Redundancy and subsumption in high-level replacement systems

System verification in the broadest sense deals with those semantic properties that can be decided or deduced by analyzing a syntactical description of the system. Hence, one may consider the notions of redundancy and subsumption in this context as they are known from the area of rule-based systems. A rule is redundant if it can be removed without affecting the semantics of the system; it is subsumed by another rule if each application of the former one can be replaced by an application of the latter one with the same effect. In this paper, redundancy and subsumption are carried over from rule-based systems to high-level replacement systems, which in turn generalize graph and hypergraph grammars. The main results presented in this paper are a characterization of subsumption and a sufficient condition for redundancy, which involves composite productions.Postprint (published version

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

A Robust Dynamic Data Masking Transformation approach To Safeguard Sensitive Data

Large amount of digital data is generated rapidly all around the corners. Providing security to digital data is the crucial issue in almost all types of organizations. According to the Identity Theft Resource Center, there were 8,069 data breaches between January 2005 and November 2017, and in recent years the number of data breaches and compromised records has skyrocketed [1]. To provide protection to the digital sensitive data, from data breaches in the need of hour. Almost all domains like insurance, banking, health care, and educational and many more are concern about security of sensitive data. Data masking is one of the vital discussions everywhere as data breach leads to threats. Masking is a philosophy or new way of thinking about safeguarding sensitive data in such a way that accessible and usable data is still available for non- production environment. In this research paper authors proposed a dynamic data masking model to protect sensitive data using random deterministic masking algorithm with shift left approach. This paper describes methodology & experimental design and results