Data management plan for the grant, "Collaborative Research: Engaging Blind and Visually Impaired Youth in Computer Science through Music Programming."

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, "Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications."

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, "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.

Article on combining hashing and abstraction in sparse high dimensional feature spaces.

This paper discusses combining lexical resources for contextual synonym expansion.

Poster for the 2014 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Annual Conference. This poster discusses a comparison of machine learning algorithms for identifying cancer types.

This paper discusses automatic recognition of humor.

In this paper, the authors explore several computational models for incongruity resolution.

This article is a study where the authors integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach.

Video from the Fall 2018 3 Minute Thesis (3MT®) Final Competition. In this video, Shraddha Piparia presents her research methods, findings, and its significance in non-technical language.

This article discusses corpus-based and knowledge-based measures of text semantic similarity.

This paper discusses how to locate emotions.

This paper describes on-going efforts to annotate a corpus of almost 16,000 answer pairs with an estimated 69,000 fine-grained entailment relationships.

Article introduces a large corpus of metaphor novelty scores for syntactically related word pairs, and releases it freely to the research community. This article describes the corpus, includes an analysis of its score distribution and the types of word pairs included in the corpus, and provides a brief overview of standard metaphor detection corpora.

Article studying the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. The authors propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings.

Article is a study analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset, natural language processing, and machine learning.

This paper discusses automatic generating of back-of-the-book indexes.

This paper discusses creating annotated data collections.

This article develops a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.

This paper discusses cross-lingual semantic relatedness using encyclopedic knowledge.

This presentation discusses wireless networks, access point selections, traffic balancing, multi-cell CDMA, user distribution modeling, and call admission control.

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.