Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

An Exploration of Auditory Brainstem Encoding of Stop Consonants in Infants and Implications for Language Outcomes

Current trends in speech-language pathology focus on early intervention as the preferred tool for promoting the best possible outcomes in children with language disorders. Neuroimaging techniques are being studied as promising tools for flagging at-risk infants. In this study, the auditory brainstem response (ABR) to the syllables /ba/ and /ga/ was examined in 41 infants between 3 and 12 months of age as a possible tool to predict language development in toddlerhood. The MacArthur-Bates Communicative Development Inventory (MCDI) was used to assess language development at 18 months of age. The current study compared the periodicity of the responses to the stop consonants and phase differences between /ba/ and /ga/ in both at-risk and low-risk groups. The study also examined whether there are correlations among ABR measures (periodicity and phase differentiation) and language development. The study found that these measures predict language development at 18 months.

Development of measurement and modeling techniques to quantify atmospheric deposition of persistent, bioaccumulative and toxic chemicals in the Great Lakes

Measurement and modeling techniques were developed to improve over-water gaseous air-water exchange measurements for persistent bioaccumulative and toxic chemicals (PBTs). Analytical methods were applied to atmospheric measurements of hexachlorobenzene (HCB), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs). Additionally, the sampling and analytical methods are well suited to study semivolatile organic compounds (SOCs) in air with applications related to secondary organic aerosol formation, urban, and indoor air quality. A novel gas-phase cleanup method is described for use with thermal desorption methods for analysis of atmospheric SOCs using multicapillary denuders. The cleanup selectively removed hydrogen-bonding chemicals from samples, including much of the background matrix of oxidized organic compounds in ambient air, and thereby improved precision and method detection limits for nonpolar analytes. A model is presented that predicts gas collection efficiency and particle collection artifact for SOCs in multicapillary denuders using polydimethylsiloxane (PDMS) sorbent. An approach is presented to estimate the equilibrium PDMS-gas partition coefficient (Kpdms) from an Abraham solvation parameter model for any SOC. A high flow rate (300 L min-1) multicapillary denuder was designed for measurement of trace atmospheric SOCs. Overall method precision and detection limits were determined using field duplicates and compared to the conventional high-volume sampler method. The high-flow denuder is an alternative to high-volume or passive samplers when separation of gas and particle-associated SOCs upstream of a filter and short sample collection time are advantageous. A Lagrangian internal boundary layer transport exchange (IBLTE) Model is described. The model predicts the near-surface variation in several quantities with fetch in coastal, offshore flow: 1) modification in potential temperature and gas mixing ratio, 2) surface fluxes of sensible heat, water vapor, and trace gases using the NOAA COARE Bulk Algorithm and Gas Transfer Model, 3) vertical gradients in potential temperature and mixing ratio. The model was applied to interpret micrometeorological measurements of air-water exchange flux of HCB and several PCB congeners in Lake Superior. The IBLTE Model can be applied to any scalar, including water vapor, carbon dioxide, dimethyl sulfide, and other scalar quantities of interest with respect to hydrology, climate, and ecosystem science.

On the analysis of notched concrete beams: From measurement with digital image correlation to identification with boundary element method of a cohesive model

A way of coupling digital image correlation (to measure displacement fields) and boundary element method (to compute displacements and tractions along a crack surface) is presented herein. It allows for the identification of Young`s modulus and fracture parameters associated with a cohesive model. This procedure is illustrated to analyze the latter for an ordinary concrete in a three-point bend test on a notched beam. In view of measurement uncertainties, the results are deemed trustworthy thanks to the fact that numerous measurement points are accessible and used as entries to the identification procedure. (C) 2010 Elsevier Ltd. All rights reserved.

Use of Performance Measurement in the Public Sector: the case of the police service

Many public organisations have been under great pressure in recent years to increase the efficiency and transparency of outputs, to rationalise the use of public resources, and to increase the quality of service delivery. In this context, public organisations were encouraged to introduce the New Public Management reforms with the goal of improving the efficiency and effectiveness of the performance organisation through a new public management model. This new public management model is based on measurement by outputs and outcomes, a clear definition of responsibilities, the transparency and accountability of governmental activities, and on a greater value for citizens. What type of performance measurement systems are used in police services? Based on the literature, we see that multidimensional models, such as the Balanced Scorecard, are important in many public organisations, like municipalities, universities, and hospitals. Police services are characterised by complex, diverse objectives and stakeholders. Therefore, performance measurement of these public services calls for a specific analysis. Based on a nationwide survey of all police chiefs of the Portuguese police force, we find that employee performance measurement is the main form of measurement. Also, we propose a strategic map for the Portuguese police service.

