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At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Analyst-to-Analyst Variability in Simulation-Based Prediction

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Creating an Interprocedural Analyst-Oriented Data Flow Representation for Binary Analysts (CIAO)

National security missions require understanding third-party software binaries, a key element of which is reasoning about how data flows through a program. However, vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. To reduce the human time burden for these analysts, we used human factors methods in a rolling discovery process to derive user-centric visual representation requirements. We encountered three main challenges: analysis projects span weeks, analysis goals significantly affect approaches and required knowledge, and analyst tools, techniques, conventions, and prioritization are based on personal preference. To address these challenges, we initially focused our human factors methods on an attack surface characterization task. We generalized our results using a two-stage modified sorting task, creating requirements for a data flow visualization. We implemented these requirements partially in manual static visualizations, which we informally evaluated, and partially in automatically generated interactive visualizations, which have yet to be integrated into workflows for evaluation. Our observations and results indicate that 1) this data flow visualization has the potential to enable novel code navigation, information presentation, and information sharing, and 2) it is an excellent time to pursue research applying human factors methods to binary analysis workflows.

42 ENGINEERING↗

NASA NTRS1988

Analyst-centered models for systems design, analysis, and development

Much has been written about the possible use of Expert Systems (ES) technology for strategic defense system applications, particularly for battle management algorithms and mission planning. It is proposed that ES (or more accurately, Knowledge Based System (KBS)) technology can be used in situations for which no human expert exists, namely to create design and analysis environments that allow an analyst to rapidly pose many different possible problem resolutions in game like fashion and to then work through the solution space in search of the optimal solution. Portions of such an environment exist for expensive AI hardware/software combinations such as the Xerox LOOPS and Intellicorp KEE systems. Efforts are discussed to build an analyst centered model (ACM) using an ES programming environment, ExperOPS5 for a simple missile system tradeoff study. By analyst centered, it is meant that the focus of learning is for the benefit of the analyst, not the model. The model's environment allows the analyst to pose a variety of what if questions without resorting to programming changes. Although not an ES per se, the ACM would allow for a design and analysis environment that is much superior to that of current technologies.

Bukley, A. P.↗

NASA NTRS1992

Analyst variability in labeling of unsupervised classifications

Analyst variability in the labeling of unsupervised classifications is tested for Landsat 5 Thematic Mapper image products covering two test sites in southern California. The accuracy of results are tested using samples from a photo interpreted base map of the area. The significance of differences between analysts is indicated by comparing Kappa statistics derived from error matrices. Analyst variability is found to be statistically significant in most cases. Certain analysts provided consistently better results for a given study area or degree of training. This work demonstrates the potential influence of analyst bias on what would otherwise seem to be a fairly objective method and suggests that controls for this subjectivity should be factored into experimental designs.

Mcgwire, Kenneth C.↗

NASA NTRS1993

The Generic Spacecraft Analyst Assistant (gensaa): a Tool for Developing Graphical Expert Systems

During numerous contacts with a satellite each day, spacecraft analysts must closely monitor real-time data. The analysts must watch for combinations of telemetry parameter values, trends, and other indications that may signify a problem or failure. As the satellites become more complex and the number of data items increases, this task is becoming increasingly difficult for humans to perform at acceptable performance levels. At NASA GSFC, fault-isolation expert systems are in operation supporting this data monitoring task. Based on the lessons learned during these initial efforts in expert system automation, a new domain-specific expert system development tool named the Generic Spacecraft Analyst Assistant (GenSAA) is being developed to facilitate the rapid development and reuse of real-time expert systems to serve as fault-isolation assistants for spacecraft analysts. Although initially domain-specific in nature, this powerful tool will readily support the development of highly graphical expert systems for data monitoring purposes throughout the space and commercial industry.

Hughes, Peter M.↗

DOE OSTI2011

Visual Text Analytics for Impromptu Analysts

The Scalable Reasoning System (SRS) is a lightweight visual analytics framework that makes analytical capabilities widely accessible to a class of users we have deemed “impromptu analysts.” By focusing on a deployment of SRS, the Lessons Learned Explorer (LLEx), we examine how to develop visualizations around analytical-oriented goals and data availability. We discuss how to help impromptu analysts to explore deeper patterns. Through designing consistent interactions, we arrive at an interdependent view capable of showcasing patterns. With the combination of SRS widget visualizations and interactions around the underlying textual data, we aim to transition the casual, infrequent user into a viable–albeit impromptu–analyst.

