In this proposal, we aim to design, build, and integrate a novel student modeling engine to provide adaptive scaffolding to programming students. We bring in our expertise in student modeling coupled with LLM's capability in deep analysis of programming snippets to address challenges associated with the temporal ill-defined nature of programming education. This entails effective representation of students' programming processes and capturing their evolving competency in foundational programming knowledge and skills.

This project directly contributes to the main goals of the NSF DRK-12 program through the design, implementation, and evaluation of a project-based integrated science and AI curriculum and technology. The project has the overarching goal of preparing a diverse, computationally-competent next-generation STEM workforce through devising an effective, engaging, and inclusive learning environment. In particular, we will create a learning environment for computational modeling that features custom code blocks that facilitate the implementation of AI algorithms in science-related contexts while obfuscating unnecessary programming complications. Our curricular modules will integrate important AI concepts with the high school STEM curriculum following NGSS standards.

With the rapidly growing recognition of the role that computer science is playing in every aspect of society, enrollments in introductory computer science courses are increasing at an unprecedented pace. As a result of this phenomenal growth, departments of computer science are seeing extraordinary demand for introductory computer science courses. The accelerating growth in enrollments poses significant challenges for introductory programming instructors, who must teach increasingly larger classes while providing effective, engaging learning experiences for students. The overarching objective of this project is to develop an introductory programming teaching support environment, INSIGHT, that will enable instructors to readily understand their students’ progress through introductory computer science coding activities. INSIGHT will fundamentally change classroom dynamics by supporting both students and instructors.

This project will support implementation and study of the Beauty and Joy of Computing (BJC) curriculum. We aim to increase implementation of BJC in New England states and beyond particularly in high-need districts. We will study the effects of BJC implementation on the participation of girls, Black, Latinx and low-income students.

This project will develop generalizable data-driven tools that addresses the conceptually and practically complex activity of constructing adaptive support for individualized learning in STEM domains.

Knowledge graphs have emerged in many domains of science and technology as a powerful means of integrating, structuring, and mining information to extract new knowledge. Recognizing the importance of this paradigm, the Proto-OKN project will create FRINK: Fabric Integrating Networked Knowledge. FRINK will create capabilities that allow for the uniform deployment, integration, and harmonization of knowledge graphs created under the Proto-OKN program into a unified Open Knowledge Network for query and analysis. FRINK will be organized around three objectives, detailing three types of fabric that will bind together the initially disparate graphs developed by Theme 1 teams.

Autonomous Driving Systems (ADS) are software systems designed to reduce the need or even replace humans in the task of driving a vehicle. They have been attracting tremendous societal interest given its potential to increase road safety, reduce traffic congestion, reduce commuting time, etc. Unfortunately, these systems are not foolproof. Physical testing of ADS-driven vehicles does not scale and can be unsafe. For those reasons, simulation-based testing (SBT) became very popular. Despite the recent advances in SBT techniques for autonomous driving, important challenges remain to be addressed. First, finding problematic inputs with simulation is time and space costly. Second, techniques often report duplicate infractions that do not contribute to information gain. Third, techniques make strong assumptions about the simulation environment. This project proposes novel approaches to mitigate these fundamental challenges, advancing the state of the art in Simulation-based Testing of Autonomous Driving Systems.

Runtime Verification (RV) monitors programs against formal specifications and reports violations when executions do not satisfy those specifications. RV can find bugs that tests miss, but it is not yet widely adopted. We address two hindrances to broad RV adoption: (1) writing specifications requires learning domain specific languages, and (2) many programming languages have no mature RV tool. We propose infrastructure for writing specifications in popular user-friendly formats, and for reusing existing RV tooling for Java to monitor programs written in other languages. We will evaluate the improved usability of the proposed infrastructure via case studies and user studies.

Recent years have seen a surge in popularity in smart home IoT products, and with the ongoing pandemic, people are spending more time interacting with such devices. However, it is unclear whether and how these IoT devices affect the security, privacy, and performance of the home network as well as the access network. In this proposal, we focus on developing privacy-preserving IoT analytics to help network providers allocate network resources accordingly and, at the same time, help consumers identify potential anomalous behavior.