Effects of anthropogenic impacts on benthic macroinvertebrates assemblages in subtropical mountain streams

The nature of the riparian and surrounding landscape has been modified by anthropogenic activities, which may subsequently alter the composition and functional structure of macroinvertebrate assemblages. The effect of these changes on function of benthic fauna is difficult to assess due to the scarce knowledge on functional structures in tropical streams. In this study we evaluate whether sites impacted and unimpacted by anthropogenic alterations differed in assemblage composition and density, richness and diversity of each functional feeding group. The selection of the sites was related to their distinct riparian characteristics, following the QBRy riparian quality index. Collector-gatherer was the dominant functional feeding group, comprising 91% of total density, whereas the proportion of shredders was very low, representing less of 0.5% of total density. Asemblage composition of macroinvertebrates differed between impacted and unimpacted sites. Predators were dominant in taxa number, representing about 60% of total taxa richness. In addition, the diversity and richness of collector-gatherers differed significantly between degraded and unimpacted sites, reflecting the sensitivity of this group to environmental changes and the utility to be used in the assessment of anthropogenic modifications. The results of this study reinforce the idea that riparian corridor management is critical for the distribution of macroinvertebrate assemblages as well as functional organization of lotic streams.

Assessment of fish assemblages in streams of different orders in the Upper Paraná River basin, Central Brazil

The aim of this study was to test whether the richness observed and the biomass per trophic group of fish assemblages vary depending on the order (1st and 2nd) of the streams located in three different basins of the Upper Paraná River Basin, Central Brazil. Samples were collected between April and September, 2009, in 27 streams of the Meia Ponte, Piracanjuba and Santa Maria River basins. A total of 4,879 specimens were collected distributed in 59 species and 19 families. The statistical analyses carried out indicate that the observed richness and biomass of omnivore fish were influenced by the interaction of two factors: stream order and basin. The 2nd order streams located in the Santa Maria basin presented significant differences in the observed richness and omnivore biomass when compared to i) 1st order streams in the same basin (only richness) or in the Piracanjuba and Meia Ponte basin; ii) 2nd order streams in the Piracanjuba (only omnivore biomass) and Meia Ponte Rivers basins. Results are discussed considering the influence of geomorphic processes on fish assemblages and food availability.

Protocol of the COSMIN study: consensus-based standards for the selection of health measurement instruments

Background: Choosing an adequate measurement instrument depends on the proposed use of the instrument, the concept to be measured, the measurement properties (e.g. internal consistency, reproducibility, content and construct validity, responsiveness, and interpretability), the requirements, the burden for subjects, and costs of the available instruments. As far as measurement properties are concerned, there are no sufficiently specific standards for the evaluation of measurement properties of instruments to measure health status, and also no explicit criteria for what constitutes good measurement properties. In this paper we describe the protocol for the COSMIN study, the objective of which is to develop a checklist that contains COnsensus-based Standards for the selection of health Measurement INstruments, including explicit criteria for satisfying these standards. We will focus on evaluative health related patient-reported outcomes (HR-PROs), i.e. patient-reported health measurement instruments used in a longitudinal design as an outcome measure, excluding health care related PROs, such as satisfaction with care or adherence. The COSMIN standards will be made available in the form of an easily applicable checklist.Method: An international Delphi study will be performed to reach consensus on which and how measurement properties should be assessed, and on criteria for good measurement properties. Two sources of input will be used for the Delphi study: (1) a systematic review of properties, standards and criteria of measurement properties found in systematic reviews of measurement instruments, and (2) an additional literature search of methodological articles presenting a comprehensive checklist of standards and criteria. The Delphi study will consist of four (written) Delphi rounds, with approximately 30 expert panel members with different backgrounds in clinical medicine, biostatistics, psychology, and epidemiology. The final checklist will subsequently be field-tested by assessing the inter-rater reproducibility of the checklist.Discussion: Since the study will mainly be anonymous, problems that are commonly encountered in face-to-face group meetings, such as the dominance of certain persons in the communication process, will be avoided. By performing a Delphi study and involving many experts, the likelihood that the checklist will have sufficient credibility to be accepted and implemented will increase.

Reactivity to accelerometer measurement of children and adolescents.

