Adaptations in SCOR based performance metrics of airline catering supply chain during COVID-19 pandemic

Purpose: There is the propensity of the Airline catering supply chain to adapt their performance measures in order to meet desired service level due to the challenges of the Covid-19 pandemic. The aim of this paper is to develop a set of metrics for airline catering organization and explore the choices of SCOR based performance metrics during the Covid-19 pandemic.Design/methodology/approach: The SCOR framework is applied in the context of the airline catering supply chain to develop performance metrics. In this case study, the performance metrics model is analysed and validated by experts. Then, metrics are prioritised using MoSCoW method based on the experience of the Covid-19 challenges.Findings: A hierarchical performance measure framework is proposed, and a set of 55 metrics is identified. The validation of these metrics recognises the initial work. With the prioritisation, 13 level-2 & level-3 metrics are considered necessary in addition to 7 level-1 metrics to mitigate Covid-19 pandemic challenges better. Research limitations/implications: This research is based on a single case study. The validation is restricted to a small sample size.Practical implications: With the development of performance metrics and prioritization, airline catering organisation able to monitor their catering logistics performance.Originality/value: The work contributes to the measurement of performance in airline catering logistics, and adapted metrics would help business to be more responsive and flexible as per the market changes to alleviate Covid-19 challenges.

Analysis of Developers’ Network and Change Burst Metrics as Predictors of Software Faults

Introduction: Many software quality metrics that can be used as proxies of measuring software quality by predicting software faults have previously been proposed. However determining a superior predictor of software faults given a set of metrics is difficult since prediction performances of the proposed metrics have been evaluated in non–uniform experimental contexts. There is need for software metrics that can guarantee consistent superior fault prediction performances across different contexts. Such software metrics would enable software developers and users to establish software quality. Objectives: This research sought to determine a predictor for software faults that requires least effort to detect software faults and has least cost of misclassifying software components as faulty or not given developers’ network metrics and change burst metrics. Methods: Experimental data for this study was derived from Jmeter, Gedit, POI and Gimp open source software projects. Logistic regression was used to predict faultiness of a file while linear regression was used to predict number of faults per file. Results: Change burst metrics model exhibited the highest fault detection probabilities with least cost of mis-classification of components as compared to the developers’ network model. Conclusion: The study found that change burst metrics could effectively predict software faults.

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.

MMSPhiD: a phoneme based phishing verification model for persons with visual impairments

Purpose This paper aims to propose a model entitled MMSPhiD (multidimensional similarity metrics model for screen reader user to phishing detection) that amalgamates multiple approaches to detect phishing URLs. Design/methodology/approach The model consists of three major components: machine learning-based approach, typosquatting-based approach and phoneme-based approach. The major objectives of the proposed model are detecting phishing URL, typosquatting and phoneme-based domain and suggesting the legitimate domain which is targeted by attackers. Findings The result of the experiment shows that the MMSPhiD model can successfully detect phishing with 99.03 per cent accuracy. In addition, this paper has analyzed 20 leading domains from Alexa and identified 1,861 registered typosquatting and 543 phoneme-based domains. Research limitations/implications The proposed model has used machine learning with the list-based approach. Building and maintaining the list shall be a limitation. Practical implication The results of the experiments demonstrate that the model achieved higher performance due to the incorporation of multi-dimensional filters. Social implications In addition, this paper has incorporated the accessibility needs of persons with visual impairments and provides an accessible anti-phishing approach. Originality/value This paper assists persons with visual impairments on detection phoneme-based phishing domains.