Women remain underrepresented in science, technology, engineering, arts and mathematics (STEAM) subjects and careers in higher education. This literature review explored the current state, challenges, strategies and best practices related to encouraging women's participation in STEAM fields in the context of Ireland and the UK. Database searches identified 21 relevant studies from the past decade (2013–2023). Results indicate that despite increasing enrollment, fewer women pursue STEAM degrees and careers compared to men. Key barriers faced by women include stereotypes, lack of sense of belonging and confidence, difficulty shaping a STEAM identity, and lack of institutional support. Strategies to boost engagement include outreach programs, peer learning, mentoring with female role models, and flexible career paths. While mentors can enhance women's self-efficacy, few studies have investigated this. Recommended best practices emphasize early interventions, visibility of female role models, leadership commitment to inclusion policies, and approaches accommodating women's lives. However, gender inequality in STEAM persists. Further research is needed to understand how evidence-based strategies can be effectively implemented. This review highlights the need for multifaceted initiatives spanning all educational levels and career stages to create a supportive ecosystem enabling women to reach their full potential in STEAM fields.

Password-based authentication systems have many weaknesses, yet they remain overwhelmingly used and their announced disappearance is still undated. The system admin overcomes the imperfection by skilfully enforcing a strong password policy and sane password management on the server side. But in the end, the user behind the password is still responsible for the password’s strength. A poor choice can have dramatic consequences for the user or even for the service behind, especially considering critical infrastructure. On the other hand, law enforcement can benefit from a suspect’s weak decisions to recover digital content stored in an encrypted format. Generic password cracking procedures can support law enforcement in this matter — however, these approaches quickly demonstrate their limitations. This article proves that more targeted approaches can be used in combination with traditional strategies to increase the likelihood of success when contextual information is available and can be exploited.

Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net and DEX.