This comprehensive 1-day course equips scientists, engineers, researchers, and technical professionals to present their science in an understandable, memorable, and persuasive way.  Through a dynamic combination of lecture, discussion, exercises, and video analysis, each participant will walk away with the skills, knowledge, and practice necessary to transform the way their work is presented. Five course objectives are covered:

1. Transform the scientific presentations skills of participants. Enable participants to utilize effective strategies for content, structure, slide design and delivery of scientific presentations.
2. Teach participants to analyze and adapt to their audience.
3. Help participants understand which scientific details to emphasize in their presentation and which details to filter out.
4. Equip participants to understand and enact the assertion evidence slide design in their own talks to make their scientific presentation slides more understandable, memorable, and engaging.
5. Assist participants in developing an engaging and confident delivery style.

*** Attendees should bring a laptop with them to the session. ***

In many Department of Defense (DoD) Test and Evaluation (T&E) applications, binary response variables are unavoidable. Many have considered D-optimal design of experiments (DOEs) for generalized linear models (GLMs). However, little consideration has been given to assessing how these new designs perform in terms of statistical power for a given hypothesis test. Monte Carlo simulations and exact power calculations suggest that D-optimal designs generally yield higher power than binary D-optimal designs, despite using logistic regression in the analysis after data have been collected. Results from using statistical power to compare designs contradict traditional DOE comparisons which employ D-efficiency ratios and fractional design space (FDS) plots. Power calculations suggest that practitioners that are primarily interested in the resulting statistical power of a design should use normal D-optimal designs over binary D-optimal designs when logistic regression is to be used in the data analysis after data collection.

A key global security concern in the nuclear weapons age is the proliferation and development of nuclear weapons technology, and a crucial part of enforcing non-proliferation policy is developing an awareness of the scientific research being pursued by other nations and organizations. Deep, transformer-based text classification models are an important piece of systems designed to monitor scientific research for this purpose. For applications like proliferation detection involving high-stakes decisions, there has been growing interest in ensuring that we can perform well-calibrated, interpretable uncertainty quantification with such classifier models. However, because modern transformer-based text classification models have hundreds of millions of parameters and the computational cost of uncertainty quantification typically scales with the size of the parameter space, it has been difficult to produce computationally tractable uncertainty quantification for these models. We propose a new variational inference framework that is computationally tractable for large models and meets important uncertainty quantification objectives including producing predicted class probabilities that are well-calibrated and reflect our prior conception of how different classes are related.

High-fidelity simulation capabilities have progressed rapidly over the past decades in computational fluid dynamics (CFD), resulting in plenty of high-resolution flow field data. Uncertainty quantification remains an unsolved problem due to the high-dimensional input space and the intrinsic complexity of turbulence. Here we developed an uncertainty quantification method to model the Reynolds stress based on Karhunen-Loeve Expansion(KLE) and Project Pursuit basis Adaptation polynomial chaos expansion(PPA). First, different representative volume elements (RVEs) were randomly drawn from the flow field, and KLE was used to reduce them into a moderate dimension. Then, we build polynomial chaos expansions of Reynolds stress using PPA. Results show that this method can yield a surrogate model with a test accuracy of up to 90%. PCE coefficients also show that Reynolds stress strongly depends on second-order KLE random variables instead of first-order terms. Regarding data efficiency, we built another surrogate model using a neural network(NN) and found that our method outperforms NN in limited data cases.

IDA is developing a Data Strategy to develop solid infrastructures and practices that allow for a rigorous data-centric approach to answering U.S. security and science policy questions. The data strategy implements data governance and data architecture strategies to leverage data to gain trusted insights, and establishes a data-centric culture. One key component of the Data Strategy is a set of research taxonomies that describe and characterize the research done at IDA. These research taxonomies, broadly divided into six categories, are a vital tool to help IDA researchers gain insight into the research expertise of staff and divisions, in terms of the research products that are produced for our sponsors. We have developed an interactive web application which consumes numerous disparate sources of data related to these taxonomies, research products, researchers, and divisions, and unites them to create quantified analytics and visualizations to answer questions about research at IDA. This tool, titled I-TREE (IDA-Taxonomical Research Expertise Explorer), will enable staff to answer questions like ‘Who are the researchers most commonly producing products for a specified research area?’, ‘What is the research profile of a specified author?’, ‘What research topics are most commonly addressed by a specified division?’, ‘Who are the researchers most commonly producing products in a specified division?’, and ‘What divisions are producing products for a specified research topic?’. These are essential questions whose answers allow IDA to identify subject-matter expertise areas, methodologies, and key skills in response to sponsor requests, and to identify common areas of expertise to build a research team with a broad range of skills. I-TREE demonstrates the use of data science and data management techniques that enhance the company’s data strategy while actively enabling researchers and management to make informed decisions.

As an institution committed to developing leaders of character, the United States Military Academy (USMA) holds a vested interest in measuring character growth. One such tool, the Periodic Developmental Review (PDR), has been used by the Academy’s Institutional Effectiveness Office for over a decade. PDRs are written counseling statements evaluating how a cadet is developing with respect to his/her peers. The objective of this research was to provide an alternate perspective of the PDR system by using statistical and natural language processing (NLP) based approaches to find whether certain dimensions of PDR data were predictive of a cadet’s overall rating. This research implemented multiple NLP tasks and techniques, including sentiment analysis, named entity recognition, tokenization, part-of-speech tagging, and word2vec, as well as statistical models such as linear regression and ordinal logistic regression. The ordinal logistic regression model concluded PDRs with optional written summary statements had more predictable overall scores than those without summary statements. Additionally, those who wrote the PDR on the cadet (Self, Instructor, Peer, Subordinate) held strong predictive value towards the overall rating. When compared to a self-reflecting PDR, instructor-written PDRs were 62.40% more probable to have a higher overall score, while subordinate-written PDRs had a probability of improvement of 61.65%. These values were amplified to 70.85% and 73.12% respectively when considering only those PDRs with summary statements. These findings indicate that different writer demographics have a different understanding of the meaning of each rating level. Recommendations for the Academy would be implementing a forced distribution or providing a deeper explanation of overall rating in instructions. Additionally, no written language facets analyzed demonstrated predictive strength, meaning written statements do not introduce unwanted bias and could be made a required field for more meaningful feedback to cadets.

The optimal time to release a software is a common problem of broad concern to software engineers, where the goal is to minimize cost by balancing the cost of fixing defects before or after release as well as the cost of testing. However, the vast majority of these models are based on defect discovery models that are a function of time and can therefore only provide guidance on the amount of additional effort required. To overcome this limitation, this paper presents a software optimal release model based on cost criteria, incorporating the covariate software defect detection model based on the Discrete Cox Proportional Hazards Model. The proposed model provides more detailed guidance recommending the amount of each distinct test activity performed to discover defects. Our results indicate that the approach can be utilized to allocate effort among alternative test activities in order to minimize cost.

Modern software development organizations rely on continuous integration and continuous delivery (CI/CD), since it allows developers to continuously integrate their code in a single shared repository and automates the delivery process of the product to the user. While modern software practices improve the performance of the software life cycle, they also increase the complexity of this process. Past studies make improvements to the performance of the CI/CD pipeline. However, there are fewer formal models to quantitatively guide process and product quality improvement or characterize how automated and human activities compose and interact asynchronously. Therefore, this talk develops a stochastic Petri net model to analyze a CI/CD pipeline to improve process performance in terms of the probability of successfully delivering new or updated functionality by a specified deadline. The utility of the model is demonstrated through a sensitivity analysis to identify stages of the pipeline where improvements would most significantly improve the probability of timely product delivery. In addition, this research provided an enhanced version of the conventional CI/CD pipeline to examine how it can improve process performance in general. The results indicate that the augmented model outperforms the conventional model, and sensitivity analysis suggests that failures in later stages are more important and can impact the delivery of the final product.

