Procedural-Reasoning Architecture for Applied Behavior Analysis-based Instructions

Autism Spectrum Disorder (ASD) is a complex developmental disability affecting as many as 1 in every 88 children. While there is no known cure for ASD, there are known behavioral and developmental interventions, based on demonstrated efficacy, that have become the predominant treatments for improving social, adaptive, and behavioral functions in children. Applied Behavioral Analysis (ABA)-based early childhood interventions are evidence based, efficacious therapies for autism that are widely recognized as effective approaches to remediation of the symptoms of ASD. They are, however, labor intensive and consequently often inaccessible at the recommended levels. Recent advancements in socially assistive robotics and applications of virtual intelligent agents have shown that children with ASD accept intelligent agents as effective and often preferred substitutes for human therapists. This research is nascent and highly experimental with no unifying, interdisciplinary, and integral approach to development of intelligent agents based therapies, especially not in the area of behavioral interventions. Motivated by the absence of the unifying framework, we developed a conceptual procedural-reasoning agent architecture (PRA-ABA) that, we propose, could serve as a foundation for ABA-based assistive technologies involving virtual, mixed or embodied agents, including robots. This architecture and related research presented in this disser- tation encompass two main areas: (a) knowledge representation and computational model of the behavioral aspects of ABA as applicable to autism intervention practices, and (b) abstract architecture for multi-modal, agent-mediated implementation of these practices

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.Comment: SIGMOD'1

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Political communications in the form of unstructured text convey rich connotative meaning that can reveal underlying group social processes. Previous research has focused on sentiment analysis at the document level, but we extend this analysis to sub-document levels through a detailed analysis of affective relationships between entities extracted from a document. Instead of pure sentiment analysis, which is just positive or negative, we explore nuances of affective meaning in 22 affect categories. Our affect propagation algorithm automatically calculates and displays extracted affective relationships among entities in graphical form in our prototype (TEAMSTER), starting with seed lists of affect terms. Several useful metrics are defined to infer underlying group processes by aggregating affective relationships discovered in a text. Our approach has been validated with annotated documents from the MPQA corpus, achieving a performance gain of 74% over comparable random guessers

Final Report: Multi-State Sharing Initiative

In 2003 a joint effort between the U.S. Department of Homeland Security (DHS) and the U.S. Department of Justice created state and metropolitan intelligence fusion centers. These fusion centers were an effort to share law enforcement, disaster, and terrorism related information and intelligence between state and local jurisdictions and to share terrorism related intelligence between state and local law enforcement agencies and various federal entities. In 2006, DHS commissioned the Oak Ridge National Laboratory to establish and manage a groundbreaking program to assist local, state, and tribal leaders in developing the tools and methods required to anticipate and forestall terrorist events and to enhance disaster response. This program, called the Southeast Region Research Initiative (SERRI), combines science and technology with validated operational approaches to address regionally unique requirements and suggest regional solutions with the potential for national application. In 2009, SERRI sponsored the Multistate Sharing Initiative (MSSI) to assist state and metropolitan intelligence fusion centers with sharing information related to a wider variety of state interests than just terrorism. While these fusion centers have been effective at sharing data across organizations within their respective jurisdictions, their organizational structure makes bilateral communication with federal entities convenient and also allows information to be further disbursed to other local entities when appropriate. The MSSI-developed Suspicious Activity Report (SAR) sharing system allows state-to-state sharing of non-terrorism-related law enforcement and disaster information. Currently, the MSSI SAR system is deployed in Alabama, Kentucky, Tennessee, and South Carolina. About 1 year after implementation, cognizant fusion center personnel from each state were contacted to ascertain the status of their MSSI SAR systems. The overwhelming response from these individuals was that the MSSI SAR system was an outstanding success and contributed greatly to the security and resiliency of their states. At least one state commented that SERRI's implementation of the MSSI SAR actually 'jump started' and accelerated deployment and acceptance of the Nationwide Suspicious Activity Reporting Initiative (NSI). While all states were enthusiastic about their systems, South Carolina and Tennessee appeared to be the heaviest users of their respective systems. With NSI taking the load of sharing SARs with other states, Tennessee has redeployed the MSSI SAR system within Tennessee to allow SAR sharing between state and local organizations including Tennessee's three Homeland Security Regions, eleven Homeland Security Districts, and more than 500 police and sheriff offices, as well as with other states. In one success story from South Carolina, the Economy SAR System was used to compile similar SARs from throughout the state which were then forwarded to field liaison officers, emergency management personnel, and law enforcement officers for action

The Heidelberg Laureate Forum on the moving frontier between mathematics and computer science

Young and early-career researchers at the 2016 Heidelberg Laureate Forum discuss how the frontier between mathematics and computer science is shifting, what the future promises, and the implications the frontier's shape and dynamics will have on both fields

cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning

Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing, is a vital application of a computer-assisted clinical decision support system. It can help clinicians extract critical information effortlessly for clinical decision-making by comprehending and answering questions from biomedical text data. While recent advances in bio-MRC consider text data from resources such as clinical notes and scholarly articles, the clinical practice guidelines (CPGs) are still unexplored in this regard. CPGs are a pivotal component of clinical decision-making at the point of care as they provide recommendations for patient care based on the most up-to-date information available. Although CPGs are inherently terse compared to a multitude of articles, often, clinicians find them lengthy and complicated to use. In this paper, we define a new problem domain – bio-MRC on CPGs – where the ultimate goal is to assist clinicians in efficiently interpreting the clinical practice guidelines using MRC systems. To that end, we develop a manually annotated and subject-matter expert-validated benchmark dataset for the bio-MRC task on CPGs – cpgQA. This dataset aims to evaluate intelligent systems performing MRC tasks on CPGs. Hence, we employ the state-of-the-art MRC models to present a case study illustrating an extensive evaluation of the proposed dataset. We address the problem of lack of training data in this newly defined domain by applying transfer learning. The results show that while the current state-of-the-art models perform well with 78% exact match scores on the dataset, there is still room for improvement, warranting further research on this problem domain. We release the dataset at https://github.com/mmahbub/cpgQA