Mohammed Aman Bhuiyan

Mohammed Aman Bhuiyan, Aritra Islam Saswato, Md. Misbah Khan et al.

The increasing demand for hygienic and contactless solutions in public and private environments has encouraged the development of automated systems for everyday applications. This paper presents the design and implementation of a motion- sensing automatic waste bin using an STM32 microcontroller, ultrasonic sensors, and a servo motor. The system detects user presence through ultrasonic sensing and automatically opens the bin lid using a servo motor controlled by the microcontroller. An additional ultrasonic sensor is used to monitor the internal waste level of the bin, while an OLED display provides real- time feedback regarding system status. The proposed system offers a low-cost, reliable, and easily deployable solution for touch-free waste disposal. Experimental evaluation demonstrates fast response time, stable sensing performance, and smooth mechanical operation. The system can be effectively deployed in homes, educational institutions, hospitals, and public facilities to improve hygiene and user convenience.

Samiul Basir Bhuiyan, Md. Sazzad Hossain Adib, Mohammed Aman Bhuiyan et al.

Large language models (LLMs) have rapidly advanced in recent years, achieving remarkable performance across a wide range of natural language processing tasks. However, this progress has come at the cost of increasingly large model sizes, which pose significant challenges for deployment, scalability, and energy efficiency. To address these limitations, post-training pruning has emerged as a promising approach for reducing model size and inference latency without the need for retraining. Despite these advantages, many existing pruning methods result in substantial performance degradation or require computationally expensive fine-tuning. In this work, we introduce Z-Pruner, a novel post-training pruning method designed to induce sparsity in pretrained LLMs without any retraining. Unlike conventional approaches, Z-Pruner leverages both weight update magnitudes and activation patterns to identify and eliminate redundant parameters more effectively. Our method is model-agnostic, efficient, and easy to implement. We evaluate Z-Pruner using multiple widely-used LLM architectures, including LLaMA-2, LLaMA-3, and OPT, across a diverse set of standard language benchmarks. Experimental results demonstrate that Z-Pruner surpasses state-of-the-art pruning methods that require intensive weight updates. Specifically, Z-Pruner achieves the lowest perplexity scores and the highest overall average score for zero-shot accuracy. We have made the corresponding codes publicly available at https://github.com/sazzadadib/Z-Pruner.

Mohammed Aman Bhuiyan, Md Sazzad Hossain Adib, Samiul Basir Bhuiyan et al.

Automatic Speech Recognition (ASR) and speaker diarization in Bangla remain challenging due to long form recordings, diverse acoustic conditions, and significant speaker variability. This work addresses these two core tasks in Bangla spoken language understanding by developing robust systems for long form ASR and speaker diarization. For ASR (Problem 1), we fine tune the tugstugi bengaliai regional asr whisper medium model on a custom-curated dataset of approximately 15,000 chunked and aligned Bangla audio segments, employing full weight training with extensive data augmentation including noise injection, reverb simulation, echo, clipping distortion, and pitch/time perturbation. For speaker diarization (Problem 2), we fine-tune the pyannote/segmentation-3.0 model using PyTorch Lightning on the competition annotated diarization dataset, swapping the fine-tuned segmentation backbone into the pyannote/speaker-diarization-community-1 pipeline while retaining the pretrained speaker embedding and clustering components. Our ASR system achieves a Word Error Rate (WER) of 0.2441, while our diarization system achieves a Diarization Error Rate (DER) of 0.2392, both evaluated on the test set, demonstrating notable improvements over the respective pretrained baselines. We describe our complete pipeline, including data preprocessing, text normalization, audio augmentation, training strategies, inference optimization, and post-processing for both tasks.