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.

Cross linguistic name matching in English and Arabic: a one to many mapping extension of the Levenshtein edit distance algorithm

Abstract: This paper presents a solution to the problem of matching personal names in English to the same names represented in Arabic script. Standard string comparison measures perform poorly on this task due to varying transliteration conventions in both languages and the fact that Arabic script does not usually represent short vowels. Significant improvement is achieved by augmenting the classic Levenshtein edit-distance algorithm with character equivalency classes.

An agent-based model for financial vulnerability

Abstract: This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the ensuing agent-based model for analyzing the vulnerability of the financial system to asset- and funding-based fire sales. The model captures the dynamic interactions of agents in the financial system extending from the suppliers of funding through the intermediation and transformation functions of the bank/dealers to the financial institutions that use the funds to trade in the asset markets. The model replicates the key finding that it is the reaction to initial losses, rather than the losses themselves, that determine the extent of a crisis. By building on a detailed mapping of the transformations and dynamics of the financial system, the agent-based model provides an avenue toward risk management that can illuminate the pathways for the propagation of key crisis dynamics such as fire sales and funding runs.

Personalized multimedia information access

Abstract: Ask questions, get personalized answers.

COVID-19 Vaccine Discourse on Twitter: A Content Analysis of Persuasion Techniques, Sentiment and Mis/Disinformation.

Abstract: This research aims to understand the persuasion techniques used in Twitter posts about COVID-19 vaccines by the different vaccine sentiments (i.e., Pro-Vaccine, Anti-Vaccine, and Neutral) using the Elaboration Likelihood Model, Social judgment Theory, and the Extended Parallel Process Model as theoretical frameworks. A content analysis was conducted on a data set of 1,000 Twitter posts. The corpus of Tweets was examined using the persuasion frameworks; tweets that were identified as emanating from bots were further examined. Results found Anti-Vaccine messages predominantly used Anecdotal stories, Humor/Sarcasm, and Celebrity figures as persuasion techniques, while Pro-Vaccine messages primarily used Information, Celebrity figures, and Participation. Results also showed the Anti-Vaccine messages primarily focused on values related to the categories of Safety, Political/Conspiracy Theories, and Choice. Finally, results revealed Anti-Vaccine messages primarily used Perceived Severity and Perceived Susceptibility, which are fear appeal elements. The findings for messages by bots were comparable to the messages in the larger corpus of tweets. Based on the findings, a response framework-Health Information Persuasion Exploration (HIPE)-is proposed to address mis/disinformation and Anti-Vaccine messaging. The results of this study and the HIPE framework can inform a national COVID-19 vaccine health campaign to increase vaccine adoption.

Using Dialogue Features to Predict Trouble During Collaborative Learning

Abstract: A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning.