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.

D3M: multicast listener mobility support mechanisms over distributed mobility anchoring architectures

The explosion in mobile data traffic is a driver for future network operator technologies, given its large potential to affect both network performance and generated revenue. The concept of distributed mobility management (DMM) has emerged in order to overcome efficiency-wise limitations in centralized mobility approaches, proposing not only the distribution of anchoring functions but also dynamic mobility activation sensitive to the applications needs. Nevertheless, there is not an acceptable solution for IP multicast in DMM environments, as the first proposals based on MLD Proxy are prone to tunnel replication problem or service disruption. We propose the application of PIM-SM in mobility entities as an alternative solution for multicast support in DMM, and introduce an architecture enabling mobile multicast listeners support over distributed anchoring frameworks in a network-efficient way. The architecture aims at providing operators with flexible options to provide multicast mobility, supporting three modes: the first one introduces basic IP multicast support in DMM; the second improves subscription time through extensions to the mobility protocol, obliterating the dependence on MLD protocol; and the third enables fast listener mobility by avoiding potentially slow multicast tree convergence latency in larger infrastructures, by benefiting from mobility tunnels. The different modes were evaluated by mathematical analysis regarding disruption time and packet loss during handoff against several parameters, total and tunneling packet delivery cost, and regarding packet and signaling overhead.

Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support

As climate change continues to impact socio-ecological systems, tools that assist conservation managers to understand vulnerability and target adaptations are essential. Quantitative assessments of vulnerability are rare because available frameworks are complex and lack guidance for dealing with data limitations and integrating across scales and disciplines. This paper describes a semi-quantitative method for assessing vulnerability to climate change that integrates socio-ecological factors to address management objectives and support decision-making. The method applies a framework first adopted by the Intergovernmental Panel on Climate Change and uses a structured 10-step process. The scores for each framework element are normalized and multiplied to produce a vulnerability score and then the assessed components are ranked from high to low vulnerability. Sensitivity analyses determine which indicators most influence the analysis and the resultant decision-making process so data quality for these indicators can be reviewed to increase robustness. Prioritisation of components for conservation considers other economic, social and cultural values with vulnerability rankings to target actions that reduce vulnerability to climate change by decreasing exposure or sensitivity and/or increasing adaptive capacity. This framework provides practical decision-support and has been applied to marine ecosystems and fisheries, with two case applications provided as examples: (1) food security in Pacific Island nations under climate-driven fish declines, and (2) fisheries in the Gulf of Carpentaria, northern Australia. The step-wise process outlined here is broadly applicable and can be undertaken with minimal resources using existing data, thereby having great potential to inform adaptive natural resource management in diverse locations.

Accessing dental anxiety online support groups: An exploratory qualitative study of motives and experiences

Objectives: In recent years, Internet access has grown markedly providing individuals with new opportunities for online information retrieval, psychological advice and support. The objectives of the present study were to explore the context through which dentally anxious individuals access an online support group and the nature of their online experiences. Methods: An online questionnaire was completed by 143 individuals who accessed the Dental Fear Central online support group bulletin board. Qualitative analysis was conducted on the responses. Results: Analysis revealed three emergent themes which reflected the motives and experiences of individuals: ‘Searching for help’, ‘Sharing fears’ and ‘I feel empowered’. Conclusion: This exploratory study suggests that for most individuals accessing this online support group was a positive and beneficial experience. Practice Implications: Online support groups may represent a convenient and beneficial tool that may assist certain individuals to confront their debilitating anxiety/phobia and successfully receive dental care.

