This article aims to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. It was presented at the 36th International Conference on Logic Programming (ICLP).

Article that introduces Natlog, a lightweight Logic Programming language, sharing Prolog's unification-driven execution model, but with a simplified syntax and semantics. The authors' proof-of-concept Natlog implementation is tightly embedded in the Python-based deep-learning ecosystem with focus on content-driven indexing of ground term datasets. As an overriding of the authors symbolic indexing algorithm, the same function can be delegated to a neural network, serving ground facts to Natlog's resolution engine. The open-source implementation is available as a Python package at t https://pypi.org/project/natlog/.

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.

Article presents research determining event outcomes in social media.

Article presents a new benchmark for natural language inference in which negation plays a critical role and shows that state-of-the-art transformers struggle making inference judgments with the new pairs.

Article studies the communication styles present in chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. The authors find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success.

Article develops a a simple, fast, and powerful method, named JS-MA, based on Jensen-Shannon divergence and agglomerative hierarchical clustering, to detect the genome-wide multi-locus interactions associated with multiple diseases.

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 "2021 NCAE-C-002 University of North Texas."

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.

This article describes a system for pupil size estimation with a user interface to allow rapid adjustment of parameters and extraction of pupil parameters of interest in order to identify Parkinson's disease (PD) as early as possible.

Article discusses how despite the advantages of online education, it lacks face-to-face settings, which makes it very difficult to analyze the students’ level of interaction, understanding, and confusion. This study proposes a novel engineering approach that uses probability-based features (PBF) for increasing the efficacy of machine learning models.

Data management plan for the grant, "Graduate Research Fellowship Program (GRFP)" for Ali Yar Khan.

Article discusses how globalisation has facilitated different industries to eliminate geographical boundaries and equipped organisations to work collectively to produce goods. The authors of the article propose a novel Distributed Ledger Technology (DLT) based transparent supply chain for PSC and proof-of-concept is implemented to analyse the scalability and efficiency of the proposed architecture.

Authors of the article created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study.

Article asserts that the foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. The authors present an instep girth measurement algorithm, and they used a pixel per metric algorithm for measurement; these algorithms were accordingly integrated with the application.

Article discusses how visual object tracking aims to continuously localize the target object of interest in a video sequence. To provide the community an overview, in this commentary, the authors discuss visual tracking from different aspects.

Article is a study proposing an approach for blood cancer disease prediction using the supervised machine learning approach to perform blood cancer prediction with high accuracy using microarray gene data.

Article conducting an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. The experimental results show that the ECTG algorithm can detect protein functional modules better.

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

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.

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.

Article proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes in order to prevent mosquito-borne infection.