This proposal introduces innovative strategies to fortify the metaverse ecosystem, aiming for secure, resilient, and privacy-enhanced digital world experiences. The proposed work will tackle physical, digital, and virtual security and privacy risks, developing reliable digital mapping and robust ML services, as well as privacy-enhancing user interactions.

The global cellular telecommunication system is critical infrastructure that has become a ubiquitous platform for Internet connectivity supporting a wide range of use cases for both consumers and industry. We are now on the cusp of widespread adoption of 5G technology. While 5G is widely marketed for its gigabit per second rates and ultra-low latency, it also also fundamentally changes the internal network architecture, providing dynamic provisioning of software-defined services that offer enhanced control to network tenants including virtual operators and enterprises. This new threat model necessitates deep investigation of the many technical components that comprise the cellular system. Whereas several initial studies have formally modeled and evaluated the security of 5G cryptographic protocols, little is known about the security of software and hardware systems that implement them. To this end, the goal of this work is to aid mobile network operators in deploying secure cellular systems through the development of tools and techniques that extract, model, and analyze security-sensitive logic of the source and binary code that exists within cellular system functional entities.

This project aims to develop an integrated lightweight and energy-efficient prosthetic care robot framework. It will enable proactive and user-specific prosthetic control to improve walking function in a variety of walking conditions found ubiquitously in daily living.

In this research, we plan to characterize the help resources available to students in Computer Science (CS) courses and analyze the order and frequency of use by the students. We will also study why students choose specific help patterns and what help they perceive to be effective for their learning. Our goals are to explicitly teach students about the help resource landscape, guide them to identify CS topics where they may need help and to empower them to be more effective in traversing the complex landscape of help resources.

Learning how to code is a key and challenging component in computer science (CS) education. Traditionally, the primary focus in programming courses has been on achieving functional correctness. However, there is a growing recognition of the importance of program performance, as evidenced by factors such as execution time, memory usage, and other metrics. This shift has garnered attention from both students and instructors, highlighting the need to incorporate performance considerations alongside functional correctness in CS education. This project will develop EduPerf, which aims to hoist program performance as the first-order metric in CS education via tightly integrating program performance analysis into different levels of CS courses for both students and instructors.

Continuous Integration (CI) has become an essential component of the modern software development cycle. Developers engineer CI scripts, commonly called workflows or pipelines, to automate most software maintenance tasks, such as testing and deployment. Security issues in workflows can have devastating effects resulting in supply-chain attacks. We propose to handle these research challenges by (1) defining a threat model and deriving security properties from first principles; (2) developing a framework based on our Workflow Intermediate Representation (WIR) that enables us to verify and define security properties in a platform-agnostic way.

We study the web differently from how users explore it, as browsers are not meant to be monitoring tools. Researchers build either ad-hoc solutions or use high-level information from the browser that is inadequate to identify some of the most advanced web attacks. This research aims at building the fundamental blocks for studying an increasingly complex web by developing a monitoring platform that sheds light into the inner workings of modern browsers and websites. Our research outcomes will allow any researcher, web developer or web user to understand better how the web works.

Fingerprinting has been a known threat to web privacy for over a decade. Yet, automated detection of fingerprinting methods and scripts has been lacking the properties for protecting web users from such an evolving web threat. Our proposed work aims to provide novel detection methods for browser fingerprinting both at its core, the browser and the evolution of its APIs, and at the page level, via dynamic analysis ofJavaScript. We also propose developing countermeasures that are capable of performing more fine-grained blocking not only at the script level, but also at the API level where an instance of a script/API will be blocked depending on inferring the underlying intent behind executing the script or accessing the API.

