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FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

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Chinese Journal of Electronics 2024年第1期33卷 139-152页

作者： Ruru ZHANG Haihong E meina song School of Computer Science Beijing University of Posts and Telecommunications Education Department Information Network Engineering Research Center Beijing University of Posts and Telecommunications

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning(FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters, but ignoring embedding network transferability and classifier adaptation(CA), failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. In this paper, we propose a simple and novel approach from two perspectives: embedding bias and classifier bias. The method learns an embedding augmented(EA) network with cross-class transfer and class-specific discriminative abilities based on self-supervised learning and modulated attention to alleviate embedding bias. Based on the adaptive incremental classifier learning scheme to realize incremental learning capability,guiding the adaptive update of prototypes and feature embeddings to alleviate classifier bias. We conduct extensive experiments on two popular natural image datasets and two medical datasets. The experiments show that our method is significantly better than the baseline and achieves state-of-the-art results.

关键词： Training Adaptation models Visualization Image recognition Prototypes Self-supervised learning Power capacitors

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Social media mining and visualization for point-o f-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 67-76,86页

作者： Ren Xingyi song meina E Haihong song Junde School of Computing Beijing University of Posts and Telecommunications Beijing 100876 China Engineering Research Center of Information Networks Beijing University of Posts and Telecommunications Beijing 100876 China

With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network （CNN） features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image＇s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation （SM-TwitterLDA） which jointly models five Twitter features, （i.e., text, image, location, timestamp and hashtag） is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

关键词： social media Twitter POI recommendation visualization

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QoS prediction algorithm used in location-aware hybrid Web service

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The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 42-49页

作者： E Haihong Tong Junjie song meina song Junde School of Computer Science Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Ministry of Education

Quality-of-Service （QoS） describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ＇hybrid user-location-aware prediction based on weighted Adamic-Adar （WAA）＇（HUWAA） was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient （IPCC）, user-based pearson correlation coefficient （UPCC） and Web service recommender system （WSRec） algorithms.

关键词： service QoS prediction data sparsity link prediction location-aware

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Traffic Prediction in 3G Mobile Networks Based on Multifractal Exploration

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Tsinghua Science and Technology 2013年第4期18卷 398-405页

作者： Yanhua Yu meina song Yu Fu Junde song the School of Computer Beijing University of Posts and Telecommunications

Traffic prediction plays an integral role in telecommunication network planning and network optimization. In this paper, we investigate the traffic forecasting for data services in 3G mobile networks. Although the Box-Jenkins model has been proven to be appropriate for voice traffic （since the arrival of calls follows a Poisson distribution）, it has been demonstrated that the Internet traffic exhibits statistical self-similarity and has to be modeled using the Fractional AutoRegressive Integrated Moving Average （FARIMA） process. However, a few studies have concluded that the FARIMA process may fail in modeling the Internet traffic. To this end, we conducted experiments on the modeling of benchmark Internet traffic and found that the FARIMA process fails because of the significant multifractal characteristic inherent in the traffic series. Thereafter, we investigate the traffic series of data services in a 3G mobile network from a province in China. Rich multifractal spectra are found in this series. Based on this observation, an integrated method combining the AutoRegressive Moving Average （ARMA） and FARIMA processes is applied. The obtained experimental results verify the effectiveness of the integrated prediction method.

关键词： time series prediction self-similar Fractional AutoRegressive Integrated Moving Average (FARIMA)

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Textual-geographical-social aware point-of-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2016年第6期23卷 24-33,67页

作者： Ren Xingyi song meina E Haihong song Junde School of Telecommunication Engineering Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Beijing University of Posts and Telecommunications

The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词： location-based social networks POI recommendation topic model geographical correlations social correlations

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Joint model of user check-in activities for point-of-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2016年第4期23卷 25-36页

作者： Ren Xingyi song meina E Haihong song Junde School of Telecommunication Engineering Beijing University of Posts and Telecommunications

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词： POI recommendation user check-in activities joint probabilistic generative model geographical influence social influence temporal effect content information popularity information

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ERCS: An Efficient and Robust Card Recognition System for Camera-Based Image

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China Communications 2020年第12期17卷 247-264页

作者： Zhonghong Ou Baiqiao Xiong Fenrui Xiao meina song Beijing University of Posts and Telecommunications No.10 Xitucheng Road Haidian DistrictBeijing 100876China

Cards Recognition Systems,(CRSs)are representative computer vision-based *** have a broad range of usage *** example,they can be used to recognize images containing business cards,personal identification cards,and bank cards *** though CRSs have been studied for many years,it is still difficult to recognize cards in camera-based images taken by ordinary devices,e.g.,mobile *** of viewpoints and complex backgrounds in the images make the recognition task *** systems employing traditional image processing schemes are not robust to varied environment,and are inefficient in dealing with natural images,e.g.,taken by mobile *** tackle the problem,we propose a novel framework for card recognition by employing a Convolutional Neutral Network(CNN)based *** system localizes the foreground of the image by utilizing a Fully Convolutional Network(FCN).With the help of the foreground map,the system localizes the corners of the card region and employs perspective transformation to alleviate the effects from *** lines in the card region are detected and recognized by utilizing CNN and Long Short Term Memory,(LSTM).To evaluate the proposed scheme,we collect a large dataset which contains 4,065 images in a variety of shooting *** results demonstrate the efficacy of the proposed ***,it is able to achieve an accuracy of 90.62%in the end-toend test,outperforming the state-of-the-art.

