Current Research Projects (by faculty)

The funded projects listed below are active projects and the funded running total for the active projects is on the left navigational menu.

REU Site: Socially Relevant Computing and Analytics
Tiffany Barnes

$359,997 by National Science Foundation
03/ 1/2017 - 02/29/2020

The REU Site at NC State University will immerse a diverse group of undergraduates in a vibrant research community of faculty and graduate students building and analyzing cutting-edge human-centric applications including games, tutors, and mobile apps. We will recruit students from underrepresented groups and colleges and universities with limited research opportunities through the STARS Computing Corps, an NSF-funded national consortium of institutions dedicated to broadening participation in computing. Using the Affinity Research Groups and STARS Training for REUs models, we will engage faculty and graduate student mentors with undergraduates to create a supportive culture of collaboration while promoting individual contributions to research through just-in-time training for both mentors and students throughout the summer.

Track 2: CS10K: BJC-STARS: Scaling CS Principles through STARS community & Leadership Development
Tiffany Barnes

$500,000 by National Science Foundation
10/ 1/2015 - 09/30/2018

BJC-STARS is a CS10K proposal to broaden access to Computer Science education through engaging colleges and universities to prepare and support regional communities of high school teachers to teach the Beauty and Joy of Computing (BJC) Computer Science Principles course. We will leverage the successful STARS model focusing on engaging faculty and students in a community committed to leading efforts to broaden participation in computing. Each year, we will engage new university faculty who will teach BJC and facilitate professional development and support to high school teachers and students. We will also build a STARS community among participating high school teachers and students, engaging them in the need to broaden participation in computing.

Type I: Collaborative Research: FRABJOUS CS - Framing a Rigorous Approach to Beauty and Joy for Outreach to Underrepresented Students in Computing at Scale (Supplement)
Tiffany Barnes

$565,874 by National Science Foundation
09/ 1/2016 - 08/31/2018

In recent decades, coding has evolved from a professional activity of a few million developers to a near universally-needed skill. However, there are still fewer than 2000 teachers prepared to teach computer science to high school students. In this supplement we propose to engage 240 teachers in NC districts, and an additional 1000 online, in BJC professional developme

Collaborative Research: Modeling Social Interaction and Performance in STEM Learning
Tiffany Barnes

$200,003 by National Science Foundation
09/ 1/2014 - 08/31/2017

Despite long-standing awareness that social interaction is an integral part of knowledge construction, efforts to study complex collaborative learning have traditionally been relegated to qualitative and small-scale methodologies. Relatively new data traces left by online learning environments, including massive open online courses (MOOCs), offer the first real hope for scaling up such analyses. The purpose of the proposed research is to develop comprehensive models for collaborative learning which in turn will enable instructional design and the authentic assessment of the individual within the group context. This task is undertaken by an interdisciplinary team of researchers with specializations in natural language processing, discourse analysis, social network analysis, educational data mining and psychometrics.

EXP: Data-driven Support for Novice Programmers
Tiffany Barnes ; Min Chi

$549,874 by National Science Foundation
09/ 1/2016 - 08/31/2019

While intelligent tutors have been shown to increase student learning in programming and other domains, and creative, exploratory programming environments are assumed to promote novice interest and motivation to learn to program, there are no environments that provide both creative tasks and intelligent support. We propose to extend our methods for data-driven hint generation, model tracing, and knowledge tracing to augment Snap and Java programming environments to be more supportive for novice programmers doing open-ended creative tasks.

CAREER: Improving Adaptive Decision Making in Interactive Learning Environments
Min Chi

$547,810 by National Science Foundation
03/ 1/2017 - 02/28/2022

For many forms of interactive environments, the system's behaviors can be viewed as a sequential decision process wherein, at each discrete step, the system is responsible for selecting the next action to take from a set of alternatives. The objective of this CAREER proposal is to learn robust interaction strategies that will lead to desirable outcomes in complex interactive environments. The central idea of this project is that strategies should not only be effective in complex interactive environments but they should also be efficient, focusing solely on the key features of the domain and the crucial decision points. These are the features and decisions that are not only associated with desirable outcomes, but without which the desirable outcomes are unlikely to occur.

Educational Data Mining for Individualized Instruction in STEM Learning Environments
Min Chi ; Tiffany Barnes

$639,401 by National Science Foundation
09/ 1/2014 - 08/31/2017

Human one-on-one tutoring is one of the most effective educational interventions. Tutored students often perform significantly better than students in classroom settings (Bloom, 1984; Cohen, Kulik, & Kulik, 1982). Computer learning environments that mimic aspects of human tutors have also been highly successful. Intelligent Tutoring Systems (ITSs) have been shown to be highly effective in improving students' learning at real classrooms (Anderson, Corbett, Koedinger, & Pelletier, 1995; Koedinger, Anderson, Hadley, & Mark, 1997; VanLehn et al., 2005). The development of ITSs has enabled schools and universities to reach out and educate students who otherwise would be unable to take advantage of one-on-one tutoring due to cost and time constraints (Koedinger, Anderson, Hadley, & Mark, 1997). Despite the high payoffs provided by ITSs, significant barriers remain. High development costs and the challenges of knowledge engineering have prevented their widespread deployment. A diverse team of software developers, domain experts, and educational theorists are required for development, testing and even maintenance. Generally speaking, it requires an average of 80 man-hours per hour of tutoring content. In this proposed work, our goal is to automatically design effective personalized ITSs directly from log data. We will combine co-pI Dr. Barnes data-driven approach on learning what to teach with PI Dr. Chi’s data-driven work on learning how to teach. More specifically, we will explore two important undergraduate stem domains: discrete math and probability; and employ two types of ITSs: an example-based ITS, the discrete math tutor, and a rule-based ITS, Pyrenees. The former can automatically generate hints directly from students’ prior solutions while the latter has hard-coded domain rules and teaches students a domain-general problem-solving strategy within the context of probability. For learning how to teach, we will apply reinforcement learning to induce adaptive pedagogical strategies directly from students’ log files and will focus on three levels of pedagogical decisions: 1) whether or not to give students hints and what level of hint (step-level); 2) whether to show students worked example or ask students to engage problem solving (problem-level); and 3) whether or not to teach students a meta-cognitive problem-solving strategy (metacognitive level).

Using Real-Time Multichannel Self-Regulated Learning Data to Enhance Student Learning and Teachers' Decision-Making with MetaDash
Min Chi - Co PI ; Roger Azevedo (Psychology) ; Soonhye Pakr - co-PI EDUCA

$914,585 by National Science Foundation
04/ 1/2017 - 03/31/2020

This 3-year project will focus on laboratory and classroom research in the Raleigh-Chapel-Hill, and Durham areas. A team of interdisciplinary researchers from NCSU's Department of Psychology (Dr. Roger Azevedo), Computer Science (Dr. Min Chi), and STEM Education (Dr. Soonhye Park) will conduct empirical and observational research aimed at improving teachers' decision-making based on their analyses of students' real-time, multi-channel self-regulated learning data. We will use multi-channel data to understand the nature of self-regulatory processes in students while using MetaTutor to understand challenging science topics (e.g., human biological systems). This will be accomplished by aligning and conducting complex computational and statistical analyses of a multitude of trace data (e.g., log-files, eye-tracking, facial expression of emotions), behavioral (e.g., human-pedagogical agent dialogue moves), and physiological measures (e.g., EDA), learning outcomes, and classroom data (e.g., teacher-student interactions, gaze behavior of teachers’ attention and use of data presented by the visualization tool). The proposed research, in the context of using MetaTutor and a visualization tool for teachers, is extremely challenging and will help us to better understand the nature and temporal dynamics of these processes in classroom contexts, how they contribute to various types of learning and use of self-regulatory skills, and provide empirical basis for designing an intelligent teacher analytics tool. The results from this grant will contribute significantly to models of social and cognitive bases of student-machine-teacher interactions; statistical and computational methods used to make inferences from complex multi-channel data; theoretical and conceptual understanding of temporally-aligned data streams; enhancing students’ understanding of complex science topics by making more sensitive and intelligent advanced learning technologies; and, enhanced understanding of how teachers use real-time student data to enhance their instructional decision-making, based on data presented in teacher analytic tools.