System resilience is the ability of a system to survive and recover from disruptive events, which finds applications in several engineering domains, such as cyber-physical systems and infrastructure. Most studies emphasize resilience metrics to quantify system performance, whereas more recent studies propose resilience models to project system recovery time after degradation using traditional statistical modeling approaches. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this talk considers alternative machine learning approaches such as (i) Artificial Neural Networks (ANN), (ii) Recurrent Neural Networks, and (iii) Long-Short Term Memory (LSTM) to model and predict system performance of alternative trends other than ones previously considered. These approaches include negative and positive factors driving resilience to understand and precisely quantify the impact of disruptive events and restorative activities. A hybrid feature selection approach is also applied to identify the most relevant covariates. Goodness of fit measures are calculated to evaluate the models, including (i) mean squared error, (ii) predictive-ratio risk, (iii) and adjusted R squared. The results indicate that LSTM models outperform ANN and RNN models requiring fewer neurons in the hidden layer in most of the data sets considered. In many cases, ANN models performed better than RNNs but required more time to be trained. These results suggest that neural network models for predictive resilience are both feasible and accurate relative to traditional statistical methods and may find practical use in many important domains.

The influence maximization problem is a popular topic in social networks with several applications in viral marketing and epidemiology. One possible way to understand the problem is from the perspective of a marketer who wants to achieve the maximum influence on a social network by choosing an optimum set of nodes of a given size as seeds. The marketer actively influences these seeds, followed by a passive viral process based on a certain influence diffusion model, in which influenced nodes influence other nodes without external intervention. Kempe et al. showed that a greedy algorithm-based approach can provide a (1-1/e)-approximation guarantee compared to the optimal solution if the influence spreads according to the Triggering model. In our current work, we consider a much more general problem where the goal is to maximize the total expected reward obtained from the nodes that are influenced by a given time (that may be finite or infinite) where the reward obtained by influencing a set of nodes can depend on the set (and not necessarily a sum of rewards from the individual nodes) as well as the times at which each node gets influenced, we can restrict ourself to a subset of the network from where the seeds can be chosen, we can choose to assign multiple units of our budget to a single node (where the maximum number of budget units that may be assigned on a node can depend on the node), and a seeded node will actually get influenced with a certain probability where the probability is a non-decreasing function of the number of budget units assigned to that node. We have formulated a greedy algorithm that provides a (1-1/e)-approximation guarantee compared to the optimal solution of this generalized influence maximization problem if the influence spreads according to the Triggering model.

In Quality Control, monitoring sequential-functional observations for characteristic changes through change-point detection is a common practice to ensure that a system or process produces high-quality outputs. Existing methods in this field often only focus on identifying when a process is out-of-control without quantifying the uncertainty of the underlying decision-making processes. To address this issue, we propose using universal residuals under a Bayesian paradigm to determine if the process is out-of-control and assess the uncertainty surrounding that decision. The universal residuals are computed by combining two non-parametric techniques: regression trees and kernel density estimation. These residuals have the key feature of being uniformly distributed when the process is in control. To test if the residuals are uniformly distributed across time (i.e., that the process is in-control), we use a Bayesian approach for hypothesis testing, which outputs posterior probabilities for events such as the process being out-of-control at the current time, in the past, or in the future. We perform a simulation study and demonstrate that the proposed methodology has remarkable detection and a low false alarm rate.

