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21st ACM International Conference on Multimedia, MM 2013

作者： Li, Xirong Snoek, Cees G. M. Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands

ISBN: (纸本)9781450324045

Image tag relevance estimation aims to automatically deter- mine what people label about images is factually present in the pictorial content. Different from previous works, which either use only positive examples of a given tag or use pos- itive and random negative examples, we argue the impor- Tance of relevant positive and relevant negative examples for tag relevance estimation. We propose a system that se- lects positive and negative examples, deemed most relevant with respect to the given tag from crowd-annotated images. While applying models for many tags could be cumbersome, our system trains efficient ensembles of Support Vector Ma- chines per tag, enabling fast classification. Experiments on two benchmark sets show that the proposed system com- pares favorably against five present day methods. Given extracted visual features, for each image our system can pro- cess up to 3,787 tags per second. The new system is both effective and efficient for tag relevance estimation. Copyright © 2013 ACM.

关键词： Image classification

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Top-k neighborhood dominating query

Top-k neighborhood dominating query

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Xie, Xike Lu, Hua Chen, Jinchuan Shang, Shuo Department of Computer Science Aalborg University Denmark Key Labs of Data Engineering and Knowledge Engineering MOE Renmin University of China China

ISBN: (纸本)9783642374869

In many real-life applications, spatial objects are associated with multiple non-spatial attributes. For example, a hotel may have price and rating in addition to its geographic location. In traditional spatial databases, spatial objects are often ranked solely based on their distance to a given query location, e.g., in a nearest neighbor search. In another line of research, domination based skyline queries are used to return best objects according to multi-criteria on non-spatial attributes. In this paper, we study how to rank spatial objects with respect to their non-spatial attributes within their spatial neighborhoods. To enable a general ranking, we design a ranking function that inherits the advantages of dominance relationship and integrates them with spatial proximity. Further, we propose an effective index structure, and a branch and bound solution that executes the ranking efficiently via the index. We conduct extensive empirical studies on real and synthetic datasets. The results demonstrate the high efficiency of our proposal compared to straightforward alternatives. © Springer-Verlag 2013.

关键词： Nearest neighbor search

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I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks 23

Publish me and protect me: Personalized and flexible locatio...

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23rd IEEE International Symposium on Quality of Service, IWQoS 2015

作者： Wu, Yao Peng, Hui Zhang, Xiaoying Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467371131

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data. © 2015 IEEE.

关键词： Location based services

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Efficient SPARQL query evaluation via automatic data partitioning

Efficient SPARQL query evaluation via automatic data partiti...

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Yang, Tao Chen, Jinchuan Wang, Xiaoyan Chen, Yueguo Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China

ISBN: (纸本)9783642374494

The volume of RDF data increases very fast within the last five years, e.g. the Linked Open data cloud grows from 2 billions to 50 billions of RDF triples. With its wonderful scalability, cloud computing platform like Hadoop is a good choice for processing queries over large data sets. Previous works on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect the performance. Specifically, a good partitioning will greatly reduce or even totally avoid cross-node joins and significantly reduce the cost of query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture where Map/Reduce takes charge of the computing tasks and an RDF query engine, RDF-3X, stores the data and evaluates join operations over local data. Based on analysis of query work-loads, we propose a novel algorithm for automatically partitioning RDF data. We also present an approximate solution to physically place the partitions in order to reduce data redundancy. All the proposed approaches are evaluated by extensive experiments over large RDF data sets. © Springer-Verlag 2013.

关键词： Resource Description Framework (RDF)

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

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Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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Rucmm at MediaEval 2015 Affective impact of movies task: Fusion of audio and visual cues

Rucmm at MediaEval 2015 Affective impact of movies task: Fus...

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Multimedia Benchmark Workshop, MediaEval 2015

作者： Jin, Qin Li, Xirong Cao, Haibing Huo, Yujia Liao, Shuai Yang, Gang Xu, Jieping Multimedia Computing Lab School of Information Renmin University of China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

This paper summarizes our efforts for the first time participation in the Violent Scene Detection subtask of the MediaEval 2015 Affective Impact of Movies Task. We build violent scene detectors using both audio and visual cues. In particular, the audio cue is represented by bag-of-audio-words with fisher vector encoding. The visual cue is exploited by extracting CNN features from video frames. The detectors are implemented using two-class linear SVM classifiers. Evaluation shows that the audio detectors and the visual detectors are comparable and complementary to each other. Among our submissions, multi-modal late fusion leads to the best performance.

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TYG: A tag-as-you-go online annotation tool for web browsing and navigation

TYG: A tag-as-you-go online annotation tool for web browsing...

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6th International Conference on knowledge Science, engineering and Management, KSEM 2013

作者： Hu, He Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

ISBN: (纸本)9783642397868

We present TYG (Tag-as-You-Go) in this paper, a chrome browser extension for personal knowledge annotation on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can group similar html nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Websites

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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Generative Zero-shot Learning Compound Fault Diagnosis of Bearings 2

Generative Zero-shot Learning Compound Fault Diagnosis of Be...

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2nd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2021

作者： Xu, Juan Li, Kang Hefei University of Technology School of Computer and Information Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (纸本)9781665427470

Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of possible failure modes. Current compound faults diagnostic methods, which are usually based on supervised or semi-supervised learning, require sufficient labeled or unlabeled training data for each compound faults. In industrial scenarios, neither labeled nor unlabeled training data of compound faults are usually difficult to collect and sometimes even inaccessible, whereas single faults samples are easy to obtain. Based on these issues, we construct a novel generative zero-shot learning (ZSL) compound faults diagnosis model identifies unseen compound faults using only single faults samples as training set. This model comprises several modules, namely semantic vector definition, feature extractor, generative adversarial modules. Firstly, we devise a unified semantic vector definition method for expressing single and compound faults based on theoretical correlation of characteristics between single fault and compound faults vibration data. Secondly, a CNN-based feature extractor is designed for extraction the fault features from the time-frequency domain of vibration data. Thirdly, a generative adversarial module performs adversarial training of semantic vectors and fault features of single faults to learn the mapping relationship between the fault features and the fault semantic vectors. Once trained, the generator is able to generate compound fault features using the compound fault semantic vectors, rather than any compound fault samples. Finally, the K-nearest neighbor method is adopted to identify the unseen compound faults by measuring the distance between the extracted feature from the testing compound fault samples and the generated features. The effectiveness of the proposed method is verified on a self-built bearing test stand. The results show

关键词： Semantics

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