The U.S. Army???s Force Modernization strategy highlights the critical role synthetic training will play in transforming Soldiers to operate as a multiple domain force. A key affordance of synthetic training environments is their capacity to support competency-based experiential learning (CBEL), which prescribes an active approach to learning and expertise development that incorporates adaptive instruction and intelligent tutoring capabilities. Although synthetic training environments show great promise for supporting CBEL, there is a lack of guidance on how synthetic training experiences should be integrated into Army schoolhouse curricula to support competency development and experiential exposure. To maximize the effectiveness of CBEL, synthetic learning experiences need to be dynamically crafted to support individual learning needs and skill development. Simulation-based training scenarios can offer trainees valuable experiences but are resource-intensive to create, and in most cases, schoolhouses have a limited supply of scenarios that they can utilize for a particular course. Competency-based scenario generators offer considerable promise for addressing these challenges by tailoring synthetic training experiences to the needs of individual learners in support of CBEL. Competency-based scenario generators can dynamically shape training experiences, scenario events, unit behaviors and states, and virtual environments in order to support CBEL. Scenario generators can leverage recent advances in machine learning to provide data-driven approaches to support competency-driven training. Recognizing the opportunity introduced by recent advances in machine learning and data-driven scenario generation, the proposed project will investigate how we can devise generalizable, data-driven scenario generation models that dynamically generate training scenarios that achieve target learning objectives to support CBEL in Army schoolhouses. We will design and develop the CompGen competency-based scenario generation framework and demonstrate its data-driven capabilities for supporting CBEL in an institutional training setting.

Developing our biomedical workforce is a critical national need. Artificial intelligence (AI) has emerged as a powerful technology that will play an important role in biomedical careers. The project will engage middle school students learning about AI in the context of biomedical careers through the design, development, and evaluation of AI4Health, a game-based learning environment that will create personalized adventures in which students will utilize AI tools to solve biomedical problems. The project will evaluate the impact of AI4Health on students??? knowledge, interest, and self-efficacy for AI and biomedical careers.

Artificial Intelligence (AI) has emerged as a foundational technology that is profoundly reshaping society. With accelerating advances in a wide array of capabilities including natural language processing, computer vision, and machine learning, AI is quickly finding broad applications in every sector of society. Critically, AI holds significant transformative potential for improving human learning. This National Artificial Intelligence (AI) Research Institutes proposal centers on the establishment of the Institute for an AI-Engaged Future of Learning. Driven by a learner-centered vision of the potential of AI-augmented learning, the ENGAGE AI Institute will conduct (1) foundational AI research on natural language technologies, computer vision, and machine learning and (2) use-inspired AI research on AI-augmented learning, thereby creating learning experiences specifically designed to promote student engagement in formal and informal learning settings. The ENGAGE AI Institute brings together an exceptional interdisciplinary team spanning five organizations with deep expertise in AI and education, including four universities (North Carolina State University, the University of North Carolina at Chapel Hill, Vanderbilt University, and Indiana University) and Digital Promise, which will serve a “nexus” role for the Institute. The Institute will create AI-augmented learning technologies with specific foci on supporting two forms of engaging collaborative inquiry learning experiences: collaborative learning (problem solving and learning that play out in groups) and embodied learning (learning processes that are grounded in the interplay between the body, movement, and senses). The Institute will focus on AI-driven narrative-centered learning environments that create engaging story-based problem-solving experiences to support collaborative inquiry learning. The Institute will explore AI-augmented learning that operates at three levels: individuals, small groups, and larger groups within a range of educational contexts (e.g., classrooms, museums).

The overarching objective of this project is to investigate how explanation-based classroom response systems can significantly improve student learning in STEM undergraduate education. It has been widely demonstrated that students who engage in self-explanation learn much more effectively than students who do not engage in self-explanation. By explaining concepts and examples as they learn, students trigger the self-explanation effect, which causes them to actively probe their own understanding, to learn much more deeply. However, students in undergraduate STEM courses have limited opportunity to engage in self-explanation. Building on our prior NSF-supported research on natural language processing-based STEM learning environments, we will investigate student learning in undergraduate STEM classrooms with an explanation-based classroom response system. The system will fundamentally change classroom dynamics by supporting both students and instructors. It will support students by instantly providing realtime formative assessment of their explanations. It will support instructors by instantly providing a summary and analysis of students’ explanations in aggregate, which will enable instructors to make immediate adjustments to pedagogy. Together, these benefits will synergistically lead to improved student learning and stronger student engagement in STEM classrooms.

