Explained: Artificial Intelligence for Propensity Score Estimation in Multilevel Educational Settings

Although educational research and evaluation generally occur in multilevel settings, many analyses ignore cluster effects. Neglecting the nature of data from educational settings, especially in non-randomized experiments, can result in biased estimates with long-term consequences. Our manuscript improves the availability and understanding of artificial neural networks, an underutilized method trending in other disciplines. This method also shows promise for dealing with challenges faced by educational researchers, such as analyzing clustered data. Therefore, we simulated data to generalize the potential benefits of artificial neural networks to different data types. We also compared artificial neural networks to more familiar methods and investigated the time it demanded to perform each technique. Hence, readers can decide when it may be more appropriate to use one method instead of another

Risk Management Decision Making for Security and Trust in Hardware Supply Chains

Modern cyber-physical systems are enabled by electronic hardware and embedded systems. The security of these sub-components is a concern during the design and operational phases of cyber-physical system life cycles. Compromised electronics can result in mission-critical failures, unauthorized access, and other severe consequences. As systems become more complex and feature greater connectivity, system owners must make decisions regarding how to mitigate risks and ensure resilience and trust. This paper provides an overview of research efforts related to assessing and managing risks, resilience, and trust with an emphasis on electronic hardware and embedded systems. The research takes a decision-oriented perspective, drawing from the perspectives of scenario planning and portfolio analysis, and describes examples related to the risk-based prioritization of cyber assets in large-scale systems

Fundamental Concepts of Cyber Resilience: Introduction and Overview

Given the rapid evolution of threats to cyber systems, new management approaches are needed that address risk across all interdependent domains (i.e., physical, information, cognitive, and social) of cyber systems. Further, the traditional approach of hardening of cyber systems against identified threats has proven to be impossible. Therefore, in the same way that biological systems develop immunity as a way to respond to infections and other attacks, so too must cyber systems adapt to ever-changing threats that continue to attack vital system functions, and to bounce back from the effects of the attacks. Here, we explain the basic concepts of resilience in the context of systems, discuss related properties, and make business case of cyber resilience. We also offer a brief summary of ways to assess cyber resilience of a system, and approaches to improving cyber resilience.Comment: This is a preprint version of a chapter that appears in the book "Cyber Resilience of Systems and Networks," Springer 201

Dynamic edge effects in small mammal communities across a conservation-agricultural interface in Swaziland

Across the planet, high-intensity farming has transformed native vegetation into monocultures, decreasing biodiversity on a landscape scale. Yet landscape-scale changes to biodiversity and community structure often emerge from processes operating at local scales. One common process that can explain changes in biodiversity and community structure is the creation of abrupt habitat edges, which, in turn, generate edge effects. Such effects, while incredibly common, can be highly variable across space and time; however, we currently lack a general analytical framework that can adequately capture such spatio-temporal variability. We extend previous approaches for estimating edge effects to a non-linear mixed modeling framework that captures such spatio-temporal heterogeneity and apply it to understand how agricultural land-uses alter wildlife communities. We trapped small mammals along a conservation-agriculture land-use interface extending 375 m into sugarcane plantations and conservation land-uses at three sites during dry and wet seasons in Swaziland, Africa. Sugarcane plantations had significant reductions in species richness and heterogeneity, and showed an increase in community similarity, suggesting a more homogenized small mammal community. Furthermore, our modeling framework identified strong variation in edge effects on communities across sites and seasons. Using small mammals as an indicator, intensive agricultural practices appear to create high-density communities of generalist species while isolating interior species in less than 225 m. These results illustrate how agricultural land-use can reduce diversity across the landscape and that effects can be masked or magnified, depending on local conditions. Taken together, our results emphasize the need to create or retain natural habitat features in agricultural mosaics.Texas A&M Agrilife Researchhttp://www.plosone.orgam2013Zoology and EntomologyMammal Research Institut

Skunk River Review 2012, vol 24

https://openspace.dmacc.edu/skunkriver/1015/thumbnail.jp

Plex: Towards Reliability using Pretrained Large Model Extensions

A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.Comment: Code available at https://goo.gle/plex-cod

SoK: Contemporary Issues and Challenges to Enable Cyber Situational Awareness for Network Security

Cyber situational awareness is an essential part of cyber defense that allows the cybersecurity operators to cope with the complexity of today's networks and threat landscape. Perceiving and comprehending the situation allow the operator to project upcoming events and make strategic decisions. In this paper, we recapitulate the fundamentals of cyber situational awareness and highlight its unique characteristics in comparison to generic situational awareness known from other fields. Subsequently, we provide an overview of existing research and trends in publishing on the topic, introduce front research groups, and highlight the impact of cyber situational awareness research. Further, we propose an updated taxonomy and enumeration of the components used for achieving cyber situational awareness. The updated taxonomy conforms to the widely-accepted three-level definition of cyber situational awareness and newly includes the projection level. Finally, we identify and discuss contemporary research and operational challenges, such as the need to cope with rising volume, velocity, and variety of cybersecurity data and the need to provide cybersecurity operators with the right data at the right time and increase their value through visualization

Kepler-102 : masses and compositions for a super-Earth and sub-Neptune orbiting an active star

Funding: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant No. 1842402. C.L.B., L.W., and D.H. acknowledge support from National Aeronautics and Space Administration (grant No. 80NSSC19K0597) issued through the Astrophysics Data Analysis Program. D.H. also acknowledges support from the Alfred P. Sloan Foundation. K.R. acknowledges support from the UK STFC via grant No. ST/V000594/1. E.G. acknowledges support from NASA grant No. 80NSSC20K0957 (Exoplanets Research Program).Radial velocity (RV) measurements of transiting multiplanet systems allow us to understand the densities and compositions of planets unlike those in the solar system. Kepler-102, which consists of five tightly packed transiting planets, is a particularly interesting system since it includes a super-Earth (Kepler-102d) and a sub-Neptune-sized planet (Kepler-102e) for which masses can be measured using RVs. Previous work found a high density for Kepler-102d, suggesting a composition similar to that of Mercury, while Kepler-102e was found to have a density typical of sub-Neptune size planets; however, Kepler-102 is an active star, which can interfere with RV mass measurements. To better measure the mass of these two planets, we obtained 111 new RVs using Keck/HIRES and Telescopio Nazionale Galileo/HARPS-N and modeled Kepler-102's activity using quasiperiodic Gaussian process regression. For Kepler-102d, we report a mass upper limit Md < 5.3 M⊕ (95% confidence), a best-fit mass Md = 2.5 ± 1.4 M⊕, and a density ρd = 5.6 ± 3.2 g cm−3, which is consistent with a rocky composition similar in density to the Earth. For Kepler-102e we report a mass Me = 4.7 ± 1.7 M⊕ and a density ρe = 1.8 ± 0.7 g cm−3. These measurements suggest that Kepler-102e has a rocky core with a thick gaseous envelope comprising 2%–4% of the planet mass and 16%–50% of its radius. Our study is yet another demonstration that accounting for stellar activity in stars with clear rotation signals can yield more accurate planet masses, enabling a more realistic interpretation of planet interiors.Publisher PDFPeer reviewe