The Experts below are selected from a list of 1782 Experts worldwide ranked by ideXlab platform
Danai Koutra - One of the best experts on this subject based on the ideXlab platform.
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Fast Network Discovery on sequence data via time-aware hashing
Knowledge and Information Systems, 2018Co-Authors: Tara Safavi, Chandra Sripada, Danai KoutraAbstract:Discovering and analyzing Networks from non-Network data is a task with applications in fields as diverse as neuroscience, genomics, climate science, economics, and more. In domains where Networks are discovered on multiple time series, the most common approach is to compute measures of association or similarity between all pairs of time series. The nodes in the resultant Network correspond to time series, which are linked by edges weighted according to the association scores of their endpoints. Finally, the fully connected Network is thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic (or higher) time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to circumvent the inefficient and wasteful intermediary step of building a fully connected graph before Network sparsification, we propose a fast Network Discovery approach based on probabilistic hashing. Our methods emphasize consecutiveness, or the intuition that time series following similar fluctuations in longer time-consecutive intervals are more similar overall. Evaluation on real data shows that our method can build graphs nearly 15 times faster than baselines (when the baselines do not run out of memory), while achieving accuracy comparable to, or better than, baselines in task-based evaluation. Furthermore, our proposals are general, modular, and may be applied to a variety of sequence similarity search tasks.
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ICDM - Scalable Hashing-Based Network Discovery
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Tara Safavi, Chandra Sripada, Danai KoutraAbstract:Discovering and analyzing Networks from non-Network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, Networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the Network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast Network Discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both Network structure and accuracy comparable to baselines in task-based evaluation.
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Scalable Hashing-Based Network Discovery
2017 IEEE International Conference on Data Mining (ICDM), 2017Co-Authors: Tara Safavi, Chandra Sripada, Danai KoutraAbstract:Discovering and analyzing Networks from non-Network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, Networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the Network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast Network Discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both Network structure and accuracy comparable to baselines in task-based evaluation.
Angelica Lo Duca - One of the best experts on this subject based on the ideXlab platform.
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ISCC - SeFLOOD: A secure Network Discovery protocol for Underwater Acoustic Networks
2011 IEEE Symposium on Computers and Communications (ISCC), 2011Co-Authors: Gianluca Dini, Angelica Lo DucaAbstract:An Underwater Acoustic Network (UAN) raises many issues in terms of security. In this paper we focus on attacks performed during the Network Discovery phase. At the state of art, all underwater Discovery protocols do not provide message authenticity so they are exposed to spoofing-based attacks against Network integrity and availability. In this paper, we focus on FLOOD [1], a Network Discovery protocol for UANs and we extend it in order to provide protection against Network authenticity and integrity attacks. In particular we show that certain attacks against integrity and leading to Denial of Service are avoided.
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SeFLOOD: A secure Network Discovery protocol for Underwater Acoustic Networks
2011 IEEE Symposium on Computers and Communications (ISCC), 2011Co-Authors: Gianluca Dini, Angelica Lo DucaAbstract:An Underwater Acoustic Network (UAN) raises many issues in terms of security. In this paper we focus on attacks performed during the Network Discovery phase. At the state of art, all underwater Discovery protocols do not provide message authenticity so they are exposed to spoofing-based attacks against Network integrity and availability. In this paper, we focus on FLOOD, a Network Discovery protocol for UANs and we extend it in order to provide protection against Network authenticity and integrity attacks. In particular we show that certain attacks against integrity and leading to Denial of Service are avoided.
A.o. Hero - One of the best experts on this subject based on the ideXlab platform.
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Gene coexpression Network Discovery with controlled statistical and biological significance
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: A.o. HeroAbstract:Many biological functions are executed as a module of coexpressed genes which can be conveniently viewed as a coexpression Network. Genes are Network vertices and significant pairwise coexpression are Network edges. Traditional Network Discovery methods control either statistical significance or biological significance, but not both. We have designed and implemented a two-stage algorithm that controls both the statistical significance (false Discovery rate, FDR) and the biological significance (minimum acceptable strength, MAS) of the discovered Network. Based on the estimation of pairwise gene profile correlation, the algorithm provides an initial Network Discovery that controls only FDR, which is then followed by a second Network Discovery which controls both FDR and MAS. We illustrate the algorithm for Discovery of coexpression Networks for yeast galactose metabolism with controlled FDR and MAS.
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ICASSP (5) - Gene coexpression Network Discovery with controlled statistical and biological significance
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: A.o. HeroAbstract:Many biological functions are executed as a module of coexpressed genes which can be conveniently viewed as a coexpression Network. Genes are Network vertices and significant pairwise coexpression are Network edges. Traditional Network Discovery methods control either statistical significance or biological significance, but not both. We have designed and implemented a two-stage algorithm that controls both the statistical significance (false Discovery rate, FDR) and the biological significance (minimum acceptable strength, MAS) of the discovered Network. Based on the estimation of pairwise gene profile correlation, the algorithm provides an initial Network Discovery that controls only FDR, which is then followed by a second Network Discovery which controls both FDR and MAS. We illustrate the algorithm for Discovery of coexpression Networks for yeast galactose metabolism with controlled FDR and MAS.
Klaus Moessner - One of the best experts on this subject based on the ideXlab platform.