PURPOSE: Awareness of being monitored can influence participants' habitual physical activity (PA) behavior. This reactivity effect may threaten the validity of PA assessment. Reports on reactivity when measuring the PA of children and adolescents have been inconsistent. The aim of this study was to investigate whether PA outcomes measured by accelerometer devices differ from measurement day to measurement day and whether the day of the week and the day on which measurement started influence these differences. METHODS: Accelerometer data (counts per minute [cpm]) of children and adolescents (n = 2081) pooled from eight studies in Switzerland with at least 10 h of daily valid recording were investigated for effects of measurement day, day of the week, and start day using mixed linear regression. RESULTS: The first measurement day was the most active day. Counts per minute were significantly higher than on the second to the sixth day, but not on the seventh day. Differences in the age-adjusted means between the first and consecutive days ranged from 23 to 45 cpm (3.6%-7.1%). In preschoolchildren, the differences almost reached 10%. The start day significantly influenced PA outcome measures. CONCLUSIONS: Reactivity to accelerometer measurement of PA is likely to be present to an extent of approximately 5% on the first day and may introduce a relevant bias to accelerometer-based studies. In preschoolchildren, the effects are larger than those in elementary and secondary schoolchildren. As the day of the week and the start day significantly influence PA estimates, researchers should plan for at least one familiarization day in school-age children and randomly assign start days.

R&D Performance Analysis: Case Studies on the Challenges and Promotion of the Evaluation and Measurement of R&D

Due to the intense international competition, demanding, and sophisticated customers, and diverse transforming technological change, organizations need to renew their products and services by allocating resources on research and development (R&D). Managing R&D is complex, but vital for many organizations to survive in the dynamic, turbulent environment. Thus, the increased interest among decision-makers towards finding the right performance measures for R&D is understandable. The measures or evaluation methods of R&D performance can be utilized for multiple purposes; for strategic control, for justifying the existence of R&D, for providing information and improving activities, as well as for the purposes of motivating and benchmarking. The earlier research in the field of R&D performance analysis has generally focused on either the activities and considerable factors and dimensions - e.g. strategic perspectives, purposes of measurement, levels of analysis, types of R&D or phases of R&D process - prior to the selection of R&Dperformance measures, or on proposed principles or actual implementation of theselection or design processes of R&D performance measures or measurement systems. This study aims at integrating the consideration of essential factors anddimensions of R&D performance analysis to developed selection processes of R&D measures, which have been applied in real-world organizations. The earlier models for corporate performance measurement that can be found in the literature, are to some extent adaptable also to the development of measurement systemsand selecting the measures in R&D activities. However, it is necessary to emphasize the special aspects related to the measurement of R&D performance in a way that make the development of new approaches for especially R&D performance measure selection necessary: First, the special characteristics of R&D - such as the long time lag between the inputs and outcomes, as well as the overall complexity and difficult coordination of activities - influence the R&D performance analysis problems, such as the need for more systematic, objective, balanced and multi-dimensional approaches for R&D measure selection, as well as the incompatibility of R&D measurement systems to other corporate measurement systems and vice versa. Secondly, the above-mentioned characteristics and challenges bring forth the significance of the influencing factors and dimensions that need to be recognized in order to derive the selection criteria for measures and choose the right R&D metrics, which is the most crucial step in the measurement system development process. The main purpose of this study is to support the management and control of the research and development activities of organizations by increasing the understanding of R&D performance analysis, clarifying the main factors related to the selection of R&D measures and by providing novel types of approaches and methods for systematizing the whole strategy- and business-based selection and development process of R&D indicators.The final aim of the research is to support the management in their decision making of R&D with suitable, systematically chosen measures or evaluation methods of R&D performance. Thus, the emphasis in most sub-areas of the present research has been on the promotion of the selection and development process of R&D indicators with the help of the different tools and decision support systems, i.e. the research has normative features through providing guidelines by novel types of approaches. The gathering of data and conducting case studies in metal and electronic industry companies, in the information and communications technology (ICT) sector, and in non-profit organizations helped us to formulate a comprehensive picture of the main challenges of R&D performance analysis in different organizations, which is essential, as recognition of the most importantproblem areas is a very crucial element in the constructive research approach utilized in this study. Multiple practical benefits regarding the defined problemareas could be found in the various constructed approaches presented in this dissertation: 1) the selection of R&D measures became more systematic when compared to the empirical analysis, as it was common that there were no systematic approaches utilized in the studied organizations earlier; 2) the evaluation methods or measures of R&D chosen with the help of the developed approaches can be more directly utilized in the decision-making, because of the thorough consideration of the purpose of measurement, as well as other dimensions of measurement; 3) more balance to the set of R&D measures was desired and gained throughthe holistic approaches to the selection processes; and 4) more objectivity wasgained through organizing the selection processes, as the earlier systems were considered subjective in many organizations. Scientifically, this dissertation aims to make a contribution to the present body of knowledge of R&D performance analysis by facilitating dealing with the versatility and challenges of R&D performance analysis, as well as the factors and dimensions influencing the selection of R&D performance measures, and by integrating these aspects to the developed novel types of approaches, methods and tools in the selection processes of R&D measures, applied in real-world organizations. In the whole research, facilitation of dealing with the versatility and challenges in R&D performance analysis, as well as the factors and dimensions influencing the R&D performance measure selection are strongly integrated with the constructed approaches. Thus, the research meets the above-mentioned purposes and objectives of the dissertation from the scientific as well as from the practical point of view.

