In today’s technology-centric era, digital transformation has made accessibility and cybersecurity critical components for developing inclusive web applications that are essential in daily operations. However, current development models often treat accessibility and cybersecurity separately, leading to solutions that may excel in one area while lacking in the other. This paper addresses this gap by proposing the Holistic Security and Accessibility Layered Architecture (HSALA), a novel architectural model that integrates accessibility and cybersecurity throughout the development process. HSALA consists of five interconnected layers, each paired with a corresponding security layer, ensuring that both accessibility and security are embedded at every level. We conduct a theoretical and analytical case study comparing HSALA with the traditional three-layered architecture, demonstrating HSALA’s superiority in addressing contemporary challenges related to accessibility, cybersecurity, scalability, and user experience. The findings indicate that HSALA provides a robust framework for developing secure and inclusive web applications, promoting a more equitable digital ecosystem. We outline a roadmap for future work, including empirical validation and practical implementation strategies. HSALA represents a significant advancement in web application development, fostering inclusivity and trust in an increasingly digital society.

The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions.

The present paper proposes a fractal-like approach to the parametric flow problem, derived from the rules and recursion of generative linguistics. In the same manner in which any sentence can be analysed in terms of "theme" of the sentence (that which is spoken about in the sentence) and "rheme" of the sentence (that which is said about the theme in the sentence), in the proposed parametric preflow-push algorithm, a "partitioning push" (a non-cancelling push of flow in the parametric residual network) might leave the node unbalanced for only a subinterval of the range of parameter values. This will lead to separating the problem for the two disjoints subintervals, which allows the algorithm to continue after the same rules, independently, on each of the partitioned subintervals. The algorithm runs as the template-like structure of a dialogue act which reveals a design where information about the items (part-of-speech) is a two sections vector with one segment for each of the used part of speech categories. The article also proposes a possible application of the algorithm in assessing legal information and in semantic evaluation of legislation.

The article presents a preflow approach for the parametric maximum flow problem, derived from the rules of constructing concepts hierarchy in text corpus. Just as generating a taxonomy can be equivalently reduced to ranking concepts within a text corpus according to a defined criterion, the proposed preflow bipush-relabel algorithm computes the maximum flow - the optimum ow that respects certain ranking constraints. The parametric preflow algorithm for generating two level concepts hierarchy in text corpus works in a parametric bipartite association network and, on each step, the maximum possible amount of ow is pushed along conditional augmenting two-arcs directed paths in the parametric residual network, for the maximum interval of the parameter values. The obtained parametric maximum ow generates concepts hierarchies (taxonomies) in text corpus for different degrees of association values described by the parameter values.