It has long been recognized that drawing can be a powerful approach to learning. Learning-by-drawing activates a complex set of cognitive processes that requires students to deeply engage with a subject matter. The project centers on the design, development, iterative refinement, and investigation of a sketch-based science learning environment. Specifically, the project will focus on the development and piloting of a sketch-based science learning environment to support students’ conceptual understanding of science with an emphasis on modeling. The project will culminate in a pilot study to investigate the effectiveness of the sketch-based learning environment for improving students’ factual understanding, their inferential understanding, and their ability to engage in science modeling. By utilizing a mixed methods approach integrating quantitative and qualitative work with learning analytics, it is anticipated that the project will yield theoretically-driven, empirically-based advances in sketch-based science learning environments that significantly improve conceptual understanding of science in upper elementary students.

As the correlation of data gains importance in many domains, high-dimensional tensors are becoming an ever more important object to represent data and analyze its inherit properties. Tensor networks targeting very high-dimensional data and extracting physically meaningful latent variables are underdeveloped because of their complicated mathematical nature, extremely high computational complexity, and more domain-dependent challenges. This work proposes Cross-layer cooRdination and Optimization for Scalable and Sparse Tensor Networks.

The aim of this project is to achieve early, rapid, and precise detection of harmful downy mildew on cucurbit plants to enhance crop health and production. This objective will be accomplished by employing quantum sensing-enabled spectroscopy, which utilizes entangled photons and a quantum machine learning receiver. The resulting quantum sensing device will be incorporated into a robotic land rover for testing in North Carolina's cucurbit fields.

Wireless devices are inherently faculty which can result in multifaceted data errors in computing, caching, and communications (C3). These errors have been widely deemed harmful, but recent studies have shown that they can be benign or even beneficial. The research objective of this project is to proactively harvest, render, and control data errors across C3 of wireless devices for significant performance gains in energy efficiency, throughput, data privacy, etc. Moreover, the research efforts will be coupled with educational innovations through the development of new laboratories, lecture contents, outreach demos and a novel undergraduate/graduate co-learning pedagogy.

This project will create a general-purpose, open-access, and programmable quantum network prototype for the quantum information science and engineering (QISE) community to experiment new quantum technologies and train teachers and students. The key applied methodology is virtualization that permits rapid and flexible experimentation via agile software controls, without resorting to daunting hardware modifications. The research team initiates a research agenda consisting of three thrusts, namely re-designing key quantum components, developing communication protocols, and implementing the prototype in the testbed. A new curriculum based on this prototype will be created and disseminated to train a large body of college students.

Digital twin is emerging as a revolutionary approach to testing and assurance for next-generation (nextG) wireless networks enabling continuous prototyping, optimization, and validation. The primary goal is to lay the foundations of digital network twin (DNT) by exploring innovative technologies to map and optimize nextG wireless networks in twins, thereby facilitating development, testing, and formal evaluation exercises of nextG wireless networks. The research agenda comprises two thrusts. Thrust 1 is focused on novel approaches of building the twin environment to replicate the physical network world. Thrust 2 shall build and optimize the network twins over actual network environments associated with communication, computing, and networking resources. The fundamental research of Thrusts 1 and 2 is then implemented in a developed DNT platform used to demonstrate the behavior and performance of designed twining and optimization approaches.

This collaborative project between NCSU and ETS is focused on developing new noninvasive process-based measurements for students engagement with writing tasks, including analyses of their writing quality, working habits, and responses to feedback. As part of this project we will develop a secure instrumented platform for online writing tasks that will provide analytical tools for instructors and researchers to monitor and evaluate student's work.

We propose to develop a transformative technology in the form of teachable agent to amplify the effect of learning by teaching that we shall call a smart teachable agent, or smart TA for short. The smart TA asks students questions to justify their reasoning while solving equations. When student???s response could be elaborated, the smart TA further provides a follow up question to solicit a response that reflects a connection between procedural operations and conceptual justifications.

