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SpeechAct: Towards Generating Whole-Body Motion from Speech

IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 3634-3644页

作者： Zhang, Jinsong Zhu, Minjie Zhang, Yuxiang Zheng, Zerong Liu, Yebin Li, Kun Tianjin University College of Intelligence and Computing Tianjin300350 China Tsinghua University Department of Automation Beijing100084 China NNKosmos Technology Hangzhou China

Whole-body motion generation from speech audio is crucial for computer graphics and immersive VR/AR. Prior methods struggle to produce natural and diverse whole-body motions from speech. In this paper, we introduce a novel method, named SpeechAct, based on a hybrid point representation and contrastive motion learning to boost realism and diversity in motion generation. Our hybrid point representation leverages the advantages of keypoint representation and surface points of 3D body model, which is easy to learn and helps to achieve smooth and natural motion generation from speech audio. We design a VQ-VAE to learn a motion codebook using our hybrid presentation, and then regress the motion from the input audio using a translation model. To boost diversity in motion generation, we propose a contrastive motion learning method according to the intuitive idea that the generated motion should be different from the motions of other audios and other speakers. We collect negative samples from other audio inputs and other speakers using our translation model. With these negative samples, we pull the current motion away from them using a contrastive loss to produce more distinctive representations. In addition, we compose a face generator to generate deterministic face motion due to the strong connection between the face movements and the speech audio. Experimental results validate the superior performance of our model. The code will be available for research purposes. © 1995-2012 IEEE.

关键词： Hands Avatars Motors Visualization Training Collaboration Memory management User experience Medical treatment Data mining

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Diff-Holo: A Residual Diffusion Model with Complex Transformer for Rapid Single-Frame Hologram Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 689-703页

作者： Bai, Ziqi Liu, Xianming Guo, Cheng Jiang, Kui Jiang, Junjun Ji, Xiangyang Harbin Institute of Technology Faculty of Computing Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China Tsinghua University Department of Automation Beijing100084 China

Deep learning approaches have gained significant traction in holographic imaging, with diffusion models—an emerging class of deep generative models—showing particular promise in hologram reconstruction. Unlike conventional neural networks that directly generate outputs, diffusion models gradually add noise to data and train neural networks to remove it, enabling them to learn implicit priors of the underlying data distribution. However, current diffusion-based hologram reconstruction methods often require hundreds or even thousands of iterations to achieve high-fidelity results, leading to processing times of several minutes or more—falling short of the fast imaging demands of holographic systems. To address this, we propose Diff-Holo, a residual diffusion model integrated with a complex transformer, designed for rapid and high-quality single-frame hologram reconstruction. Specifically, we create a shorter and more efficient Markov chain by controlling the residuals between clean images and those degraded by twin-image artifacts. Additionally, we incorporate complex-valued priors into the network by using a complex window-based transformer as the backbone, enhancing the network's ability to process complex-valued data in the reverse reconstruction process. Experimental results demonstrate that Diff-Holo achieves high-quality single-frame reconstructions in as few as 15 sampling steps, reducing reconstruction time from minutes to under 2.2 seconds. © 2015 IEEE.

关键词： Markov chains

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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310058China School of Computing University of PortsmouthPO13HEUK

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases,with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC).Although deep learning holds promise in mitigating missed polyp rates,modern endoscopy examinations pose additional challenges,such as image blurring and *** study explores lightweight yet powerful attention mechanisms,introducing the spatial-channel transformer(SCT),an innovative approach that leverages spatial channel relationships for attention weight *** method utilizes rotation operations for inter-dimensional dependencies,followed by residual transformation,encoding inter-channel and spatial information with minimal computational *** experiments on the CVC-Clinic DB polyp detection dataset,addressing endoscopy pitfalls,underscore the superiority of our SCT over other state-of-the-art *** proposed model maintains high performance,even in challenging scenarios.

关键词： diagnosis utilize holds

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Robust facial expression recognition via lightweight reinforcement learning for rehabilitation robotics

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Optoelectronics Letters 2025年第2期21卷 97-104页

作者： CHEN Yifan FAN Weiming GAO Hongwei YU Jiahui JU Zhaojie School of Computing University of PortsmouthPO13HEUK School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110159China Department of Biomedical Engineering Zhejiang UniversityHangzhou 310027China

This paper proposes a lightweight reinforcement network (LRN) and auxiliary label distribution learning (ALDL)based robust facial expression recognition (FER) *** designed representation reinforcement (RR) network mainly comprises two modules,i.e.,the RR module and the auxiliary label space construction (ALSC) *** RR module highlights key feature messaging nodes in feature maps,and ALSC allows multiple labels with different intensities to be linked to one ***,LRN has a more robust feature extraction capability when model parameters are greatly reduced,and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training *** tested our method on FER-Plus and RAF-DB datasets,and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

关键词： rehabilitation lightweight auxiliary

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Research on Garbled Circuits-Based Identity Authentication and Ciphertext Hidden Access Control in Vehicular Networks 24

Research on Garbled Circuits-Based Identity Authentication a...

