Requirements for Data Quality Metrics

Morphological variability or inter-observer bias? A methodological toolkit to improve data quality of multi-researcher datasets for the analysis of morphological variation

OBJECTIVES

The investigation of morphological variation in animals is widely used in taxonomy, ecology, and evolution. Using large datasets for meta-analyses has dramatically increased, raising concerns about dataset compatibilities and biases introduced by contributions of multiple researchers.

MATERIALS AND METHODS

We compiled morphological data on 13 variables for 3073 individual mouse lemurs (Cheirogaleidae, Microcebus spp.) from 25 taxa and 153 different sampling locations, measured by 48 different researchers. We introduced and applied a filtering pipeline and quantified improvements in data quality (Shapiro-Francia statistic, skewness, and excess kurtosis). The filtered dataset was then used to test for genus-wide sexual size dimorphism and the applicability of Rensch's, Allen's, and Bergmann's rules.

RESULTS

Our pipeline reduced inter-observer bias (i.e., increased normality of data distributions). Inter-observer reliability of measurements was notably variable, highlighting the need to reduce data collection biases. Although subtle, we found a consistent pattern of sexual size dimorphism across Microcebus, with females being the larger (but not heavier) sex. Sexual size dimorphism was isometric, providing no support for Rensch's rule. Variations in tail length but not in ear size were consistent with the predictions of Allen's rule. Body mass and length followed a pattern contrary to predictions of Bergmann's rule.

DISCUSSION

We highlighted the usefulness of large multi-researcher datasets for testing ecological hypotheses after correcting for inter-observer biases. Using genus-wide tests, we outlined generalizable patterns of morphological variability across all mouse lemurs. This new methodological toolkit aims to facilitate future large-scale morphological comparisons for a wide range of taxa and applications.

A Multifactor Ring Signature based Authentication Scheme for Quality Assessment of IoMT Environment in COVID-19 Scenario

The quality of the healthcare environment has become an essential factor for healthcare users to access quality services. Smart healthcare systems use the Internet of Medical Things (IoMT) devices to capture patients’ health data for treatment or diagnostic purposes. This sensitive collected patient data is shared between the different stakeholders across the network to provide quality services. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. In the COVID-19 scenario, when collaborative medical consultation is required, the quality assessment of the framework is essential to protect the privacy of doctors and patients. In this paper, a ring signature-based anonymous authentication and quality assessment scheme is designed for collaborative medical consultation environments for quality assessment and protection of the privacy of doctors and patients. This scheme also uses a new KMOV Cryptosystem to ensure the quality of the network and protect the system from different attacks that hamper data confidentiality.

Quality 4.0 – a review of and framework for quality management in the digital era

Purpose

Industry 4.0 has brought about a paradigm shift in value delivery with the introduction of disruptive technologies. This has resulted in efforts by organizations to re-invent their business processes and reskill their workforce while attempting to realize digital transformation. Quality management in the context of Industry 4.0 is still in its nascent stage with researchers trying to identify key and relevant components of quality management with respect to Industry 4.0. The current study attempts to address the knowledge gap through a literature review and subsequently provide a conceptual framework for quality in the digital transformation context.

Design/methodology/approach

An integrative literature review was conducted to analyze and abstract knowledge from the literature on Quality 4.0 and a conceptual framework was developed based on the review.

Findings

The review revealed the motivators, building blocks and challenges for Quality 4.0. The conceptual framework discusses the salient points relevant to Quality 4.0 with respect to the people, process and technology dimensions and their sub-dimensions that can be used to build 4.0 capabilities. The proposed framework is represented to depict the conceptualization and the relationships among its components.

Originality/value

This study aims to contribute to the model building efforts of researchers towards Quality 4.0. The points discussed here provide an actionable direction to augment the efforts of practitioners and organizations in quality management in the context of Industry 4.0, especially digital transformation.

AI System Engineering—Key Challenges and Lessons Learned

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.