Recent years have seen a growing recognition of the national STEM workforce shortage. Although problems abound in all STEM disciplines, the shortage is particularly acute in information and communications technology. This is especially true in artificial intelligence (AI), a field of computer science that focuses on the design of computing systems that solve problems involving human-like capabilities including reasoning, learning, and natural language. Engaging middle-grade students, especially those from underserved populations, in artificial intelligence through the creation of lifelike AI for digital games offers a promising approach to encouraging students to pursue innovative computing careers. The AI Play project will engage students in a broad range of computing activities centered on creating AI for games. The project will see the development of a learning environment and curriculum that introduces artificial intelligence into middle school emphasizing connections to the CSTA K-12 Computer Science Standards. The AI Play project will host a series of five-day camps for underserved populations where students will engage in hands-on learning activities under the guidance of teachers and undergraduate computer scientists, who will serve as mentors and role models as the students engage in artificial intelligence, while designing and developing AI for games. The final year of the project will see an evaluation of the AI Play program and its impact on students’ learning and interest in artificial intelligence.

The objective of this work is to (1) develop sample programs that utilize the SCR library and can serve as benchmark examples to the community as well as (2) devise novel methodologies for improving the performance of checkpoint/restart on modern HPC systems with an implementation and evaluation.

The primary goal of the QACTI quantum system and technology demonstrator is to build an advantage-class trapped-ion quantum-computer capable of being used by the broader scientific community remotely. The secondary goals are to discover algorithms suited for near-term quantum computers, improve and democratize ion trap quantum technology, and develop a workforce capable of utilizing and building advantage-class machines.These goals only become achievable by performing device-oriented experiments at fine-grained control levels that are not available on commercial platforms, thereby contributing to the development of a combined hardware/software stack in an open-source manner.

The Institute for Robust Quantum Simulation will focus on using quantum simulation to gain insight into—and thereby exploit—the rich behavior of complex quantum systems. Combining expertise from researchers in computer science, engineering, and physics, our team will address the challenge of robustly simulating classically intractable quantum systems of practical interest, and verifying the correctness of the simulation result.

Open-source robot software aims to enable rapid system development but comes with little or no tooling for automated testing and analysis. This work utilizes model checking of behavior trees and abstract type inference of physical units to automatically suggest system tests and to help ensure the absence of certain classes of software defects. This CAREER proposal examines whole system representation and tooling across interdisciplinary boundaries. We aim to substantially reduce the cost and improve the scalability of lightweight formal methods for robotic software systems, thus laying the foundation for the next generation of automated testing and analysis of robotic systems.

Cognition is central to any programming task---from understanding and reading source code, selecting programming abstractions and algorithms, and problem-solving and debugging implementations. Despite its vast capacity and associative powers, the human brain limits what programming tasks can be performed without process or tools to support it. In this project, we use brain imaging techniques to study software engineers, by examining them perform programming tasks under various conditions. From these studies, we are able to explain the neural mechanics of cognition in programming and derive more effective mental representations, strategies, and training techniques. Finally, we design more effective tools and processes for understanding and supporting programmer cognition.

Machine learning (ML) is a powerful computing tool for building models from data, which is becoming a vital skill across STEM disciplines. However, ML is a challenging subject, requiring students to construct complex ML "pipelines," often with little one-on-one support from instructors. The goal of this CAREER proposal is to aid students in learning to design and implement ML pipelines through a data-driven tutoring system. To do so, the project will develop novel techniques for evidence-centered, real-time assessment of students' ML knowledge and novel forms of automated support for ML, including design feedback, and adaptive code examples.

Machine learning (ML) is a powerful computing tool for building models from data, which is becoming a vital skill across STEM disciplines. However, ML is a challenging subject, requiring students to construct complex ML "pipelines," often with little one-on-one support from instructors. The goal of this CAREER proposal is to aid students in learning to design and implement ML pipelines through a data-driven tutoring system. To do so, the project will develop novel techniques for evidence-centered, real-time assessment of students' ML knowledge and novel forms of automated support for ML, including design feedback, and adaptive code examples.

The goal of this work is to investigate the role of self-regulated learning (SRL) in computing education by validating and analyzing fine-grained trace data from students' interactions with programming tools. We will: 1) Conduct instructor interviews and classroom observations to identify SRL strategies related to programming tool use; 2) Instrument the tools to record student behavior, adding a priori design choices that make students' SRL strategies more visible; 3) Conduct laboratory studies and collect think-aloud protocols, then code the data with strategies identified earlier; 4) develop educational data mining techniques to identify SRL behaviors from log data; 5) deploy the SRL detectors in both introductory and more advanced CS classrooms, using the detected behaviors to validate and extend SRL theories in the domain of CS.

