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Adaptive Occlusion Face Recognition Feature Fusion Network Based on Precise Guidance Mask Extraction

Journal of Shanghai Jiaotong University (science) 2025年 1-12页

作者： Ye, Jihua Zhang, Lei Wang, Chao Tu, Qixuan Zheng, Rehua Huang, Huiyuan Jiang, Aiwen School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Provincial Key Laboratory for High Performance Computing Jiangxi Normal University Nanchang330022 China State International Science & Technology Cooperation Base of Networked Supporting Software Jiangxi Normal University Nanchang330022 China

The uncertainty in the position and size of occluding objects greatly affects the extraction of identity features in facial recognition, which is a challenge that existing methods fail to effectively address. To tackle this issue, this paper introduces a novel feature fusion network for occluded facial recognition that adaptively handles arbitrary occlusions and accurately extracts occlusion information masks. The network effectively mitigates the impact of occlusions on facial identity features through a feature fusion module. The proposed method employs an occlusion classification module and an occlusion classification loss to precisely guide the extraction of accurate occlusion masks. Subsequently, an occlusion-aware hybrid attention module is introduced, which focuses on the relationship between facial features and occlusion masks across channel and spatial dimensions. This fusion strategy enables the extraction of facial identity features that are resilient to occlusions. Experimental results indicate that the proposed method holds a significant advantage in handling facial recognition tasks with arbitrary occlusions. On the LFW and MF1 synthetic occlusion datasets, the average accuracy rates reached 93.19% and 72.57%, respectively, showing improvements of 1.7 and 2.39 percent points over mainstream methods. © Shanghai Jiao Tong University 2025.

关键词： A feature fusion occluded facial recognition occlusion mask occlusion perception TP391.41

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Transformer-based multi-level attention integration network for video saliency prediction

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Multimedia Tools and Applications 2025年第13期84卷 11833-11854页

作者： Tan, Rui Sun, Minghui Liang, Yanhua Software College Jilin University Qianjin Street Jilin Changchun130012 China College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

Most existing models for video saliency prediction heavily rely on 3D convolutional operations to extract spatio-temporal features. However, it is worth noting that 3D convolution produces a local receptive field, which may struggle to capture long-range spatio-temporal dependencies effectively. To compensate for such shortage, this paper introduces a novel approach called the Transformer-based Multi-level Attention Integration Network (TMAI-Net) for video saliency prediction. TMAI-Net is designed as a two-stream encoder-decoder model, carefully integrating multi-level features of semantic information. Our model incorporates a Multi-level Interactive Attention(MLIA) module and a Transformer, both implemented based on self-attention mechanism, which are placed at different levels of the model to capture long-range spatio-temporal feature dependencies. Additionally, our model operates on input video frames and attentional patches, allowing the Transformer module to capture structural similarities between related objects in global features and attention features. This, in turn, enables the model to allocate increased attention to salient areas. The efficacy of our proposed approach is validated through extensive experiments conducted on three widely recognized benchmark datasets. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Optimal Illumination Distance Metrics for Person Re-identification 21st

Optimal Illumination Distance Metrics for Person Re-identif...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Chao Wang, Zhongyuan Hu, Ruimin Wang, Xiaochen Zhou, Wen 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 University Wuhan China College of Information Engineering Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9789819601240

Person re-identification (Re-ID) has been widely used in public security and surveillance. Due to the influence of different shooting times and locations, can lead to lighting variations in the images captured by the camera, and the lighting variations seriously affect the Re-ID performance. Existing methods utilize images under different lighting conditions to transform to uniform lighting conditions to eliminate the effect of lighting on Re-ID. However, these methods ignore the fact that different illumination metric distances have different recognition performances. Therefore, we propose the optimal illumination distance metric, by analyzing different light distances for pedestrians in different light conditions, we find that there exists an optimal light condition in which the distance between image pairs is smaller than the distance in other light conditions. The distance under the optimal lighting condition is used in the retrieval process as the distance between its final sorted image pairs. To verify the effectiveness of our method, Market-1501-IA and Duke-MTMC-IA datasets are constructed, and a large number of experiments are conducted under different lighting conditions to verify the existence of the optimal lighting conditions and the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting

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Defending Against Adversarial Examples Via Modeling Adversarial Noise

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International Journal of computer Vision 2025年 1-18页

作者： Zhou, Dawei Wang, Nannan Han, Bo Liu, Tongliang Gao, Xinbo State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi’an710071 China TMLR Group Department of Computer Science Hong Kong Baptist University Hong Kong Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia School of Electronic Engineering Organization Xidian University Shaanxi Xi’an710071 China