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9th International Conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Jiao, Zhilong Bai, Fenhua Zhang, Chi Zhang, Xiaohui Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology China Kunming China Department of Computing Beijing University of Technology China Beijing China

ISBN: (纸本)9798400717161

Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to various attacks. This can result in communication messages being tampered with and the leakage of vehicle locations and identity privacy. From a security perspective, this paper introduces Yao's garbled circuit mechanism, combining random bytes with the solutions of linear equations as identity information assigned to vehicle nodes. By employing dual authentication, the proposed approach addresses issues such as privacy protection, anonymity, replay attacks, and collusion attacks in vehicular networks, thereby enhancing security. Moreover, considering the post-authentication access control scenario, this paper proposes a novel LSSS matrix transformation algorithm for ciphertext hidden access control strategy. It realizes a partially hidden policy, which makes matrix generation more efficient by supporting both AND and OR gates, thus improving the access control efficiency while ensuring security. Performance analysis indicates that in terms of identity authentication, the scheme reduces computational costs by an average of 96.25%, and in access control, the scheme reduces key generation time by an average of 80%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ciphertext

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VITMST++: Efficient Hyperspectral Reconstruction Through Vision Transformer-Based Spatial Compression

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 398-404页

作者： Silveira, Ana C. Caznok do Carmo, Diedre S. Ueda, Lucas H. Fantinato, Denis G. Costa, Paula D. P. Rittner, Leticia Universidade Estadual de Campinas Department of Computer Engineering and Automation School of Electrical and Computer Engineering Campinas13083-852 Brazil Universidade Estadual de Campinas CPQD Recod.AI Institute of Computing Campinas13083-852 Brazil

Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations. © 2020 IEEE.

关键词： Image compression

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The Challenges of Cyber Resilience in the Maritime Sector: Addressing the Weak Awareness of the Dangers Caused by Cyber Threats

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Journal of Marine Science and Engineering 2025年第4期13卷

作者： Ćelić, Jasmin Vukšić, Marko Baždarić, Robert Cuculić, Aleksandar Department of Electrical Engineering Automation and Computing Faculty of Maritime Studies University of Rijeka Rijeka51000 Croatia

The maritime industry plays a key role in the global supply chain. Advanced digital technologies bring significant economic benefits to ports and shipowners, but at the same time increase the risks of cyber threats and attacks. This article aims to provide guidelines and examples of good practice that will help in the effective implementation of cyber risk assessment, cyber resilience and cyber sustainability, which are the products of increasingly pronounced challenges. The interconnection of ports requires operators to achieve and maintain a baseline level of cybersecurity to ensure security across the entire port ecosystem. The development of new technologies in areas such as the Internet of Things, cloud computing, artificial intelligence, etc., contributes to the fact that monitoring and control systems in the maritime industry are becoming increasingly exposed to cyber threats and various forms of cyberattacks. The connection of vessels with systems on land in real time presents a necessary element in meeting the intended goals in the digital transformation of the maritime sector. This results in increasingly frequent work on specific software solutions within the maritime sector. With the adoption of new operational technologies (OT) and information technologies (IT), the desire for more efficient supply chains and operations of shipping in general has been realized, but at the same time the level of cybersecurity has decreased. The research results aim to encourage port operators and shippers to develop a series of good practices in order to develop an appropriate level of cybersecurity, resilience, and sustainability. © 2025 by the authors.

关键词： Marine industry

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Latent Landmark Graph for Efficient Explorationexploitation Balance in Hierarchical Reinforcement Learning

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Machine Intelligence Research 2025年第2期22卷 267-288页

作者： Qingyang Zhang Hongming Zhang Dengpeng Xing Bo Xu Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Future Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computing Science University of AlbertaEdmonton T6G 2E8Canada School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Goal-conditioned hierarchical reinforcement learning(GCHRL)decomposes the desired goal into subgoals and conducts exploration and exploitation in the subgoal *** effectiveness heavily relies on subgoal representation and ***,existing works do not consider distinct information across hierarchical time scales when learning subgoal representations and lack a subgoal selection strategy that balances exploration and *** this paper,we propose a novel method for efficient exploration-exploitation balance in HIerarchical reinforcement learning by dynamically constructing Latent Landmark graphs(HILL).HILL transforms the reward maximization problem of GCHRL into the shortest path planning on *** effectively consider the hierarchical time-scale information,HILL adopts a contrastive representation learning objective to learn informative latent *** on these representations,HILL dynamically constructs latent landmark graphs and selects subgoals using two measures to balance exploration and *** implement two variants:HILL-hf generates graphs periodically,while HILL-lf generates graphs *** results on continuous control tasks with sparse rewards demonstrate that both variants outperform state-of-the-art baselines in sample efficiency and asymptotic performance,with HILL-lf further reducing training time by 40%compared to HILL-hf.

关键词： Hierarchical reinforcement learning representation learning latent landmark graph contrastive learning exploration and exploitation.

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Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework

arXiv

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arXiv 2025年

作者： Li, Hongyi Xu, Jun Armstrong, William Ward The Department of Automation Harbin Institute of Technology Shenzhen518055 China The Department of Computing Science University of Alberta Edmonton Canada

This paper introduces a novel tree-based model, Learning Hyperplane Tree (LHT), which outperforms state-of-the-art (SOTA) tree models for classification tasks on several public datasets. The structure of LHT is simple and efficient: it partitions the data using several hyperplanes to progressively distinguish between target and non-target class samples. Although the separation is not perfect at each stage, LHT effectively improves the distinction through successive partitions. During testing, a sample is classified by evaluating the hyperplanes defined in the branching blocks and traversing down the tree until it reaches the corresponding leaf block. The class of the test sample is then determined using the piecewise linear membership function defined in the leaf blocks, which is derived through least-squares fitting and fuzzy logic. LHT is highly transparent and interpretable-at each branching block, the contribution of each feature to the classification can be clearly observed. Copyright © 2025, The Authors. All rights reserved.

关键词： Membership functions

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