The Impact of Information Literacy-Related Instruction in the Science Classroom: Clickers Versus Nonclickers

The goal of information literacy instruction is to enable students to develop skills that they can use for life to facilitate their empowerment through information. Instruction librarians, particularly those teaching Millenials whose need for “hands on” instruction has been widely emphasized, are constantly searching for methodologies that will provide appropriate levels of interactive instruction. Many methods for enhancing the relevance of library instruction have been discussed in the literature. This study, designed and developed by a collaborative team of librarians and science faculty, describes the effects of providing course-integrated, interactive (with clickers) information literacy instruction to undergraduates at a small private nonprofit university in the Southeast

Designing Disease-Specific mHealth Apps for Clinical Value

mHealth apps for patient use are promising but continue to face a plateau in usage. Current apps work for a limited segment of the patient population, i.e., those who enjoy tracking for intrinsic rewards. There are many opportunities to support patient care in between health care provider visits that are not currently being met for many diseases and patient types (personas). This is an area of great potential growth for mHealth apps and could contribute greatly to patient health and wellness. In this chapter, we propose a framework for how to think about the between-visit needs of patients that would motivate continued use of mhealth apps. We view the app design process from the following perspectives: 1) disease-specific needs, 2) non-disease specific needs, 3) behavioral theoretical aspects of app usage and 4) app-intrinsic usage motivators. Myasthenia gravis serves as the use case for illustrating these perspectives and how to use them in designing a disease-specific mHealth app

Field Scanner Design for MUSTANG of the Green Bank Telescope

MUSTANG is a bolometer camera for the Green Bank Telescope (GBT) working at a frequency of 90 GHz. The detector has a field of view of 40 arcseconds. To cancel out random emission change from atmosphere and other sources, requires a fast scanning reflecting system with a few arcminute ranges. In this paper, the aberrations of an off-axis system are reviewed. The condition for an optimized system is provided. In an optimized system, as additional image transfer mirrors are introduced, new aberrations of the off-axis system may be reintroduced, resulting in a limited field of view. In this paper, different scanning mirror arrangements for the GBT system are analyzed through the ray tracing analysis. These include using the subreflector as the scanning mirror, chopping a flat mirror and transferring image with an ellipse mirror, and chopping a flat mirror and transferring image with a pair of face-to-face paraboloid mirrors. The system analysis shows that chopping a flat mirror and using a well aligned pair of paraboloids can generate the required field of view for the MUSTUNG detector system, while other systems all suffer from larger off-axis aberrations added by the system modification. The spot diagrams of the well aligned pair of paraboloids produced is only about one Airy disk size within a scanning angle of about 3 arcmin.Comment: 7 pages, 9 figure

Effects of Smart Position Only (SPOT) Tag Deployment on White Sharks Carcharodon carcharias in South Africa

We present 15 individual cases of sub-adult white sharks that were SPOT tagged in South Africa from 2003–2004 and have been re-sighted as recently as 2011. Our observations suggest SPOT tags can cause permanent cosmetic and structural damage to white shark dorsal fins depending on the duration of tag attachment. SPOT tags that detached within 12–24 months did not cause long term damage to the dorsal fin other than pigmentation scarring. Within 12 months of deployment, tag fouling can occur. After 24 months of deployment permanent damage to the dorsal fin occurred. A shark survived this prolonged attachment and there seems little compromise on the animal's long term survival and resultant body growth. This is the first investigation detailing the long term effects of SPOT deployment on the dorsal fin of white sharks

