Cybersecurity Leaders: Knowledge Driving Human Capital Development

Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to become smarter and remain ahead of attackers? How might these technologies hasten the understanding of the ‘what,’ ‘how,’ and ‘why’ reasons and key drivers for organizational behaviors. This article offers comparative analyses for cybersecurity leaders to engage in the questioning of practices, scrutinize entrenched assumptions about technology, customary practices, and query technology’s outputs by pursuing to comprehend all assumptions that could influence operations. Because understanding continues to rely upon progressively multifaceted epistemic technologies, outcomes of the research suggest that the salience of status distinctions is of central significance to the development of ongoing and proactive technological learning and up scaling solutions.

The Up-scaling Organization Structure Design: an Axioms Based Concept

Theoretical and Industry 4.0 also Society 5.0 phenomena adaptation research for formulating a new up-scaling organization structure design and concept visualized by mathematical axioms.

The Up-scaling Organization Structure Design: an Axioms Based Concept

Theoretical and Industry 4.0 also Society 5.0 phenomena adaptation research for formulating a new up-scaling organization structure design and concept visualized by mathematical axioms.

Looking at redefining sex(uality)

This article examines the dubbing of the 2014 American gay-themed series Looking and its treatment of sexual references into Castilian Spanish with a view to exploring the role of audiovisual translation in the discursive construction of homosexuality. While some scholars have decried a historical tendency in translation to attenuate or even suppress references in connection with non-normative sex, the dubbing of Looking, I claim, amplifies these references by way of two strategies: up-scaling and increased explicitness. Drawing upon Jeremy Munday’s (2012) concept of “evaluation” and on appraisal theory as expounded by Martin and White (2005), I aim at revealing the significance of the translator’s lexicogrammatical selections and how these may alter the semiotic import of the characters and, thus, of a certain portrayal of homosexuality. Additionally, such choices may be indicative of the translator’s own stance towards issues of sexuality. Though the strategies analyzed may appear to perpetuate commonplaces regarding gay sexual experience, they ultimately serve, I argue, as a device to generate a language that goes beyond diluted expressions of homosexuality.

Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools

Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.