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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong xie Jinbao Wang Weicheng xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Learning Multimodal Attention Mixed with Frequency Domain Information as Detector for Fake News Detection

Learning Multimodal Attention Mixed with Frequency Domain In...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zihan Ma Huan Liu Zhi Zeng Hao Guo xiang Zhao Minnan Luo School of Computer Science and Technology Xi’an Jiaotong University Xi'an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi'an China Laboratory for Big Data and Decision Nation University of Defence Technology Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Detecting fake news on social media has become a crucial task in combating online misinformation and countering malicious propaganda. Existing methods rely on semantic consistency across modalities to fuse features and determine news authenticity. However, cunning fake news publisher manipulate image to ensure a high level of semantic consistency between news post and image, making it more difficult to distinguish fake news. To this end, we propose MHFFD (Mixed High-Frequency Feature Detector), a novel fake news detection framework that utilizes token-level semantic consistency evaluation to identify key elements in news content and provide guidance for discovering image manipulation and learning better news representations. Extensive experiments demonstrate that MHFFD outperforms state-of-the-art methods on two widely used fake news detection datasets. Further research also validates the effectiveness of token-level semantic alignment and manipulation detection.

关键词： Social networking (online) Fuses Frequency-domain analysis Semantics Detectors Feature extraction Fake news

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

arXiv

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arxiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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Is Really Correlation Information Represented Well in Self-Attention for Skeleton-based Action Recognition?

Is Really Correlation Information Represented Well in Self-A...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wentian xin Hongkai Lin Ruyi Liu Yi Liu Qiguang Miao School of Computer Science and Technology Xidian University China Xi’an Key Laboratory of Big Data and Intelligent Vision Xidian University China Ministry of Education Key Laboratory of Collaborative Intelligence Systems Xidian University China

Transformer has shown significant advantages by various vision tasks. However, the lack of representation of correlation information about data properties makes it difficult to match the excellent results consistent with GCNs in skeleton-based action recognition. In this paper, we propose a Topology and Frames-guided Spatial-Temporal ConvFormer Network (TF-STCFormer), which is well suited for dynamically extracting topological and inter-frame uniqueness & co-occurrence information. Three essential components make up the proposed framework: (1) Grouped Physical-guided Spatial Transformer for focusing on learning essential spatial features and physical topology. (2) Global and Focal Temporal Transformer for promoting the relationship of different joints in consecutive frames and improving the representation of discriminative key-frames. (3) Grouped Dilation Temporal Convolution for connecting the intermediate output obtained by the previous transformers in the feature channels of different dilation. Experiments on four standard datasets (NTU RGB+D, NTU RGB+D 120, NW-UCLA, and UAV-Human) demonstrate that our approach prominently outperforms state-of-the-art methods on all benchmarks.

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期 44-58页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Electronic Engineering Xi'an University of Posts and Telecommunications

Multifunctional therapeutic peptides(MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods,the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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A Provably Secure Certificateless Signature Scheme With Anonymity for Healthcare IIoT

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IEEE Internet of Things Journal 2025年

作者： Qiao, Zirui Xu, Ran Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Xi’an University of Posts and Telecommunications School of Cyberspace Security Xi’an China Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Shaanxi Normal University School of Computer Science Xi’an China City University of Macau Faculty of Data Science Macau China Hubei University of Technology School of Computer Wuhan China

The industrial Internet of Things (IIoT) is changing our way of life and work. As the number of mobile devices connected to IIoT increases, users will face many security challenges such as insecure communication environments. The certificateless signature (CLS) scheme can ensure the integrity and validity of data and provide secure identity authentication for the IIoT, and several pairing-free CLS schemes have been proposed in recent years. However, we find they are vulnerable to the signature forgery attack of malicious key generation centers by safety analysis, which does not realize the security they have claimed. To solve this problem, we show an improved CLS scheme that achieves anonymity and formally proves its security under the hardness of the discrete logarithm problem in the random oracle. Comprehensive performance analysis and comparison show that our scheme has less communication and computation costs with higher security, and our construction is suitable for scenarios with limited resources. Finally, we apply this scheme to construct a mutual identity authentication protocol in the healthcare IIoT environment. 2014 IEEE.

关键词： Anonymity

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A Model that Learns Discriminative Features for Automatic Sleep Stage Classification Based on Raw Single-Channel EEG

SSRN

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SSRN 2023年

作者： Heng, xia Wang, Miao Wang, Zhong-Min Zhang, Jie He, Lang Fan, Lin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

Sleep staging is the basis of sleep quality assessment. In the process of scoring each sleep stage, some automatic sleep staging models often fail to effectively capture the more accurate long-range correlation coupling between the input sleep EEG signals and the output sleep stage, which leads to the extracted features cannot effectively distinguish the different sleep stage. In order to alleviate the long-term time series problem of sleep EEG signals and learn the discriminative features of each sleep stage in sleep EEG signals, we propose an end-to-end automatic sleep stage classification method based on raw single-channel sleep EEG signals, which can improve the attention to the discriminative features of each sleep stage. In the construction of the model, in order to learn the source domain information of the key part of the sleep EEG signal, SE-Block is added to Convolutional Neural Network (CNN) to highlight the time-frequency domain features related to the sleep stage. Meanwhile, the attention mechanism is added to the decoding part of Bidirectional Recurrent Unit (Bi-GRU) to enhance the long-term memory ability of Bi-GRU and learn the conversion rules between sleep stages. To validate the accuracy and stability of the model, the Fpz-Cz channel and Pz-Oz channel EEG signals in the Sleep-EDF sleep dataset are used for 10-fold cross-validation. The classification accuracies obtained are 88.48% and 87.56%, respectively. The results show that our model has stronger ability to extract discriminative features, better representation ability, more stable performance and relatively simple structure. © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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