We propose to develop infrastructure to enhance and scale CSEd research by leveraging the power of data-driven AI and ML. To do so, we need to overcome 3 challenges: data (there is not enough quantity and quality of data), analytics (developing and sharing data mining and AI methods for CSEd is highly siloed and disconnected) and evaluation (AI-based interventions and tools are not easily deployed and replicated). To address these challenges, we will develop a large collection of resources including datasets, analytical approaches, reusable smart learning content, and tools and user services that enables the community to reuse the resources and contribute to the collection.

Telephone users are regularly besieged by unsolicited sales and scam calls, cannot verify identities of callers, and enterprises frequently fall prey to expensive compromises of their telephone infrastructure. This proposal will deliver techniques to detect these issues, conduct network-wide systematic measurement, and provide practical defenses for these problems. The vision of this 5-year project is to provide technologies that will restore the telephone network to its former status as a trusted and trustworthy network.

Educating the next generation of cybersecurity professionals is a critical need for the State of North Carolina and the United States. We are utilizing our expertise in cybersecurity research to prepare undergraduate and Masters computer science students at NC State for cybersecurity jobs. Scholarship for Service (SFS) will provide students from North Carolina and the United States, especially from underrepresented groups, the opportunity to receive a high quality cybersecurity focused degree. SFS students will be part of a larger cohort of cybersecurity students who will participate in supplemental activities, events, and conferences as part of their educational experience.

Animal-Assisted Interventions (AAIs) are goal-oriented programs that intentionally incorporate animals, such as dogs, for therapeutic benefits. AAIs are widely used in a variety of settings, including for cancer patients and veterans with Post-Traumatic Stress Disorder (PTSD). AAI has proven to provide physiological, psychological, and symptom benefits. The positive effects of AAI are posited to be, in part, due to the dynamic human-animal bond (HAB). Despite AAI???s popularity, neither comprehensive AAI assessment methods nor stakeholder-informed, standardized AAI protocols exist???activities critical for understanding AAI mechanisms of action, the role of the HAB, and the mechanisms by which the HAB is formed and maintained. The overall objective is to develop, test, and evaluate an IoT software and hardware system for dyadic physiological monitoring of humans and animals in AAI settings, and to innovate in analytic methods for interpreting the data. The work is important, careful, and systematic and will yield novel capabilities and information for AAI outcome assessment and intervention development. Our team has developed and tested an innovative platform that incorporates wearable, wireless sensors that will simultaneously gather physiological data (i.e., activity, heart rate/variability, respiratory rate, electrodermal activity) from both humans and dogs involved in AAIs. This novel system will be combined with psychological (i.e., distress, well- being) and symptom data (i.e., pain, fatigue) collected from the patient and dog handler. Tasks will include qualitative methods (i.e., focus groups) to elucidate first stakeholder-informed, standardized AAI protocols. Conducting focus groups including patients, handlers, and providers will provide information to optimize a structured AAI protocol that can be delivered with a high level of intervention fidelity, lead to beneficial patient outcomes, and provide controlled settings for objective, continuous measurement of both patient and dog physiology and behavior.

The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North Carolina’s sweet potato growers.

NSF Computer and Information Science and Engineering Graduate Fellowship Award (CSGrad4US) for incoming CSC PhD student Jordan Esiason.

This proposal will generate novel abstractions for computing that extend serverless functions to better leverage unique hardware characteristics, and for memory to allow more automated leveraging of workload characteristics such as locality and compute intensity. Further, this work expands currently limited secure enclaves to include parallel, heterogeneous hardware needed to support a wide range of applications, and enhances serverless databases to leverage heterogeneous compute resources.