REDUCING TEACHER ATTRITION: THE ROLES AND RESPONSIBILITIES OF BUILDING PRINCIPALS IN A LARGE URBAN DISTRICT TO SUPPORT TEACHER RETENTION

The high rate of teacher attrition in urban schools is well documented. While this does not seem like a problem in Carter County, this equates to hundreds of teachers that need to be replaced annually. Since school year (SY) 2007-08, Carter County has lost over 7,100 teachers, approximately half of (50.1%) of whom resigned, often going to neighboring, higher-paying jurisdictions as suggested by exit survey data (SY2016-2020 Strategic Plan). Included in this study is a range of practices principals use to retain teachers. While the role of the principal is recognized as a critical element in teacher retention, few studies explore the specific practices principals implement to retain teachers and how they use their time to accomplish this task. Through interviews, observations, document analysis and reflective notes, the study identifies the practices four elementary school principals of high and relatively low attrition schools use to support teacher retention. In doing so, the study uses a qualitative cross-case analysis approach. The researcher examined the following leadership practices of the principal and their impact on teacher retention: (a) providing leadership, (b) supporting new teachers, (c) training and mentoring teaching staff, (d) creating opportunities for collaboration, (d) creating a positive school climate, and (e) promoting teacher autonomy. The following research questions served as a foundational guide for the development and implementation of this study: 1. How do principals prioritize addressing teacher attrition or retention relative to all of their other responsibilities? How do they allocate their time to this challenge? 2. What do principals in schools with low attrition rates do to promote retention that principals in high attrition schools do not? What specific practices or interventions are principals in these two types of schools utilizing to retain teachers? Is there evidence to support their use of the practices? The findings that emerge from the data revealed the various practices principals use to influence and support teachers do not differ between the four schools.

How do servant leaders ignite absorptive capacity?: the role of epistemic motivation and organizational support

This study examines the role of servant leadership in absorptive capacity. Data from manufacturing and service sector organizations found that: a) there was moderation of servant leadership influence on knowledge identification through POS by high need for cognition, b) there was moderation of servant leadership influence on knowledge application through POS by low time pressure, and c) POS mediated relationship between servant leadership and knowledge dissemination. The findings illustrate and support the importance of a comprehensive model integrating servant leadership, POS, and epistemic motivation in determining absorptive capacity.

SVR-GARCH com misturas de kernels gaussianos

Dissertação (mestrado)—Universidade de Brasília, Departamento de Administração, Programa de Pós-graduação em Administração, 2016.

About time : life-cycle and cohort effects on support for Quebec sovereignty

L’appui à la souveraineté du Québec diminue-t-il avec l’âge, ou est-il le reflet de préférences générationnelles ? Cette recherche se base sur les théories du changement générationnel et de la socialisation politique pour répondre à cette question. À l’aide de données de sondages de 1985 à 2014, nous mesurons l’impact de l’âge et de la génération sur l’appui à cette option constitutionnelle chez les Québécois francophones. Nos deux hypothèses de recherche sont confirmées dans une certaine mesure. Premièrement, les Québécois ont moins tendance à appuyer la souveraineté en vieillissant. La relation négative entre ces variables devient par contre plus faible au début des années 2000. Deuxièmement, les Baby boomers (nés entre 1945 et 1964) ont une probabilité plus élevée d’être souverainistes que les autres générations, et ce peu importe leur âge. Ils sont suivis, dans l’ordre, par les Aînés (nés en 1944 et moins), la Génération X (nés entre 1965 et 1979) et les Milléniaux (nés en 1980 ou plus).

Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.

Aedes aegypti Pharate First Instar Aseasonal Quiescence Cues Anticipatory Plasticity with Implications for Urban Vector Ecology and Control

The eggs of the dengue fever vector Aedes aegypti possess the ability to undergo an extended quiescence period hosting a fully developed first instar larvae within its chorion. As a result of this life history stage, pharate larvae can withstand months of dormancy inside the egg where they depend on stored reserves of maternal origin. This adaptation known as pharate first instar quiescence, allows A. aegypti to cope with fluctuations in water availability. An examination of this fundamental adaptation has shown that there are trade-offs associated with it. Aedes aegypti mosquitoes are frequently associated with urban habitats that may contain metal pollution. My research has demonstrated that the duration of this quiescence and the extent of nutritional depletion associated with it affects the physiology and survival of larvae that hatch in a suboptimal habitat; nutrient reserves decrease during pharate first instar quiescence and alter subsequent larval and adult fitness. The duration of quiescence compromises metal tolerance physiology and is coupled to a decrease in metallothionein mRNA levels. My findings also indicate that even low levels of environmentally relevant larval metal stress alter the parameters that determine vector capacity. My research has also demonstrated that extended pharate first instar quiescence can elicit a plastic response resulting in an adult phenotype distinct from adults reared from short quiescence eggs. Extended pharate first instar quiescence affects the performance and reproductive fitness of the adult female mosquito as well as the nutritional status of its progeny via maternal effects in an adaptive manner, i.e., anticipatory phenotypic plasticity results as a consequence of the duration of pharate first instar quiescence and alternative phenotypes may exist for this mosquito with quiescence serving as a cue possibly signaling the environmental conditions that follow a dry period. M findings may explain, in part, A. aegypti’s success as a vector and its geographic distribution and have implications for its vector capacity and control.

