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Kazi Ababil Azam

Publications and source records attributed to Kazi Ababil Azam.

2 recordsLinked to original sources

Tracing Users' Privacy Concerns Across the Lifecycle of a Romantic AI Companion

Romantic AI chatbots have quickly attracted users, but their emotional use raises concerns about privacy and safety. As people turn to these systems for intimacy, comfort, and emotionally significant interaction, they often disclose highly sensitive information. Yet the privacy implications of such disclosure remain poorly understood in platforms shaped by persistence, intimacy, and opaque data practices. In this paper, we examine public Reddit discussions about privacy in romantic AI chatbot ecosystems through a lifecycle lens. Analyzing 2,909 posts from 79 subreddits collected over one year, we identify four recurring patterns: disproportionate entry requirements, intensified sensitivity in intimate use, interpretive uncertainty and perceived surveillance, and irreversibility, persistence, and user burden. We show that privacy in romantic AI is best understood as an evolving socio-technical governance problem spanning access, disclosure, interpretation, retention, and exit. These findings highlight the need for privacy and safety governance in romantic AI that is staged across the lifecycle of use, supports meaningful reversibility, and accounts for the emotional vulnerability of intimate human-AI interaction.

cs.CY

Towards Intelligent Traffic Signaling in Dhaka City Based on Vehicle Detection and Congestion Optimization

The vehicular density in urbanizing cities of developing countries such as Dhaka, Bangladesh result in a lot of traffic congestion, causing poor on-road experiences. Traffic signaling is a key component in effective traffic management for such situations, but the advancements in intelligent traffic signaling have been exclusive to developed countries with structured traffic. The non-lane-based, heterogeneous traffic of Dhaka City requires a contextual approach. This study focuses on the development of an intelligent traffic signaling system feasible in the context of developing countries such as Bangladesh. We propose a pipeline leveraging Real Time Streaming Protocol (RTSP) feeds, a low resources system Raspberry Pi 4B processing, and a state of the art YOLO-based object detection model trained on the Non-lane-based and Heterogeneous Traffic (NHT-1071) dataset to detect and classify heterogeneous traffic. A multi-objective optimization algorithm, NSGA-II, then generates optimized signal timings, minimizing waiting time while maximizing vehicle throughput. We test our implementation in a five-road intersection at Palashi, Dhaka, demonstrating the potential to significantly improve traffic management in similar situations. The developed testbed paves the way for more contextual and effective Intelligent Traffic Signaling (ITS) solutions for developing areas with complicated traffic dynamics such as Dhaka City.

cs.AR