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.

Synchronized multiple access apparatus and method for a local area network

Abstract: The present access protocol for contention networks results in synchronous network operation. Each node determines the next transmission instant by counting from the beginning of the most recently received packet of information a time equal to that packet's labeled length minus the previously measured round trip propagation delay seen by the node. Subsequent instants will occur at intervals equal to the maximum propagation delay allowed on the network. Each time a subsequent packet is received, synchronization is rederived based on the packet. If no packets are received within predetermined slots, the network is considered to be in asynchronous mode, with transmissions allowed at any time following the next slot. The disclosed packet synchronized multiple access (PSMA) protocol improves the performance measures by which local area networks are evaluated.

Dynamic Queuing Network Model for Flow Contingency Management

Abstract: We introduce a queuing network model that can comprehensively represent traffic flow dynamics and flow management capabilities in the U.S. National Airspace System (NAS). We envision this model as a framework for tractably evaluating and designing coordinated flow management capabilities at a multi-Center or even NAS-wide spatial scale and at a strategic (2-15 h) temporal horizon. As such, the queuing network model is expected to serve as a critical piece of a strategic flow contingency management solution for the Next Generation Air Traffic System (NextGen). Based on this perspective, we outline, in some detail, the evaluation and design tasks that can be performed using the model, as well as the construction of the flow network underlying the model. Finally, some examples are presented, including one example that replicates traffic in Atlanta Center on an actual bad-weather day, to illustrate simulation of the model and interpretation/use of model outputs.

How to Evaluate your Question Answering System Every Day and Still Get Real Work Done

Abstract: In this paper, we report on Qaviar, an experimental automated evaluation system for question answering applications. The goal of our research was to find an automatically calculated measure that correlates well with human judges' assessment of answer correctness in the context of question answering tasks. Qaviar judges the response by computing recall against the stemmed content words in the human-generated answer key. It counts the answer correct if it exceeds agiven recall threshold. We determined that the answer correctness predicted by Qaviar agreed with the human 93% to 95% of the time. 41 question-answering systems were ranked by both Qaviar and human assessors, and these rankings correlated with a Kendall's Tau measure of 0.920, compared to a correlation of 0.956 between human assessors on the same data.

Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

Abstract: The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, an extraordinary capacity which has profound implications for our understanding of human behavior. Given the growing assortment of sentiment-measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of a given text’s lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale.

Machine learning of user profiles: representational issues

Abstract: As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.