I/UCRC: Site application to join I/UCRC known as CHMPR
Rada Chirkova

$298,533 by National Science Foundation
09/ 1/2016 - 08/31/2019

Abstract: The objective of this proposal is to indicate that North Carolina State University (NCSU) will join, as a site, the Center of Hybrid Multicore Productivity Research (CHMPR) in Year 2 of its phase II I/UCRC renewal. The focus of NCSU within the center will be the science of technologies for end-to-end enablement of data. The research at NCSU complements well the work being done at the other I/UCRC centers and at the other sites of the CHMPR center. NCSU has had extensive positive experience with I/UCRC centers over the years, and is very comfortable with the model.

Membership for Center of Hybrid Multicore Productivity Research (CHMPR), Full Member
Rada Chirkova

$80,000 by Northrup Grumman
01/ 1/2016 - 12/31/2017

Membership dues from Northrup Grumman for Center of Hybrid Multicore Productivity Research. Exploring and analyzing the available data is key to making the right decisions. It is well known that “data wrangling,” which includes many kinds of end-to-end data enablement, makes up 60-80% of the total effort in analytics on large-scale data. We look to address the challenge of maximizing the usefulness of the available data, by providing tools, science, and talent for next-generation technologies and infrastructure. We focus on empowering organizations that wish to unlock the value of decisions based on their data, and envision a future where technologies and tools for data enablement provide significant business advantages to such organizations. At NCSU, we will lead national and international efforts in this space, by developing and providing technologies and tools for bridging the time gap between the acquisition of data and real-time and long-term decision making.

BD Spokes: PLANNING: SOUTH: Collaborative: Rare Disease Observatory
Rada Chirkova

$71,143 by National Science Foundation
09/ 1/2016 - 08/31/2017

One of the greatest challenges in the rare disease domain is access to trusted, verified data. With advances in mapping the human genome, over 7000 rare diseases have been identified. However no integrated, comprehensive patient registries exist that reliably collect data on these patients and their conditions and would allow for queries such as outcomes and economic impact. This planning grant will concentrate on building a large data system that can be accessed by a large collaborative community of those in the rare disease space, including state and federal agencies, clinicians, investigators, patient advocacy groups, industry and SPOKE: South.

NeTS: Small: Collaborative Research: Enabling Robust Communication in Cognitive Radio Networks with Multiple Lines of Defense
Rudra Dutta

$249,901 by National Science Foundation
10/ 1/2013 - 09/30/2017

Cognitive radio is an emerging advanced radio technology in wireless access, with many promising benefits including dynamic spectrum sharing, robust cross-layer adaptation, and collaborative networking. Opportunistic spectrum access (OSA) is at the core of cognitive radio technologies, which has received great attention recently, focusing on improving spectrum utilization efficiency and reliability. However, the state-of-the-art still suffers from one severe security vulnerability, which has been largely overlooked by the research community so far. That is, a malicious jammer can always disrupt the legitimate network communication by leveraging the public-available channel statistic information to effectively jam the channels and thus lead to serious spectrum underutilization. In this proposal, we propose to address the challenge of effective anti-jamming communication in cognitive radio networks (CRNs). We propose a multiple lines of defense approach, which considers and integrates defense technologies from different dimensions, including frequency hopping, power control, cooperative communication, and signal processing. The proposed defense approach enables both reactive and proactive protection, from evading jammers to competing against jammers, and to expelling jamming signals, and thus guarantees effective anti-jamming communication under a variety of network environments.

TWC: Medium: Collaborative: Improving Mobile-Application Security via Text Analytics
William Enck

$300,000 by National Science Foundation
07/ 1/2015 - 06/30/2018

Computing systems that make security decisions often fail to take into account human expectations. This failure occurs because human expectations are commonly drawn from textual sources (e.g., mobile application description) and are hard to extract and codify. This proposal seeks to extract expectation context from natural-language text artifacts presented to users as they find, install, and run software. The proposed work focuses specifically mobile applications to demonstrate concrete and practical advances in our scientific understanding of applying user expectation context to security decisions. These findings will advance the state-of-the-art in identifying and classifying malware and grayware, as well as identify better methods of communicating risk to users. We will also gain a better understanding of the unique challenges of applying text analytics the security domain.

TWC: Medium: Collaborative: Improving Mobile-Application Security via Text Analytics (supplement)
William Enck

$8,000 by National Science Foundation
07/ 1/2015 - 06/30/2018

Smartphones and mobile devices have become a dominant computing platform for billions of users. There are many static and dynamic program analysis tools to detecting security and privacy concerns in mobile applications. However, few approaches bridge the semantic gap between code and visual presentation. Ignoring this context results in analysis that falsely reports an application as malicious (e.g., the user really wanted to use an app to record phone calls), or fails to detect suspicious behavior (e.g., an app that collects sensitive information via text input). We propose to use a hybrid static / dynamic approach to extract the text labels from the Android application UIs followed by text analytics to semantically label the type of input asked for by the application. Doing so will better characterize the amount of security and privacy information entered into Android applications, as well as enable outlier detection to identify applications that ask for unexpected (e.g., SSN) information for their semantic class (e.g., banking applications). This analysis will be applied at scale to identify potential privacy infringements in mobile application stores.

CAREER: Secure OS Views for Modern Computing Platforms
William Enck

$400,000 by National Science Foundation
02/ 1/2013 - 01/31/2018

Controlling the access and use of information is a fundamental challenge of computer security. Emerging computing platforms such as Android and Windows 8 further complicate access control by relying on sharing and collaboration between applications. When more than two applications participate in a workflow, existing permission systems break down due to their boolean nature. In this proposal, we seek to provide applications with residual control of their data and its copies. To do this, we propose secure OS views, which combines a new abstraction for accessing data with whole-system information tracking. We apply secure OS views to modern operating systems (e.g., Android and Windows 8), which use database-like abstractions for sharing and accessing information. Similar to a database view, secure OS views uses runtime context to dynamically define the protection domain, allowing the return of the value, a fake value, or nonexistence of the record.

TWC: Frontier: Collaborative: Rethinking Security in the Era of Cloud Computing
William Enck ; Peng Ning ; Mladen Vouk

$749,996 by National Science Foundation
09/ 1/2013 - 08/31/2018

Increased use of cloud computing services is becoming a reality in today's IT management. The security risks of this move are active research topics, yielding cautionary examples of attacks enabled by the co-location of competing tenants. In this project, we propose to mitigate such risks through a new approach to cloud architecture defined by leveraging cloud providers as trusted (but auditable) security enablers. We will exploit cooperation between cloud providers and tenants in preventing attacks as a means to tackle long-standing open security problems, including protection of tenants against outsider attacks, improved intrusion detection and security diagnosis, and security-monitoring inlays.