Modeling and simulation (M&S) can be a useful tool for testers and evaluators when they need to augment the data collected during a test event. During the planning phase, testers use experimental design techniques to determine how much and which data to collect. When designing a test that involves M&S, testers can use Space-Filling Designs (SFD) to spread out points across the operational space. Fast Flexible Space-Filling Designs (FFSFD) are a type of SFD that are useful for M&S because they work well in nonrectangular design spaces and allow for the inclusion of categorical factors. Both of these are recurring features in defense testing. Guidance from the Deputy Secretary of Defense and the Director of Operational Test and Evaluation encourages the use of open and interoperable software and recommends the use of SFD. This project aims to address those. IDA analysts developed a function to create FFSFD using the free statistical software R. To our knowledge, there are no R packages for the creation of an FFSFD that could accommodate a variety of user inputs, such as categorical factors. Moreover, by using this function, users can share their code to make their work reproducible. This presentation starts with background information about M&S and, more specifically, SFD. The briefing uses a notional missile system example to explain FFSFD in more detail and show the FFSFD R function inputs and outputs. The briefing ends with a summary of the future work for this project.

This work describes the development of a statistical test created in support of ongoing verification, validation, and accreditation (VV&A) efforts for modeling and simulation (M&S) environments. The test decides between a null hypothesis of agreement between the simulation and reality, and an alternative hypothesis stating the simulation and reality do not agree. To do so, it generates a Wald-type statistic that compares the coefficients of two generalized linear models that are estimated on live test data and analogous simulated data, then determines whether any of the coefficient pairs are statistically different. The test was applied to two logistic regression models that were estimated from live torpedo test data and simulated data from the Naval Undersea Warfare Center’s (NUWC) Environment Centric Weapons Analysis Facility (ECWAF). The test did not show any significant differences between the live and simulated tests for the scenarios modeled by the ECWAF. While more work is needed to fully validate the ECWAF’s performance, this finding suggests that the facility is adequately modeling the various target characteristics and environmental factors that affect in-water torpedo performance. The primary advantage of this test is that it is capable of handling cases where one or more variables are estimable in one model but missing or inestimable from the other. While it is possible to simply create the linear models on the common set of variables, this results in the omission of potentially useful test data. Instead, this approach identifies the mismatched coefficients and combines them with the model’s intercept term, thus allowing the user to consider models that are created on the entire set of available data. Furthermore, the test was developed in a generalized manner without any references to a specific dataset or system. Therefore, other researchers who are conducting VV&A processes on other operational systems may benefit from using this test for their own purposes.

Energetic defect characterizations in munitions is a task requiring further refinement in military manufacturing processes. Convolutional neural networks (CNN) have shown promise in defect localization and segmentation in recent studies. These studies supplement that we may utilize a CNN architecture to localize casting defects in X-ray images. The U.S. Armament center has provided munition images for training to develop a system against MILSPEC requirements to identify and categorize defect munitions. In our approach, we utilize preprocessed munitions images and transfer learning from prior studies’ model weights to compare the localization accuracy of this dataset for application in the field.

Resilience is the ability of a system to respond, absorb, adapt, and recover from a disruptive event. Dozens of metrics to quantify resilience have been proposed in the literature. However, fewer studies have proposed models to predict these metrics or the time at which a system will be restored to its nominal performance level after experiencing degradation. This talk presents three alternative approaches to model and predict performance and resilience metrics with techniques from reliability engineering, including (i) bathtub-shaped hazard functions, (ii) mixture distributions, and (iii) a model incorporating covariates related to the intensity of events that degrade performance as well as efforts to restore performance. Historical data sets on job losses during seven different recessions in the United States are used to assess the predictive accuracy of these approaches, including the recession that began in 2020 due to COVID-19. Goodness of fit measures and confidence intervals as well as interval-based resilience metrics are computed to assess how well the models perform on the data sets considered. The results suggest that both bathtub-shaped functions and mixture distributions can produce accurate predictions for data sets exhibiting V, U, L, and J shaped curves, but that W and K shaped curves that respectively experience multiple shocks, deviate from the assumption of a single decrease and subsequent increase, or suffers a sudden drop in performance cannot be characterized well by either of those classes proposed. In contrast, the model incorporating covariates is capable of tracking all of types of curves noted above very well, including W and K shaped curves such as the two successive shocks the U.S. economy experienced in 1980 and the sharp degradation in 2020. Moreover, covariate models outperform the simpler models on all of the goodness of fit measures and interval-based resilience metrics computed for all seven data sets considered. These results suggest that classical reliability modeling techniques such as bathtub-shaped hazard functions and mixture distributions are suitable for modeling and prediction of some resilience curves possessing a single decrease and subsequent recovery, but that covariate models to explicitly incorporate explanatory factors and domain specific information are much more flexible and achieve higher goodness of fit and greater predictive accuracy. Thus, the covariate modeling approach provides a general framework for data collection and predictive modeling for a variety of resilience curves.

Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs) are susceptible to adversarial scenarios. In these scenarios, an attacker attempts to manipulate or deceive a machine learning model by providing it with malicious input, necessitating quantitative reliability and resilience evaluation of ML algorithms. This can result in the model making incorrect predictions or decisions, which can have severe consequences in applications such as security, healthcare, and finance. Failure in the ML algorithm can lead not just to failures in the application domain but also to the system to which they provide functionality, which may have a performance requirement, hence the need for the application of software reliability and resilience. This talk demonstrates the applicability of software reliability and resilience tools to ML algorithms providing an objective approach to assess recovery after a degradation from known adversarial attacks. The results indicate that software reliability growth models and tools can be used to monitor the performance and quantify the reliability and resilience of ML models in the many domains in which machine learning algorithms are applied.

Traditional software reliability growth models (SRGM) characterize software defect detection as a function of testing time. Many of those SRGM are modeled by the non-homogeneous Poisson process (NHPP). However, those models are parametric in nature and do not explicitly encode factors driving defect or vulnerability discovery. Moreover, NHPP models are characterized by a mean value function that predicts the average of the number of defects discovered by a certain point in time during the testing interval, but may not capture all changes and details present in the data and do not consider them. More recent studies proposed SRGM incorporating covariates, where defect discovery is a function of one or more test activities documented and recorded during the testing process. These covariate models introduce an additional parameter per testing activity, which adds a high degree of non-linearity to traditional NHPP models, and parameter estimation becomes complex since it is limited to maximum likelihood estimation or expectation maximization. Therefore, this talk assesses the potential use of neural networks to predict software defects due to their ability to remember trends. Three different neural networks are considered, including (i) Recurrent neural networks (RNNs), (ii) Long short-term memory (LSTM), and (iii) Gated recurrent unit (GRU) to predict software defects. The neural network approaches are compared with the covariate model to evaluate the ability in predictions. Results suggest that GRU and LSTM present better goodness-of-fit measures such as SSE, PSSE, and MAPE compared to RNN and covariate models, indicating more accurate predictions.

The purpose of this research is to see the use and application of Topological Data Analysis (TDA) in the real of Cyber Security. The methods used in this research include an exploration of different Python libraries or C++ python interfaces in order to explore the shape of data that is involved using TDA. These methods include, but are not limited to, the GUDHI, GIOTTO, and Scikit-tda libraries. The project’s results will show where the literal holes in cyber security lie and will offer methods on how to better analyze these holes and breaches.

A shortfall of the Derringer and Suich (1980) desirability function for multi-objective optimization has been a lack of inferential methods to quantify uncertainty. Most articles for addressing uncertainty involve robust methods, providing a point estimate that is less affected by variation. Few articles address confidence intervals or bands but not specifically for the widely used Derringer and Suich method. 8 methods are presented to construct 100(1-alpha) confidence intervals around Derringer and Suich desirability function optimal values. First order and second order models using bivariate and multivariate data sets are used as examples to demonstrate effectiveness. The 8 proposed methods include a simple best/worst case method, 2 generalized methods, 4 simulated surface methods, and a nonparametric bootstrap method. One of the generalized methods, 2 of the simulated surface methods, and the nonparametric method account for covariance between the response surfaces. All 8 methods seem to perform decently on the second order models; however, the methods which utilize an underlying multivariate-t distribution, Multivariate Generalized (MG) and Multivariate t Simulated Surface (MVtSSig) are recommended methods from this research as they perform well with small samples for both first order and second order models with coverage only becoming unreliable at consistently non-optimal solutions. MG and MVtSSig inference could also be used in conjunction with robust methods such as Pareto Front Optimization to help ascertain which solutions are more likely to be optimal before constructing confidence interval.

