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作者： Runyi Chen Data Science and Big Data Technology Nanjing University of Information Science &Technology

Face recognition has emerged as a new trend in the era of intelligence due to the rapid growth of artificial intelligence and other cutting-edge technologies. This paper investigates the research and application of face recognition technology based on deep learning through a comprehensive literature review and analysis. The discussion encompasses the classification and processes of facial recognition technology, the incorporation of deep learning into face recognition, and the application of face recognition across a variety of *** emphasis is placed on the use of deep belief networks(DBN) and convolutional neural networks(CNN) in face recognition. Face recognition technology, facilitated by deep learning techniques, has become pervasive in our daily lives, vastly enhancing our quality of life and productivity.

关键词： face recognition deep learning convolutional neural network(CNN) deep belief network(DBN) 2D and 3D face recognition

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Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

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Intelligent Automation & Soft Computing 2023年第8期37卷 2059-2075页

作者： Changshun Liu Yan Liu Jingjing Song Taihua Xu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhejiang Ocean UniversityZhoushan316022China

Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from *** granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute *** information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is *** fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information *** first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence *** second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information *** results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the *** strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

关键词： Attribute reduction information granulation mapping-equiva-lence relation neighborhood rough set

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Real-time Student Classroom Feedback with the RFMNet Framework in a Smart Classroom 13

Real-time Student Classroom Feedback with the RFMNet Framewo...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Tang, Xianpiao Hu, Pengyun Yu, Dewei Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions. © 2023 IEEE.

关键词： Students

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Security analysis and secured access design for networks of image remote sensing

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Digital Communications and Networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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Learning to Unlearn for Bayesian Personalized Ranking via Influence Function

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Chinese Journal of Electronics 2025年第3期34卷 990-1001页

作者： Chen Jundong Zhang Honglei Li Haoxuan Li Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing 100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing 100044 China School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Center for Data Science Peking University Beijing 100871 China

Learning recommender models from vast amounts of behavioral data has become a mainstream paradigm in recent information systems. Conversely, with privacy awareness grown, there has been increasing attention to the removal of sensitive or outlier data from well-trained recommendation models (known as recommendation unlearning). However, current unlearning methods primarily focus on fully/partially retraining the entire model. Despite considerable performance, it inevitably introduces significant efficiency bottlenecks, which is impractical for latency-sensitive streaming services. While recent efforts exploit efficient unlearning in point-wise recommender tasks, these approaches overlook the partial order relationships between items, resulting in suboptimal performance in both recommendation and unlearning capabilities. In light of this, we explore learning to unlearn for Bayesian personalized ranking via influence function, which relies on a pair-wise ranking loss to model user preferences and item characteristics, making unlearning more challenging than in point-wise settings. Specifically, we propose an influence function-guided unlearning framework tailored for pair-wise ranking models to efficiently perform unlearning requests, which involves unlearning partial order relationships while handling negative samples appropriately during the unlearning process. Besides, we prove that our proposed method can theoretically match the performance of retraining counterparts. Finally, we conduct extensive experiments to validate the effectiveness and efficiency of our model.

关键词： Recommendation unlearning Influence function Bayesian personalized ranking The right to be forgotten

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Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

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Chinese Physics B 2023年第1期32卷 380-391页

作者：刘华清姜锐田钧方朱凯旋 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of TransportBeijing Jiaotong UniversityBeijing 100044China College of Management and Economics Tianjin UniversityTianjin 300072China Suzhou Institute of Technology Jiangsu University of Science and TechnologyZhangjiagang 215600China

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

关键词： traffic flow connected and automated vehicles(CAVs) lane drop optimization-based control algorithm Heuristic rules-based algorithm

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Text-based Q&A System technology for Medical Aesthetic Field 22

Research on Text-based Q&A System Technology for Medical Aes...

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22nd IEEE/ACIS International Conference on Computer and Information science, ICIS 2022

作者： Liu, JunZhen Zhang, YiXuan Gong, Bin Lu, Wei Xiao, Yue University of Macau Faculty of Data Science City China Zhuhai College of Science and Technology School of Aliyun Big Data Applications Zhuhai China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Medical aesthetics is the process of re-homing the shape of various parts of the body using medical means in order to meet the subjective definition of beauty. A text-based question and answer system in the field of medical aesthetics can provide quick answers to patients' various medical search needs and help them to consult online and get professional explanations of their medical aesthetic problems. We propose the GPT2 Medchat model as an important part of the system and need to set up the following aspects to complete the system: 1. We prepare a new dataset in the medical aesthetics domain, which covers a wide range of questions and answers with strong professionalism. 2. In the pre-training phase, high-quality dialogue texts in the medical aesthetics domain are used as training materials. The system is built based on the pre-training phase. The medical text is used as training material, and the GPT2 Medchat model is trained and fine-tuned by relying on the pkuseg professional medical model to segment the medical text. The optimized segmentation results are used as a medical dictionary. 3. Sampling strategy to ensure the quality and diversity of the quizzes under the specified medical aesthetic domain questions. Both objective and subjective evaluations show that the GPT2 Medchat model has achieved a high practical and reference value for quizzing in the medical aesthetics domain. © 2022 IEEE.

关键词： Medical problems

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Learning to Restore Fire Smoke Monitoring Images: A dataset and A Transformer-Based Method

Learning to Restore Fire Smoke Monitoring Images: A Dataset ...

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Sensors and Information technology (ICSI), International Conference on

作者： Xinran Wu Wei Jiang School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China School of Data Science and Big Data Technology Anhui Sanlian University Hefei China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Image desmoking is an important task in computer vision, aimed at recovering clear visual content from images affected by smoke. Smoke causes reduced contrast and loss of details, which negatively impacts subsequent image analysis and understanding tasks. Traditional desmoking methods often rely on physical models or prior knowledge, but they are limited when handling complex scenes and non-uniform smoke. With the development of deep learning, CNN-based methods have made significant progress. However, these methods focus on local features, potentially neglecting the global contextual information. Recently, Transformer models have shown unique advantages in desmoking tasks due to their self-attention mechanism. Transformer-based models can capture both local features and global context, improving desmoking performance. This paper proposes a Transformer-based desmoking method called DesmokeFormer. This method focuses on both the details and global context of the image during the desmoking process, thereby enhancing the desmoking effect. To validate the effectiveness of DesmokeFormer, we construct the Desmoke6k dataset, which includes hazy and clear image pairs from various indoor fire smoke scenarios. Experimental results demonstrate that DesmokeFormer outperforms existing state-of-the-art desmoking methods in terms of image quality and perceptual evaluation, proving its potential for practical applications.

关键词： Image sensors Image quality Degradation Deep learning Visualization Transformers Image restoration Sensors Information technology Monitoring

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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