Collaborative Research: Research in Student Peer Review: A Cooperative Web-Services Approach
Edward Gehringer

$1,034,166 by NSF
09/ 1/2014 - 08/31/2017

Peer review between students has a 40-year history in academia. During the last half of that period, web-based peer-review systems have been used in tens of thousands of classes. Many online systems have been developed, in diverse settings and with diverse purposes. The systems, however, have common concerns: assigning capable reviewers to each student submission, insuring review quality, and delivering reliable scores, in cases where the systems are used for summative review of student work. Many strategies have been proposed to meet those concerns, and tested in relatively small numbers of courses. The next step is to scale up the studies to learn how well they perform in diverse settings, and with large numbers of students. This project brings together researchers from several peer-review systems, including some of the largest, to build web services that can be incorporated into existing systems to test these strategies and visualize the results.

Collaborative Research: Research in Student Peer Review: A Cooperative Web-Services Approach (Supplement)
Edward Gehringer

$40,000 by National Science Foundation
09/ 1/2014 - 08/31/2017

The students assist our efforts to build a database of peer-review responses that can be mined for quantitative research studies. The database will be composed of anonymized data from the peer-review systems of the constituent projects: CritViz, CrowdGrader, Expertiza, and Mobius/Slip. Among other items, it will contain peer feedback and ratings, and links to submitted work. They will embark on a qualitative research study to determine what STEM students value about the peer-review process. They will use a common set of research protocols to investigate three research questions: What do students value about receiving reviews? What do they value about giving reviews? Do their reactions differ, based on demographics, age/level of study, or academic major?

CSR: Medium:Collaborative Research: Holistic, Cross-Site, Hybrid System Anomaly Debugging for Large Scale Hosting Infrastructures
Xiaohui (Helen) Gu

$518,000 by National Science Foundation
08/ 1/2015 - 07/31/2019

Hosting infrastructures provide users with cost-effective computing solutions by obviating the need for users to maintain complex computing infrastructures themselves. Unfortunately, due to their inherent complexity and sharing nature, hosting infrastructures are prone to various system anomalies caused by various external or internal faults.The goal of this project is to investigate a holistic,cross-site, hybrid system anomaly debugging framework that intelligently integrates production-site black-box diagnosis and developer-site white-box analysis into a more powerful hosting infrastructure anomaly debugging system.

Identification of Translational Hormone-Response Gene Networks and Cis-Regulatory Elements
Steffen Heber(co-PI) ; Jose Alonso(Lead PI-CALS) ; Anna Stepanova (CALS) ; Cranos Williams (ECE)

$897,637 by National Science Foundation
08/ 1/2015 - 07/31/2020

Plants, as sessile organisms, need to constantly adjust their intrinsic growth and developmental programs to the environmental conditions. These environmentally triggered “adjustments“ often involve changes in the developmentally predefined patterns of one or more hormone activities. In turn, these hormonal changes result in alterations at the gene expression level and the concurrent alterations of the cellular activities. In general, these hormone-mediated regulatory functions are achieved, at least in part, by modulating the transcriptional activity of hundreds of genes. The study of these transcriptional regulatory networks not only provides a conceptual framework to understand the fundamental biology behind these hormone-mediated processes, but also the molecular tools needed to accelerate the progress of modern agriculture. Although often overlooked, understanding of the translational regulatory networks behind complex biological processes has the potential to empower similar advances in both basic and applied plant biology arenas. By taking advantage of the recently developed ribosome footprinting technology, genome-wide changes in translation activity in response to ethylene were quantified at codon resolution, and new translational regulatory elements have been identified in Arabidopsis. Importantly, the detailed characterization of one of the regulatory elements identified indicates that this regulation is NOT miRNA dependent, and that the identified regulatory element is also responsive to the plant hormone auxin, suggesting a role in the interaction between these two plant hormones. These findings not only confirm the basic biological importance of translational regulation and its potential as a signal integration mechanism, but also open new avenues to identifying, characterizing and utilizing additional regulatory modules in plants species of economic importance. Towards that general goal, a plant-optimized ribosome footprinting methodology will be deployed to examine the translation landscape of two plant species, tomato and Arabidopsis, in response to two plant hormones, ethylene and auxin. A time-course experiment will be performed to maximize the detection sensitivity (strong vs. weak) and diversity (early vs. late activation) of additional translational regulatory elements. The large amount and dynamic nature of the generated data will be also utilized to generate hierarchical transcriptional and translational interaction networks between these two hormones and to explore the possible use of these types of diverse information to identify key regulatory nodes. Finally, the comparison between two plant species will provide critical information on the conservation of the regulatory elements identified and, thus, inform research on future practical applications. Intellectual merit: The identification and characterization of signal integration hubs and cis-regulatory elements of translation will allow not only to better understand how information from different origins (environment and developmental programs) are integrated, but also to devise new strategies to control this flow for the advance of agriculture. Broader Impacts: A new outreach program to promote interest among middle and high school kids in combining biology, computers, and engineering. We will use our current NSF-supported Plants4kids platform (ref) with a web-based bilingual divulgation tools, monthly demos at the science museum and local schools to implement this new outreach program. Examples of demonstration modules will include comparison between simple electronic and genetic circuits.

Collaborative Research: Transforming Computer Science Education Research Through Use of Appropriate Empirical Research Methods: Mentoring and Tutorials
Sarah Heckman

$406,557 by National Science Foundation
09/ 1/2015 - 08/31/2020

The computer science education (CSEd) research community consists of a large group of passionate CS educators who often contribute to other disciplines of CS research. There has been a trend in other disciplines toward more rigorous and empirical evaluation of various hypotheses. However, many of the practices that we apply to demonstrate rigor in our discipline research are ignored or actively avoided when performing research in CSEd. This suggests that CSEd is “theory scarce” because most publications are not research and do not provide the evidence or replication required for meta-analysis and theory building . An increase in empiricism in CSEd research will move the field from “scholarly teaching” to the “scholarship of teaching and learning” (SoTL) providing the foundation for meta-analysis and the generation of theories about teaching and learning in computer science. We propose the creation of training workshops and tutorials to educate the educators about appropriate research design and evaluation of educational interventions. The creation of laboratory packages, “research-in-a-box,” will support sound evaluation and replication leading to meta-analysis and theory building in the CSEd community.

TrackWhere: An App for Tracking and Visualizing Communication Across Teams During Product Lifecycle
Arnav Jhala ; Christopher Parnin

$29,999 by Lenovo
04/15/2017 - 01/15/2018

This project seeks to create an intelligent monitoring and visualization framework for communication across different groups involved in design, development, and release of hardware and software products within Lenovo.

Collaborative Research: Big Data from Small Groups: Learning Analytics and Adaptive Support in Game-based Collaborative Learning
James Lester

$1,249,611 by National Science Foundation
10/ 1/2016 - 09/30/2021

The proposed project focuses on integrating models of game-based and problem-based learning in a computer-supported collaborative learning environment (CSCL). As groups of students solve problems in these environments, their actions generate rich and dynamic streams of fine-grained multi-channel data that can be instrumented for investigating students' learning processes and outcomes. Using the big data generated by small groups, we will leverage learning analytics to provide adaptive support for collaboration that will allow these models to be used at larger scales in real classrooms. The project will study CSCL in the context of an environmental-science-based digital game that will employ specific strategies to support the problem-based learning goals of helping students construct explanations, reason effectively, and become self-directed learners. In problem-based learning, students are active, intentional learners who collaboratively negotiate meaning. The project will embed models induced using learning analytic techniques inside of a digital game environment to enable students to cultivate collaborative learning competencies that translate to non-digital classroom settings.

