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arXiv 2025年

作者： Xia, Jiahao Xu, Min Huang, Wenjian Zhang, Jianguo Zhang, Haimin Xiao, Chunxia Faculty of Engineering and IT University of Technology Sydney UltimoNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Computer Science Wuhan University Hubei Wuhan430072 China

Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets with different landmark annotations. The limited training samples in a single dataset commonly result in fragile robustness in this field. To mitigate knowledge discrepancies among different datasets and train a task-agnostic unified face alignment (TUFA) framework, this paper presents a strategy to unify knowledge from multiple datasets. Specifically, we calculate a mean face shape for each dataset. To explicitly align these mean shapes on an interpretable plane based on their semantics, each shape is then incorporated with a group of semantic alignment embeddings. The 2D coordinates of these aligned shapes can be viewed as the anchors of the plane. By encoding them into structure prompts and further regressing the corresponding facial landmarks using image features, a mapping from the plane to the target faces is finally established, which unifies the learning target of different datasets. Consequently, multiple datasets can be utilized to boost the generalization ability of the model. The successful mitigation of discrepancies also enhances the efficiency of knowledge transferring to a novel dataset, significantly boosts the performance of few-shot face alignment. Additionally, the interpretable plane endows TUFA with a task-agnostic characteristic, enabling it to locate landmarks unseen during training in a zero-shot manner. Extensive experiments are carried on seven benchmarks and the results demonstrate an impressive improvement in face alignment brought by knowledge discrepancies mitigation. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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P-I2Prange: An Automatic Construction Architecture for Scenarios in I2P Ranges

P-I2Prange: An Automatic Construction Architecture for Scena...

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International Joint Conference on Neural Networks (IJCNN)

作者： Runnan Tan Qingfeng Tan Haiyan Wang Yushun Xie Peng Zhang Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of New Networks PengCheng Laboratory Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Anonymous networks play a crucial role in preserving information privacy, yet simultaneously spark technical conflicts and gamesmanship between their maintainers and regulatory authorities. To solve the above conflicts and economic losses in real networks, it is imminent to realize adversarial exercises in anonymous networks and validate new technologies and scenarios in the cyber range. It is well known that the two dominant types of anonymity networks are Tor and Invisible Internet Project (I2P). This paper establishes a cyber range based on I2P to realize a task-driven automated scenario construction technique. The proposed task-driven automated scenario construction technique can solve the problem of decoupling between business scenarios and I2P cyber range network infrastructure. Specifically, our approach allows users to upload models/executable code to extend flexibly and fine-grained control I2P nodes, protocols, and traffic behavior characteristics. Then it empowers users to define application scenarios as needed, enabling them to handle intricate business scenarios programmatically. Therefore, it further supports diverse adversarial exercises and the validation of new technologies and scenarios within the dark web.

关键词： Measurement Privacy Costs Protocols Dark Web Scalability Neural networks

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On which nodes does GCN fail? enhancing GCN from the node perspective 24

On which nodes does GCN fail? enhancing GCN from the node pe...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jincheng Huang Jialie Shen Xiaoshuang Shi Xiaofeng Zhu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science City University of London London UK School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China and Sichuan Artificial Intelligence Research Institute Yibin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China and Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

The label smoothness assumption is at the core of Graph Convolutional Networks (GCNs): nodes in a local region have similar labels. Thus, GCN performs local feature smoothing operation to adhere to this assumption. However, there exist some nodes whose labels obtained by feature smoothing conflict with the label smoothness assumption. We find that the label smoothness assumption and the process of feature smoothing are both problematic on these nodes, and call these nodes out of GCN's control (OOC nodes). In this paper, first, we design the corresponding algorithm to locate the OOC nodes, then we summarize the characteristics of OOC nodes that affect their representation learning, and based on their characteristics, we present DaGCN, an efficient framework that can facilitate the OOC nodes. Extensive experiments verify the superiority of the proposed method and demonstrate that current advanced GCNs are improvements specifically on OOC nodes; the remaining nodes under GCN's control (UC nodes) are already optimally represented by vanilla GCN on most datasets.

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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A Survey of fMRI to Image Reconstruction

arXiv

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arXiv 2025年

作者： Guo, Weiyu Sun, Guoying He, Jianxiang Shao, Tong Wang, Shaoguang Chen, Ziyang Hong, Meisheng Sun, Ying Xiong, Hui Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Control Science and Engineering Shandong University China

Functional magnetic resonance imaging (fMRI)based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies. © 2025, CC BY.