Measurement of social value creation with non-financial measures

This research examines the concept of social entrepreneurship which is a fairly new business model. In the field of business it has become increasingly popular in recent years. The growing awareness of the environment and concrete examples of impact created by social entrepreneurship have encouraged entrepreneurs to address social problems. Society’s failures are tried to redress as a result of business activities. The purpose of doing business is necessarily no longer generating just profits but business is run in order to make a social change with the profit gained from the operations. Successful social entrepreneurship requires a specific nature, constant creativity and strong desire to make a social change. It requires constant balancing between two major objectives: both financial and non-financial issues need to be considered, but not at the expense of another. While aiming at the social purpose, the business needs to be run in highly competitive markets. Therefore, both factors need equally be integrated into an organization as they are complementary, not exclusionary. Business does not exist without society and society cannot go forward without business. Social entrepreneurship, its value creation, measurement tools and reporting practices are under discussion in this research. An extensive theoretical basis is covered and used to support the findings coming out of the researched case enterprises. The most attention is focused on the concept of Social Return on Investment. The case enterprises are analyzed through the SROI process. Social enterprises are mostly small or medium sized. Naturally this sets some limitations in implementing measurement tools. The question of resources requires the most attention and therefore sets the biggest constraints. However, the size of the company does not determine all – the nature of business and the type of social purpose need to be considered always. The mission may be so concrete and transparent that in all cases any kind of measurement would be useless. Implementing measurement tools may be of great benefit – or a huge financial burden. Thus, the very first thing to carefully consider is the possible need of measuring value creation.

Characterization of human and instrument related measurement errors

Measurement is the act or the result of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or the human being involved. The first part of the study is devoted to understanding the human errors in measurement. For that, selected person related and selected work related factors that could affect measurement errors have been identified. Though these are well known, the exact extent of the error and the extent of effect of different factors on human errors in measurement are less reported. Characterization of human errors in measurement is done by conducting an experimental study using different subjects, where the factors were changed one at a time and the measurements made by them recorded. From the pre‐experiment survey research studies, it is observed that the respondents could not give the correct answers to questions related to the correct values [extent] of human related measurement errors. This confirmed the fears expressed regarding lack of knowledge about the extent of human related measurement errors among professionals associated with quality. But in postexperiment phase of survey study, it is observed that the answers regarding the extent of human related measurement errors has improved significantly since the answer choices were provided based on the experimental study. It is hoped that this work will help users of measurement in practice to better understand and manage the phenomena of human related errors in measurement.

Measurement Quality in Indicators of Compositions: a Compositional Multitrait-Multimethod Approach

Compositional data, also called multiplicative ipsative data, are common in survey research instruments in areas such as time use, budget expenditure and social networks. Compositional data are usually expressed as proportions of a total, whose sum can only be 1. Owing to their constrained nature, statistical analysis in general, and estimation of measurement quality with a confirmatory factor analysis model for multitrait-multimethod (MTMM) designs in particular are challenging tasks. Compositional data are highly non-normal, as they range within the 0-1 interval. One component can only increase if some other(s) decrease, which results in spurious negative correlations among components which cannot be accounted for by the MTMM model parameters. In this article we show how researchers can use the correlated uniqueness model for MTMM designs in order to evaluate measurement quality of compositional indicators. We suggest using the additive log ratio transformation of the data, discuss several approaches to deal with zero components and explain how the interpretation of MTMM designs di ers from the application to standard unconstrained data. We show an illustration of the method on data of social network composition expressed in percentages of partner, family, friends and other members in which we conclude that the faceto-face collection mode is generally superior to the telephone mode, although primacy e ects are higher in the face-to-face mode. Compositions of strong ties (such as partner) are measured with higher quality than those of weaker ties (such as other network members)