Learning Environments Across Disciplines LEADS: Supporting Technology Rich Learning Across Disciplines: Affect Generation and Regulation During Co-Regulated Learning in Game-Based Learning Environments (Supplement
James Lester

$114,672 by McGill University/Social Sciences and Humanities Research Council of Canada
04/ 1/2012 - 02/28/2020

Contemporary research on multi-agent learning environments has focused on self-regulated learning (SRL) while relatively little effort has been made to use co-regulated learning as a guiding theoretical framework (Hadwin et al., 2011). This oversight needs to be addressed given the complex nature that self-and other-regulatory processes play when human learners and artificial pedagogical agents (APAs) interact to support learners? internalization of cognitive, affective, and metacognitive (CAM) SRL processes. We will use the Crystal Island learning environment to investigate these issues.

Learning Environments Across Disciplines LEADS: Supporting Technology Rich Learning Across Disciplines: Affect Generation and Regulation During Co-Regulated Learning in Game-Based Learning Environments ((Supplement)
James Lester

$3,737 by McGill University
04/ 1/2017 - 03/31/2018

Contemporary research on multi-agent learning environments has focused on self-regulated learning (SRL) while relatively little effort has been made to use co-regulated learning as a guiding theoretical framework (Hadwin et al., 2011). This oversight needs to be addressed given the complex nature that self-and other-regulatory processes play when human learners and artificial pedagogical agents (APAs) interact to support learners? internalization of cognitive, affective, and metacognitive (CAM) SRL processes. We will use the Crystal Island learning environment to investigate these issues.

SCH: INT: Collaborative Research: A Self-Adaptive Personalized Behavior Change System for Adolescent Preventive Healthcare
James Lester

$952,818 by National Science Foundation
10/ 1/2013 - 09/30/2017

Although the majority of adolescent health problems are amenable to behavioral intervention, and most adolescents are comfortable using interactive computing technology, few health information technology interventions have been integrated into adolescent care. The objective of the proposed research is to design, implement, and investigate INSPIRE, a self-adaptive personalized behavior change system for adolescent preventive healthcare. With a focus on adolescents, INSPIRE will enable adolescents to be active participants in dynamically generated, personalized narrative experiences that operationalize theoretically grounded interventions for behavior change through interactive narratives? plot structures and virtual character interactions.

REFLECT: Improving Science Problem Solving with Adaptive Game-Based Reflection Tools
James Lester ; Roger Azevedo (Psychology)

$1,300,000 by National Science Foundation
04/15/2017 - 03/31/2020

Reflection has long been recognized as a central component of effective learning. With the overarching goal of improving middle school students' science problem solving and learning outcomes, the REFLECT project has the objective of investigating a suite of theoretically grounded, adaptive game-based reflection tools to scaffold students' cognitive and metacognitive processes. The project will center on the design, development, and investigation of game-based learning tools for science education that adaptively scaffold students’ reflection through both embedded and retrospective support. It will culminate in a classroom experiment to study the impact of the adaptive reflection tools on both problem solving and learning. The results from this project will contribute significantly to theoretical and computational models of reflection, and produce both design principles and learning technologies that support the creation of effective learning environments.

CHS: Medium: Adapting to Affect in Multimodal Dialogue-Rich Interaction with Middle School Students
James Lester ; Kristy Boyer ; Bradford Mott ; Eric Wiebe

$1,184,073 by National Science Foundation
08/ 1/2014 - 07/31/2017

Despite the great promise offered by learning environments for K-12 science education, realizing its potential poses significant challenges. In particular, current learning environments do not adaptively support children's affect. This project investigates computational models of affect that support multimodal dialogue-rich interaction. With an emphasis on devising scalable solutions, the project focus on machine-learning techniques for automatically acquiring affect and dialogue models that can be widely deployed in natural classroom settings.

ENGAGE: A Game-Based Curricular Strategy for Infusing Computational Thinking into Middle School Science.
James Lester ; Brad Mott ; Eric Wiebe (Friday Instit

$2,498,862 by National Science Foundation
08/15/2016 - 07/31/2019

Recent years have seen a growing recognition that computer science is vital for scientific inquiry. The middle school grade band is critical for shaping students’ aspirations and skills, and many issues relating to workforce underproduction and underrepresentation of diverse students in computer science can be traced back to middle school. To address this problem, the project will deeply integrate computer science into middle school science classrooms. Centered on a game-based learning environment that features collaborative learning, the project will have a specific focus on addressing gender issues in middle school computer science education with the goal of creating learning interactions that are both effective and engaging for all students.

Tutorial Planning with Markov Decision Processes for Counterinsurgency Training Environments
James Lester ; Bradford Mott ; Jonathan Rowe

$1,072,237 by US Army - Army Research Laboratory
04/10/2015 - 04/ 9/2018

Intelligent tutoring systems (ITSs) are highly effective for education and training. Tutorial planning is a critical component of ITSs, controlling how scaffolding is structured and delivered to learners. Devising data-driven tutorial planners that automatically induce scaffolding models from corpora of student data holds significant promise. This project investigates a data-driven framework for tutorial planning that is based on modular reinforcement learning. This framework explicitly accounts for the inherent uncertainty in how learners respond to different types of tutorial strategies and tactics, and automatically induces and refines tutorial planning policies in order to optimize measures of learning outcomes.

Collaborative Research: PRIME: Engaging STEM Undergraduate Students in Computer Science with Intelligent Tutoring Systems
James Lester ; Bradford Mott ; Eric Wiebe (Friday Instit

$1,499,828 by National Science Foundation
09/ 1/2016 - 08/31/2020

Significant advances in intelligent tutoring systems have paved the way for engaging STEM undergraduates in computer science. This research has spawned a new generation of personalized learning environments that offer significant promise for providing students with adaptive learning experiences that are crafted to their individual needs. Spurred by this significant promise and building on a research infrastructure developed by the project team in a series of NSF-supported projects, the PRIME project will transform introductory computer science education with state-of-the-art intelligent tutoring systems technologies.

The Effectiveness of Intelligent Virtual Humans in Facilitating Self-Regulated Learning in STEM with MetaTutor
James Lester (Co-PI) ; Roger Azevedo

$1,365,603 by National Science Foundation
09/ 1/2014 - 08/31/2017

Intelligent virtual humans (IVHs) are able to connect with real people in powerful, meaningful, and complex ways. They can mimic the behavior of real people and therefore add a rich social dimension to computer interactions, providing not only a wealth of information but presenting information in more personals ways. This 3-year project will focus on testing the effectiveness of IVHs in facilitating college students self-regulated learning in STEM with MetaTutor. More specifically, we plan to test IVHs detection, monitoring, and modeling (both facially and verbally) the temporal dynamics of learners self-regulatory processes to enhance learners deployment of effective learning strategies, accurate metacognitive judgments, and appraisals of emotional states. This will be accomplished by aligning and conducting complex computational and statistical analyses of a multitude of trace data (e.g., log-files, eye-tracking), behavioral (e.g., human-virtual human dialogue moves), physiological measures (e.g., GSR, ECG, EEG), and learning outcome data collected in real-time. The proposed research, in the context of using IVHs, is extremely challenging and will help us to better understand the nature and temporal dynamics of these processes, how they contribute to various types of learning, and provide the empirical basis for designing intelligent virtual human systems. The results from this grant will contribute significantly to models and theories of social, cognitive, and physiological bases of human-virtual human interactions; statistical and computational methods to make inferences from complex multi-channel data; theoretical and conceptual understanding of temporally-aligned data streams, and enhancing students understanding of complex science topics by making more sensitive and intelligent virtual humans.

