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30th International Conference on Software engineering and knowledge engineering, SEKE 2018

作者： Sun, Caihong Zhang, Meina Zuo, Meiyun Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

ISBN: (纸本)1891706446

In this paper, we focus on examining the effects of Ad-context on the click-Through rate (CTR) for the online advertising. Many researches have shown that ad-context congruity is a key factor to CTR, but the features of ad-context are rarely introduced in CTR prediction algorithms. By leveraging data from various sources, and using text mining and sentiment analysis techniques, our proposed approach extracts three types of features (i.e. users, advertisements and ad-context features) to predict CTR. User features describe "Who" is browsing the webpage, advertisement features depict "How" an ad serving is, and ad-context features include "What" the product is, "Where" an ad displays, as well as the "Mood" of the context. Experiment results show that our proposed approach outperforms the benchmark models by introducing ad-context features. Novel ad-context features we proposed make good contributions to the prediction of CTR. The research highlights the power of ad-context in online advertising CTR prediction. Moreover it gives an insight into supporting effective advertising and improving users' satisfaction. © 2018 Universitat zu Koln. All rights reserved.

关键词： Sentiment analysis

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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Optimization framework for distributed clustering scheme in wireless sensor networks

Optimization framework for distributed clustering scheme in ...

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SNPD 2007: 8th ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing

作者： Yongxuan, Lai Xiaobo, Fan Hong, Chen Tingting, Xie School of Information Renmin University of China Beijing Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)0769529097

Recently there have been growing interests in the applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach and data gathering protocol in wireless sensor networks. In this paper, we propose a novel optimization framework called MICA (MICa Adjustments) for clustering scheme in wireless sensor networks. MICA takes the cluster as a whole, and dynamically adjusts the clusters according to the real-lime workload of the local clusters to balance energy consumption among clusters. The adjustments can also deal with link failures. Simulation results show that MICA can be easily embedded into existing clustering schemes to balance the energy consumptions among the clusters, and to prolong the lifetime of wireless sensor networks. © 2007 IEEE.

关键词： Wireless sensor networks

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A WSMO-based semantic web services discovery framework in heterogeneous ontologies environment

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2nd International Conference on knowledge Science, engineering and Management, KSEM 2007

作者： Haihua, Li Xiaoyong, Du Xuan, Tian School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9783540767183

Nowadays, WSMO (Web Service Modeling Ontology)1 has received great attention of academic and business communities, since its potential to achieve dynamic and scalable infrastructure for web services is extracted. Therefore, we design an ontology-based Semantic Web Services (SWSs) discovery framework based on WSMO so as to searching dynamically web services located at different nodes. Also, we provide several SWSs matching techniques based on this framework. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Semantic Web

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CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

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Journal of Computer Science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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Weighted neural bag-of-n-grams model: New baselines for text classification 26

Weighted neural bag-of-n-grams model: New baselines for text...

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26th International Conference on Computational Linguistics, COLING 2016

作者： Li, Bofang Zhao, Zhe Liu, Tao Wang, Puwei Du, Xiaoyong School of Information Renmin University of China Key laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9784879747020

NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5% accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at https://***/zhezhaoa/neural-BOW-toolkit. © 1963-2018 ACL.

关键词： Classification (of information)

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Reduce pruning cost to accelerate multimedia kNN search over MBRs based index structures

Reduce pruning cost to accelerate multimedia kNN search over...

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3rd International Conference on Multimedia Information Networking and Security, MINES 2011

作者： Li, Hui Zhang, Xiao Wang, Shan School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

ISBN: (纸本)9780769545592

MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR based index structures. However, with the increase of dimensionality, the computation costs of above two metrics are expensive, and the involved distance computations may become the considerable component. In this paper, to improve the performance of kNN search over multimedia data, we proposed an approach to reduce the computation cost of pruning heuristics by use the cheap upper bound of MINMAXDIST instead of its precise value, and then we derive enhanced cheap heuristics based on this upper bound for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. We conducted an extensive experimental study on a large real-life multimedia dataset called MSRA-MM 2.0, obtained from the commercial Bing Image search engine. The results show that the proposed approach significantly reduced the computation cost and boosted the overall performance of MBR based multimedia kNN search. © 2011 IEEE.

关键词： Cost reduction

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Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

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Journal of Systems engineering and Electronics 2023年第3期34卷 682-695页

作者： SUN Rui YANG Zi ZHAO Zhenghui ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification is a prevalent technology deployed on intelligent *** have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open *** real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera *** low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR *** address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification ***,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR ***,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various *** qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.

关键词： cross-resolution feature invariant learning person re-identification residual knowledge transfer wavelet transform

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Attributes-based person re-identification via CNNs with coupled clusters loss

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Journal of Systems engineering and Electronics 2020年第1期31卷 45-55页

作者： SUN Rui HUANG Qiheng FANGWei ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and *** attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and *** paper proposes an end-to-end framework of deep learning for attribute-based person *** the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN ***,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id *** coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of *** combined features are mapped into the Euclidean *** L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the *** experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

关键词： person re-identification(re-id) convolutions neural network(CNN) attributes coupled clusters loss(CCL)

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XRCJ: Supporting keyword search in XML and relation co-occurrence

XRCJ: Supporting keyword search in XML and relation co-occur...

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1st International Workshop on Graph database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Zhang, Song Du, Xiaoyong Key Lab. of Data Engineering and Knowledge Engineering School of Information Renmin University of China China

ISBN: (纸本)3642167195

keyword search is a proven, user-friendly way to query html documents in the World Wide Web. With the trend of integrating IR technologies into DB community, state-of-the-art studies have introduced keyword search into XML and relation. In this paper we introduce keyword search into a new circumstance called XRC (XML and relation co-occurrence) where XML and relational data exist at the same storage. By exploring semantic relationships between data from relation and XML, we enable users who intend to visit data from XML and relation to access information in an easier manner. To achieve this target, we propose a novel operation called XRCJ which connects data from XML and relation together when answering search query. We also improve traditional inverted index and design a mixed inverted index. Through conducting extensive experiments on synthetic data from real data set DBLP, we find that our method can achieve a good performance in XML and relation co-occurrence circumstance. © 2010 Springer-Verlag.

关键词： XML

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