Feature Set Selection for Improved Classification of Static Analysis Alerts

With the extreme growth in third party cloud applications, increased exposure of applications to the internet, and the impact of successful breaches, improving the security of software being produced is imperative. Static analysis tools can alert to quality and security vulnerabilities of an application; however, they present developers and analysts with a high rate of false positives and unactionable alerts. This problem may lead to the loss of confidence in the scanning tools, possibly resulting in the tools not being used. The discontinued use of these tools may increase the likelihood of insecure software being released into production. Insecure software can be successfully attacked resulting in the compromise of one or several information security principles such as confidentiality, availability, and integrity. Feature selection methods have the potential to improve the classification of static analysis alerts and thereby reduce the false positive rates. Thus, the goal of this research effort was to improve the classification of static analysis alerts by proposing and testing a novel method leveraging feature selection. The proposed model was developed and subsequently tested on three open source PHP applications spanning several years. The results were compared to a classification model utilizing all features to gauge the classification improvement of the feature selection model. The model presented did result in the improved classification accuracy and reduction of the false positive rate on a reduced feature set. This work contributes a real-world static analysis dataset based upon three open source PHP applications. It also enhanced an existing data set generation framework to include additional predictive software features. However, the main contribution is a feature selection methodology that may be used to discover optimal feature sets that increase the classification accuracy of static analysis alerts

Quantifying Soil Greenhouse Gas Emissions And Soil Carbon Storage To Determine Best Management Practices In Agroecosystems

Intensive agriculture, coupled with an increase in nitrogen fertilizer use, has contributed significantly to the elevation of atmospheric greenhouse gases (GHGs), including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Rising GHG emissions usually mean a decrease in soil carbon. Currently, soil C is twice that of all standing crop biomass, making it an extremely important player in the C cycle. Fortunately, agricultural management practices have the potential to reduce agricultural GHG emissions whilst increasing soil C. Management practices that impact GHG emissions and soil C include various tillage practices, different N fertilization amounts and treatments (synthetic N, cattle manure, or a combination of both), the use of cover crops, aeration, and water levels. Employing agricultural best management practices (BMPs) can assist in the mitigation and sequestration of CO2, N2O and soil C. Measuring soil carbon storage and GHG emissions and using them as metrics to evaluate BMPs are vital in understanding agriculture\u27s role in climate change. The objective of this research was to quantify soil carbon and CO2 and N2O emissions in agroecosystems (dairy, crop, and meat producing farms) under differing management practices. Three farms were selected for intensive GHG emissions sampling: Shelburne Farm in Shelburne, VT, a dairy in North Williston, VT, and Borderview Farm in Alburgh, VT. At each site, I collected data on GHG (CO2 and N2O) emissions and soil carbon and nitrogen storage to a depth of 1 meter. Soil emissions of CO2 and N2O were taken once every two weeks (on average) from June 2015 through November, 2015 using static flux chambers and a model 1412 Infrared Photoacoustic Spectroscopy (PAS) gas analyzer (Innova Air Tech Instruments, Ballerup, Denmark). Fluxes were measured on 17 dates at Shelburne Farms, 13 dates at the Williston site, and 13 dates in the MINT trial. Gas samples were taken at fixed intervals over a 10-14 minute time frame, with samples normally taken every one or two minutes. I also measured soil carbon to a depth of 1m in six BMPs at Borderview Farm. Overall, I found that manure injection increased N2O and CO2 emissions, but decreased soil C storage at depth. Tillage had little to no impact on N2O emissions, except at Shelburne Farms, where aeration tillage decreased N2O emissions (marginally significant, P \u3c 0.1). No-till did, however, decrease CO2 emissions relative to other conservation tillage practices (strip and vertical tillage) but we were unable to detect a significant change in soil C due to tillage practices. At Borderview farm, N2O emissions increased with soil NO3 and soil moisture, while CO2 emissions increased with soil temperature and nitrate. At Williston, CO2 emissions only increased with temperature; at Shelburne CO2 emissions increased with nitrate. N2O fluxes at Shelburne and Williston were not associated with any of the measured covariates

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects

Funder: The Health FoundationFunder: National Institute for Health ResearchAbstract: Background: The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on “what good looks like” might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. Methods: We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team’s experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. Results: A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. Conclusions: This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness

A modified Delphi study to identify the features of high quality measurement plans for healthcare improvement projects

Funder: The Health FoundationFunder: National Institute for Health ResearchAbstract: Background: The design and execution of measurement in quality improvement (QI) initiatives is often poor. Better guidance on “what good looks like” might help to mitigate some of the problems. We report a consensus-building process that sought to identify which features are important to include in QI measurement plans. Methods: We conducted a three-stage consensus-building approach: (1) identifying the list of features of measurement plans that were potential candidates for inclusion based on literature review and the study team’s experience; (2) a two-round modified Delphi exercise with a panel of experts to establish consensus on the importance of these features; and (3) a small in-person consensus group meeting to finalise the list of features. Results: A list of 104 candidate questions was generated. A panel of 19 experts in the Delphi reviewed these questions and produced consensus on retaining 46 questions in the first round and on a further 22 in the second round. Thematic analysis of open text responses from the panellists suggested a number of areas of debate that were explicitly considered by the consensus group. The exercise yielded 74 questions (71% of 104) on which there was consensus in five categories of measurement relating to: design, data collection and management, analysis, action, and embedding. Conclusions: This study offers a consensus-based view on the features of a good measurement plan for a QI project in healthcare. The results may be of use to QI teams, funders and evaluators, but are likely to require further development and testing to ensure feasibility and usefulness

