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This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Given the rising integration of digital tools into educational practices in our century, it is important to comprehend how seasoned educators adjust to these developments. This qualitative research study explored the struggles and coping mechanisms of the seasoned secondary math teachers in incorporating technology into their lessons. Using phenomenological approach, fifteen seasoned math teachers from Digos City Schools Division were interviewed using the In-depth interview I to learn about their actual experiences. The results reveal the four main challenges: seasoned teachers' technological difficulties, technological resource constraint, resistance to change, curriculum integration. Teachers use a variety of strategies to address these issues, troubleshooting skills, innovative solutions, seeking help and collaborations, and continuous professional development. The report also lists the types of assistance that school administrators should provide, professional development and learning opportunities, sharing experience and peer collaboration, resource allocation and infrastructure support, and building healthy relationships with stakeholder. The results imply that with theories of social support, resilience, and stress and coping highlight the value of specialized support systems in assisting educators in successfully navigating the challenges of teaching mathematics using technology. [ABSTRACT FROM AUTHOR]

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original architecture to 94.4% obtained with CyBERT. Furthermore, we evaluated CyBERT for the impact of randomness in the initialization, training, and data-sampling phases. CyBERT demonstrated a standard deviation of ±0.6% during validation across 100 random seed values. Finally, we also compared the performance of CyBERT to other well-established language models including GPT2, ULMFiT, and ELMo, as well as neural network models such as CNN, LSTM, and BiLSTM. The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

Disaster preparedness has played a vital role in mitigating disaster risks. A well-planned disaster risk reduction program is effective in minimizing hazards and casualties. The study determined the extent of disaster preparedness practices of households in the coastal communities of a congressional district in the Philippines, particularly on typhoons, in terms of disaster preparedness literacy, participation in community disaster preparedness, and disaster preparedness on supplies and kit contents. A descriptive-comparative research design was used to administer a researcher-made survey instrument to 341 coastal household members. Statistical techniques such as Kolmogorov Smirnov, Mann Whitney U test, Kruskal Wallis, Mean, and Standard Deviation were used in the study. The findings revealed a great extent of disaster preparedness practices with significant differences when respondents were grouped according to household income and educational attainment of the household members. However, there were no significant differences when grouped according to household size, type of housing unit, and type of housing structure. Household income is very minimal in purchasing food/kit supplies was identified as the most significant challenge experienced by the households. The study recommends an enhanced Barangay Disaster Risk Reduction Management (BDRRM) Plan to strengthen the programs, projects, and activities to increase the adaptive capacity and involvement of households toward disaster preparedness.