关键词： card localization card recognition optical character recognition CNN

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Vision-based positioning system

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The Journal of China Universities of Posts and Telecommunications 2016年第5期23卷 88-96页

作者： song meina Ou Zhonghong E Haihong song Junde Zhao Xuejun School of Computer Science Beijing University of Posts and Telecommunications

Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system （GPS） and global navigation satellite system （GLONASS）. The latest advances from wearable, e.g., BaiduEye and Google Glass, have enabled new approaches to leverage information from the surrounding environment. For example, they enable the change from passively receiving information to actively requesting information. Thus, such changes might inspire brand new application scenarios that were not possible before. In this work, we propose a vision-based navigation system based on wearable like Baidu Eye. We discuss the associated challenges and propose potential solutions for each of them. The system utilizes crowd sensing to collect and build a traffic signpost database for positioning reference. Then it leverages context information, such as cell identification （Cell ID）, signal strength, and altitude combined with traffic sign detection and recognition to enable real-time positioning. A hybrid cloud architecture is proposed to enhance the capability of sensing devices （SD） to realize the proposed vision.

关键词： vision-based positioning system wearable machine vision

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FGM-SPCL:Open-Set Recognition Network for Medical Images Based on Fine-Grained Data Mixture and Spatial Position Constraint Loss

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Chinese Journal of Electronics 2024年第4期33卷 1023-1033页

作者： Ruru ZHANG Haihong E Lifei YUAN Yanhui WANG Lifei WANG meina song School of Computer Science Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Ministry of Education Hebei Eye Hospital

The current intelligent auxiliary diagnosis models all follow the closed-set recognition setting. After the model is deployed online, the input data is often not completely controlled. Diagnosing an untrained disease as a known category would lead to serious medical malpractice. Therefore, realizing the open-set recognition is significant to the safe operation of the intelligent auxiliary diagnosis model. Currently, most open-set recognition models are studied for natural images, and it is very challenging to obtain clear and concise decision boundaries between known and unknown classes when applied to fine-grained medical images. We propose an open-set recognition network for medical images based on fine-grained data mixture and spatial position constraint loss(FGM-SPCL) in this *** the fine graininess of medical images and the diversity of unknown samples, we propose a fine-grained data mixture(FGM) method to simulate unknown data by performing a mixing operation on known data to expand the coverage of unknown data difficulty levels. In order to obtain a concise and clear decision boundary, we propose a spatial position constraint loss(SPCL) to control the position distribution of prototypes and samples in the feature space and maximize the distance between known classes and unknown classes. We validate on a private ophthalmic OCT dataset, and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.

关键词： Analytical models Fine-grained image recognition Prototypes Aerospace electronics Spatial databases Robustness Data models

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Dynamically modulated synthesis of hollow metal-organic frameworks for selective hydrogenation reactions

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Nano Research 2023年第8期16卷 11334-11341页

作者： Liyun Xiao Chuanqi Cheng Zhixi Li Chaoyang Zheng Jing Du meina song Yue Wan Shaopeng Li Guo Jun Meiting Zhao Department of Chemistry Institute of Molecular Aggregation ScienceTianjin Key Laboratory of Molecular Optoelectronic SciencesTianjin UniversityTianjin 300072China Institute of New Energy Materials School of Materials Science and EngineeringTianjin UniversityTianjin 300072China State Key Laboratory of Separation Membranes and Membrane Processes School of ChemistryTiangong UniversityTianjin 300387China

Hollow metal-organic frameworks(MOFs)have attracted increasing attention in the field of catalysis in recent years due to their unique cavity structure with fast mass-diffusion rates and easily accessible active ***,we report the use of dynamic modulators,which are formed by the in-situ imine condensation reaction of 4-aminobenzoic acid and 4-formylbenzoic acid,to regulate the growth of MOFs to synthesize well-defined hollow thioether functionalized UiO-67(denoted as H-UiO-67-S)single *** supporting Pd nanoparticles,the designed catalysts Pd@H-UiO-67-S show excellent conversion(>99.9%),selectivity(>99.9%),and stability(10 cycles)in the selective hydrogenation of nitrobenzenes with other reducible *** functional theory calculations and the experimental results reveal that Pd nanoparticles not only selectively adsorb the nitro-groups on nitrobenzene,but also restrict the adsorption of the aniline product,due to the interaction of thioether with Pd in the confined pores of H-UiO-67-S,finally result in a significant increase in selectivity of nitro-hydrogenation.

关键词： hollow structure metal-organic frameworks(MOFs) dynamically modulated synthesis selective hydrogenation nitrobenzene

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