Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

Parallel proximity detection and the distribution list algorithm

Abstract: Generalized proximity detection for moving objects in a logically correct parallel discrete-event simulation is an interesting and fundamentally challenging problem. Determining who can see whom in a manner that is fully scalable in terms of CPU usage, number of messages, and memory requirements is highly non-trivial.A new scalable approach has been developed to solve this problem. This algorithm, called The Distribution List, has been designed and tested using the object-oriented Synchronous Parallel Environment for Emulation and Discrete-Event Simulation (SPEEDES) operating system. Preliminary results show that the Distribution List algorithm achieves excellent parallel performance.

Scalable and explainable legal prediction

Abstract: Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs This paper describes two approaches to an important form of legal decision support—explainable outcome prediction—that obviate both annotation of an entire decision corpus and manual processing of new cases The first approach, which uses an attention network for prediction and attention weights to highlight salient case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects The second approach, termed semi-supervised case annotation for legal explanations, exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value

Learning rules from highly unbalanced data sets

Abstract: This paper presents a simple and effective rule learning algorithm for highly unbalanced data sets By using the small size of the minority class to its advantage this algorithm can conduct an almost exhaustive search for patterns within the known fraudulent cases This algorithm was designed for and successfully applied to a law enforcement problem, which involves discovering common patterns of fraudulent transactions

Automated flight track taxonomy for measuring benefits from performance based navigation

Abstract: A flight track taxonomy is presented which decomposes a set of radar tracks according to their lateral, vertical, and conformance segments. These identifications are based upon a novel set of filtering, segment identification and track decomposition algorithms. These algorithms have been optimized such that they can batch process large data sets efficiently and robustly.

Gene Name Extraction Using FlyBase Resources

Abstract: Machine-learning based entity extraction requires a large corpus of annotated training to achieve acceptable results. However, the cost of expert annotation of relevant data, coupled with issues of inter-annotator variability, makes it expensive and time-consuming to create the necessary corpora. We report here on a simple method for the automatic creation of large quantities of imperfect training data for a biological entity (gene or protein) extraction system. We used resources available in the FlyBase model organism database; these resources include a curated lists of genes and the articles from which the entries were drawn, together a synonym lexicon. We applied simple pattern matching to identify gene names in the associated abstracts and filtered these entities using the list of curated entries for the article. This process created a data set that could be used to train a simple Hidden Markov Model (HMM) entity tagger. The results from the HMM tagger were comparable to those reported by other groups (F-measure of 0.75). This method has the advantage of being rapidly transferable to new domains that have similar existing resources.