This project will develop IRIS-D: a data flow-enabled portable memory abstraction for seamlessly orchestrating memory in diverse heterogeneity. Using the data-flow analysis, IRIS-D will guard against race conditions while multiple heterogeneous devices can access memory objects and optimize data movement both between the host and devices and between devices across multiple nodes in the distributed multi-node systems. As a result, the proposed IRIS-D will provide high programming productivity, performance, and portability for distributed multi-device heterogeneous execution in HPC and cloud systems with diverse co-existence of architectures from different vendors, including CPUs, NVIDIA GPUs, AMD GPUs, field-programmable gate arrays (FPGAs), and Hexagon digital signal processors (DSPs).

Coastal areas are highly susceptible to significant flood damage from sea level rise, high tides, storm surges, and extreme rainfall due to dense populations, high property values, and disproportionately vulnerable populations in low-lying areas. Understanding and predicting the consequences of stresses and shocks in this coupled land-ocean system is vital for the future viability and sustainability of the region. Researchers, encompassing faculty, postdocs, graduate, and undergraduate students, are eager to engage in Environmental Science (ES) research, a critical endeavor to safeguard regions against environmental disasters. However, the ES data collected from distributed sensors with various monitoring resolutions worldwide, both diverse and complex, presents challenges but also opportunities for scientists engaged in understanding and predicting environmental phenomena using numerical modeling and/or artificial intelligence (AI) algorithms. To this end, a widely accessible AI research cyberinfrastructure (CI) that brings together powerful computational resources, data, testbeds, algorithms, software, services, networks, and expertise is important and helps democratize the AI research landscape and enable more efficient ES research. Recognizing this, our project aims to train the next generation of CI professionals and contributors in the two universities located in the coastal states: Florida International University (FIU) and North Carolina State University (NCSU). Our long-term goal is to build a sustainable and transdisciplinary CI community to support the nation???s advanced CIs that can ensure broad adoption of advanced CI resources and expert services including platforms, tools, methods, software, data, and networks for research communities, to catalyze foundational AI research advances, and to enhance researchers' abilities to lead the development of new CI through education, training, and workforce development.

Software testing is a critical skill for computer science graduates entering technical positions. Software tests, and in particular unit tests, have several uses in education. The purpose of this proposal is to create pedagogy and tools around writing unit tests for CS3 and Software Engineering (SE) courses. Building on our preliminary work, we develop and evaluate the impact of a lightweight intervention with explicit testing strategies on the test quality of student-written tests. Then, we investigate the impact of the process of writing tests on student outcomes.

This project aims to address the following question: How to model the combined information of a pan-genome collection succinctly (and in a biologically meaningful way) such that the genomic analysis on that representation is both easy-to-compute and accurate? Pan-genome collections may be represented as high-scoring Multiple Sequence Alignment (MSA) data, indexed text data, or the more popular graph-based representations (pan-genome graphs). These models need to support read mapping queries efficiently. This research will lead to a new class of string/graph algorithms for the analysis of pan-genomic data.

Fueled by machine learning (ML) model and hardware advancements, intelligence is transforming every walk of life. For critical applications like autonomous vehicles, ensuring inference dependability is essential. Unfortunately, current hardware cannot provide such a promise. This CAREER project aims to create a new paradigm of safeguarding ML execution against both passive hardware faults and active fault attacks. The novelties lie in the new capability development inside ML processing, and the cross-layer exploration of algorithm, architecture, and hardware security. The broader impacts include yielding practical solutions for ensuring the root of trust of accelerated intelligence services and abundant educational opportunities.

The North Carolina Partnership for Cybersecurity Excellence (NC-PaCE) is a coalition of industry, government, and educational organizations committed to outpacing attackers through a partnership of cybersecurity excellence in research, education, and service. The Secure Computing Institute at North Carolina State University (NCSU) leads NC-PaCE organizations to address a growing cybersecurity workforce gap through educational opportunities; to protect financial assets and intellectual property (IP); and to drive economic growth of North Carolina’s public agencies and private sector businesses through cybersecurity research and service. Synergistically, NC-PaCE supports entrepreneurial and economic growth in North Carolina. NC-PaCE partners are NSA National Centers of Academic Excellence in Cybersecurity (CAE-C) educational institutions and include: NCSU, East Carolina University (ECU), Forsyth Technical Community College (FTCC), North Carolina A&T (NCAT), UNC-Charlotte (UNC-C), UNC-Wilmington (UNCW), and Wake Technical Community College (WTCC). The educational institutions will work together to close the cybersecurity talent gap highlighted in the Cyberseek (https://www.cyberseek.org) Supply/Demand Heat Map. Cyberseek is an organization funded by the National Initiative for Cybersecurity Education (NICE), a program of the National Institute of Standards and Technology (NIST) to provide detailed, actionable data about supply and demand in the cybersecurity job market. Based upon Cyerseek’s data, North Carolina is third in the country in terms of supply/demand ratio of cybersecurity workers. The need for cybersec urity-trained professionals is real in North Carolina.

