The protagonists of John Updike

My purpose in this paper is to show that the protagonists of Updike can be categorized into groups and that these protagonists are as real for me as they are for Updike. It is because of these protagonists that the works of Updike will live for many years to come

Hardening DGA classifiers utilizing IVAP

Domain Generation Algorithms (DGAs) are used by malware to generate a deterministic set of domains, usually by utilizing a pseudo-random seed. A malicious botmaster can establish connections between their command-and-control center (C&C) and any malware-infected machines by registering domains that will be DGA-generated given a specific seed, rendering traditional domain blacklisting ineffective. Given the nature of this threat, the real-time detection of DGA domains based on incoming DNS traffic is highly important. The use of neural network machine learning (ML) models for this task has been well-studied, but there is still substantial room for improvement. In this paper, we propose to use Inductive Venn-Abers predictors (IVAPs) to calibrate the output of existing ML models for DGA classification. The IVAP is a computationally efficient procedure which consistently improves the predictive accuracy of classifiers at the expense of not offering predictions for a small subset of inputs and consuming an additional amount of training data

Student thinking about the divergence and curl in mathematics and physics contexts

Undergraduate physics students are known to have difficulties with understanding mathematical tools, and with applying their knowledge of mathematics to physical contexts. Using survey statements based on student interviews and written responses to open-ended questions, we investigated the prevalence of correct and incorrect conceptions regarding the divergence and curl of vector fields, among both mathematics and physics students. We compare and contrast pre-instruction responses from intermediate-level E&M students at KU Leuven and the University of St Andrews, with post-instruction responses from St Andrews students enrolled in a vector calculus course. The differences between these student populations were primarily in areas having to do with physics-related concepts and graphical representations of vector fields. Our comparison of pre- and post-instruction responses from E&M students shows that their understanding of the divergence and curl improved significantly in most areas, though not as much as would be desired.Comment: Physics Education Research Conference 2015 (submitted

New insights into pharyngo-esophageal bolus transport revealed by pressure-impedance measurement

Author version made available in accordance with the publisher's policy.Introduction: Pharyngeal propulsion, strength of peristalsis and esophago-gastric junction (EJG) resistance are determinants of esophageal bolus transport. This study used pressure-impedance methods to correlate pharyngo-esophageal function with the esophageal bolus trajectory pathway and pressures generated during bolus transport. Methods: Pharyngo-esophageal pressure-impedance measurements were performed in 20 healthy adult controls. Pharyngeal automated impedance manometry was performed to derive pharyngeal swallow function variables. The esophageal time of nadir impedance (TZn) was used to track bolus trajectory pathway. The inflexion, or flow stasis point (FSP), of the trajectory curve was determined as were the pressures within the bolus (PZn) above and below the FSP. The size of 20mmHg isocontour defect measured the integrity of the peristaltic wave. Results: For viscous boluses, weaker pharyngeal bolus propulsion correlated with the FSP being located higher in the esophagus. Pressure within the bolus was observed to increase at the FSP and below the FSP in a manner that correlated with the magnitude of esophageal peak pressures. Larger 20mmHg isocontour defects were associated with lower pressures within the bolus at the FSP and below. Conclusion: The FSP of the bolus trajectory pathway appears to represent a switch from bolus propulsion due to pharyngeal mechanisms to bolus propulsion due to esophageal mechanisms. 20mmHg isocontour defects significantly reduce bolus driving pressure at or below the FSP

Ultra high definition video decoding with motion JPEG XR using the GPU

Many applications require real-time decoding of highresolution video pictures, for example, quick editing of video sequences in video editing applications. To increase decoding speed, parallelism can be exploited, yet, block-based image and video coding standards are difficult to decode in parallel because of the high number of dependencies between blocks. This paper investigates the parallel decoding capabilities of the new JPEG XR image coding standard for use on the massively-parallel architecture of the GPU. The potential of parallelism of the hierarchical frequency coding scheme used in the standard is addressed and a parallel decoding scheme is described suitable for real-time decoding of Ultra High Definition (4320p) Motion JPEG XR video sequences. Our results show a decoding speed of up to 46 frames per second for Ultra High Definition (4320p) sequences with high-dynamic range (32-bit/ 4: 2: 0) luma and chroma components

CharBot: A Simple and Effective Method for Evading DGA Classifiers

Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). CharBot is very simple, effective and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date

Qualitative investigation into students’ use of divergence and curl in electromagnetism

Many students struggle with the use of mathematics in physics courses. Although typically well trained in rote mathematical calculation, they often lack the ability to apply their acquired skills to physical contexts. Such student difficulties are particularly apparent in undergraduate electrodynamics, which relies heavily on the use of vector calculus. To gain insight into student reasoning when solving problems involving divergence and curl, we conducted eight semistructured individual student interviews. During these interviews, students discussed the divergence and curl of electromagnetic fields using graphical representations, mathematical calculations, and the differential form of Maxwell’s equations. We observed that while many students attempt to clarify the problem by making a sketch of the electromagnetic field, they struggle to interpret graphical representations of vector fields in terms of divergence and curl. In addition, some students confuse the characteristics of field line diagrams and field vector plots. By interpreting our results within the conceptual blending framework, we show how a lack of conceptual understanding of the vector operators and difficulties with graphical representations can account for an improper understanding of Maxwell’s equations in differential form. Consequently, specific learning materials based on a multiple representation approach are required to clarify Maxwell’s equations.Publisher PDFPeer reviewe

HIV prevalence and associated risk factors among individuals aged 13-34 years in Rural Western Kenya.

OBJECTIVES: To estimate HIV prevalence and characterize risk factors among young adults in Asembo, rural western Kenya. DESIGN: Community-based cross-sectional survey. METHODS: From a demographic surveillance system, we selected a random sample of residents aged 13-34 years, who were contacted at home and invited to a nearby mobile study site. Consent procedures for non-emancipated minors required assent and parental consent. From October 2003 - April 2004, consenting participants were interviewed on risk behavior and tested for HIV and HSV-2. HIV voluntary counseling and testing was offered. RESULTS: Of 2606 eligible residents, 1822 (70%) enrolled. Primary reasons for refusal included not wanting blood taken, not wanting to learn HIV status, and partner/parental objection. Females comprised 53% of 1762 participants providing blood. Adjusted HIV prevalence was 15.4% overall: 20.5% among females and 10.2% among males. HIV prevalence was highest in women aged 25-29 years (36.5%) and men aged 30-34 years (41.1%). HSV-2 prevalence was 40.0% overall: 53% among females, 25.8% among males. In multivariate models stratified by gender and marital status, HIV infection was strongly associated with age, higher number of sex partners, widowhood, and HSV-2 seropositivity. CONCLUSIONS: Asembo has extremely high HIV and HSV-2 prevalence, and probable high incidence, among young adults. Further research on circumstances around HIV acquisition in young women and novel prevention strategies (vaccines, microbicides, pre-exposure prophylaxis, HSV-2 prevention, etc.) are urgently needed

CharBot : a simple and effective method for evading DGA classifiers

Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names, which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this paper, we present a novel DGA called CharBot, which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of the DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). The CharBot is very simple, effective, and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, the CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date