Mohammed Usman Hussain

arXiv2025

JobSphere: An AI-Powered Multilingual Career Copilot for Government Employment Platforms

Users of government employment websites commonly face engagement and accessibility challenges linked to navigational complexity, a dearth of language options, and a lack of personalized support. This paper introduces JobSphere, an AI-powered career assistant that is redefining the employment platform in Punjab called PGRKAM. JobSphere employs Retrieval-Augmented Generation (RAG) architecture, and it is multilingual, available in English, Hindi and Punjabi. JobSphere technique uses 4-bit quantization, allowing the platform to deploy on consumer-grade GPUs (i.e., NVIDIA RTX 3050 4GB), making the implementation 89% cheaper than that of cloud-based systems. Key innovations include voice-enabled interaction with the assistant, automated mock tests, resume parsing with skills recognition, and embed-based job recommendation that achieves a precision@10 score of 68%. An evaluation of JobSphere's implementation reveals 94% factual accuracy, a median response time of 1.8 seconds, and a System Usability Scale score of 78.5/100, a 50% improvement compared to the baseline PGRKAM platform context. In conclusion, JobSphere effectively fills significant accessibility gaps for Punjab/Hindi-speaking users in rural locations, while also affirming the users access to trusted job content provided by government agencies.

cs.AI↗

arXiv2025

Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection

As cyber threats continue to evolve in complexity and frequency, robust endpoint protection is essential for organizational security. This paper presents "Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection" a modular, real-time security solution for Windows endpoints. The agent leverages native tools like WMI and ETW for lowlevel monitoring of system activities such as process execution, registry modifications, and network behaviour. A machine learning-based detection engine, trained on labelled datasets of benign and malicious activity, enables accurate threat identification with minimal false positives. Detection techniques are mapped to the MITRE ATT&CK framework for standardized threat classification. Designed for extensibility, the system includes a centralized interface for alerting and forensic analysis. Preliminary evaluation shows promising results in detecting diverse attack vectors with high accuracy and efficiency.

cs.CR↗