PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts

Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory trade-off that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain.Comment: 6 pages, 3 figures, ieeeTran conference. International Conference on Machine Learning and Applications 201

Seasonal Aeration Rates for the Eastern United States Based on Long-Term Weather Patterns

Most aeration fans are sized to produce a minimum airflow rate of 0.1 m3/min/t (0.1 cfm/bu) in on-farm grain storage structures. At this airflow rate a significant amount of time is required to move a cooling front completely through a bin. The desired grain temperature and prevailing weather conditions will have a significant effect on required fan size. Thirty years of weather data were analyzed for the eastern United States to determine the amount of time available in temperature windows between 0 to 15.C and 0 to 17.C. Contour maps were generated with ArcMap 8.3 for the percentage of each month within the given temperature windows. A substantial amount of time (over 4% of the month) is available within temperature limits of 0 and 17.C between September and April. This indicates that airflow rates of at least 0.6 m3/min/t (0.5 cfm/bu) would be more adequate to completely move an aeration front through a bin for summer harvested grain in Southern regions of the United States. However, during July and August only the northern half of the United States would have a sufficient amount of time available for cooling grain below 17.C using an airflow rate of 0.1 m3/min/t (0.1 cfm/bu). The maps generated provide a starting point for sizing aeration fans in the eastern United States

Aeration Strategies and Fan Cost Comparisons for Wheat in Mid-South Production Regions

Numerous factors influence the sizing of aeration fans for summer-harvested crops. Thirty years of weather data for Lexington, Kentucky, were analyzed and the cost of aeration was compared for two axial fans (afan1, afan2) and one centrifugal fan (cfan1). Aeration costs were defined as the sum of the following components: the cost of owning the fan, the cost of electricity for operating the fan, a cost for wheat shrinkage during aeration, and a cost for dry matter loss (DML). The fans were selected to deliver airflow rates of approximately one, two, and three times the recommended aeration rate of 0.11 m3/min/t (0.1 cfm/bu). Aeration fan investment costs ranged from $709 (afan1) to$1739 (cfan1). Aeration costs for each fan were compared for four initial grain temperatures: 21.1°C, 23.9°C, 26.7°C, and 29.4°C (70°F, 75°F, 80°F, and 85°F); four harvest dates: 1 June, 15 June, 1 July, and 15 July; and two aeration temperature windows (0 to 15°C and 0 to 17°C). Generally, the total aeration cost increased with initial grain temperature, decreased with later harvest dates, and was not significantly affected by aeration temperature window. When the total cost of aerating the wheat was considered, the results showed that the most expensive fan (cfan1) was not appreciably more costly than the least expensive (afan1). It was also found that using fans with airflow rates above the minimum recommendation were successful in reducing the amount of wheat shrinkage and dry matter loss, which should provide the producer with a larger volume of better quality grain at market

Alignments of the Dominant Galaxies in Poor Clusters

We have examined the orientations of brightest cluster galaxies (BCGs) in poor MKW and AWM clusters and find that, like their counterparts in richer Abell clusters, poor cluster BCGs exhibit a strong propensity to be aligned with the principal axes of their host clusters as well as the surrounding distribution of nearby (< 20/h Mpc) Abell clusters. The processes responsible for dominant galaxy alignments are therefore independent of cluster richness. We argue that these alignments most likely arise from anisotropic infall of material into clusters along large-scale filaments.Comment: 8 pages, 5 figure

Packing Factors of Feed Products in Storage Structures

Experiments were conducted to measure the changes in bulk density of cracked corn, corn meal, soybean meal, cotton seed meal, and distillers dried grain (without solubles) when subjected to simulated overburden pressures. All materials were tested at two moisture content levels (approximately 8% and 12% w.b.) and seven pressures between 0 and 69 kPa (0 and 10 psi). A mathematical model was fitted to the data to predict the bulk density of each feed ingredient as a function of pressure and moisture content. These relationships were inserted into a previously developed computer model to predict ingredient packing within conventional storage structures based on Janssen\u27s equation as a function of feed product type, moisture content of the material, friction characteristics of the bin wall material, material height, and bin diameter. Cracked corn experienced the smallest amount of packing (approximately 4.3% in a bin with a diameter of 1.8 m and a height of 1.8 m), while distillers dried grain (without solubles) had approximately 8.1% packing in the same sized bin. With a bin diameter of 5.5 m and a height of 5.5 m, distillers dried grain (without solubles) and cracked corn had a packing factor of 13.3% and 6.8%, respectively. As moisture content increased the amount of packing increased for all materials. The data presented can be used for inventory control and management