Adversarial examples have become a major threat to the reliable application of deep learning models. Meanwhile, this issue promotes the development of adversarial defenses. Adversarial noise contains well-generalizing and misleading features, which can manipulate predicted labels to be flipped maliciously. Motivated by this, we study modeling adversarial noise for defending against adversarial examples by learning the transition relationship between adversarial labels (i.e., flipped labels caused by adversarial noise) and natural labels (i.e., real labels of natural samples). In this work, we propose an adversarial defense method from the perspective of modeling adversarial noise. Specifically, we construct an instance-dependent label transition matrix to represent the label transition relationship for explicitly modeling adversarial noise. The label transition matrix is obtained from the input sample by leveraging a label transition network. By exploiting the label transition matrix, we can infer the natural label from the adversarial label and thus correct wrong predictions misled by adversarial noise. Additionally, to enhance the robustness of the label transition network, we design an adversarial robustness constraint at the transition matrix level. Experimental results demonstrate that our method effectively improves the robust accuracy against multiple attacks and exhibits great performance in detecting adversarial input samples. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adversarial attack Adversarial defense Modeling adversarial noise Transition matrix

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction 27th

Leveraging Data from Vast Unexplored Seas: Positive Unlabel...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Konii, Haruki Nakano, Teppei Miyazawa, Yasumasa Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9783031781919

This paper proposes a Positive Unlabeled (PU) learning approach to narrow down the prediction area in the context of good fishing ground prediction. PU learning, a type of semi-supervised learning, is particularly suitable for scenarios characterized by limited positive examples and abundant unlabeled data, as often encountered in fishing ground prediction tasks, where the explored sea areas identified as productive fishing spots are treated as positive instances and the vast unexplored sea areas as unlabeled data. Conventional methods often struggle to accurately model the characteristics of good fishing grounds, resulting in overly broad prediction areas or overly restrictive constraints. To tackle this challenge, we present a PU learning-based method designed to identify negative examples from the unlabeled data and consequently refine the area predicted as positive. Specifically, we train a prediction model for fishing duration, which can be considered a surrogate indicator of good fishing grounds. Subsequently, we apply this model to predict the fishing duration for unexplored areas;those areas exhibiting short fishing durations are deemed reliable negative examples. By incorporating both past positive examples and selected negative examples into a binary classification framework for predicting good fishing grounds, we aim to fine-tune the prediction area. To the best of our knowledge, this study represents the first application of PU learning in the domain of good fishing ground prediction. Experimental comparisons conducted using data from bullet tuna trolling validate the efficacy of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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Mechanistic switch in corrosion behavior of magnesium alloy diamond lattice structures induced by argon plasma treatment

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Journal of Magnesium and Alloys 2025年第1期13卷 101-119页

作者： Viviana M.Posadaa Alexandru Marin Tonny Naranjo Juan Ramírez Patricia Fernández-Morales Ken and Mary Alice Lindquist Department of Nuclear Engineering Pennsylvania State UniversityPA USA Department of Mechanical Engineering School of MinesUniversidad Nacional de ColombiaBogotáColombia Department of Nuclear Plasma and Radiological EngineeringCollege of EngineeringUniversity of Illinois at Urbana-ChampaignILUSA Medicine Faculty Universidad Pontificia BolivarianaAntioquiaColombia Corporación para Investigaciones Biológicas AntioquiaColombia Industrial Engineering Faculty Universidad Pontificia BolivarianaAntioquiaColombia Materials Science and Engineering Department Pennsylvania State UniversityUniversity ParkPA16802USA Surface Analysis Laboratory Institute for Nuclear Research PitestiMioveni15400Romania Department of Electronics and Computer Science Pontificia Universidad Javeriana de CaliColombia

Advancing 3D magnesium(Mg)development beyond current limitations requires controlling Mg alloy degradation in pre-designed,low-dimension *** study reveals a mechanistic switch in the corrosion behavior of Mg alloy(3.6%Al,0.8%Zn)diamond lattice structures,induced by plasma nanosynthesis(400 eV Ar^(+)ions,fluence 1×10^(17) ions/cm^(2)).Plasma treatment of the Mg alloy increases surface Mg from 1.5%to 14.5%,enhances carbonate formation,and generates a nanostructured surface with a Mg carbonate layer over an oxide/hydroxide *** vitro and in vivo analyses over 8 wk demonstrate how this treatment fundamentally alters the degradation process and stability of these 3D *** untreated samples initially formed a protective film that subsequently diminished,DPNS-treated samples demonstrated an inverse corrosion behavior.X-ray photoelectron spectroscopy(XPS)and electrochemical impedance spectroscopy(EIS)confirmed the presence of a stable,protective layer composed of magnesium oxide,magnesium hydroxide,and magnesium carbonate on the DPNS-treated *** 14 days,the DPNS-treated sample exhibited a more positive corrosion potential(-0.69 V versus-1.36 V)and a marginally lower current density(0.73 mA/cm^(2)compared to 0.75 mA/c^(2))relative to the *** protective layer,combined with modified surface topology,initiated a core-to-periphery degradation pattern that maintained structural integrity for up to 8 wk *** findings support the conclusion that the DPNS-treated scaffold demonstrates sustained improved corrosion resistance over time compared to the untreated ***-CT revealed plasma-treated samples retained larger struts(504.9±95.3μm at 8 wk)and formed larger H_(2) pockets extending 14.2 mm from the implant center,versus 4.9 mm in *** corrosion behavior switch enhances stability but risks pore clogging,offering insights for tailoring Mg alloy degradation and H_(2) evolution in 3D architectures

关键词： Mg Corrosion resistance Cellular materials Nanotopography Nano-medicine Diirected plasma nanosynthesis

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