About time : life-cycle and cohort effects on support for Quebec sovereignty

L’appui à la souveraineté du Québec diminue-t-il avec l’âge, ou est-il le reflet de préférences générationnelles ? Cette recherche se base sur les théories du changement générationnel et de la socialisation politique pour répondre à cette question. À l’aide de données de sondages de 1985 à 2014, nous mesurons l’impact de l’âge et de la génération sur l’appui à cette option constitutionnelle chez les Québécois francophones. Nos deux hypothèses de recherche sont confirmées dans une certaine mesure. Premièrement, les Québécois ont moins tendance à appuyer la souveraineté en vieillissant. La relation négative entre ces variables devient par contre plus faible au début des années 2000. Deuxièmement, les Baby boomers (nés entre 1945 et 1964) ont une probabilité plus élevée d’être souverainistes que les autres générations, et ce peu importe leur âge. Ils sont suivis, dans l’ordre, par les Aînés (nés en 1944 et moins), la Génération X (nés entre 1965 et 1979) et les Milléniaux (nés en 1980 ou plus).

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Wavelets And Adaptive Grids For The Discontinuous Galerkin Method

In this paper, space adaptivity is introduced to control the error in the numerical solution of hyperbolic systems of conservation laws. The reference numerical scheme is a new version of the discontinuous Galerkin method, which uses an implicit diffusive term in the direction of the streamlines, for stability purposes. The decision whether to refine or to unrefine the grid in a certain location is taken according to the magnitude of wavelet coefficients, which are indicators of local smoothness of the numerical solution. Numerical solutions of the nonlinear Euler equations illustrate the efficiency of the method. © Springer 2005.

Leptin's effect on puberty in mice is relayed by the ventral premammillary nucleus and does not require signaling in Kiss1 neurons

Studies m hum ins and rodents indicate that a minimum amount of stored energy is required for normal pubertal development The adipocyte-derived hormone leptin is a key metabolic signal to the neuroendocrine reproductive axis Humans and mice lacking leptin or the leptin receptor (LepR) (ob/ob and db/db mice, respectively) are infertile and fail to enter puberty Leptin administration to leptin-deficient subjects and ob/ob mice induces puberty and restores fertility, but the exact site or sites of leptin action are unclear Here, we found that genetic deletion of LepR selectively from hypothalamic Kiss1 neurons m mice had no effect on puberty or fertility, indicating that direct leptin signaling m Kiss1 neurons is not required for these processes However, bilateral lesions of the ventral premammillary nucleus (PMV) of ob/ob mice blunted the ability of exogenous leptin to induce sexual maturation Moreover, unilateral reexpression of endogenous LepR m PMV neurons was sufficient to induce puberty and improve fertility m female LepR-null mice This LepR reexpression also normalized the increased hypothalamic GnRH content characteristic of leptin-signaling deficiency These data suggest that the PMV is a key site for leptin's permissive action at the onset of puberty and support the hypothesis that the multiple actions of leptin to control metabolism and reproduction at e anatomically dissociated

Exclusive vector meson production in electron-ion collisions

We calculate the nuclear cross section for coherent and incoherent vector meson production within the QCD color dipole picture, including saturation effects. Theoretical estimates for scattering on both light and heavy nuclei are given over a wide range of energy.