Testing SOAR Tools in Use

Modern security operation centers (SOCs) rely on operators and a tapestry of logging and alerting tools with large scale collection and query abilities. SOC investigations are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, and correlate the data into information and then document results in a ticketing system. Security orchestration, automation, and response (SOAR) tools are a new technology that promise to collect, filter, and display needed data; automate common tasks that require SOC analysts' time; facilitate SOC collaboration; and, improve both efficiency and consistency of SOCs. SOAR tools have never been tested in practice to evaluate their effect and understand them in use. In this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and 6 commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools and a methodology for testing them. We describe configuration of the test environment in a cyber range, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. We found that SOAR configuration is critical, as it involves creative design for data display and automation. We found that SOAR tools increased efficiency and reduced context switching during investigations, although ticket accuracy and completeness (indicating investigation quality) decreased with SOAR use. Our findings indicated that user preferences are slightly negatively correlated with their performance with the tool; overautomation was a concern of senior analysts, and SOAR tools that balanced automation with assisting a user to make decisions were preferred

MUSTANG 3.3 Millimeter Continuum Observations of Class 0 Protostars

We present observations of six Class 0 protostars at 3.3 mm (90 GHz) using the 64-pixel MUSTANG bolometer camera on the 100-m Green Bank Telescope. The 3.3 mm photometry is analyzed along with shorter wavelength observations to derive spectral indices (S_nu ~ nu^alpha) of the measured emission. We utilize previously published dust continuum radiative transfer models to estimate the characteristic dust temperature within the central beam of our observations. We present constraints on the millimeter dust opacity index, beta, between 0.862 mm, 1.25 mm, and 3.3 mm. Beta_mm typically ranges from 1.0 to 2.4 for Class 0 sources. The relative contributions from disk emission and envelope emission are estimated at 3.3 mm. L483 is found to have negligible disk emission at 3.3 mm while L1527 is dominated by disk emission within the central beam. The beta_mm^disk <= 0.8 - 1.4 for L1527 indicates that grain growth is likely occurring in the disk. The photometry presented in this paper may be combined with future interferometric observations of Class 0 envelopes and disks.Comment: 19 pages, 3 figures, AJ accepted, in pres

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial Intelligence Applications to Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files $\times$ 8 tools), a software framework is designed to choreographed the experiment into a completely automated, time-synced, and reproducible workflow with substantial parallelization. A cost-benefit model was configured to integrate the tools' recall, precision, time to detection, and resource requirements into a single comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens through which to reason about the varied statistical viewpoints of the results. These statistical and cost-model results provide insights on state of commercial malware detection

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection

There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). The result is that the efficacy and gaps among the available approaches are opaque, inhibiting end users from making informed network security decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four market-leading malware detection tools to assist an organization with two primary questions: (Q1) To what extent do ML-based tools accurately classify never-before-seen files without sacrificing detection ability on known files? (Q2) Is it worth purchasing a network-level malware detector to complement host-based detection? We tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) including over 400 zero-day malware, and tested with a variety of file types and protocols for delivery. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of a recent cost-benefit evaluation procedure by Iannaconne & Bridges that incorporates all the above metrics into a single quantifiable cost. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected.Comment: Includes Actionable Takeaways for SOC

FIREBall-2: advancing TRL while doing proof-of-concept astrophysics on a suborbital platform

Here we discuss advances in UV technology over the last decade, with an emphasis on photon counting, low noise, high efficiency detectors in sub-orbital programs. We focus on the use of innovative UV detectors in a NASA astrophysics balloon telescope, FIREBall-2, which successfully flew in the Fall of 2018. The FIREBall-2 telescope is designed to make observations of distant galaxies to understand more about how they evolve by looking for diffuse hydrogen in the galactic halo. The payload utilizes a 1.0-meter class telescope with an ultraviolet multi-object spectrograph and is a joint collaboration between Caltech, JPL, LAM, CNES, Columbia, the University of Arizona, and NASA. The improved detector technology that was tested on FIREBall-2 can be applied to any UV mission. We discuss the results of the flight and detector performance. We will also discuss the utility of sub-orbital platforms (both balloon payloads and rockets) for testing new technologies and proof-of-concept scientific ideasComment: Submitted to the Proceedings of SPIE, Defense + Commercial Sensing (SI19