The New Paradigm of Interaction: Emotional Computing, Affective Interfaces, and the Path to Seamless Human-AI Collaboration
A Constraint-Based Automated Timetable Generator for Educational Institutions
A Novel Hybrid Deep Learning Model Integrating Vision Transformer and LSTM for Effective DDOS Detection
Process Tree Analysis using GraphDB
Development of an Application for Data Privacy, Access Control and Data Leak
AI-Based Code Generation and Analysis: A Paradigm Shift in Software Development
Digital Accessibility in Computing Education: A Global South Perspective
Ensuring Software Quality in Engineering Environments
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
The development of artificial intelligence is rapidly evolving from functional, task-oriented systems to collaborative partners who can interpret and react to situations in a human context. This study investigates the undeniable fusion of emotional computing (or affective computing) and affective interfaces as two foundational aspects essential for attaining human-AI collaboration. It is proposed that for AI to go beyond acting as a tool and move to being a teammate, it must have the capacity to perceive, interpret, and respond to human emotional states. This paper reviews the technologies that constitute emotional computing, namely multimodal affect recognition, such as text, voice, face, and physiological signals, and affective generation, and examines the design principles for affective interfaces that can enable this emotional intelligence to manifest as natural, intuitive, and trustworthy interactions. In closing, the architectural diagrams for implementing these attributes into collaborative AI systems are discussed, along with compelling use cases from diverse areas such as healthcare, education, and creative industries. Major ethical and privacy concerns are also examined. The findings emphasize that developing emotional intelligence in AI represents not merely an incremental advancement but a transformative evolution enabling humans and machines to work together seamlessly, efficiently, and symbiotically.
Creating academic timetables for educational institutions is a complex and time-consuming task that must account for constraints such as subject requirements, teacher availability, classroom resources, and schedule conflicts. This research presents the design and implementation of an automated school timetable generation system using Python and the Flask web framework. The system enables administrators to input parameters, including teacher-subject assignments, class sections, and weekly time limits. It employs a hybrid scheduling algorithm that combines constraint-based logic with randomized and greedy allocation techniques to generate conflict-free schedules. Comparative evaluation was conducted against two existing scheduling approaches—a standard genetic algorithm and a simulated annealing model—using datasets from three different schools. The proposed system achieved an average scheduling accuracy of 96.3% and a generation time 28% faster than the best-performing baseline. It also demonstrated higher adaptability in handling complex constraints such as laboratory sessions and skill-based periods. These results confirm the model's effectiveness for small-to medium-sized institutions, offering a flexible, scalable, and computationally efficient alternative to manual and conventional automated scheduling methods. The automation substantially reduces administrative workload while improving consistency and efficiency in academic planning.
Distributed Denial of Service (DDoS) attacks remain one of the most catastrophic threats within the digital flow of life. They continue to grow in both scale and sophistication within the cybercriminal toolbox. Regardless of whether the DDoS attacks are carried out by cybercriminals, cyberterrorists, or other malicious actors, their impact remains significant. DDoS attacks destroy the reliability and availability of online services, of which IoTs and cloud infrastructures are subject to much greater risk than previous methods. A hybrid deep learning method is proposed in this study that implements a Vision Transformer (ViT) combined with Long Short-Term Memory (LSTM) networks that can learn spatial representations from the ViT and temporal patterns from the LSTM for the purpose of being able to cope with the numerous IoT cyber risks that continue to surface within the modern online environment. Solutions to class imbalance are used by the SMOTE Borderline technique to improve the occurrence of the minority class and therefore improve the robustness of classification. The model is evaluated against the CICIDS2017 dataset, a realistic benchmark for benign and malicious attacks. The experimental results demonstrate the ViT+LSTM framework achieves a very high accuracy of 99.78%, providing robust resiliency from the class imbalance of the data.
In modern cybersecurity and system monitoring, understanding the behavior and relationships between processes is essential for detecting anomalies, malware, and suspicious activities. Traditional relational databases have trouble showing complex hierarchies or linked process relationships. This paper introduces a method for analyzing process trees using a graph database, which provides a natural and efficient way to model and query the structure of processes. By representing processes, hosts, and users as distinct nodes and linking them through edges that capture relationships like parent–child processes, host-to-process connections, and user-to-process associations, a graph database allows fast traversal and provides rich contextual insights and deeper analysis of process trees. This approach helps in finding odd process behaviors, tracking where processes come from, and making it easier to look into threats. This method works well in places where there's a lot of changing and linked data, like in endpoint detection and response systems. Testing outcomes show how Graph DB successfully streamlines intricate process tree examination while boosting query speed when compared to conventional approaches.
This paper presents a safe desktop client for data privacy, access control, and leakage prevention of data in high- security settings. The system incorporates Advanced Encryption Standard (AES) for encrypting files, Firebase-based multi- factor authentication for safe authentication, and Google Drive integration for encrypted cloud storage. A real-time chat feature allows safe communication, while watermarking, screenshot protection, and monitoring of access allow controlled use of data. Unlike the existing file-sharing systems, the app provides an end-to-end solution with strong cryptographic techniques, safe authentication, and cloud integration to ensure confidentiality, integrity, and availability of data. The design offers modularity to allow flexibility for future additions like watermark-based traceability and enhanced monitoring features.
Artificial Intelligence (AI) is transforming software engineering by enabling automated code generation, intelligent debugging, and quality assurance workflows. This paper presents CodeSynth+, a hybrid framework that integrates transformer based code generation with Graph Neural Network (GNN)-based semantic analysis to produce syntactically correct, semantically validated, and secure multi-language code (Python, Java, C++). CodeSynth+ operates in an iterative feedback loop: natural language requirements are converted to initial code by a fine- tuned transformer, the code is parsed into Abstract Syntax Trees (ASTs) and semantic graphs, and a GNN inspects structural and data-flow properties to detect logic errors and vulnerabilities. We describe dataset construction (public code corpora, competitive programming solutions, and curated GitHub projects), formalize evaluation metrics (Syntactic Accuracy, Semantic Precision, Maintainability Index, Security Vulnerability Score, Regeneration Success Rate, and Time to Production), and detail baseline configurations (CodeT5, Codex-like evaluation, and SonarQube rules). Ten experiments demonstrate consistent improvements versus transformer only baselines: increased semantic accuracy and vulnerability detection, improved maintainability scores, and reduced time-to-production. We provide statistical validation (multiple seeds, means ± std, confidence intervals, and significance testing) and reproducibility artifacts (scripts and configs) in the supplemental repository. CodeSynth+ represents a step toward autonomous, interpretable, and secure code generation in modern software engineering.
Digital accessibility is essential for fair participation in computing education, especially in the Global South, where there are ongoing issues related to infrastructure and socio-economic differences. This paper looks at how accessible computing education is by examining policies, technology readiness, and teaching methods that affect inclusivity in developing regions. A qualitative research approach was used, mixing literature review with case studies from India, Kenya, and Brazil. The study uses data from UNESCO, World Bank, and ITU reports, as well as academic articles and NGO findings, to evaluate how accessibility challenges appear in different socio-economic situations. The analysis identifies four main barriers: poor infrastructure, weak policy enforcement, low awareness among teachers, and high costs of assistive technologies. Despite these challenges, there are encouraging developments such as community-led initiatives, open-source assistive tools, and AI-supported accessibility technologies that show potential for scalable solutions. The paper suggests a multi-layered strategy that focuses on universal design principles, policy incentives, teacher training, and international collaboration to improve digital accessibility. These insights aim to create a sustainable plan for an inclusive computing education and to reduce the accessibility gap between the Global North and South.