Guiding Understanding via Information from Digital Environments (GUIDE)
James Lester Co-PI ; Eric Wiebe Lead PI

$1,238,549 by Concord Consortium via National Science Foundation
09/15/2015 - 08/31/2019

This project will utilize research and development groups at the Concord Consortium and NC State University. Educational software for teaching high school multi-level genetics developed by the Concord Consortium will be enhanced by intelligent agents and machine-based tutoring system technologies developed at NC State to help enhance the learning experience for students. These groups will collaborate closely to develop and research a hybrid system that combines technological intervention and teacher pedagogical expertise to illuminate and guide student learning in deeply digital curricula and classrooms.

SHF:Medium:Collaborative:Transfer Learning in Software Engineering
Tim Menzies

$464,609 by National Science Foundation
08/ 2/2014 - 06/30/2017

Software engineers need better ways to recognize best practices in past projects, and to understand how to transfer and adapt those experiences to current projects. No project is exactly like previous projects- hence, the trick is to find which parts of the past are most relevant and can be transferred into the current project. We propose novel automated methods to apply the machine learning concept of transfer learning to adapt lessons from past software engineering project data to new conditions.

SHF:Medium:Scalable Holistic Autotuning for Software Analytics
Timothy Menzies ; Xipeng Shen

$898,349 by National Science Foundation
07/ 1/2017 - 06/30/2021

This research proposes to advance the state of the art to holistic scalable autotuners, which tunes all levels of options for multiple optimization objectives at the same time. It will achieve this ambitious goal through the development of a set of novel techniques that efficiently handles the tremendous tuning space. These techniques take advantage of the synergies between all those options and goals by exploiting relevancy filtering (to quickly dispose of unhelpful options), locality of inference (that enables faster updates to out- dated tunings) and redundancy reduction (that reduces the search space for better tunings). This new autotuner will be a faster method for finding better tunings that satisfy more goals. To test this claim, this research will assess if this new tool can reduce the total computational resources required for effective SE data analytics by orders of magnitude.

HPC Power Modeling and Active Control
Frank Mueller

$386,290 by Lawrence Livermore National Laboratory via US Department of Energy
10/25/2016 - 09/30/2019

As we approach the exascale era, power has become a primary bottleneck. The US Department of Energy has set a power constraint of 20MW on each exascale machine. To be able achieve one exaflop in 20MW,it is necessary that we use power intelligently to maximize performance under a power constraint. In this work, we propose to alleviate the shortcomings of current HPC systems in addressing power constraints by (1) power-aware machine partitioning, (2) power-constrained job scheduling, (3) systematic provisioning and procurement of hardware under a power cap, (4)modeling of network, deep memories, and storage, as well as (5)investigating the inter-dependence between power and cooling.

A Deep-Learning Approach Towards Auto-Tuning CFD Codes
Frank Mueller

$90,000 by Virginia Tech vai US Air Force Office of Scientific Research
06/ 1/2017 - 02/14/2018

Heterogeneous computing systems are increasingly becoming the norm in high-performance computing (HPC) norm. For instance, as of the November 2015 TOP500 List, more than 35% of the computational power on the TOP500 now comes from systems containing heterogeneous computing devices, e.g., CPUs, GPUs, APUs, Xeon Phis, and even FPGAs. However,significant hurdles impede a domain scientist ability to extract high performance out of such heterogeneous devices, including (1) selecting the appropriate algorithm(s) for the target heterogeneous device, (2)setting the runtime parameters, and (3) configuring the hardware relative to some evaluation metric, e.g., performance, power, or energy efficiency. Unfortunately, exploring hardware and software design choices often requires time-consuming simulations, and while some brute-force auto-tuning support has been proposed, the results are heuristic that are often narrow in scope, i.e., only applicable to a particular problem. This proposal seeks to study, analyze, and synthesis deep-learning approaches that expose the various parameters as "knobs" that can be tuned via deep learning to optimize for the metric of interest, whether it be performance, power, or energy efficiency.

Failure Prediction with Exact Localization
Frank Mueller

$84,684 by Lawrence Berkeley National Lab via US Department of Energy
10/18/2016 - 08/16/2017

Extreme-scale computing platforms are increasingly suffering from job failures due to hardware and software faults.Past work has predicted system availability but cannot predicting the locality of failures. The objective of this work is to assess the potential of machine learning techniques for pin-pointing failures before they happen with high true positive and low false positive rates. We propose to employ a combination of machine learning (ML) techniques for offline training followed by online real-time prediction of failure locations in a timely manner to take preemptive measurements and ``work around'' predicted trouble spots (nodes, network links/switches).

CAREER:Expanding Developers' Usage of Software Tools by Enabling Social Learning
Emerson Murphy-Hill

$495,721 by National Science Foundation
08/ 1/2013 - 07/31/2018

Tools can help software developers alleviate the challenge of creating and maintaining software. Unfortunately, developers only use a small subset of the available tools. The proposed research investigates how social learning, an effective mechanism for discovering new tools, can help software developers to discover relevant tools. In doing so, developers will be able to increase software quality while decreasing development time.

FSE 2016 Doctoral Consortium and Mentorship Sessions Program
Emerson Murphy-Hill

$24,952 by National Science Foundation
07/ 1/2016 - 06/30/2017

The two proposed events at the 2016 Symposium on the Foundations of Software Engineering will build connections and enhance research capacity for junior researchers. Specifically, doctoral symposium and mentorship sessions will support senior doctoral students and pre-tenure faculty make connections with senior researchers, get feedback on their work, and foster the creation of a supportive community of scholars and a spirit of collaborative research.

NeTS: Small: Collaborative Research: Creating Semantically-Enabled Programmable Networked Systems (SERPENT)
Kemafor Ogan

$278,271 by National Science Foundation
10/ 1/2015 - 09/30/2018

The separation of control and data plane in SDN architectures helps merge packet and circuit paradigms into a single architecture and enables logical centralization of the control function. This enables new thinking about solutions to path optimization problems frequently encountered in networking, from routing to traffic engineering. The SERPENT project proposes to develop effective solutions for representing, storing and manipulating network state using rich semantic models such that path and topology embedding problems can be solved using a semantic database framework. This will simplify creation of novel network control and management systems able to cope with increasingly complex user requirements.

III: Small: Optimization Techniques for Scalable Semantic Web Data Processing in the Cloud
Kemafor Ogan

$446,942 by National Science Foundation
09/ 1/2012 - 08/31/2017

Achieving scalable processing of the increasing amount of publicly-available Semantic Web data will hinge on parallelization. The Map-Reduce programming paradigm recently emerged as a de-facto parallel data processing standard and has demonstrated effectiveness with respect to structured and unstructured data. However, Semantic Web data presents challenges not adequately addressed by existing techniques due to its flexible, fine-grained data model and the need to reason beyond explicitly represented data. This project will investigate optimization techniques that address these unique challenges based on rethinking Semantic Web data processing on Map-Reduce platforms from the ground, up - from query algebra to query execution.

