The Performance of Elliptic Curve Based Group Diffie-Hellman Protocols for Secure Group Communication over Ad Hoc Networks

The security of the two party Diffie-Hellman key exchange protocol is currently based on the discrete logarithm problem (DLP). However, it can also be built upon the elliptic curve discrete logarithm problem (ECDLP). Most proposed secure group communication schemes employ the DLP-based Diffie-Hellman protocol. This paper proposes the ECDLP-based Diffie-Hellman protocols for secure group communication and evaluates their performance on wireless ad hoc networks. The proposed schemes are compared at the same security level with DLP-based group protocols under different channel conditions. Our experiments and analysis show that the Tree-based Group Elliptic Curve Diffie-Hellman (TGECDH) protocol is the best in overall performance for secure group communication among the four schemes discussed in the paper. Low communication overhead, relatively low computation load and short packets are the main reasons for the good performance of the TGECDH protocol.

Demography and population viability of polar bears in the Gulf of Boothia, Nunavut

We estimated demographic parameters and harvest risks for polar bears (Ursus maritimus) inhabiting the Gulf of Boothia, Nunavut, from 1976 to 2000. We computed survival and abundance from capture–recapture and recovery data (630 marks) using a Burnham joint live–dead model implemented in program MARK. Annual mean total survival (including harvest) was 0.889 ± 0.179 ( x ± 1 SE) for cubs, 0.883 ± 0.087 for subadults (ages 1–4), 0.919 ± 0.044 for adult females, and 0.917 ± 0.041 for adult males. Abundance in the last 3 yr of study was 1,592 ± 361 bears. Mean size of newborn litters was 1.648 ± 0.098 cubs. By age 7, 0.97 ± 0.30 of available females were producing litters. Harvest averaged 38.4 ± 4.2 bears/year in the last 5 yr of study; however, the 2002–2007 kill averaged 56.4 bears/yr. We used a harvested Population Viability Analysis (PVA) to examine impacts of increasing rates of harvest. We estimated the current population growth rate, λH, to be 1.025 ± 0.032. Although this suggests the population is growing, progressive environmental changes may require more frequent population inventory studies to maintain the same levels of harvest risk.

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.