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 analyses the impact of Covid-19 on various cyber-security related aspects.

Article exploring graph node property prediction. Originally presented as part of the application track at the 38th International Conference on Logic Programming in Haifa, Israel.

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 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 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.

Article on deep-learning framework for edge intelligence EDL-EI (event-driven deep learning for edge intelligence). To verify the proposed framework, the authors include a case study of air-quality scenarios based for the most polluted cities in South Korea and China.

This article examines the relationships between spatial patterns of long-term ozone exposure and respiratory illness within Dallas-Fort Worth to better understand impacts on health outcomes.

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.

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.

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.

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