Analyst↗

DOE OSTI2022

Applying Waveform Correlation and Waveform Template Metadata to Mining Blasts to Reduce Analyst Workload

Organizations that monitor for underground nuclear explosive tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high - quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. In this study we use waveform template event metadata to seek corroborating detections from multiple stations in the International Monitoring System of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization. We build upon events detected in a prior waveform correlation study of mining blasts in two geographic regions, Wyoming and Scandinavia. Using a set of expert analyst-reviewed waveform correlation events that were declared to be true positive detections, we explore criteria for choosing the waveform correlation detections that are most likely to lead to bulletin-worthy events and reduction of analyst effort.

47 OTHER INSTRUMENTATION↗

NASA NTRS1979

USDA analyst review of the LACIE IMAGE-100 hybrid system test

Fifty operational segments from the U.S.S.R., 40 test segments from Canada, and 24 test segments from the United States were used to provide a wide range of geographic conditions for USDA analysts during a test to determine the effectiveness of labeling single pixel training fields (dots) using Procedure 1 on the 1-100 hybrid system, and clustering and classifying on the Earth Resources Interactive Processing System. The analysts had additional on-line capabilities such as interactive dot labeling, class or cluster map overlay flickers, and flashing of all dots of equal spectral value. Results on the 1-100 hybrid system are described and analyst problems and recommendations are discussed.

Ashburn, P.↗

NASA NTRS1991

The Generic Spacecraft Analyst Assistant (GenSAA): A tool for automating spacecraft monitoring with expert systems

Flight Operations Analysts (FOAs) in the Payload Operations Control Center (POCC) are responsible for monitoring a satellite's health and safety. As satellites become more complex and data rates increase, FOAs are quickly approaching a level of information saturation. The FOAs in the spacecraft control center for the COBE (Cosmic Background Explorer) satellite are currently using a fault isolation expert system named the Communications Link Expert Assistance Resource (CLEAR), to assist in isolating and correcting communications link faults. Due to the success of CLEAR and several other systems in the control center domain, many other monitoring and fault isolation expert systems will likely be developed to support control center operations during the early 1990s. To facilitate the development of these systems, a project was initiated to develop a domain specific tool, named the Generic Spacecraft Analyst Assistant (GenSAA). GenSAA will enable spacecraft analysts to easily build simple real-time expert systems that perform spacecraft monitoring and fault isolation functions. Lessons learned during the development of several expert systems at Goddard, thereby establishing the foundation of GenSAA's objectives and offering insights in how problems may be avoided in future project, are described. This is followed by a description of the capabilities, architecture, and usage of GenSAA along with a discussion of its application to future NASA missions.

Hughes, Peter M.↗

NASA NTRS2011

Multi-Instance Learning Models for Automated Support of Analysts in Simulated Surveillance Environments

New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.

Birisan, Mihnea↗

DOE OSTI2018

Women in Science: Fermilab cybersecurity analyst Jeny Teheran

With 11 February marking the International Day of Women and Girls in Science, female physicists, engineers and computer scientists from CERN and from Fermilab share their experiences of building a career in science. Jeny Teheran is a security analyst and cybersecurity researcher at Fermi National Accelerator Laboratory. That means keeping up with and taking care of hardware and software vulnerabilities so that the experiments can carry out their science in a secure manner. It's a fast-paced job where you have to come with the best solution you can put in place, right in the moment. "I would recommend this job because it challenges you. It pushes you to be on top of your game. You have to improve your analytical skills; you have to react fast; you have to communicate better." - Jeny says. Jeny came to Fermilab from the Caribbean coast of Colombia. She grew up in a house with few toys but lots of books, and says she has always felt close to science. With a degree in systems and computing engineering, she arrived at Fermilab four years ago as an intern to work on the offline production team for neutrino experiments. A year later, she was hired as a security analyst. "And I'm loving it," she says. Credits: Video production - Fermilab Creative Services Music Copyright: Universal Production Music Astral Light Chevalier Idea originally conceived by Tanya Levshina, Fermilab and Maria Dimou, CERN.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

