arXiv subjects

Haitao Fu

Publications and source records attributed to Haitao Fu.

3 recordsLinked to original sources

arXiv2026

QSolver: A Quantum Constraint Solver

With the growing interest in quantum programs, ensuring their correctness is a fundamental challenge. Although constraint-solving techniques can overcome some limitations of traditional testing and verification, they have not yet been sufficiently explored in the context of quantum programs. To address this gap, we present QSolver, the first quantum constraint solver. QSolver provides a structured framework for handling five types of quantum constraints and incorporates an automated assertion generation module to verify quantum states. QSolver transforms quantum programs and multi-moment constraints into symbolic representations, and utilizes an SMT solver to obtain quantum states that satisfy these constraints. To validate the correctness of the generated input states, QSolver automatically generates assertion programs corresponding to each constraint. Experimental results show that QSolver efficiently processes commonly used quantum gates and demonstrates good scalability across quantum programs of different sizes.

quant-ph↗

arXiv2024

PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset

With the rapid advancement of generative AI, multimodal deepfakes, which manipulate both audio and visual modalities, have drawn increasing public concern. Currently, deepfake detection has emerged as a crucial strategy in countering these growing threats. However, as a key factor in training and validating deepfake detectors, most existing deepfake datasets primarily focus on the visual modal, and the few that are multimodal employ outdated techniques, and their audio content is limited to a single language, thereby failing to represent the cutting-edge advancements and globalization trends in current deepfake technologies. To address this gap, we propose a novel, multilingual, and multimodal deepfake dataset: PolyGlotFake. It includes content in seven languages, created using a variety of cutting-edge and popular Text-to-Speech, voice cloning, and lip-sync technologies. We conduct comprehensive experiments using state-of-the-art detection methods on PolyGlotFake dataset. These experiments demonstrate the dataset's significant challenges and its practical value in advancing research into multimodal deepfake detection.

cs.SD↗

arXiv2023

Channel-Spatial-Based Few-Shot Bird Sound Event Detection

In this paper, we propose a model for bird sound event detection that focuses on a small number of training samples within the everyday long-tail distribution. As a result, we investigate bird sound detection using the few-shot learning paradigm. By integrating channel and spatial attention mechanisms, improved feature representations can be learned from few-shot training datasets. We develop a Metric Channel-Spatial Network model by incorporating a Channel Spatial Squeeze-Excitation block into the prototype network, combining it with these attention mechanisms. We evaluate the Metric Channel Spatial Network model on the DCASE 2022 Take5 dataset benchmark, achieving an F-measure of 66.84% and a PSDS of 58.98%. Our experiment demonstrates that the combination of channel and spatial attention mechanisms effectively enhances the performance of bird sound classification and detection.

eess.AS↗