The purpose of the Skyborg Data Pipeline is to allow for the rapid turnover of flight data collect during a test event, using collaborative easily access tool sets available in the AFOTEC Data Vault. Ultimately the goal of this data pipeline is to provide a working up to date dashboard that leadership can utilize shortly after a test event.

Circular Error Probable (CEP) is a measure of a weapon system’s precision developed based on the Bivariate Normal Distribution. Failing to understanding the theory behind CEP can result in misuse of equations developed to help estimation. Estimation of CEP is also much more straightforward given situations such as single samples where factors are not being manipulated. This brief aims to help build a theoretical understanding of CEP, and then presents a non-trivial example in which CEP is estimated via multilevel regression. The goal is to help build an understanding of CEP so it can be properly estimated in trivial (single sample) and non-trivial cases (e.g. regression and multilevel regression).

Ranking data are collected by presenting a respondent with a list of choices, and then asking what are the respondent’s favorite, second favorite, and so on. The rankings may be complete, the respondent rank orders the complete list, or partial, only the respondent’s favorite two or three, etc. Given a sample of rankings from a population, one goal may be to estimate the most favored choice from the population. Another may be to compare the preferences of one subpopulation to another. In this presentation I will introduce ranking data and probability models that form the foundation for statistical inference for them. The Plackette-Luce model will be the main focus. After that I will introduce a real data set containing ranking data assembled by the National Institute of Standards and Technology (NIST) based on the results of a national survey of first responders. The survey asked about how first responders use communication technology. With this data set, questions such as do rural and urban/suburban first responders prefer the same types communication devices, can be explored. I will conclude with some ideas for incorporating rankning data into test and evaluation settings.

With the rise in availability of larger datasets, there is a growing need of tools and methods to help inform data-driven decisions. Data that vary over a continuum, such as time, exist in a wide array of fields, such as defense, finance, and medicine. One such class of methods that addresses data varying over a continuum is functional data analysis (FDA). FDA methods typically make three assumptions that are often violated in real datasets: all observations exist over the same continuum interval (such as a closed interval [a,b]), all observations are regularly and densely observed, and if the dataset consists of multiple covariates, the covariates are independent of one another. We look to address violation of the latter two assumptions. In this talk, we will discuss methods for analyzing functional data that are irregularly and sparsely observed, while also accounting for dependencies between covariates. These methods will be used to estimate the reconstruction of partially observed multivariate functional data that contain measurement errors. We will begin with a high-level introduction of FDA. Next, we will introduce functional principal components analysis (FPCA), which is a representation of functions that our estimation methods are based on. We will discuss a specific approach called principal components analysis through conditional expectation (PACE) (Yao et al, 2005), which computes the FPCA quantities for a sparsely or irregularly sampled function. The PACE method is a key component that allows us to estimate partially observed functions based on the available dataset. Finally, we will introduce multivariate functional principal components analysis (MFPCA) (Happ & Greven, 2018), which utilizes the FPCA representations of each covariate’s functions in order to compute a principal components representation that accounts for dependencies between covariates. We will illustrate these methods through implementation on simulated and real datasets. We will discuss our findings in terms of the accuracy of our estimates with regards to the amount and portions of a function that is observed, as well as the diversity of functional observations in the dataset. We will conclude our talk with discussion on future research directions.