This paper investigates the gender gap in South Africa’s cybersecurity sector and its effects on both the sector and the nation’s economic growth. There is a significant shortage of skilled workers in the sector, an...
This paper investigates the gender gap in South Africa’s cybersecurity sector and its effects on both the sector and the nation’s economic growth. There is a significant shortage of skilled workers in the sector, and women are notably underrepresented. The main research question examines how the gender gap affects the industry’s development, innovation, and national security posture, emphasizing the importance of utilizing the nation’s talent pool to its fullest capacity to combat the evolving cyber threats. Insights and perspectives on the gender disparity in the cybersecurity sector were gathered through a questionnaire circulated amongst cybersecurity professionals and academics. These participants were chosen due to their knowledge and direct engagement in the subject, which offers valuable insights into the obstacles that women encounter in their workplace. The study highlights a number of problems, including gender stereotypes, a lack of access to education and training, and a non-inclusive workplace culture, that contribute to the gender gap. The paper makes the case that in order to solve these problems and advance diversity and inclusivity in the workplace, all stakeholders must work together to close the gender gap. In order to overcome the gender gap in cybersecurity in South Africa, the study suggests special programs aimed at expanding access to education and training opportunities, promoting gender diversity and inclusivity, and creating a more hospitable and equal work environment. This will boost the country's economic growth and strengthen security against cyberattacks.
Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature...
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Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing com...
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ISBN:
(数字)9798331540012
ISBN:
(纸本)9798331540029
Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs); GORE-based approach to Energy Management Systems (EnMS); Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services; GORE and reference architecture approach for microgrid systems; and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems.
Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation. Existing state-of-the-art met...
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In today’s digital world, information security, and person In today’s digital world, information security, and personal authentication are major concerns. As technology has advanced, so have the methods used to auth...
In today’s digital world, information security, and person In today’s digital world, information security, and personal authentication are major concerns. As technology has advanced, so have the methods used to authenticate users. Multimodal biometric selection combines several biometric methods to authenticate users. However, before any biometric system can be used in the public domain, it must be analysed and evaluated to ensure that it is usable and that the system considers the ethical aspect of the end-user. This process is necessary to ensure that all individuals affected by the implementation are aware of the ethical implications of biometric use and that any risks are properly assessed. To ensure the accuracy of the selection process, survey and evaluation techniques have been implemented to enhance the multimodal biometric selection processes for public domain authentication. Furthermore, the paper discusses the ethical implications and the potential impact of the use of multimodal biometrics in the public domain. The model was assessed using a confirmatory factor analysis quantitative research survey. In the experimental study, 352 eligible responses were received, where more than 200 participants were expected to complete the survey to evaluate the model and determine what is important to the public end-user when using biometric systems in this context.
Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer frame...
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Research areas in cooperative multiagent learning have found success in using selection-based methods, such as Evolutionary Algorithms (EAs), in order for agents to measure their own progress within the system, guided...
Research areas in cooperative multiagent learning have found success in using selection-based methods, such as Evolutionary Algorithms (EAs), in order for agents to measure their own progress within the system, guided by a fitness function. There have also been approaches which combine Genetic Algorithms (GAs) with supervised learning problems, such as decision trees. This research introduces a holonic agent-oriented implementation of a recursively nested Island genetic algorithm, called CLISDE, that builds a decision tree learner. The unique contribution of this paper is to show that Island genetic algorithms can be implemented in a holonic multiagent-based environment, and it can result in a model expressing self-similarity of the problem type and the architecture, achieved by integrating the learning algorithm of decision trees directly into the evolutionary process. The CLISDE GA and three known parallel GA models, namely the Island GA, Master-Slave GA and Hierarchical GA, were evaluated on a classification dataset and their performance metrics were compared. The results showed that the CLISDE GA produces a stronger decision tree predictor.
Adversaries initiate their cyberattacks towards different entities such as healthcare or business institutes, and a successful attack causes data breaches. They publish their success stories in public forums for ranki...
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During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts ne...
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Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained m...
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