Digital innovation is the source of competitiveness and value creation for many types of businesses. The universal desire for rapid digital innovation demands efficient reuse of software code building blocks, which has increased the dependence upon open source and third-party libraries and tools that comprise the software supply chain. Adversaries have moved from finding and exploiting vulnerabilities in end products to a new generation of supply chain attacks where attackers aggressively implant malicious code directly into artifacts in the supply chain and find their way into build and deployment pipelines. Digital innovation depends upon confidence in the software supply chain. As such, our research will enable the following vision: The software industry can rapidly innovate with confidence in the security of their software supply chain. The challenge of software supply chain security has recently received significant interest from industry and government. However, discussions with key stakeholders indicate that the state-of-the-art is preliminary, motivating scientific research to address the underlying fundamental challenges that will limit the practical success of existing approaches. We tackle the challenges of secure software supply chain through three thrusts: prevention, detection, and response, with an explicit objective of moving toward preventing security failures. For each thrust, we consider five hard security problems: (1) Scalability and Composability, such as detecting malicious commits and hardening containers; (2) Policy-governed Secure Collaboration, such as effective use of Software Bill of Materials; (3) Predictive Security Metrics, such as measuring the exploitability of vulnerabilities; (4) Resilient Architectures, such as isolation and sandboxing of components; and (5) Human Behavior, such as studying how to make software developers make more secure decisions. The project will impact the software industry by engaging with current industry players/community, enabling their participation in our research thrusts. Additionally, the project will involve educating the next generation of engineers to eradicate software supply chain security issues and training current employees to make them aware of these issues to help reduce them. To solve these challenging issues, we have created a multidisciplinary proposing team committed to diversity.

This proposal aims to develop techniques that enable application of robust predictive intelligence algorithms in the new spectrum era. The goal is to ensure robustness of predictive intelligence when handling critical spectrum-related tasks, including but not limited to: spectrum management, spectrum trading and spectrum monitoring.

This project aims to investigate the possibility and develop the technical foundation of building an open, decentralized wireless access ecosystem. The core contribution is around building contract overlay networks to enable on-demand spectrum leasing and wireless access, enabling verifiable contract fulfillment, and incentivizing broad and honest participation in the ecosystem.

This project seeks to develop theoretical tools (models and algorithms) for analyzing and optimizing a hybrid continuous-discrete variable quantum network architecture for the future quantum internet.

Abstract: The goal of this CAREER project is to fill the gap between growing application complexity and performance requirements, and existing application-agnostic network management, to enable and incentivize rigorous performance guarantees for distributed real-time applications at the network edge. The core contribution is the design, analysis, and evaluation of WolfPack, a general edge resource provisioning framework for real-time applications. The PI will focus on three key thrusts: 1) modeling and optimization of edge resource provisioning, 2) stochastic models and robustness techniques to control the risk, and 3) incentive mechanisms to enable truthful and competitive network edge resource trading.

Stroke is a leading cause of motor disability. A majority of stroke survivors exhibit upper and lower limb motor impairments, ranging from incapability of reaching and grasping objects to limited ambulation. The objective of this project is to develop a personalized, community-based rehabilitation system to improve daily functions of stroke survivors. The system will include three essential components – a nanomaterial-enabled multifunctional wearable sensor network to monitor arm and leg functional activities; a low-power data acquisition, processing, and transmission protocol; and a user interface (i.e., smart phone APP) to communicate training outcomes to the users and clinicians and receive feedback from the users and clinicians. The proposed community-based rehabilitation system will enable personalized, continuous rehabilitation during daily activities.