This article describes the theoretical background and experimental design of new research based on previous multi-agent research on collaborative agents. The research extends the concept of agent decommitment into a resource scheduling domain. The problem is no longer time constrained, however, the number of agents is increased dramatically. The actual experimentation is yet to be performed. The authors state that results of the experimentation will be reported in a future article.

This article provides a review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. The authors also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered.

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 utilizing active learning and support vector machine (SVM) algorithms to classify facial action units (AU) for human facial expression recognition. Experimental results show that the proposed algorithm can effectively suppress correlated noise and achieve higher recognition rates than principal component analysis and a human observer on seven different facial expressions.

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 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 proposes a novel flower classification method that combines enhanced VGG16 (E-VGG16) with decision fusion.