Jim N. Treadwell

A Stigmergy Approach for Open Source Software Developer Community Simulation

The stigmergy collaboration approach provides a hypothesized explanation about how online groups work together. In this research, we presented a stigmergy approach for building an agent based open source software (OSS) developer community collaboration simulation. We used group of actors who collaborate on OSS projects as our frame of reference and investigated how the choices actors make in contribution their work on the projects determinate the global status of the whole OSS projects. In our simulation, the forum posts and project codes served as the digital pheromone and the modified Pierre-Paul Grasse pheromone model is used for computing developer agent behaviors selection probability.

A genetic algorithm for learning significant phrase patterns in radiology reports

Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. Recently, the focus has been on classifying abnormal or suspicious reports, but even this process needs further layers of clustering and gradation, so that individual lesions can be more effectively classified. Using a genetic algorithm, the approach described here successfully learns phrase patterns for two distinct classes of radiology reports (normal and abnormal). These patterns can then be used as a basis for automatically analyzing, categorizing, clustering, or retrieving relevant radiology reports for the user.

A Text Analysis Approach to Motivate Knowledge Sharing via Microsoft SharePoint

Creating incentives for knowledge workers to share their knowledge within an organization continues to be a challenging task. Strong, innate behaviors of the knowledge worker, such as self-preservation and self-advancement, are difficult to overcome, regardless of the level of knowledge. Many incentive policies simply focus on providing external pressure to promote knowledge sharing. This work describes a technical approach to motivate sharing. Utilizing text analysis and machine learning techniques to create an enhanced knowledge sharing experience, a prototype system was developed and tested at Oak Ridge National Laboratory that reduces the overhead cost of sharing while providing a quick, positive payoff for the knowledge worker. This work describes the implementation and experiences of using the prototype in a corporate production environment.

Hierarchical clustering and visualization of aggregate cyber data

Most commercial intrusion detections systems (IDS) can produce a very high volume of alerts, and are typically plagued by a high false positive rate. The approach described here uses Splunk to aggregate IDS alerts. The aggregated IDS alerts are retrieved from Splunk programmatically and are then clustered using text analysis and visualized using a sunburst diagram to provide an additional understanding of the data. The equivalent of what the cluster analysis and visualization provides would require numerous detailed queries using Splunk and considerable manual effort.

The Swarm Model in Open Source Software Developer Communities

Most of the current swarm model studies and applications try to mimic the collective behaviors of social animals, such as birds and ants. The studies seek to solve tasks similar to patterns and behaviors exhibited in those animal colonies. In this research, we demonstrated that the swarm model is also the major collaboration and organization model of Open Source Software (OSS) developer communities. OSS developers swarm together and spend their time attempting to achieve their relatively simple goals, while their contributions emerged as a collection of useful and sophisticated functionality that can compete with commercial software. The results discovered in this research will be helpful in demonstrating that the swarm model can not only be considered as a feasible approach to classical optimization problems, but can also be applied to constructing highly sophisticated systems.

Observing Community Resiliency in Social Media

In spite of social media’s lack of structural integrity, accuracy, and reduced noise with respect to other forms of communication, it plays an increasingly vital role in the observation of societal actions before, during, and after significant events. In October 2012, Hurricane Sandy making landfall on the northeastern coasts of the United States demonstrated this role. This work provides a preliminary view into how social media could be used to monitor and gauge community resilience to such natural disasters. We observe, evaluate, and visualize how Twitter data evolves over time before, during, and after a natural disaster such as Hurricane Sandy and what opportunities there may be to leverage social media for situational awareness and emergency response.

Decision-level information fusion to assess threat likelihood in shipped containers

A significant challenge in the distribution of goods is assessing the potential threat that an individual shipping container poses. Due to the high volume of shipped goods, a primary concern is balancing accuracy and container scan time. The application of information fusion to the problem enables automated threat determination and the presentation of relevant data to an operator, in a decision support capacity, in order to maintain a sufficient level of processing. This paper outlines an approach to container threat assessment that combines data from multiple sources in order to reliably score the likelihood that a given container holds a threat. Fused data is also leveraged as a tool to optimize the routing of containers through a scanning system comprised of multiple data acquisition stations and providing data in multiple modes. Furthermore, we propose methods for the consolidated presentation of fused data to an operator in order to both minimize the time expended in container evaluation and maximize the accuracy of the assessment.

Particle Swarm Social Model for Group Social Learning in Adaptive Environment

This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as human communities.

ShadowNet: an active defense infrastructure for insider cyber attack prevention

The ShadowNet infrastructure for insider cyber attack prevention is comprised of a tiered server system that is able to dynamically redirect dangerous/suspicious network traffic away from production servers that provide web, ftp, database and other vital services to cloned virtual machines in a quarantined environment. This is done transparently from the point of view of both the attacker and normal users. Existing connections, such as SSH sessions, are not interrupted. Any malicious activity performed by the attacker on a quarantined server is not reflected on the production server. The attacker is provided services from the quarantined server, which creates the impression that the attacks performed are successful. The activities of the attacker on the quarantined system are able to be recorded much like a honeypot system for forensic analysis.

Knowledge Discovery, Knowledge Management and Enterprise-Wide Information Technology Tools Final Report

A final report on an ORNL task to establish a knowledge discovery and management tool to retrieve and recommend information from existing S&T documents for the Office of Naval Research Global.