关键词： Image reconstruction

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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Design and Analysis of a Single Lower Limb Rehabilitation Exoskeleton Robot

Design and Analysis of a Single Lower Limb Rehabilitation Ex...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Li, Ang Luan, Fujin Zhang, Faxiang Lu, Sheng Gao, Guanbin Liu, Meihong Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Kunming University of Science and Technology Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan Key Laboratory of Digital Orthopedics The First People's Hospital of Yunnan Province Kunming650034 China

ISBN: (纸本)9798350309003

Exoskeleton robots are a novel type of rehabilitation device. Existing exoskeleton robots typically provide assistance and rehabilitation training to both lower limbs simultaneously, which is not suitable for patients with unilateral limb movement impairments caused by hemiplegia, trauma, and other conditions. In this study, we designed a single lower limb rehabilitation exoskeleton robot that enables the affected limb to follow the healthy limb during rehabilitation training. Firstly, we applied biomechanics principles to design the exoskeleton robot. The backpack surface, the angles of hip and knee joint components are designed to make them conform better to the human body. Secondly, to obtain the positions of the exoskeleton's links in joint space and the required joint torques, we established the kinematic and dynamics models of the exoskeleton using the Denavit-Hartenberg method and Lagrange's equations, respectively. Finally, we conducted simulations by wearing the exoskeleton on a human lower limb model using AnyBody software. The simulations included both passive exoskeleton mode and active exoskeleton mode, which simulated the biological forces on the human body during exoskeleton-assisted movements. The results indicated that, while wearing the exoskeleton, the muscle forces and joint torques at the hip and knee joints during the flexion/extension cycle of the lower limb were significantly reduced, with reduced fluctuations. This suggests that the exoskeleton provides good wearing comfort and stable assistive effects. © 2023 IEEE.

关键词： Kinematics

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Benthic bioturbations weaken the stability of blue carbon storage

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National science Open 2025年第2期4卷 205-218页

作者： Yingrong Huang Peng Zhang He-Bo Peng Feng Pan Yan Liu Kai Wang Yu Cai Jianyu Wang Zhenyan Wang Chunmiao Zheng Hailong Li Ding He Junjian Wang Chenyuan Dang Pengbao Wu Ji Chen Damien Maher Kai Xiao School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds Institute of Environmental and Ecological EngineeringGuangdong University of TechnologyGuangzhou 510006China Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems Fujian Key Laboratory of Coastal Pollution Prevention and ControlCollege of the Environment&EcologyXiamen UniversityXiamen 361102China Department of Ocean Science and Engineering Southern University of Science and TechnologyShenzhen 318055China Center for Turbulence Research Stanford UniversityStanfordCA 94305USA Eastern Institute for Advanced Study Eastern Institute of TechnologyNingbo 315200China Department of Ocean Science and Center for Ocean Research in Hong Kong and MacaoThe Hong Kong University of Science and TechnologyHong Kong 999077China Hubei Key Laboratory of Multi-Media Pollution Cooperative Control in Yangtze Basin School of Environmental Science&EngineeringHuazhong University of Science and TechnologyWuhan 430074China School of Geography and Tourism Huizhou UniversityHuizhou 516007China State Key Laboratory of Loess and Quaternary Geology Institute of Earth EnvironmentChinese Academy of SciencesXi’an 710061China Faculty of Science and Engineering Southern Cross UniversityLismoreNew South Wales 2480Australia CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone ResearchChinese Academy of SciencesYantai 264003China

Coastal ecosystems are known for their ability to sequester organic carbon(OC),termed“blue carbon”.The molecular composition of dissolved organic matter(DOM)can affect sediment OC content;however,the impact of benthic bioturbation on DOM properties and OC storage stability is not well *** study examined the effects of bio-turbation by fiddler crabs on DOM molecular properties and OC storage stability along the Chinese *** findings indicate that crab bioturbation enhanced the release of labile molecules by 59%on *** increase is controlled by the coupling reactions of iron and manganese minerals,and is influenced by climatic ***,fiddler crab bioturbation diminishes the durability of blue carbon storage,with the most significant effects observed in mangrove forests,followed by bare mudflats,tidal creek banks,and *** results underscore the critical role of benthic bioturbation in global blue carbon budgets.

关键词： blue carbon crab bioturbation Fe and Mn minerals climate change molecular level continental scale

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

arXiv

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arXiv 2024年

作者： Li, Mingxian Sun, Hao Lei, Yingtie Zhang, Xiaofeng Dong, Yihang Zhou, Yilin Li, Zimeng Chen, Xuhang School of Computer Science and Engineering Huizhou University China Faculty of Science and Technology University of Macau China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Electronic and Communication Engineering Shenzhen Polytechnic University China

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 × 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer’s superior performance over state-of-the-art methods on the StainDoc dataset and its variants StainDoc Mark and StainDoc Seal, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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