Characteristics of intensive care units in Michigan: Not an open and closed case

OBJECTIVE: Delivery of critical care by intensivists has been recommended by several groups. Our objective was to understand the delivery of critical care physician services in Michigan and the role of intensivists and nonintensivist providers in providing care. DESIGN: Descriptive questionnaire. PARTICIPANTS AND SETTING: Intensive care unit (ICU) directors and nurse managers at 96 sites, representing 115 ICUs from 72 hospitals in Michigan. MEASUREMENTS AND RESULTS: The primary outcome measure was the percentage of sites utilizing a closed vs. an open model of ICU care. Secondary outcome measures included the percentage of ICUs utilizing a high-intensity service model, hospital size, ICU size, type of clinician providing care, and clinical activities performed. Twenty-four (25%) sites used a closed model of intensive care, while 72 (75%) had an open model of care. Hospitals with closed ICUs were larger and had larger ICUs than sites with open ICUs ( P < 0.05). Hospitalists serving as attending physicians were strongly associated with an open ICU (odds ratio [OR] = 12.2; 95% confidence interval [CI] = 2.5-60.2), as was the absence of intensivists in the group (OR = 12.2; 95%CI = 1.4-105.8), while ICU and hospital size were not associated. At 18 sites (20%) all attendings were board certified in Critical Care. Sixty sites had less than 50% board-certified attending physicians. CONCLUSIONS: The closed intensivist-led model of intensive care delivery is not in widespread use in Michigan. In the absence of intensivists, alternate models of care, including the hospitalist model, are frequently used. Journal of Hospital Medicine 2010;5:4–9. © 2010 Society of Hospital Medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64903/1/567_ftp.pd

Effective board governance of safe care: a (theoretically underpinned) cross-sectioned examination of the breadth and depth of relationships through national quantitative surveys and in-depth qualitative case studies

Background: Recent high-profile reports into serious failings in the quality of hospital care in the NHS raise concerns over the ability of trust boards to discharge their duties effectively. Objectives: Our study aimed to generate theoretically grounded empirical evidence on the associations between board governance, patient safety processes and patient-centred outcomes. The specific aims were as follows: (1) to identify the types of governance activities undertaken by hospital trust boards in the English NHS with regard to ensuring safe care in their organisation; (2) in foundation trusts, to explore the role of boards and boards of governors with regards to the oversight of patient safety in their organisation; (3) to assess the association between particular hospital trust board oversight activities and patient safety processes and clinical outcomes; (4) to identify the facilitators and barriers to developing effective hospital trust board governance of safe care; and (5) to assess the impact of external commissioning arrangements and incentives on hospital trust board oversight of patient safety. Methods: The study comprised three distinct but interlocking strands: (1) a narrative systematic review in order to describe, interpret and synthesise key findings and debates concerning board oversight of patient safety; (2) in-depth mixed-methods case studies in four organisations to assess the impact of hospital board governance and external incentives on patient safety processes and outcomes; and (3) two national surveys exploring board management in NHS acute and specialist hospital trusts in England, and relating board characteristics to whole-organisation outcomes. Results: A very high proportion of trust boards reported the kinds of desirable characteristics and board-related processes that research says may be associated with higher performance. Our analysis of the symbolic aspects of board activities highlights the role and differences in local processes of organising the governance of patient safety. Most boards do allocate considerable amount of time to discussing patient safety and quality-related issues and were using a wide range of hard performance metrics and soft intelligence to monitor its organisation with regard to patient safety. Although the board of governors is generally perceived to be well-meaning, they were also considered to be being largely ineffective in helping to promote and deliver safer care for their organisations. We did not find any statistically significant relationship between board attributes (self-reported) and processes and any patient safety outcome measures. However, we did find a significant relationship between two dimensions of the Board Self-Assessment Questionnaire and two specific-and-related national staff survey organisational ‘process’ measures: (1) staff feeling safe to raise concerns about errors, near-misses and incidents and (2) staff feeling confident that their organisation would address their concerns, if raised. We also found that contracting and external financial incentives appeared to play only a relatively minor role in incentivising quality and safety improvement. Conclusions: Our research is the first large-scale mixed-methods study of hospital board activity and behaviour related to the oversight of patient safety in the English NHS and the key findings should be used to influence the design of future governance arrangements as well as the training and support of board. Funding: The National Institute for Health Research Health Services and Delivery Research programme