Article proposes a Social- and Content-aware Video content delivery Prediction method (SCVP) to address the problem of predicting whether a video will be watched by a user for efficient video content delivery in mobile social networks.

Article describes study which sought to evaluate methods for activity recognition for toddlers.

Article proposes a novel Bayesian method, named BAM, for simultaneously partitioning Single Nucleotide Polymorphisms (SNPs) into Linkage Disequilibrium(LD)-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient.

Article addresses the processing of negation in Spanish by presenting a machine learning system that processes negation in Spanish and providing a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.

Article presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. In addition to the corpus, the authors release evaluation scripts and a baseline model for the task.

This article proposes a new truncated statistic method (TS) by utilizing a truncated method to find the genes that have a true contribution to the genetic association. The proposed truncated statistic outperforms existing methods. It can be employed to detect novel traits associated genes using GWAS summary data.

Article reviews the current techniques of low-light image enhancement.

This article presents a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. The authors discuss the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in massive MIMO systems and discuss state-of-the-art mitigation techniques. Recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems are outlined. Finally, future research for massive MIMO systems for 5G and beyond is discussed.

Article investigating HUMIRA effects mentioned in Twitter posts using a relational similarity-based method. The authors were able to identify effects previously known as well as potentially unreported, which demonstrates the power of this method and its potential for studying effects of other medications shared by Twitter users.

Article and accompanying poster presenting a prototype for the use of Edge with MR devices to provide cooperative perception capability to the MR device.

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 presents research that creates an automatic repetition counting system that is flexible enough to measure multiple distinct and repeating movements during physical therapy without being trained on the specific motion.

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.

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 presents research determining event outcomes in social media.

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 investigates whether translating negation is an issue for modern MT systems using 17 translation directions as test bed and provides a linguistically motivated analysis that explains the majority of the findings. The authors release their annotations and code to replicate analysis here: https://github.com/mosharafhossain/negation-mt.

Article implements and tests the performances of several approximation algorithms for computing the minimum dominating set of a graph. This article belongs to the Special Issue: Algorithms for Hard Graph Problems.

Article proposing the use of anonymization techniques coupled with graph algorithms over homomorphically encrypted (HE) graphs as a basis of analysis for this accumulated data. This approach ensures individuals’ privacy and anonymity while preserving the usefulness of the plaintext data. This article was originally presented at the 18th International Conference on Security and Cryptography - SECRYPT.

This article aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI), to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs), and to develop a classifier to predict the fat distribution clusters using the BSDs.

This article studies the problem of comparing the similarity and dissimilarity between two distinct vehicular trajectories by proposing an adjacency-based metric. This approach has a broad application in building truthfulness by comparing the similarity between two vehicles and evaluating the dissimilarity between two distinct paths in hazardous materials transportation.

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.

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.

This article presents a novel approach of introducing a weighted temporal feature to bias a topic clustering toward articles in a similar time frame, performed over a set of finance journal abstracts from 1974 to 2020 to demonstrate how time can be emphasized in trend detection. The authors detect trending finance topics that are not identifiable when we use a standard clustering approach with no temporal bias.