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Proceedings of the 2022 6th International Conference on Electronic Information Technology and computer engineering

作者： Min Liu Siwen Jin Luo Jin Shuohan Wang Yu Fang Yuliang Shi School of Software Beijing University of Technology China Computer Department Beijing Technology and Business University China Department of Computer Science Wenzhou-Kean University China School of Electronic Information Engineering Sias University China School of Mechanical and Marine Engineering Beibu Gulf University China

ISBN: (纸本)9781450397148

Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. However, the class imbalance problem is widespread and can seriously affect the model's performance. Reducing the adverse effects of imbalanced datasets on model training is crucial to improve the model's performance. Therefore, a new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem. Firstly, we propose sample mismeasurement distance to filter edge-hard samples and simple samples based on the distribution. Then, the weight coefficients are defined based on the mismeasurement distance and used in the loss function weighting term, so that the loss function focuses only on valuable samples. Experiments on several benchmarks demonstrate that our loss function can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task.

关键词： Imbalanced dataset Loss function Graph Neural Network Node classification

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CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation

arXiv

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arXiv 2023年

作者： Pei, Jialun Jiang, Tao Tang, He Liu, Nian Jin, Yueming Fan, Deng-Ping Heng, Pheng-Ann The School of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Software Engineering Huazhong University of Science and Technology Wuhan China The Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Department of Electrical and Computer Engineering National University of Singapore Singapore Nankai University Shenzhen China College of Computer Science Nankai University Tianjin China

In this study, we propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320×480 input size on the COME15K-E test set, which significantly surpasses the alternative frameworks. Our code and dataset will be publicly available at: https://***/PJLallen/CalibNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Energy Efficiency Optimization for UAV-Assisted Cellular Networks: A Periodic Clustering-Based MATD3 Approach

Energy Efficiency Optimization for UAV-Assisted Cellular Net...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Fuhao Liu Haoqiang Chen Jiansong Miao Tao Zhang Chuan Zhang Jiawen Kang Dusit Niyato School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Smartchip Microelectronics Technology Company Limited Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the advancement of unmanned aerial vehicles (UAVs) technology, UAV-assisted cellular networks (UACNs) have emerged as a new communication paradigm aimed at enhancing the coverage and capacity of ground networks. Unfortunately, the limited energy capacity of UAVs significantly restricts their operational duration, so optimizing energy efficiency is of importance. However, existing optimization schemes often overlook the impact of ground user mobility on user association, lacking ability to achieve optimal energy efficiency. In this paper, the K-Means method is applied to optimize user association by periodically clustering users. Additionally, given the dynamic nature of the wireless channels, we utilize the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach to jointly optimize 3D trajectory and power allocation. The objective is to maximize the sum energy efficiency while meeting the constraints included maximum power, minimum achievable data rate and spatial limitation. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other benchmark algorithms.

关键词： Three-dimensional displays Heuristic algorithms Simulation Clustering algorithms Dynamic scheduling Energy efficiency Trajectory Resource management Vehicle dynamics Optimization

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Ponzi Scheme Detection in Smart Contracts Using Heterogeneous Semantic Graph 6th

Ponzi Scheme Detection in Smart Contracts Using Heterogeneo...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Chen, Wei Jiang, Xinjun Lan, Tian Liu, Leyuan Li, Chengyu Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610000 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China

ISBN: (纸本)9789819614103

Behind the booming decentralized finance (DeFi) ecosystem driven by blockchain technology, various financial risks lurking, including money laundering, gambling, Ponzi schemes, and phishing. Due to the decentralization and anonymity of Ethereum, Ponzi schemes can be easily deployed, causing huge economic losses to investors. Existing detection methods based on transaction data are difficult to provide early risk warnings, while detection methods based on smart contract source code and opcodes have insufficient feature fusion at multiple levels. We propose an approach for constructing a multi-level Heterogeneous Semantic Graph (HSG) of smart contracts, and improve the HAN model to detect Ponzi scheme smart contracts based on the Heterogeneous Semantic Graph. The experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 97.21%, a precision of 94.29%, and an F1 score of 90.41%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Lombard-VLD: Voice Liveness Detection Based on Human Auditory Feedback

Lombard-VLD: Voice Liveness Detection Based on Human Auditor...