REU Site: Science of Software
Christopher Parnin ; Emerson Murphy-Hill ; Sarah Heckment

$355,365 by National Science Foundation
01/ 1/2016 - 12/31/2018

There are not enough skilled data science researchers, particularly in software engineering. Hence, this REU Site in Science of Software (SOS) will engage undergraduates as data scientists studying exciting and meaningful SE research problems. Students work side-by-side with faculty mentors to gain experience in qualitative and quantitative research methods in SOS. Activities include dataset challenges, pair research, literature reviews, and presentations. Ultimately, each student works independently toward a published research result with their faculty mentors.

An Innovative Curriculum for Cybersecurity Education
Douglas Reeves ; Jason King

$234,839 by National Security Agency
04/ 3/2017 - 04/ 2/2018

N.C. State will develop, document, and deliver course materials for institutions wishing to educate and prepare Cybersecurity graduates to meet the workforce needs of the Federal government and industry, in support of the Cybersecurity National Action Plan. This curriculum addresses key elements of the identified knowledge, skill, and ability areas. Individual courses will be modularized and packaged for use by others, including syllabi, teaching materials, readings, and fully documented, hands-on student exercises. A course roadmap will be presented, along with a means for prospective students to self-assess their needs. An educator's workshop will be organized to train teachers in the use of these materials.

EAGER: Formal Models of Trainer Feedback for I-Learning Theoretical Guarantees
David Roberts

$70,043 by National Science Foundation
08/15/2016 - 07/31/2017

Machines learning from feedback is a well-studied problem. If robots and software systems can successfully adapt, they can remain useful in changing environments, in situations unanticipated at design time, and can take direction from human users. This proposal contributes a new algorithm, Income Learning (I-learning), that is designed to thrive in these scenarios. The emphasis of the work will be on theoretical and empirical analyses of how I-learning and existing temporal difference methods (that maximize the expected reward) differ in performance on a variety of tasks, and how I-learning is better able to take advantage of human teaching.

In Situ Indexing and Query Processing of AMR Data
Nagiza Samatova

$383,000 by US Department of Energy
09/ 1/2014 - 08/31/2017

One of the most significant advances for large-scale scientific simulations has been the advent of Adaptive Mesh Refinement, or AMR. By using dynamic gridding, AMR can achieve substantial savings in memory, computation, and disk resources while maintaining or even increasing simulation accuracy, relative to static, uniform gridding. However, the resultant non-uniform structure of the simulation mesh produced by AMR methods cause inefficient access patterns during data analysis and visualization. Given the exponential increase in simulation output, the massive I/O operations are becoming a substantial bottleneck in simulations and analysis. To efficiently analyze AMR data, we propose an integrated, three-prong approach that aims: (a) To devise an AMR query model; (b) To develop effective in situ indexing and query processing methods for AMR data analytics; and (c) To investigate data storage layout strategies for AMR data retrieval optimized for analytics-induced heterogeneous data access patterns. The results, algorithms, and software will be in the public domain.

Joint Faculty Agreement For Nagiza Samatova
Nagiza Samatova

$686,881 by Oak Ridge National Laboratory
08/ 9/2007 - 08/ 8/2017

Dr. Nagiza Samatova's joint work with NC State University and Oak Ridge National Laboratory (ORNL) will provide the interface between the two organizations aiming to collaboratively address computational challenges in the Scientific Data Management, and the Large-Scale Analysis of DOE-mission applications. (Supplement)

Consortium for Nonproliferation Enabling Capabilities
Nagiza Samatova, co-PI ; Robin Gardner (Nuclear Eng

$9,744,249 by US Department of Energy
07/31/2014 - 07/30/2019

NC State University, in partnership with University of Michigan, Purdue University, University of Illinois at Urbana Champaign, Kansas State University, Georgia Institute of Technology, NC A&T State University, Los Alamos National Lab, Oak Ridge National Lab, and Pacific Northwest National lab, proposes to establish a Consortium for Nonproliferation Enabling Capabilities (CNEC). The vision of CNEC is to be a pre-eminent research and education hub dedicated to the development of enabling technologies and technical talent for meeting the grand challenges of nuclear nonproliferation in the next decade. CNEC research activities are divided into four thrust areas: 1) Signatures and Observables (S&O); 2) Simulation, Analysis, and Modeling (SAM); 3) Multi-source Data Fusion and Analytic Techniques (DFAT); and 4) Replacements for Potentially Dangerous Industrial and Medical Radiological Sources (RDRS). The goals are: 1) Identify and directly exploit signatures and observables (S&O) associated with special nuclear material (SNM) production, storage, and movement; 2) Develop simulation, analysis, and modeling (SAM) methods to identify and characterize SNM and facilities processing SNM; 3) Apply multi-source data fusion and analytic techniques to detect nuclear proliferation activities; and 4) Develop viable replacements for potentially dangerous existing industrial and medical radiological sources. In addition to research and development activities, CNEC will implement educational activities with the goal to develop a pool of future nuclear non-proliferation and other nuclear security professionals and researchers.

Lecture Hall Polytopes, Inversion Sequences, and Eulerian Polynomials
Carla Savage

$30,000 by Simons Foundation
09/ 1/2012 - 08/31/2017

Over the past ten years, lecture hall partitions have emerged as fundamental structures in combinatorics and number theory, leading to new generalizations and new interpretations of several classical theorems. This project takes a geometric view of lecture hall partitions and uses polyhedral geometry to investigate their remarkable properties.

NeTS: Small: Fine-grained Measurement of Performance Metrics in the Internet of Things
Muhammad Shahzad

$449,999 by National Science Foundation
10/ 1/2016 - 09/30/2019

PI proposes to develop a framework for passive and fine-grained measurements of the performance metrics in the Internet of Things, which include both Quality of Service metrics such as latency, loss, and throughput and Resource Utilization metrics such as power consumption, storage utilization, and radio on time etc. Measurements of these performance metrics can be used reactively by network operators to perform tasks such as detecting and localizing offending flows that are responsible for causing delay bursts, throughput deterioration, or even power surges. These measurements can also be used proactively by network operators to locate and preemptively update any potential bottlenecks.

CSR:Small:Collaborative Research: Scalable Fine-Grained Cloud Monitoring for Empowering IoT
Muhammad Shahzad

$257,996 by National Science Foundation
09/15/2016 - 08/31/2019

Due to the rapid adoption of the cloud computing model, the size of the data centers and the variety of the cloud services is increasing at an unprecedented rate. Due to this, fine-grained monitoring of the health and the usage of data center resources is becoming increasingly important and challenging. In this work, we address the problem of efficiently acquiring and transporting cloud management and monitoring data. For data acquisition, we address the crucial challenge of controlling data size. For data transportation, we focus on efficiently moving the data from the point it is collected inside the data center to the point it needs to be stored for processing.

CRII: CSR: Pervasive Gesture Recognition Using Ambient Light
Muhammad Shahzad

$174,878 by National Science Foundation
05/ 1/2016 - 04/30/2018

The PI proposes to use ambient light for recognizing human gestures. The intuition behind the proposed approach is that as a user performs a gesture in a room that is lit with light, the amount of light that he/she reflects and blocks changes, resulting in a change in the intensity of light in all parts of the room. This change can be measured and the pattern of change in the intensity of light is different for different gestures. Leveraging this observation, the proposed approach first learns these patterns for different gestures and then recognizes the gestures in real-time.

