Institution
University of Victoria
Education•Victoria, British Columbia, Canada•
About: University of Victoria is a education organization based out in Victoria, British Columbia, Canada. It is known for research contribution in the topics: Population & Galaxy. The organization has 14994 authors who have published 41051 publications receiving 1447972 citations. The organization is also known as: Victoria College.
Topics: Population, Galaxy, Large Hadron Collider, Health care, Poison control
Papers published on a yearly basis
Papers
More filters
••
TL;DR: Experimental results reported here indicate that the Markov modelling approach generally achieves much better data compression than that observed with competing methods on typical computer data.
Abstract: A method of dynamically constructing Markov chain models that describe the characteristics of binary messages is developed. Such models can be used to predict future message characters and can therefore be used as a basis for data compression. To this end, the Markov modelling technique is combined with Guazzo's arithmetic coding scheme to produce a powerful method of data compression. The method has the advantage of being adaptive: messages may be encoded or decoded with just a single pass through the data. Experimental results reported here indicate that the Markov modelling approach generally achieves much better data compression than that observed with competing methods on typical computer data.
255 citations
••
National Center for Atmospheric Research1, Geophysical Fluid Dynamics Laboratory2, National Oceanic and Atmospheric Administration3, University of Washington4, University of Reading5, University of Leeds6, Russian Academy of Sciences7, Langley Research Center8, University of Victoria9, Goddard Institute for Space Studies10, Japan Agency for Marine-Earth Science and Technology11, Imperial College London12
TL;DR: In this paper, a radiative transfer model intercomparison between the forcings computed by the radiative parameterizations of AOGCMs and by benchmark line-by-line (LBL) codes is presented.
Abstract: The radiative effects from increased concentrations of well-mixed greenhouse gases (WMGHGs) represent the most significant and best understood anthropogenic forcing of the climate system. The most comprehensive tools for simulating past and future climates influenced by WMGHGs are fully coupled atmosphere-ocean general circulation models (AOGCMs). Because of the importance of WMGHGs as forcing agents it is essential that AOGCMs compute the radiative forcing by these gases as accurately as possible. We present the results of a radiative transfer model intercomparison between the forcings computed by the radiative parameterizations of AOGCMs and by benchmark line-by-line (LBL) codes. The comparison is focused on forcing by CO2, CH4, N2O, CFC-11, CFC-12, and the increased H2O expected in warmer climates. The models included in the intercomparison include several LBL codes and most of the global models submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In general, the LBL models are in excellent agreement with each other. However, in many cases, there are substantial discrepancies among the AOGCMs and between the AOGCMs and LBL codes. In some cases this is because the AOGCMs neglect particular absorbers, in particular the near-infrared effects of CH4 and N2O, while in others it is due to the methods for modeling the radiative processes. The biases in the AOGCM forcings are generally largest at the surface level. We quantify these differences and discuss the implications for interpreting variations in forcing and response across the multimodel ensemble of AOGCM simulations assembled for the IPCC AR4.
255 citations
••
TL;DR: The substantial role of women in fisheries is overlooked in management and policy as mentioned in this paper, despite a lack of quantitative data describing the scale of women's participation and contribution in marine fisheries, which has profound implications for management, poverty alleviation and development policy.
255 citations
••
TL;DR: In this article, a model that simulates methane emissions from northern peatlands is incorporated directly into a dynamic global vegetation model, and a sensitivity test was conducted to identify the most important factors influencing methane emissions, followed by a parameter fitting exercise to find the best combination of parameter values for individual sites and over all sites.
Abstract: . For the first time, a model that simulates methane emissions from northern peatlands is incorporated directly into a dynamic global vegetation model. The model, LPJ-WHyMe (LPJ Wetland Hydrology and Methane), was previously modified in order to simulate peatland hydrology, permafrost dynamics and peatland vegetation. LPJ-WHyMe simulates methane emissions using a mechanistic approach, although the use of some empirical relationships and parameters is unavoidable. The model simulates methane production, three pathways of methane transport (diffusion, plant-mediated transport and ebullition) and methane oxidation. A sensitivity test was conducted to identify the most important factors influencing methane emissions, followed by a parameter fitting exercise to find the best combination of parameter values for individual sites and over all sites. A comparison of model results to observations from seven sites resulted in normalised root mean square errors (NRMSE) of 0.40 to 1.15 when using the best site parameter combinations and 0.68 to 1.42 when using the best overall parameter combination.
255 citations
••
19 Jul 2011TL;DR: This paper proposes a new approach for characterizing and detecting botnets using network traffic behaviors, and focuses on detecting P2P bots, which represent the newest and most challenging types of botnets currently available.
Abstract: Botnets have become one of the major threats on the Internet for serving as a vector for carrying attacks against organizations and committing cybercrimes. They are used to generate spam, carry out DDOS attacks and click-fraud, and steal sensitive information. In this paper, we propose a new approach for characterizing and detecting botnets using network traffic behaviors. Our approach focuses on detecting the bots before they launch their attack. We focus in this paper on detecting P2P bots, which represent the newest and most challenging types of botnets currently available. We study the ability of five different commonly used machine learning techniques to meet online botnet detection requirements, namely adaptability, novelty detection, and early detection. The results of our experimental evaluation based on existing datasets show that it is possible to detect effectively botnets during the botnet Command-and- Control (C&C) phase and before they launch their attacks using traffic behaviors only. However, none of the studied techniques can address all the above requirements at once.
255 citations
Authors
Showing all 15188 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
D. M. Strom | 176 | 3167 | 194314 |
Sw. Banerjee | 146 | 1906 | 124364 |
Robert J. Glynn | 146 | 748 | 88387 |
Manel Esteller | 146 | 713 | 96429 |
R. Kowalewski | 143 | 1815 | 135517 |
Paul Jackson | 141 | 1372 | 93464 |
Mingshui Chen | 141 | 1543 | 125369 |
Ali Khademhosseini | 140 | 887 | 76430 |
Roger Jones | 138 | 998 | 114061 |
Tord Ekelof | 137 | 1212 | 91105 |
L. Köpke | 136 | 950 | 81787 |
M. Morii | 134 | 1664 | 102074 |
Arnaud Ferrari | 134 | 1392 | 87052 |
Richard Brenner | 133 | 1108 | 87426 |