Facebook Group in ACC 202

ACC 202 has three sections of 100-120 students in each section each semester.https://digitalscholarship.unlv.edu/btp_expo/1018/thumbnail.jp

Internships for Accounting Students

ACC 400 is the first upper division class for accounting majors and students are introduced to the importance of internships.https://digitalscholarship.unlv.edu/btp_expo/1019/thumbnail.jp

Drill string gas data

Data and supporting documentation were compiled and analyzed for 26 cases of gas grab samples taken during waste-tank core sampling activities between September 1, 1995 and December 31, 1997. These cases were tested against specific criteria to reduce uncertainties associated with in-tank sampling location and conditions. Of the 26 possible cases, 16 qualified as drill-string grab samples most likely to represent recently released waste gases. The data from these 16 ``confirmed`` cases were adjusted to remove non-waste gas contributions from core-sampling activities (argon or nitrogen purge), the atmospheric background, and laboratory sampler preparation (helium). The procedure for subtracting atmospheric, laboratory, and argon purge gases was unambiguous. No reliable method for determining the exact amount of nitrogen purge gas was established. Thus, the final set of ``Adjusted`` drill string gas data for the 6 nitrogen-purged cases had a greater degree of uncertainty than the final results for the 10 argon-purged cases. Including the appropriate amounts of uncertainty, this final set of data was added to the set of high-quality results from the Retained Gas Sampler (RGS), and good agreement was found for the N{sub 2}, H{sub 2}, and N{sub 2}O mole fractions sampled from common tanks. These results indicate that under favorable sampling conditions, Drill-String (DS) grab samples can provide reasonably accurate information about the dominant species of released gas. One conclusion from this set of total gas data is that the distribution of the H{sub 2} mole fractions is bimodal in shape, with an upper bound of 78%

Total Cross Sections for Neutron Scattering

Measurements of neutron total cross-sections are both extensive and extremely accurate. Although they place a strong constraint on theoretically constructed models, there are relatively few comparisons of predictions with experiment. The total cross-sections for neutron scattering from $^{16}$O and $^{40}$Ca are calculated as a function of energy from $50-700$~MeV laboratory energy with a microscopic first order optical potential derived within the framework of the Watson expansion. Although these results are already in qualitative agreement with the data, the inclusion of medium corrections to the propagator is essential to correctly predict the energy dependence given by the experiment.Comment: 10 pages (Revtex 3.0), 6 fig

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar