Engineering topics

Sanfilippo, Antonio P.

Publications and source records attributed to Sanfilippo, Antonio P..

6 records

DOE OSTI2014

Current Trends in the Detection of Sociocultural Signatures: Data-Driven Models

The harvesting of behavioral data and their analysis through evidence-based reasoning enable the detection of sociocultural signatures in their context to support situation awareness and decision making. Harvested data are used as training materials from which to infer computational models of sociocultural behaviors or calibrate parameters for such models. Harvested data also serve as evidence input that the models use to provide insights about observed and future behaviors for targets of interest. The harvested data is often the result of assembling diverse data types and aggregating them into a form suitable for analysis. Data need to be analyzed to bring out the categories of content that are relevant to the domain being addressed in order to train or run a model. If, for example, we are modeling the intent of a group to engage in violent behavior using messages that the group has broadcasted, then these messages need to be processed to extract and measure indicators of violent intent. The extracted indicators and the associated measurements (e.g. rates or counts of occurrence) can then be used to train/calibrate and run computational models that assess the propensity for violence expressed in the source message. Ubiquitous access to the Internet, mobile telephony and technologies such as digital photography and digital video have enabled social media application platforms such as Facebook, YouTube, and Twitter that are altering the nature of human social interaction. The fast increasing pace of online social interaction introduces new challenges and opportunities for gathering sociocultural data. Challenges include the development of harvesting and processing techniques tailored to new data environments and formats (e.g. Twitter, Facebook), the integration of social media content with traditional media content, and the protection of personal privacy. As these and other challenges are addressed, a new wealth of behavioral data and data analysis methods becomes available that are shaping social computing as a strongly data-driven experimental discipline with an increasingly stronger impact on the decision-making process of groups and individuals alike. In this chapter, we review current advances and trends in the detection of sociocultural signatures. Specific embodiments of the issues discussed are provided with respect to the assessment of violent intent and sociopolitical contention. We begin by reviewing current approaches to the detection of sociocultural signatures in these domains. Next, we turn to the review of novel data harvesting methods for social media content. Finally, we discuss the application of sociocultural models to social media content, and conclude by commenting on current challenges and future developments.

Sanfilippo, Antonio P.↗

DOE OSTI2014

Computer Modeling of Violent Intent: A Content Analysis Approach

We present a computational approach to modeling the intent of a communication source representing a group or an individual to engage in violent behavior. Our aim is to identify and rank aspects of radical rhetoric that are endogenously related to violent intent to predict the potential for violence as encoded in written or spoken language. We use correlations between contentious rhetoric and the propensity for violent behavior found in documents from radical terrorist and non-terrorist groups and individuals to train and evaluate models of violent intent. We then apply these models to unseen instances of linguistic behavior to detect signs of contention that have a positive correlation with violent intent factors. Of particular interest is the application of violent intent models to social media, such as Twitter, that have proved to serve as effective channels in furthering sociopolitical change.

Sanfilippo, Antonio P.↗

DOE OSTI2013

Psychosocial and Cultural Modeling in Human Computation Systems: A Gamification Approach

“Gamification”, the application of gameplay to real-world problems, enables the development of human computation systems that support decision-making through the integration of social and machine intelligence. One of gamification’s major benefits includes the creation of a problem solving environment where the influence of cognitive and cultural biases on human judgment can be curtailed through collaborative and competitive reasoning. By reducing biases on human judgment, gamification allows human computation systems to exploit human creativity relatively unhindered by human error. Operationally, gamification uses simulation to harvest human behavioral data that provide valuable insights for the solution of real-world problems.

Sanfilippo, Antonio P.↗

DOE OSTI2013

Automated Detection of Anomalous Shipping Manifests to Identify Illicit Trade

We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.

classification↗

DOE OSTI2013

Modeling Emergence in Neuroprotective Regulatory Networks

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.

agent-based modeling↗

DOE OSTI2012

Technosocial Predictive Analytics for Security Informatics

Challenges to the security, health, and sustainable growth of our society keep escalating asymmetrically due to the growing pace of globalization and global change. The increasing velocity of information sharing, social networking, economic forces, and environmental change has resulted in a rapid increase in the number and frequency of “game-changing moments” that a community can face. Social movements that once took a decade to build now take a year; shifts in public opinion that once took a year to take root now take a couple of months. More and more frequently, these critical moments occur too suddenly for the affected communities to succeed in countering the consequent adversities or seizing the emerging opportunities. Now more than ever, we need anticipatory reasoning technologies to forecast and manage change in order to secure and improve our way of life and the environment we inhabit.

analytical gaming↗