Jonathan T. McClain

Evaluation of the Bible as a Resource for Cross-Language Information Retrieval

An area of recent interest in cross-language information retrieval (CLIR) is the question of which parallel corpora might be best suited to tasks in CLIR, or even to what extent parallel corpora can be obtained or are necessary. One proposal, which in our opinion has been somewhat overlooked, is that the Bible holds a unique value as a multilingual corpus, being (among other things) widely available in a broad range of languages and having a high coverage of modern-day vocabulary. In this paper, we test empirically whether this claim is justified through a series of validation tests on various information retrieval tasks. Our results appear to indicate that our methodology may significantly outperform others recently proposed.

Measuring Expert and Novice Performance Within Computer Security Incident Response Teams

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.

Data Privacy and Security Considerations for Personal Assistants for Learning (PAL)

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.

The Controlled, Large Online Social Experimentation Platform (CLOSE)

We present a new platform to do online, social influence experiments – the Controlled, Online Social Experimentation (CLOSE) system. We describe it’s development, potential uses and justification for use. The CLOSE platform can be used to do long term (weeks to months) experiments in which we can manipulate the interaction networks (within the experiment) of a diverse (drawn from Amazon Mechanical Turk (AMT)) subject pool.

Measuring Human Performance within Computer Security Incident Response Teams

Human performance has become a pertinen t 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 p ersonnel 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 orga nizations. As an alternative, the current research has focused on data collection during cyb er security training exercises. These events draw individuals with a range of knowledge and experience extending from seasoned professionals to recent college gradu ates 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.

Analysis of Social Interaction Narratives in Unaffected Siblings of Children with ASD Through Latent Dirichlet Allocation

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.

A neurophysiologically inspired hippocampus based associative-ART Artificial neural network architecture

Hippocampus within medial temporal lobe of the brain is essentially involved in episodic memory formation. Rather than simply being a mechanism of storing information, episodic memory associates information such as the spatial and temporal context of an event. Using hippocampus neurophysiology and functionality as an inspiration, we have developed an artificial neural network architecture called Associative-ART to associate k-tuples of inputs. In this paper we present an overview of hippocampus neurophysiology, explain the design of our neural network architecture, and present experimental results from an implementation of our architecture.