliminary categorization of uses is provided. Then, both grouping results are used as input variables in a K-nearest neighbors (KNN) classification model to determine the exact land use. The KNN method assumes that the category of an object must be similar to the category of the closest neighbors. After training the models, the findings reveal that this approach provides a precise land use categorization, yielding the best accuracy results for the major categories of land uses in the studied area. Moreover, as a result, the weekend data certainly contributes to finding more precise land uses as those obtained by just weekday data. In particular, the percentage of correctly predicted categories using both weekday and weekend is around 80%, while just weekday data reach 67%. The comparison with actual land uses also demonstrates that this approach is able to provide useful information, identifying zones with a specific clear dominant use (residential, industrial, and commercial), as well as multiactivity zones (mixed). This fact is especially useful in the context of urban environments where multiple activities coexist.Unión Europea Programa Operativo FEDER de Andalucía 2011–2015Ministerio de Economía y Competitividad PTQ-13-0642
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