How Analysts Cognitively “Connect the Dots”

As analysts attempt to make sense of a collection of documents, such as intelligence analysis reports, they may wish to “connect the dots” between pieces of information that may initially seem unrelated. This process of synthesizing information between information requires users to make connections between pairs of documents, creating a conceptual story. We conducted a user study to analyze the process by which users connect pairs of documents and how they spatially arrange information. Users created conceptual stories that connected the dots using organizational strategies that ranged in complexity. We propose taxonomies for cognitive connections and physical structures used when trying to “connect the dots” between two documents. We compared the user-created stories with a data-mining algorithm that constructs chains of documents using co-occurrence metrics. Using the insight gained into the storytelling process, we offer design considerations for the existing data mining algorithm and corresponding tools to combine the power of data mining and the complex cognitive processing of analysts.

data mining↗

DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗

DOE OSTI2016

Special Nuclear Material Gamma-Ray Signatures for Reachback Analysts

These are slides on special nuclear material gamma-ray signatures for reachback analysts for an LSS Spectroscopy course. The closing thoughts for this presentation are the following: SNM materials have definite spectral signatures that should be readily recognizable to analysts in both bare and shielded configurations. One can estimate burnup of plutonium using certain pairs of peaks that are a few keV apart. In most cases, one cannot reliably estimate uranium enrichment in an analogous way to the estimation of plutonium burnup. The origin of the most intense peaks from some SNM items may be indirect and from ‘associated nuclides.' Indirect SNM signatures sometimes have commonalities with the natural gamma-ray background.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

SNL will be able to provide well-calibrated models to weapons systems analysts by integrating an engine for constitutive model calibration with the GRANTA materials database

Researchers at Sandia National Laboratories have integrated the GRANTA materials database with the MatCal calibration engine to calibrate material models from characterization data. GRANTA is gaining acceptance across the NNSA Tri-lab complex and is being populated with weapons-specific test data by Sandia experimentalists. To use that data to create material models for use by weapons systems analysts, MatCal has been enabled import calibration data and test conditions from GRANTA to quickly and reproducibly produce a calibrated set of parameters for a given constitutive model. The team is currently working to store the parameters characterizing material behavior in GRANTA to make them accessible by all weapons analysts.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Applying Waveform Correlation and Waveform Template Metadata to Aftershocks in the Middle East to Reduce Analyst Workload

Organizations that monitor for underground nuclear explosive tests are interested in techniques that automatically characterize recurring events such as aftershocks to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is a technique that is effective in finding similar waveforms from repeating seismic events. In this study, we apply waveform correlation in combination with template event metadata to two aftershock sequences in the Middle East to seek corroborating detections from multiple stations in the International Monitoring System of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization. We use waveform templates from stations that are within regional distance of aftershock sequences to detect subsequent events, then use template event metadata to discover what stations are likely to record corroborating arrival waveforms for recurring aftershock events at the same location, and develop additional waveform templates to seek corroborating detections. We evaluate the results with the goal of determining whether applying the method to aftershock events will improve the choice of waveform correlation detections that lead to bulletin-worthy events and reduction of analyst effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

DOE Radiological Triage Program: Analyst Open Book Exam 2021

This document contains the open book exam for Radiological Triage Program Analysts for Los Alamos National Laboratory. "The following questions test your knowledge relevant to the DOE Radiological Triage program. There is no time limit, and you may use any reference materials or software you like. You may discuss with other analysts but please show your own work. Spectra are provided in electronic form. There are 10 questions and up to 1 point of extra credit is available. A score of 80% is required to pass. Please send your answers in any convenient form to Mercer@LANL.gov."

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

DOE Radiological Triage Program Analyst Open Book Exam 2022

This document contains the 2022 open book examination for Radiological Triage Program Analysts for Los Alamos National Laboratory. "The following questions test your knowledge relevant to the DOE Radiological Triage program. There is no time limit, you may use any reference materials or software you like, and you may discuss with other analysts. No electronic spectra are provided or needed. Please send your answers in any convenient form to Mercer@LANL.gov. You may also take the exam using Survey Monkey (which will provide immediate feedback). A score of 80% is required to pass. There are 25 multiple-choice questions each worth 1 point."

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