Data visualizations and analysis from diagrams, such as bar charts and pie charts, remain largely inaccessible to blind and visually impaired individuals, despite their widespread use in education, science, and public communication. In this work, we present a learning-based framework that automatically detects, extracts, and interprets the contents of bar charts, translating them into multimodal outputs such as text-to-speech narratives and personified representations. Our system employs a YOLOv8 object detection model trained on a custom-generated data set of annotated bar charts, capable of identifying key component of the chart, including bars, slices, angles, axes, titles and labels. Following detection, we integrate Optical Character Recognition (OCR) to decode textual labels and numerals, while a pixel-to-value mapping algorithm interprets bar heights relative to axis scales. The extracted information is dynamically translated into audio descriptions and interactive query-based speech outputs, enabling non-visual comprehension of chart data. To ensure usability, we designed the output flow to follow a hierarchical structure from summary to specific values. Preliminary evaluations show that the system achieves high accuracy in detecting component parts of the chart and generating bar values, with promising feedback from a blind user in pilot tests. This research contributes to inclusive AI, opening new directions for accessible data communication through the integration of computer vision, speech synthesis, and assistive reasoning in an abstract environment.
This study presents a comprehensive investigation into optimizing a SQL Server in a business production environment that was experiencing severe performance problems and frequent system freezing. We implemented a systematic methodology consisting of three key phases: thorough diagnosis, strategic optimization, and longitudinal monitoring. Our findings demonstrate statistically significant improvements across critical performance indicators, including a 26% reduction in CPU utilization, a 4.3% increase in available memory, stabilization of the Page Life Expectancy indicator at optimal values exceeding 300 seconds, and complete elimination of system freezing episodes. This research provides a transferable methodological framework for SQL Server optimization across diverse business environments while contributing valuable insights to the efficient management of relational databases in high-availability contexts.
Web-based supervisory interfaces for distribution networks require a communication channel that delivers timely telemetry to browser clients while preserving ordered and consistent delivery with low overhead. This paper presents the communication core of LineWatch, a campus-scale monitoring platform that adopts a WebSocket-based design to stream feeder measurements and fault notifications over topic-scoped channels such as telemetry/line/feederId and event/fault. A lightweight server broadcasts compact JSON envelopes, and a browser dashboard binds map markers, device tables, and per-device histories directly to the most recent frames. Functional validation from the running system demonstrates four key mechanisms: (1) selective fan-out that keeps the device list and map view synchronized without overloading unsubscribed clients with irrelevant telemetry, (2) in-place popover binding that exposes the latest server timestamp and prevents regressions, (3) an ordered and coherent snapshot across devices in the tabular view with identical render timestamps, and (4) bounded per-device replay retrieved in reverse chronological order at a stable five-second cadence, enabling contextual inspection without interrupting live delivery. The findings indicate that a small, domain-aware contract layered on standard WebSockets can satisfy the timeliness and consistency requirements of real-time distribution dashboards while remaining simple to deploy behind conventional web infrastructure. The paper also discusses security and scalability considerations for long-lived browser connections, including TLS termination, token-based access control, topic-level authorization, and brokered fan-out.
The integration of Virtual Reality (VR) into higher education has gained significant attention for its ability to provide immersive and experiential learning opportunities. This pilot study examines the impact of using VR on Year-2 students' engagement, learning, and professional skills development within the Ethical and Professional Issues module in a UK University, with a specific focus on employability skills. The research utilizes BodySwaps, a VR simulation tool designed to enhance students' soft skills, particularly job interview skills. Through interactive and immersive experiences, students engaged in simulated interviews, receiving real-time feedback on their articulation, body language, eye contact, and use of filler words.To evaluate the effectiveness of VR-based interview training, feedback collected from students is used for sentiment analysis. The study examines deep learning models for sentiment analysis techniques for classification. BERT and DistilBERT are applied to detailed feedback from participants. Each approach is used to classify feedback into either positive or negative categories, with the most effective method determined based on accuracy, precision, recall and F1-Score. DistilBERT model slightly outperform the BERT model with higher speed and lower resources.
Effort estimation is an important asset for software project management, which supports in efficient planning, scheduling and cost estimations. However, it remains a challenge to predict effort precisely as requirements are uncertain, environment is dynamic and software systems evolve with frequent additions of new functionality and removal of old functionality. Traditionally, solo techniques such as expert judgment, regression models, analogy-based estimation, support vector machines and neural networks have been applied to predict software development effort. While these methods have achieved some degree of success, predictability for them is mostly limited by their stability. In attempting to be responsive to such demands, ensemble learning methods have gained importance over the recent years. These involve aggregating several models in order to minimize bias, variance, and enhance robustness. This paper investigates whether ensemble methods outperform solo techniques in enhancing the accuracy of software effort estimation. We did empirical analysis by evaluating popular ensemble models, including ExtraTrees, HistGB, BootstrapNN, and Random Forest against solo machine learning approaches. The evaluation of these models has been performed using standard performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the R-squared (R2) statistic. Findings from this study shows that ensemble techniques generally deliver higher estimation accuracy than solo models, demonstrating their strength in handling heterogeneous and uncertain software project datasets. On the other hand, some single models, like BootstrapNN and Linear Regression, performed better than ensemble models on specific datasets. This shows that ensemble models tend to be more reliable and accurate, solo models can still deliver strong results, especially when the dataset has certain characteristics.
