Monitoring Networked Applications With Incremental Quantile Estimation

Networked applications have software components that reside on different computers. Email, for example, has database, processing, and user interface components that can be distributed across a network and shared by users in different locations or work groups. End-to-end performance and reliability metrics describe the software quality experienced by these groups of users, taking into account all the software components in the pipeline. Each user produces only some of the data needed to understand the quality of the application for the group, so group performance metrics are obtained by combining summary statistics that each end computer periodically (and automatically) sends to a central server. The group quality metrics usually focus on medians and tail quantiles rather than on averages. Distributed quantile estimation is challenging, though, especially when passing large amounts of data around the network solely to compute quality metrics is undesirable. This paper describes an Incremental Quantile (IQ) estimation method that is designed for performance monitoring at arbitrary levels of network aggregation and time resolution when only a limited amount of data can be transferred. Applications to both real and simulated data are provided.Comment: This paper commented in: [arXiv:0708.0317], [arXiv:0708.0336], [arXiv:0708.0338]. Rejoinder in [arXiv:0708.0339]. Published at http://dx.doi.org/10.1214/088342306000000583 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rejoinder: Monitoring Networked Applications With Incremental Quantile Estimation

Rejoinder: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]Comment: Published at http://dx.doi.org/10.1214/088342306000000592 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections

Transitions in the morphological features, habitat use, and diet of young-of-the-year goosefish (Lophius americanus)

This study was designed to improve our understanding of transitions in the early life history and the distribution, habitat use, and diets for young-of-the-year (YOY) goosefish (Lophius americanus) and, as a result, their role in northeastern U.S. continental shelf ecosystems. Pelagic juveniles (>12 to ca. 50 mm total length [TL]) were distributed over most portions of the continental shelf in the Middle Atlantic Bight, Georges Bank, and into the Gulf of Maine. Most individuals settled by 50−85 mm TL and reached approximately 60−120 mm TL by one year of age. Pelagic YOY fed on chaetognaths, hyperiid amphipods, calanoid copepods, and ostracods, and benthic YOY had a varied diet of fishes and benthic crustaceans. Goosefish are widely scattered on the continental shelf in the Middle Atlantic Bight during their early life history and once settled, are habitat generalists, and thus play a role in many continental shelf habi

Using the Pattern-of-Life in Networks to Improve the Effectiveness of Intrusion Detection Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available high- level information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination

Camera Trap Success Among Carnivores and Prey Animals in Tazewell County, Virginia

Obtaining basic ecological information on occurrence and activity levels in cryptic and elusive species is often difficult. Camera trapping provides a relatively inexpensive opportunity to acquire such data. We used infrared-triggered cameras to assess trap success and activity levels of several species across four consecutive seasons, including: Ursus americanus (black bear), Lynx rufus (bobcat), Canis latrans (coyote), Vulpes vulpes (red fox), Urocyon cinereoargenteus (gray fox), Procyon lotor (raccoon), Odocoileus virginianus (white-tailed deer), Didelphis virginiana (opossum), Sciurus carolinensis (gray squirrel), and Meleagris gallopavo (wild turkey). With a total of 396 trap nights (TN) at one station over the span of four consecutive seasons, overall trap success rate was 86.87 captures per 100 TN. Trap success was highest in wild turkeys (31.57/100 TN), followed by raccoons (15.66/100 TN), gray squirrels (10.86/100 TN), gray foxes (8.59/100 TN), white-tailed deer (8.08/100 TN), opossums (5.56/100 TN), coyotes (1.52/100 TN), red foxes (1.26/100 TN), and bobcats (0.76/100 TN). Overall trap success significantly varied across all target species combined (Kruskal Wallis Chi-Square = 349, d.f. = 10, p \u3c 0.0001). However, trap success did not vary across all seasons for all target species combined (Kruskal Wallis Chi-Square = 0.99, d.f. = 3, p = 0.78). This study is the first to use camera trapping to examine species presence and activity levels in a longitudinal manner for cryptic and elusive species of southwest Virginia

Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only

If electrodes move during geoelectrical resistivity monitoring and their new positions are not incorporated in the inversion, then the resulting tomographic images exhibit artefacts that can obscure genuine time-lapse resistivity changes in the subsurface. The effects of electrode movements on time-lapse resistivity tomography are investigated using a simple analytical model and real data. The correspondence between the model and the data is sufficiently good to be able to predict the effects of electrode movements with reasonable accuracy. For the linear electrode arrays and 2D inversions under consideration, the data are much more sensitive to longitudinal than transverse or vertical movements. Consequently the model can be used to invert the longitudinal offsets of the electrodes from their known baseline positions using only the time-lapse ratios of the apparent resistivity data. The example datasets are taken from a permanently installed electrode array on an active lobe of a landslide. Using two sets with different levels of noise and subsurface resistivity changes, it is found that the electrode positions can be recovered to an accuracy of 4 % of the baseline electrode spacing. This is sufficient to correct the artefacts in the resistivity images, and provides for the possibility of monitoring the movement of the landslide and its internal hydraulic processes simultaneously using electrical resistivity tomography only

Opportunities for epidemiologists in implementation science: A primer

The field of epidemiology has been defined as the study of the spread and control of disease. However, epidemiology frequently focuses on studies of etiology and distribution of disease at the cost of understanding the best ways to control disease. Moreover, only a small fraction of scientific discoveries are translated into public health practice, and the process from discovery to translation is exceedingly slow. Given the importance of translational science, the future of epidemiologic training should include competency in implementation science, whose goal is to rapidly move evidence into practice. Our purpose in this paper is to provide epidemiologists with a primer in implementation science, which includes dissemination research and implementation research as defined by the National Institutes of Health. We describe the basic principles of implementation science, highlight key components for conducting research, provide examples of implementation studies that encompass epidemiology, and offer resources and opportunities for continued learning. There is a clear need for greater speed, relevance, and application of evidence into practice, programs, and policies and an opportunity to enable epidemiologists to conduct research that not only will inform practitioners and policy-makers of risk but also will enhance the likelihood that evidence will be implemented

Propofol Waste Reduction in the Operating Room

Propofol is the most wasted intravenous medication used in anesthesia in the operating room (OR) (More, Dabhade, & Ghongane, 2015). Propofol, a sedative anxiolytic, is utilized as a continuous infusion in sedation procedures, or monitored anesthesia care (Nagelhout & Elisha, 2014). Inherent to these infusions is unpredictability in determining the amount of medication needed for the duration of a procedure. This project implemented customized propofol preparation charts consistent with the literature to aid the administering professional in determining the approximate milliliter requirement for the duration of a procedure. The pre-intervention steps consisted of voluntary data collection among anonymous anesthesia professionals in the OR regarding infusion characteristics. Patient weight, a commonly used infusion rate, and the duration of procedure were used in the calculation within customized charts. Post-intervention data collection was conducted in the same manner to capture the impact of the charts. This data was measured simultaneously at a local healthcare system’s main hospital and separate surgery center site, and waste reduction from baseline data was found to be 49% and 60%, respectively. This project was conducted over a four-month period and translates to a potential yearly savings of greater than $15,000 for the healthcare system. A culture of waste reduction leads to savings for anesthesia departments and healthcare organizations as a whole

Antimicrobial Resistance in Humans and Animals: Rapid Review of Psychological and Behavioral Determinants

A rapid review of current evidence examining psychological issues regarding the use of antibiotics and antimicrobials and resistance to these in both human and animal populations was conducted. Specific areas of interest were studies examining psychological determinants of AMR and interventions which attempt to change behavior with regard to AMR in the general population; animals; and fish, in particular. Although there is some evidence of the effectiveness of behavior change in general human populations, there is limited evidence in farmed animals, with a particular dearth in fish farming. We conclude there is an urgent need for more psychological research to identify major barriers and facilitators to change and evaluate the effectiveness of theory-based interventions aimed at reducing AM use in food production animals, including the promotion of alternatives to AMs, such as vaccination