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Energy-Efficient WLAN Offloading Through Network Discovery Period Optimization
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Dionysia Triantafyllopoulou, Klaus MoessnerAbstract:In this paper, we present an analytical framework to improve the energy consumption of mobile nodes through traffic offloading via wireless local area Networks (WLANs), taking into account the energy consumption for both data transmission and Network Discovery operations. More specifically, we formulate an optimization problem, according to which the Network scanning period is optimized to minimize the total energy consumption and the energy consumption per transmitted bit in a scenario where a user moves with a constant, either pedestrian or vehicular, speed along a road covered by a long-range cellular Network and a number of randomly deployed WLANs. The performance of the system that employs the proposed framework, which uses information on the user speed as well as on the availability and the load level of neighboring Networks and performs periodic Network scanning with the optimal period, is compared against a suboptimal system that does not take into consideration the user and Network context information when determining the Network scanning period. According to performance evaluation results, the use of the optimal Network scanning period achieves significant improvement in terms of total energy consumption, energy efficiency, and Network detection delay.
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VTC Fall - Optimal Network Discovery Period for Energy-Efficient WLAN Offloading
2013 IEEE 78th Vehicular Technology Conference (VTC Fall), 2013Co-Authors: Dionysia Triantafyllopoulou, Klaus MoessnerAbstract:In this paper we present an analytical framework that aims to improve the energy efficiency of traffic offloading via Wireless Local Area Networks, taking into account the energy consumption for both data transmission and Network Discovery operations. More specifically, the Network scanning period is optimized in order to minimize the energy consumption in a vehicular scenario where a user moves along a road covered by a long range cellular Network and a number of randomly deployed Wireless Local Area Networks. The performance of the system that performs periodic Network scanning with the optimal period is compared against a sub-optimal system that does not take into consideration the user and Network context information when determining the Network scanning period. According to performance evaluation results, the use of the optimal Network scanning period achieves significant improvement in terms of energy consumption and Network detection delay.
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Optimal Network Discovery Period for Energy-Efficient WLAN Offloading
2013 IEEE 78th Vehicular Technology Conference (VTC Fall), 2013Co-Authors: Dionysia Triantafyllopoulou, Klaus MoessnerAbstract:In this paper we present an analytical framework that aims to improve the energy efficiency of traffic offloading via Wireless Local Area Networks, taking into account the energy consumption for both data transmission and Network Discovery operations. More specifically, the Network scanning period is optimized in order to minimize the energy consumption in a vehicular scenario where a user moves along a road covered by a long range cellular Network and a number of randomly deployed Wireless Local Area Networks. The performance of the system that performs periodic Network scanning with the optimal period is compared against a sub-optimal system that does not take into consideration the user and Network context information when determining the Network scanning period. According to performance evaluation results, the use of the optimal Network scanning period achieves significant improvement in terms of energy consumption and Network detection delay.
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CAMAD - Energy efficient ANDSF-assisted Network Discovery for non-3GPP access Networks
2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2012Co-Authors: Dionysia Triantafyllopoulou, Klaus MoessnerAbstract:The aim of this paper is to improve the energy efficiency during Network Discovery in heterogeneous Networking environments. To this end, we propose a novel Network Discovery algorithm that exploits both user and Network context information in order to efficiently adapt the Network scanning period, thus avoiding unnecessary energy-consuming scanning or mis-detection of available Networks that can be used as targets of handover. The performance of the proposed algorithm is compared against a system that performs Network scanning in a periodic manner, without taking into consideration the user and Network context information. According to simulation results, the system that employs the proposed Network Discovery algorithm achieves significant performance improvement in terms of energy consumption and Network detection delay, with no loss in the Network detection rate.
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Energy efficient ANDSF-assisted Network Discovery for non-3GPP access Networks
2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2012Co-Authors: Dionysia Triantafyllopoulou, Klaus MoessnerAbstract:The aim of this paper is to improve the energy efficiency during Network Discovery in heterogeneous Networking environments. To this end, we propose a novel Network Discovery algorithm that exploits both user and Network context information in order to efficiently adapt the Network scanning period, thus avoiding unnecessary energy-consuming scanning or mis-detection of available Networks that can be used as targets of handover. The performance of the proposed algorithm is compared against a system that performs Network scanning in a periodic manner, without taking into consideration the user and Network context information. According to simulation results, the system that employs the proposed Network Discovery algorithm achieves significant performance improvement in terms of energy consumption and Network detection delay, with no loss in the Network detection rate.
Chun Ying - One of the best experts on this subject based on the ideXlab platform.
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SIP Network Discovery by using SIP message probing
NOMS 2008 - 2008 IEEE Network Operations and Management Symposium, 2008Co-Authors: Jin Zhou, Jie Li, Chun YingAbstract:Currently more and more SIP based Networks have been deployed in enterprises and communication carrierspsila domain to provide NGN services. With the widely adoption of SIP, information Discovery in SIP Network, which is the first step of SIP management, becomes a key challenge to operators and service providers. To address the problem, this paper presents an automated SIP Network Discovery approach based on SIP message probing. With our approach, minimal number of SIP request messages will be sent out to probe SIP entities and then targeted information could be obtained by wrapping and parsing the received responses. Experiment results show that the approach can effectively obtain SIP information for management while it costs limited probing messages.
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NOMS - SIP Network Discovery by using SIP message probing
NOMS 2008 - 2008 IEEE Network Operations and Management Symposium, 2008Co-Authors: Jin Zhou, Jie Li, Chun YingAbstract:Currently more and more SIP based Networks have been deployed in enterprises and communication carrierspsila domain to provide NGN services. With the widely adoption of SIP, information Discovery in SIP Network, which is the first step of SIP management, becomes a key challenge to operators and service providers. To address the problem, this paper presents an automated SIP Network Discovery approach based on SIP message probing. With our approach, minimal number of SIP request messages will be sent out to probe SIP entities and then targeted information could be obtained by wrapping and parsing the received responses. Experiment results show that the approach can effectively obtain SIP information for management while it costs limited probing messages.
