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Universiti Malaya

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Research library214linked papers
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Selected work

Representative Papers

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Feb 12, 2018Computational Intelligence and Neuroscience

This work addresses the challenges of illumination variation, camera jitter, and scale changes in non-stationary aerial videos by proposing a robust real-time human detection method that integrates optical flow with deep features. The study introduces a novel framework that, for the first time, combines hierarchical extreme learning machines (H-ELM) with optical flow to achieve efficient detection. It systematically evaluates the trade-offs between accuracy and speed among supervised CNNs, pre-trained CNN feature extractors, and H-ELM in complex aerial scenarios. Experimental results on the UCF-ARG dataset show that the pre-trained CNN achieves an average accuracy of 98.09%, while H-ELM attains 95.9% accuracy with only 445 seconds of training time on a CPU, significantly outperforming conventional methods such as SVM and offering an effective balance between high accuracy and low computational cost.

42 citations2 influentialRead paper

AlphaSyndrome: Tackling the Syndrome Measurement Circuit Scheduling Problem for QEC Codes

Jan 18, 2026

This work addresses the lack of a general automated method for scheduling stabilizer measurements in non-surface-code quantum error correction, which often leads to significant fluctuations in logical error rates. We propose the first optimization framework tailored to generic swap-based stabilizer codes, formulating measurement scheduling as an optimization problem that controls error propagation pathways. By integrating Monte Carlo Tree Search (MCTS) with feedback from heuristic decoders, our approach automatically discovers optimal measurement orderings and parallelization strategies that steer error propagation away from logical operators while keeping it within the correctable range of the decoder. Evaluated across diverse code families, system sizes, and decoders, the method reduces logical error rates by 80.6% on average—up to 96.2% in the best case—matching the performance of Google’s hand-optimized surface code schedules and surpassing IBM’s existing strategy for Bivariate Bicycle codes.

1 citationsRead paper

Pigment network detection and classification in dermoscopic images using directional imaging algorithms and convolutional neural networks

Jan 01, 2025Biomedical Signal Processing and Control

This work proposes a two-stage method to automatically identify regular and irregular pigment networks (PN) in dermoscopic images for aiding early melanoma diagnosis. In the first stage, PN regions are precisely localized by integrating directional imaging, principal component analysis (PCA), and contrast enhancement. The second stage employs a lightweight convolutional neural network (CNN) to classify typical versus atypical PN patterns. Evaluated on a small dataset of 200 images, the approach achieves 90% accuracy, 90% sensitivity, and 89% specificity, with a 100% PN detection rate—significantly outperforming existing techniques under limited data conditions. By effectively combining classical image processing with deep learning, the method enhances both the automation and reliability of melanoma screening.

1 citationsRead paper

ViLBias: A Comprehensive Framework for Bias Detection through Linguistic and Visual Cues , presenting Annotation Strategies, Evaluation, and Key Challenges

Dec 22, 2024

This paper addresses the challenge of detecting implicit bias in news—such as linguistic framing bias and image-text inconsistency—by proposing ViLBias, a multimodal bias detection framework. Methodologically, it jointly leverages textual and visual cues through coordinated invocation of large language models (LLMs), vision-language models (VLMs), and small language models (SLMs), and introduces a novel hybrid annotation paradigm combining LLM-assisted labeling with human verification. Key contributions include: (1) the first systematic evaluation of SLMs, LLMs, and VLMs for bimodal bias detection, demonstrating LLMs’ superior fine-grained recognition capability over SLMs; (2) empirical validation that image-text joint modeling improves detection accuracy by 3–5%; and (3) release of the first benchmark dataset for multimodal news bias, covering diverse news sources and featuring fine-grained, human-verified annotations. These results provide both a novel methodology and reproducible resources for multimodal bias assessment.

1 citationsRead paper
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