Poster for the 2014 UNT Graduate Exhibition in the Computer Science and Information Technology category. This poster discusses data mining techniques for predicting breast cancer survivability among women in the United States.

This paper proposes a novel code-based threshold ring signature scheme with a leader-participant model.

Data management for the grant, "Collaborative Research: Framework Implementations: CSSI: CANDY: Cyberinfrastructure for Accelerating Innovation in Network Dynamics." Research addressing the lack of a comprehensive cyberinfrastructure that supports innovative research challenges in large-scale, complex, dynamic networks by developing a novel platform, called CANDY (Cyberinfrastructure for Accelerating Innovation in Network Dynamics), based on efficient, scalable parallel algorithm design for dynamic networks and high-performance software development with performance optimization.

Data management plan for the grant, "REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach." This Research Experiences for Undergraduates (REU) Site Program at the University of North Texas will enhance the knowledge and research skills of a diverse cohort of undergraduate students through empowering, innovative, and interdisciplinary research experiences in developing Deep Learning applications and systems. The program aims to 1) expose undergraduate students to real-world and cutting-edge research focused on accelerated deep learning through combined hardware and software development; 2) encourage more undergraduate students to continue their academic careers and seek graduate degrees in computer science, computer engineering, and related disciplines; 3) develop research skills and improve communication and collaborative skills in undergraduate students.

Data management plan for the grant, "CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems." Research seeking to reinvent on-chip networks for GPU-accelerated systems to remove a communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck.

Patent relating to improved methods for utilizing 911 services and for implementing 911 dispatch protocols by accessing mobile phone sensors and applications.

Patent relating to a method, system and apparatus for automatic keyword extraction.

Data management plan for the grant, "SaTC: CORE: Small: Customizable Geo-Obfuscation to Protect Users' Location Privacy in Mobile Crowdsourcing."

This paper discusses reconfigurable block encryption logic. Existing block cipher function designs have tended to deploy the secret bits in a specific and limited way. The authors generalize the role of the secret as truth tables of Boolean gates in a carefully designed logic schema.

Data management plan for the grant, "REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates." TaMaLe (Testing and Machine Learning for Context-Driven Systems), a renewal Research Experience for Undergraduates (REU) Site at University of North Texas, engages 10 undergraduate students for 10 weeks with problems in the context-driven system domain. The students explore research problems to improve the reliability and security of context-driven systems. Context-driven systems, such as mobile apps, face constant streams of input from both users and context changes in their environments. Users interact with apps through touch and speech interfaces. These systems also respond to context events that occur in their environments such as changes to network connection, battery level, screen orientation, and more. The combined explosion of possible user events and context event sequences pose new challenges that require cost effective testing solutions. Students and mentors in this REU program work in small teams to develop and empirically evaluate new software testing techniques for context-driven systems using strategies such as reinforcement learning and combinatorial-based techniques.

Data management plan for the grant "Travel: NSF Student Travel Grant for 2023 Great Lakes Bioinformatics Conference (GLBIO)"

Data management plan for the grant "Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response." Research to develop open source integrative computational tools that perform secondary analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response.

Data management plan for the grant "Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)." Research relating to creating a community infrastructure for researchers using multilayer networks (MLN). This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching.

Data management plan for the grant, "Collaborative Research: SaTC: CORE: Small: Privacy protection of Vehicles location in Spatial Crowdsourcing under realistic adversarial models." Research to develop new location privacy protection techniques by considering vehicles’ mobility features in the road network, and consequently lead to a more secure and trustworthy computing environment in location-based services (LBSs). As privacy concerns are still among the main obstacles for mobile users to participate in many advanced LBSs, this project is poised to contribute to the wider adoption of LBSs for many applications (e.g. navigation systems and location-based recommendation systems). The project will also provide a set of diverse and interesting topics for undergraduate and graduate students and outreach activities for the community.

Data management plan for the grant "IUCRC Planning Grant University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)." Research concentrating on interdisciplinary research, aiming to initiate and accelerate the transformation of mobility methods from conventional vehicles to electric, connected and autonomous vehicles by creating innovative electric, connected and autonomous technologies. The grant will create the Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT). A partnership between Wayne State University (WSU), University of North Texas (UNT), and Clarkson University (Clarkson), the center not only serves as an apparatus of academic researchers collaborating with industry on important problems, but also provides industry partners opportunities to access advanced synergic research produced from a diverse group of researchers.

Data management plan for the grant, "Increasing Skills in Accessibility through Integration in the Undergraduate Computer Science and Information Technology Curricula."

Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.

Data management plan for the grant, "SaTC: CORE: Small: Probing Fairness of Ocular Biometrics Methods Across Demographic Variations."

Data management plan for the grant, "CICI: UCSS: Secure Containers in High-Performance Computing Infrastructure." Ensuring the security and privacy of high-performance computing (HPC) infrastructures is of utmost importance due to their handling of sensitive data and critical scientific computations. HPC infrastructures commonly employ containers, which provide lightweight and isolated environments for running applications. Nevertheless, containers in HPC infrastructures encounter security challenges, including insecure container images and vulnerabilities related to isolation. Existing container image scanners face a major challenge of low coverage, while current container runtimes struggle to ensure both security and performance for HPC workloads simultaneously. This project addresses these challenges by developing secure containers specifically tailored for HPC infrastructures. The project introduces innovative solutions, including the development of an efficient image vulnerability scanner and a secure container runtime.

Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.

This article proposes a novel preconditioned and accelerated Gauss–Siedel algorithm referred to as Symmetric Successive Overrelaxation Preconditioned Gauss-Seidel (SSORGS) to address the signal detection challenges associated with massive MIMO technology.