Cognitive Computing-Based Compilation (YUE ZHAO)
Xipeng Shen

$22,500 by IBM Canada Limited
01/ 1/2017 - 12/31/2017

This project proposes to leverage IBM Watson-like cognitive computing engines for improving the efficacy of IBM commercial compilers (XLC/C++, XLFortran) and for enabling intelligent compilationas-a-service on cloud.

Exploring Deep Learning and Ontology-Based Framework
Xipeng Shen

$30,000 by Lawrence Livermore National Laboratory via US Dept of Energy
06/20/2017 - 12/31/2017

This work is to explore the potential of deep learning in optimizing sparse matrix computations, and to complete an ontology-based framework with a web interface for enabling effective knowledge accumulation and knowledge sharing for HPC.

Optimizing CNN Ensemble-based Deep Learning (Randall Pittman)
Xipeng Shen

$28,296 by Oak Ridge National Laboratory
05/15/2017 - 08/31/2017

This project aims to optimize the training process of CNN-based deep learning, especially when an ensemble of networks is used.

Context-Aware Correlation-Based Program Optimizations
Xipeng Shen

$28,000 by IBM Canada Limited
07/ 1/2014 - 06/30/2017

A component (e.g., a function or loop) in a program often exhibits different behaviors (e.g., execution paths) in a different context. Such a context sensitivity exists in High Performance Computing (HPC) applications, and even more commonly in Business Analytics and Optimizations (BOA) programs. This collaboration with IBM aims at developing context-aware correlation-based program optimizations, a new way to tackle context sensitivity in code specializations that effectively removes some limitations in current compiler technology.

Context-Aware Correlation-Based Program Optimizations (Supplement)
Xipeng Shen

$28,000 by IBM Canada Limited
07/ 1/2014 - 06/30/2017

In this project, we propose to build up context-aware correlation-based program optimizations, a new way to tackle context sensitivity in code specializations that effectively removes some limitations in current compiler technology.

Cognitive Computing-Based Compilation (YUE ZHAO)
Xipeng Shen

$10,846 by IBM Canada Limited
06/30/2016 - 06/29/2017

This project proposes to leverage IBM Watson-like cognitive computing engines for improving the efficacy of IBM commercial compilers (XLC/C++, XLFortran). Recent years have witnessed some exiting improvement of cognitive computing engines as demonstrated by the increasing impact of IBM Watson. In this project, we propose to leverage such engines to allow compilers to automatically accumulate the knowledge on appropriate ways to compile programs, learn from it, and then apply it to new programs. The success will remove the difficulties for users to choose the appropriate compilation flags, largely reduce the required tuning efforts from users, and help compilers to make better decisions to produce high-quality code

Enabling Portable Optimizations of Data Placement on GPU Memory (Guoyang Chen)
Xipeng Shen

$17,044 by IBM Canada Limited
06/29/2016 - 06/28/2017

Modern GPU memory systems manifest more varieties, increasing complexities, and rapid changes. For instance, there are more than eight types of memory on a single NVIDIA Kepler GPU. Different placements of data on memory systems often cause substantial, sometimes several times, difference in program performance. Most current GPU compilers rely on programmers to indicate the appropriate placements, but finding the appropriate placements is difficult for programmers in practice, thanks to the complexity and fast changes of memory systems, as well as the input sensitivity of appropriate data placements---that is, the best placements often differ when a program runs on a different input data set. We propose to solve the problem by developing a compiler-based software framework named PORPLE. Through PORPLE, the data of a GPU program gets automatically placed appropriately on memory. Moreover, when new memory systems arrive, it can easily adapt the placements accordingly. Experiments on three types of GPU systems show that PORPLE consistently finds optimal or near-optimal placement, yielding up to 2.08X (1.59X on average) speedups compared to programmers' decisions. As an extension of our CAS project funded last year, this proposal proposes to expand our exploration to build the PORPLE techniques into IBM XLC compiler and runtime to effectively map OpenMP code to GPU

SHF: Small: Improving Memory Performance on Fused Architectures through Compiler and Runtime Innovations
Xipeng Shen ; Frank Mueller

$470,000 by National Science Foundation
08/ 1/2015 - 07/31/2018

Contemporary architectures are adopting an integrated design of conventional CPUs with accelerators on the same die with access to the same memory, albeit with different coherence models. Examples include AMD's Fusion architecture, Intel's integrated main-stream CPU/GPU product line, and NVIDIA Tegra's integrated graphics processor family. Integrated GPUs feature shared caches and a common memory interconnect with multicore CPUs, which intensify resource contention in the memory hierarchy. This creates new challenges for data locality, task partitioning and scheduling, as well as program transformations. Most significantly, a program running on GPU warps and CPU cores may adversely affect performance and power of one another. The objective of this work is to understand these novel implications of fused architectures by studying their effects, qualifying their causes and quantifying the impacts on performance and energy efficiency. We propose to advance the state-of-the-art by creating spheres of isolation between CPU and GPU execution via novel systems mechanisms and compiler transformations that reduce cross-boundary contention with respect to shared hardware resources. This synergy between systems and compiler techniques has the potential to significantly improve performance and power guarantees for co-scheduling pgrams fragments on fused architectures. impact: The proposed work, if successful, has the potential to transform resource allocation and scheduling at the systems level and compiler optimizations at the program level to create a synergistic development environment with significant performance and power improvements and vastly increased isolation suitable for synergistic co-deployment of programs crossing boundaries on innovative fused architectures.

EAGER/Cybermanufacturing: Just-In-Time Compilation of Product Manufacturing Data to Machine Instructions via an Industrial Machine Operating System
Xipeng Shen Co-PI ; Binil Starly Lead PI ISE

$299,999 by National Science Foundation
09/ 1/2015 - 08/31/2017

Intelligent machines are purported to be the back-bone of the cybermanufacturing initiative.Yet, the conventional approach to making a machine ‘cyber-enabled’, is to outfit the machine with an array of multi-modal sensors which is then integrated to the network and enterprise system through communication and computing platforms. To make further development challenging, almost all industrial machine vendors have closed hardware and software architecture which makes it difficult for extensibility and adaptation to a cyber-manufacturing environment. We propose a new architecture, which we term as the – ‘Industrial Machine Operating System - iMOS’, will be a flexible framework for writing machine software. It will be a collection of hardware configurations, data structures, tools, libraries and semantics to simplify the task of creating a cyber-physical enabled manufacturing machine, designed to operate across a wide variety of manufacturing process platforms. (Total Award Amount $299,999.00

EAGER: Cognitive Modeling of Strategies for Dealing with Errors in Mobile Touch Interfaces
Robert St. Amant ; Emerson Murphy-Hill

$281,076 by National Science Foundation
09/ 1/2014 - 08/31/2017

Touch interfaces on mobile phones and tablets are notoriously error prone in use. One plausible reason for slow progress in improving usability is that research and design efforts in HCI take a relatively narrow focus on isolating and eliminating human error. We take a different perspective: failure represents breakdowns in adaptations directed at coping with complexity. The key to improved usability is understanding the factors that contribute to both expertise and its breakdown. We propose to develop cognitive models of strategies for touch interaction. Our research will examine the detailed interactions between users perceptual, cognitive, and motor processes in recognizing, recovering from, and avoiding errors in touch interfaces. Our proposal is for three stages of research: exploratory experiments, analysis and modeling, and finally validation experiments.

