We are working to create the first automatic deductive synthesis of a unification algorithm. The program is extracted from a proof of the existence of an output substitution that satisfies a given logical specification.
Cyber & formal methods publications
Semantic Instrumentation of Virtual Environments for Training
Interactive virtual environments are useful tools for hands-on learning or rehearsing of procedural tasks. However, task training applications typically provide a constrained course of action for the learner, forcing them down a single specific solution path. We discuss an approach in which the virtual environment is semantically instrumented in order to allow for the tracking of and reasoning about open-ended learner activity therein. Our approach leverages ontology-based knowledge representation which allows for a structured and meaningful description of virtual objects and of the learner actions that may be performed upon them. This is facilitated by the association of specific ontological classes with geometric components of the objects which populate a training exercise. These classes, together with their attributes, relationships and rules, characterize the environment and user actions in a readily understandable manner. As a result, a training system is able to observe the learner activity, render an assessment of that activity, and provide meaningful feedback to the learner. We also present an authoring tool which allows content developers to semantically annotate three-dimensional models for such an environment.
Diagnosis cloud: Sharing knowledge across cellular networks
This paper presents a novel diagnosis cloud framework that enables the extraction and transfer of knowledge from one network to another. It also presents use cases and requirements. We present the implementation details of the diagnosis cloud framework for two specific types of models: topic models and Markov Logic Networks (MLNs).
Keys Under Doormats
In this report, a group of computer scientists and security experts, many of whom participated in a 1997 study of these same topics, has convened to explore the likely effects of imposing extraordinary access mandates.
Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery
We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked.
Weakness in Depth: A Voting Machine’s Demise
Virginia’s government recently examined the AVS WinVote and learned that the vulnerabilities are more serious than any other voting system, allowing complete exploitation over a Wi-Fi network.
Discrete Vs. Dense Times in the Analysis of Cyber-Physical Security Protocols
This paper investigates the foundational differences and the impacts on the analysis when using models with discrete time and models with dense time.
The SRI AVEC-2014 Evaluation System
We explore a diverse set of features based only on spoken audio to understand which features correlate with self-reported depression scores according to the Beck depression rating scale.
Safety Envelope for Security
We present an approach for detecting sensor spoofing attacks on a cyber-physical system. Our approach consists of two steps. In the first step, we construct a safety envelope of the system. Under nominal conditions (that is, when there are no attacks), the system always stays inside its safety envelope. In the second step, we build an attack detector: a monitor that executes synchronously with the system and raises an alarm whenever the system state falls outside the safety envelope. We synthesize safety envelopes using a modifed machine learning procedure applied on data collected from the system when it is not under attack. We present experimental results that show effectiveness of our approach, and also validate the several novel features that we introduced in our learning procedure.