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9th International Conference on Web-Age Information Management, WAIM 2008

作者： Xiang, Li Zhou, Da Meng, Xiaofeng School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9780769531854

Compared with traditional magnetic disks, Flash memory has many advantages and has been used as external storage media for a wide spectrum of electronic devices (such as PDA, MP3, Digital Camera and Mobile Phone) in recent years. As the capacity increases and price drops, it looks like a perfect alternative for magnetic disks. However, due to hardware limitations of flash memory, techniques including storage subsystem and indexing originally designed for magnetic disks can not run smoothly in a flash memory without any modification. In this paper we explore problems of indexing flash-resided data and present a new dynamical hash index for flash memory in two schemas. The analysis and experimental results validate the efficiency of our design. © 2008 IEEE.

关键词： Flash memory

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Towards video management over relational database

Towards video management over relational database

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12th International Asia Pacific Web Conference, APWeb 2010

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

ISBN: (纸本)9780769540122

Video has become popular in our daily life for both professional and consumer applications. Both low level video processing and high level semantic video analysis are critically computational tasks in application domains. Most of current video computing tools are developed for specific analytic tasks, they are lack higher level interoperability with database and treat database merely as a relational data storage engine rather than an analytic platform, which causes inefficient data access and massive amount of data movement. In this paper, we study how to support video data management over relational database, and present our initial solutions of video data storage mechanism, video data access method and efficient video analytics. We also illustrate our ongoing prototype system HybVideo that developed in a novel architecture. It integrates above solutions to tackle the major challenges of providing a platform for both storage and analysis of video data. © 2010 IEEE.

关键词： Information management

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Mining Bytecode Features of Smart Contracts to Detect PonziScheme on Blockchain

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Computer Modeling in engineering & Sciences 2021年第6期127卷 1069-1085页

作者： Xiajiong Shen Shuaimin Jiang Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy ***,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in *** negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been *** solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using *** this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the ***,We innovatively transformed the Ponzi schemes detection into an anomaly detection ***,an anomaly detection algorithm is used to identify Ponzi schemes in smart *** results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme ***,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.

关键词： Ponzi scheme blockchain security smart contracts anomaly detection

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RJMIM: A new feature selection method based on joint mutual information

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Revista de la Facultad de Ingenieria 2016年第4期31卷 59-69页

作者： Peng, Liwen Liu, YongGuo Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application areas like data mining, bioinformatics and machine learning. But drawbacks of these methods are the neglect of the feature interaction and overestimation of features significance due to the limitations of goal functions criterion. To address this problem, we proposed a new feature goal function RJMIM. The method employed joint mutual information and information interaction, which alleviates the shortcomings of overestimation of the feature significance as demonstrated both theoretically and experimentally. The experiments conducted to verify the performance of the proposed method, it compared with four well-known feature selection methods use three publically available datasets from UCI. The average classification accuracy and C4.5 classifier is used to assess the effectiveness of RJMIM method.

关键词： Classification (of information)

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Phase spectrum based automatic ship detection in synthetic aperture radar images

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Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Larger, Cheaper, but Faster: SSD-SMR hybrid storage boosted by a new SMR-oriented cache framework 33

Larger, Cheaper, but Faster: SSD-SMR hybrid storage boosted ...

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33rd International Conference on Massive Storage Systems and Technology, MSST 2017

作者： Wang, Chunling Wang, Dandan Chai, Yupeng Wang, Chuanwen Sun, Diansen Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

By utilizing the new Shingled Magnetic Recording (SMR) technique, the emerging SMR disks achieve higher storage density and lower costs. Together with Flash-based SSDs, SMR disks can be used to construct a new hybrid storage system that is larger, but cheaper than the widely used conventional diskbased storage systems. It is appropriate for the fast growing big data applications which have strict requirements on both capacity and cost, e.g., the scientific computing in the fields of meteorology and astronomy. However, the most serious challenge of the SSD-SMR hybrid storage lies in its weak fine-grained random write performance caused by the write amplification in SMR disks, especially when I/Os are spread randomly across the Logical Block Address (LBA) range of the entire disk. Traditional cache algorithms like LRU usually lead to tens of or even hundreds of times more data written into magnetic plates than the original requests, thus resulting in poor I/O performance, because they do not consider and limit the LBA distribution of the eviction victims at all. Therefore, the cache algorithms for SSD-SMR hybrid storage systems should take this new dimension into account except for the data popularity. In this paper, we propose a new SMR-oriented cache framework, i.e., Partially Open Region for Eviction (PORE), which restricts the LBA range of evicted data from SSD caches appropriately to promote the overall performance of the hybrid storage by making a better tradeoff between the cached data popularity and the SMR write amplification. The experiments based on real-world traces reveal that PORE can boost the SSD-SMR hybrid storage performance by a factor of 3.26∼10.2 with only a 2% SSD cache compared with a conventional disk-based storage on average, while the hybrid storage coupled with LRU is slower than the conventional storage sometimes. PORE makes the hybrid storage much faster than traditional storage besides the advantages on capacity and cost. © 33rd Internati

关键词： Hard disk storage

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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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Does ad-context matter on the effectiveness of online advertising? 30

Does ad-context matter on the effectiveness of online advert...

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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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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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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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