CHS: SMALL: Direct Physical Grasping, Manipulation, and Tooling of Simulated Objects
Robert St.Amant ; Christopher Healey

$496,858 by National Science Foundation
08/ 1/2014 - 07/31/2017

This proposal is for the development and evaluation of CAPTIVE, a Cube with Augmented Physical Tools, to support exploration of three-dimensional information. The design of CAPTIVE is founded on the concept of tool use, in which physical objects (tools) are used to modify the properties or presentation of target objects. CAPTIVE integrates findings across a wide range of areas in human-computer interaction and visualization, from bimanual and tangible user interfaces to augmented reality. CAPTIVE is configured as a desktop augmented reality/fishtank virtual reality system [120], with a stereo- scopic display, a haptic pointing device, and a user-facing camera. In one hand the user holds a wireframe cube that contains virtual objects, in the other the pointing device, augmented to reflect its function as a tool: a probe probes for pointing at, choosing, and moving objects; a magnifying or semantic lens for filter- ing, recoding, and elaborating information; a cutting plane that shows slices or projection views. CAPTIVE supports visualization with more fluid and natural interaction techniques, improving the ability of users to explore and understand 3D information.

SHF: Medium: Collaborative Research: Semi and Fully Automated Program Repair and Synthesis via Semantic Code Search
Kathryn Stolee

$387,661 by National Science Foundation
07/ 1/2016 - 06/30/2020

Software plays an integral role in our society. However, software bugs are common, routinely cause security breaches, and cost our economy billions of dollars annually. The software industry struggles to overcome this challenge: Software is so inherently complex, and mistakes so common, that new bugs are typically reported faster than developers can fix them. Recent research has demonstrated the potential of automated program repair techniques to address this challenge. However, these techniques often produce low-quality repairs that break existing functionality. In this research, we develop new techniques to fix bugs and implement new features automatically, producing high-quality code.

Parameterized Algorithms Respecting Structure in Noisy Graphs (PARSiNG).
Blair Sullivan

$249,140 by US Navy - Space and Naval Warfare Systems Center (SPAWAR)via DARPA
09/30/2014 - 07/30/2017

This extension to the PARSiNG project focuses on issues related to improving accessibility and usability for downstream analysts of the related open source software toolkit. This may include new features (such as a graphical interface), improved I/O and data format support, extension of the modular framework to additional DARPA-relevant problems, and testing and incorporation of algorithmic coloring advances.

Algorithms for Exploiting Approximate Network Structure Research Area 10: Network Science
Vida Blair Sullivan

$538,199 by US Army-Army Research Office
05/15/2017 - 05/14/2020

We propose a new framework for efficient, robust, and noise-tolerant network algorithms that guarantee near-optimal solutions to NP-hard problems by exploiting structure inherent in real-world networks. We model networks as consisting of a majority that belongs to a structural graph class, plus a few deviations resulting from measurement errors, unusual behaviors, and/or unexplained exceptions. We will develop algorithms which exploit this more approximate form of graph structure and guarantee near-optimal solutions and polynomial running time for any network that is ``close'' to a structural graph class, initially focusing on hierarchical/tree-like networks (e.g. those arising in biology and social behavior).

Moore Foundation Data-Driven Discovery Investigator
Vida Blair Sullivan

$1,500,000 by Gordon and Betty Moore Foundation
11/10/2014 - 12/ 1/2019

Understanding and identifying intermediate-scale structure is key to designing robust tools for data analysis, just as the interdependence of local interactions and global behavior is key in many science domains. We thus focus on constructing a theory and tools for using this structure to improve analysis and identification of relationships in massive graph data. Through careful integration of tools from graph theory, computational complexity, statistics, and parallel algorithm design, the proposed work will derive novel measures of graph similarity based on structural representations and application-inspired features of interest. We will design efficient, scalable sampling algorithms which leverage inherent sparsity and structure to de-noise and improve accuracy of parameter estimation. As a specific example of science domain impact, we focus on improving understanding of the brain. Applying our new tools for characterizing graph-theoretic structure in such networks, scientists will be able to build higher fidelity models of brain network formation and evolution. Additionally, efficient algorithms from the associated parameterized framework will enable rapid comparison of regions and identification of discrepancies, abnormalities, and influential components for specific tasks.

CRII: CSR: Rethinking the FTL in SSDs -- a file translation layer instead of a flash translation layer
Hung-Wei Tseng

$174,998 by National Science Foundation
03/15/2017 - 02/28/2019

SSDs (solid state drives) nowadays become popular in all kinds of computing systems. However, these systems still leverage the existing block interface to manage SSDs, resulting in multiple layers of indirections, under-utilized parallelism inside the SSD, overheads for in-storage computing,and difficulties in sharing file among heterogeneous computing devices. This work will reshape the current system stack and simplifies the software/hardware interface for SSDs by using the SSD to directly map files into physical block addresses on the SSD. This work will demonstrate the effect of the proposed system in applications ranging from file systems, datacenter storage, and virtualized machines.

Property Rights and Land Tenure in the Slums of Bangalore
Ranga Vatsavai

$21,237 by Duke University via Omidyar Network
01/ 1/2017 - 02/ 8/2018

Slums have become an inescapable feature of cities in the developing world, and the number of people living in slums has increased rapidly, coming close to 1 billion and rising higher (UN-Habitat 2010). Relatively little is known, however, about patterns of slum development over periods of time and about factors associated with progressive improvements. One of the objectives of this research is to develop a prototype methodology for semi-automatic slum identification and categorization that can speedily and reliably be adapted for use in other cities.

EDU: Motivating and Reaching Students and Professionals with Software Security Education
Laurie Williams ; Emerson Murphy-Hill ; Kevin Oliver (Education)

$300,000 by National Science Foundation
09/ 1/2013 - 08/31/2017

According to a 2010 report that was based on the interviews from 2,800 Information Technology professionals worldwide, the gap between hacker threats and suitable security defenses is widening, and the types and numbers of threats are changing faster than ever before . In 2010, Jim Gosler, a fellow at the Sandia National Laboratory who works on countering attacks on U.S. networks, claimed that there are approximately 1,000 people in the country with the skills needed for cyber defense. Gosler went on to say that 20 to 30 times that many are needed. Additionally, the Chief Executive Officer (CEO) of the Mykonos Software security firm indicated that today's graduates in software engineering are unprepared to enter the workforce because they lack a solid understanding of how to make their applications secure. Particularly due to this shortage of security expertise, education of students and professionals already in the workforce is paramount. In this grant we provide a plan for motivating and providing software security education to students and professionals.

HCC:Small:Collaborative Research:Integrating Cognitive and Computational Models of Narrative for Cinematic Generation
R. Michael Young

$352,696 by the National Science Foundation
08/ 1/2013 - 07/31/2017

Virtual cinematography, the use of a virtual camera within a three dimensional virtual world, is being used increasingly to communicate both in entertainment contexts as well as serious ones (e.g., training, education, news reporting). While many aspects of the use of virtual cameras are currently automated, the control of the camera is currently determined using either a pre-programmed script or a human operator controlling the camera at the time of filming. This project seeks to develop a model of virtual cinematography that is both computational -- providing a software system capable of generating camera control directives automatically -- and cognitive -- capable of modeling a viewer's understanding of an unfolding cinematic.