This report describes the results of a seven day effort to assist subject matter experts address a problem related to COVID-19. In the course of this effort, we analyzed the 29K documents provided as part of the White House's call to action. This involved applying a variety of natural language processing techniques and compression-based analytics in combination with visualization techniques and assessment with subject matter experts to pursue answers to a specific question. In this paper, we will describe the algorithms, the software, the study performed, and availability of the software developed during the effort.
Children with autism spectrum disorders (ASD) and their unaffected siblings (US) are frequent targets of social bullying, which leads to severe physical, emotional, and social consequences. Understanding the risk factors is essential for developing preventative measures. We suggest that one such risk factor may be a difficulty to discriminate different biological body movements (BBM), a task that requires fast and flexible processing and interpretation of complex visual cues, especially during social interactions. Deficits in cognition of BBM have been reported in ASD. Since US display an autism endophenotype we expect that they will also display deficits in social interpretation of BBM. Methods. Participants: 8 US, 8 matched TD children, age 7-14; Tasks/Measurements: Social Blue Man Task: Narrative interpretation with a Latent Dirichlet Allocation [LDA] analysis; Social Experience Questionnaires with children and parents. Results. The US displayed as compared to TD: (i) low self-awareness of social bullying in contrast to high parental reports; (ii) reduced speed in identifying social cues; (iii) lower quality and repetitious wording in social interaction narratives (LDA). Conclusions. US demonstrate social endophenotype of autism reflected in delayed identification, interpretation and verbalization of social cues; these may constitute a high risk factor for becoming a victim of social bullying.
Analysts across national security domains are required to sift through large amounts of data to find and compile relevant information in a form that enables decision makers to take action in high-consequence scenarios. However, even the most experienced analysts are unable to be 100 % consistent and accurate based on the entire dataset, unbiased towards familiar documentation, and are unable to synthesize and process large amounts of information in a small amount of time. Sandia National Laboratories has attempted to solve this problem by developing an intelligent web crawler called Huntsman. Huntsman acts as a personal research assistant by browsing the internet or offline datasets in a way similar to the human search process, only much faster (millions of documents per day), by submitting queries to search engines and assessing the usefulness of page results through analysis of full-page content with a suite of text analytics. This paper will discuss Huntsman’s capability to both mirror and enhance human analysts using intelligent web crawling with analysts-in-the-loop. The goal is to demonstrate how weaknesses in human cognitive processing can be compensated for by fusing human processes with text analytics and web crawling systems, which ultimately reduces analysts’ cognitive burden and increases mission effectiveness.
Human performance has become a pertinent issue within cyber security. However, this research has been stymied by the limited availability of expert cyber security professionals. This is partly attributable to the ongoing workload faced by cyber security professionals, which is compound ed by the limited number of qualified personnel and turnover of personnel across organizations. Additionally, it is difficult to conduct research, and particularly, openly published research, due to the sensitivity inherent to cyber ope rations at most organizations. As an alternative, the current research has focused on data collection during cyber security training exercises. These events draw individuals with a range of knowledge and experience extending from seasoned professionals to recent college graduates to college students. The current paper describes research involving data collection at two separate cyber security exercises. This data collection involved multiple measures which included behavioral performance based on human - machine transactions and questionnaire - based assessments of cyber security experience.
A hypothetical scenario is utilized to explore privacy and security considerations for intelligent systems, such as a Personal Assistant for Learning (PAL). Two categories of potential concerns are addressed: factors facilitated by user models, and factors facilitated by systems. Among the strategies presented for risk mitigation is a call for ongoing, iterative dialog among privacy, security, and personalization researchers during all stages of development, testing, and deployment.
There is a great need for creating cohesive, expert cybersecurity incident response teams and training them effectively. This paper discusses new methodologies for measuring and understanding expert and novice differences within a cybersecurity environment to bolster training, selection, and teaming. This methodology for baselining and characterizing individuals and teams relies on relating eye tracking gaze patterns to psychological assessments, human-machine transaction monitoring, and electroencephalography data that are collected during participation in the game-based training platform Tracer FIRE. We discuss preliminary findings from two pilot studies using novice and professional teams.