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» Measuring and Predicting Object Importance
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EDBT
2009
ACM
302views Database» more  EDBT 2009»
15 years 6 months ago
RankClus: integrating clustering with ranking for heterogeneous information network analysis
As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on in...
Yizhou Sun, Jiawei Han, Peixiang Zhao, Zhijun Yin,...
MMNS
2003
100views Multimedia» more  MMNS 2003»
15 years 19 days ago
Unicast Probing to Estimate Shared Loss Rate
Abstract. The paper introduces a new receiver-based active end-toend measurement technique, called the Single-Double Unicast Probing (SDUP), to estimate the rate of losses which oc...
Dinh-Dung Luong, Attila Vidács, Józs...
IPPS
2007
IEEE
15 years 5 months ago
Towards A Better Understanding of Workload Dynamics on Data-Intensive Clusters and Grids
This paper presents a comprehensive statistical analysis of workloads collected on data-intensive clusters and Grids. The analysis is conducted at different levels, including Virt...
Hui Li, Lex Wolters
TOG
2010
202views more  TOG 2010»
14 years 6 months ago
A comparative study of image retargeting
The numerous works on media retargeting call for a methodological approach for evaluating retargeting results. We present the first comprehensive perceptual study and analysis of ...
Michael Rubinstein, Diego Gutierrez, Olga Sorkine,...
ACCV
2009
Springer
15 years 5 months ago
Robust Real-Time Multiple Target Tracking
We propose a novel efficient algorithm for robust tracking of a fixed number of targets in real-time with low failure rate. The method is an instance of Sequential Importance Resa...
Nicolai von Hoyningen-Huene, Michael Beetz