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IEEE Symposium on Security and Privacy

作者： Hongcheng Zhu Zongkun Sun Yanzhen Ren Kun He Yongpeng Yan Zixuan Wang Wuyang Liu Yuhong Yang Weiping Tu School of Cyber Science and Engineering Wuhan University Wuhan China State Grid Wuhan Electric Power Supply Company Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Voice Liveness Detection (VLD) aims to protect speaker authentication from speech spoofing by determining whether speeches come from live speakers or loudspeakers. Previous methods mainly focus on their differences at the signal level. In this paper, we propose the first VLD that uses the human auditory feedback mechanism (i.e., the Lombard effect), called Lombard-VLD. The key idea is that live speakers can physiologically and involuntarily adjust their speaking patterns in a noisy background but loudspeakers cannot. Moreover, we design a reference-based dual input mode and a differential SE-ResBlock to model the acoustic differences caused by the Lombard effect. Experimental results show that Lombard-VLD achieves 0% and 0.24% EER in two datasets, outperforming the state-of-the-art methods. It is robust to various environmental factors, including different distances, postures of the speaker, and environmental noise, with an average accuracy of over 98.51%. It also has a good generalization to unseen speakers, genders, and datasets, with EER lower than 2.68%, 3.44%, and 7.32%, respectively. This work shows the advantages of the Lombard effect in VLD, which has fewer user limitations and better detection performance.

关键词： Loudspeakers Privacy Accuracy Working environment noise Authentication Physiology Environmental factors Acoustics Security Noise measurement

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EMALG: An Enhanced Mandarin Lombard Grid Corpus with Meaningful Sentences

EMALG: An Enhanced Mandarin Lombard Grid Corpus with Meaning...

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

作者： Baifeng Li Qingmu Liu Yuhong Yang Hongyang Chen Weiping Tu Song Lin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. China

This study investigates the Lombard effect, where individuals adapt their speech in noisy environments. We introduce an enhanced Mandarin Lombard grid (EMALG) corpus with meaningful sentences, enhancing the Mandarin Lombard grid (MALG) corpus. EMALG features 34 speakers and improves recording setups, addressing challenges faced by MALG with nonsense sentences. Our findings reveal that in Mandarin, meaningful sentences are more effective in enhancing the Lombard effect. Additionally, we uncover that female exhibit a more pronounced Lombard effect than male when uttering meaningful sentences. Moreover, our results reaffirm the consistency in the Lombard effect comparison between English and Mandarin found in previous research.

关键词：

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ER-NeRF++: Efficient region-aware Neural Radiance Fields for high-fidelity talking portrait synthesis

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Information Fusion 2024年 110卷

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zheng, Jin Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Brisbane Australia RIKEN AIP Tokyo Japan The University of Tokyo Tokyo Japan

Despite conditional Neural Radiance Fields (NeRF) achieving great success in modeling audio-driven talking portraits, the generation quality is increasingly hampered by the lack of efficient use of space information. This paper presents ER-NeRF, a novel conditional NeRF-based architecture for talking portrait synthesis, and its variant version ER-NeRF++ to concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Inspired by the unequal contribution of spatial regions, we propose two modules in ER-NeRF to guide the talking portrait modeling: (1) A compact and expressive Tri-Plane Hash Representation to improve the accuracy of dynamic head reconstruction by pruning empty spatial regions with three planar hash encoders. (2) A Region Attention Module for the audio–visual feature fusion, including a novel cross-modal attention mechanism to connect audio features with different spatial regions explicitly for local motion priors. Additionally, to tackle the difficulty in learning large facial motions, we propose a deformable variant ER-NeRF++ by including a Deformation Grid Transformer to enable the reuse of cross-regional spatial features for large motion representation. Compared to ER-NeRF, our ER-NeRF++ framework achieves a significant improvement in facial motion quality while maintaining the ability of fast training and real-time rendering. For the torso part, a directAdaptive Pose Encoding is introduced to simplify the pose information for a better head-torso connection. Extensive experiments demonstrate that both of our proposed frameworks can efficiently render lifelike talking portrait videos with rich realistic details, performing better in image quality and audio-lip synchronization compared to previous methods. © 2024 Elsevier B.V.

关键词： Signal encoding

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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COVID-19 Identification Using Deep Capsule Network: A Perspective of Super-Resolution CNN on Low-Quality CXR Images 21

COVID-19 Identification Using Deep Capsule Network: A Perspe...

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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Grace U. Nneji Jingye Cai Jianhua Deng Happy N. Monday Edidiong C. James Bona D. Lemessa Abel Z. Yutra Yobsan B. Leta Saifun Nahar School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer and Information System University of Missouri St.Louis USA

ISBN: (纸本)9781450385190

Chest X-ray has become a useful method in the detection of coronavirus disease-19 (COVID-19). Due to the extreme global COVID-19 crisis, using the computerized diagnosis method for COVID-19 classification upon CXR images could significantly decrease clinician workload. We explicitly addressed the issue of low CXR image resolution by using Super-Resolution Convolutional Neural Network (SRCNN) to effectively reconstruct high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents. Then, the HRCXR images are fed into the modified capsule network to retrieve distinct features for the classification of COVID-19. We demonstrate the proposed model on a public dataset and achieve ACC of 97.3%, SEN of 97.8%, SPE of 96.9%, and AUC of 98.0%. This new conceptual framework is proposed to play a vital task in the issue facing COVID-19 and related ailments.

关键词： capsule network COVID-19 super-resolution chest x-ray Deep learning Convolutional Neural Network (CNN)

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