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3rd IEEE International Conference on Big Data Analysis, ICBDA 2018

作者： Zhou, Aiping Zhu, Huisheng Liu, Lijun Zhu, Chengang School of Computer Science and Technology Taizhou University Taizhou China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781538647936

Detection of super nodes is the task of determining hosts which contact with numerous hosts in the time window or have significant connection change between two continuous time windows. This is a basic problem with many practical applications, for example, traffic measurement and anomaly detection. The main challenge is to detect super nodes in real time in the constraint of limited memory. To solve the problem, we propose a novel solution to detect super nodes based on parallel sketch using connection metrics (connection number and connection change), called PSketch. It constructs a novel data structure, and combines connection number and connection change, so that the accuracy and time efficiency of our algorithm is improved obviously. The experimental results illustrate that our algorithm is superior to CSE (Compact Spread Estimator) and JDS (Joint Data Streaming and Sampling) in terms of accuracy, memory utilization and time efficiency. © 2018 IEEE.

关键词： Anomaly detection

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Learning to Hash for Efficient Search Over Incomplete Knowledge Graphs

Learning to Hash for Efficient Search Over Incomplete Knowle...

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IEEE International Conference on Data Mining (ICDM)

作者： Meng Wang Haomin Shen Sen Wang Lina Yao Yinlin Jiang Guilin Qi Yang Chen School of Computer Science and Engineering Southeast University Nanjing China MOE Key Laboratory of Computer Network and Information Integration China Southeast University Nanjing China The University of Queensland Brisbane Australia The University of New South Wales Sydney Australia

Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional, continuous vectors, and thus enables machine learning models to be easily adapted to KG completion and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to learn compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring Hamming distance instead of Euclidean distance or dot-product. Unfortunately, most of learning-to-hash methods cannot be directly applied to KG structure encoding. In this paper, we introduce a novel framework for encoding incomplete KGs and graph queries in Hamming space. To preserve KG structure information from embeddings to hash codes and address the ill-posed gradient issue in optimization, we utilize a continuation method with convergence guarantees to jointly encode queries and KG entities with geometric operations. The hashed embedding of a query can be utilized to discover target answers from incomplete KGs whilst the efficiency has been greatly *** compared our model with state-of-the-art methods on real-world KGs. Experimental results show that our framework not only significantly speeds up the searching process, but also provides good results for unanswerable queries caused by incomplete information.

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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Location-Time-Sociality Aware Personalized Tourist Attraction Recommendation in LBSN

Location-Time-Sociality Aware Personalized Tourist Attractio...

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International Conference on computer Supported Cooperative Work in Design

作者： Ziqing Zhu Jiuxin Cao Chenghao Weng Key Laboratory of Computer Network and Information Integration of MoE of China School of Computer Science and Engineering

With the development of the tourism, vast amount of tourist attraction information makes it complex and time-consuming for users to obtain satisfactory travel destination. Rich topological, temporal and spatial information in Location-Based Social network (LBSN) helps to mine user preference deeply and evokes effectiveness of tourist attraction recommendation. In this paper, a personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity. Firstly, a large number of check-ins in LBSN are filtered for further geographical space clustering to get real tourist attraction check-ins by DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm. Then, a location-aware LDA model is used to excavate user's latent preference topic distribution and region's latent topic distribution on tourist attractions. After that, these two attributes together with social relationship are used to make user interest scoring function. Furthermore, another two scoring functions are established respectively based on tourist attraction location distance and popularity. At last, a personalized tourist attraction recommendation algorithm is designed to recommend tourist attractions to users, which ranks tourist attractions based on weighted average by combining the above three scores. Experiments are carried out on Foursquare data sets and results show that our method can perform well on recommendation in home city and new city.

关键词： Urban areas Clustering algorithms Semantics Resource management computer networks Social network services Trajectory

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偏标记学习的研究

偏标记学习的研究

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第六届演化计算与学习研讨会(ECOLE)

作者：张敏灵 School of Computer Science and Engineering MOE Key Lab.of Computer Network & Information Integration Southeast UniversityChina

Traditional Supervised Learning Cnput Space represented by a single instance(feature vector characterizing its properties Dutput Space associated with a single label characterizing its *** Assumption:Strong Supervision label supervision information key factor tor successful learning(encoding semantics and regularities for the learning problen Strong supervision assumption Sufficient labeling abundant labeled training data are available Explicit labeling object labeling is unique and unambiguous.

关键词：偏标记学习流程设计计算性能

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Measuring web page complexity by analyzing TCP flows and HTTP headers

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The Journal of China Universities of Posts and Telecommunications 2017年第6期24卷 1-13页

作者： Cheng Weiqing Hu Yangyang Yin Qiaofeng Chen Jiajia School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Computer Network and Information Integration Southeast University Jiangsu Education Information Center Jiangsu Provincial Department of Education

To understand website complexity deeply, a web page complexity measurement system is developed. The system measures the complexity of a web page at two levels： transport-level and content-level, using a packet trace-based approach rather than server or client logs. Packet traces surpass others in the amount of information contained. Quantitative analyses show that different categories of web pages have different complexity characteristics. Experimental results show that a news web page usually loads much more elements at more accessing levels from much more web servers within diverse administrative domains over much more concurrent transmission control protocol （TCP） flows. About more than half of education pages each only involve a few logical servers, where most of elements of a web page are fetched only from one or two logical servers. The number of content types for web game traffic after login is usually least. The system can help web page designers to design more efficient web pages, and help researchers or Internet users to know communication details.

关键词： hyper text transfer protocol concurrent TCP flows world wide web web page complexity

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Deep octonion networks

arXiv

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arXiv 2019年

作者： Wu, Jiasong Xu, Ling Kong, Youyong Senhadji, Lotfi Shu, Huazhong Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China INSERM U1099 RennesF-35000 France Université de Rennes 1 LTSI RennesF-35042 France

Deep learning is a research hot topic in the field of machine learning. Real-value neural networks (Real NNs), especially deep real networks (DRNs), have been widely used in many research fields. In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions. The octonion algebra, which is an extension of complex algebra and quaternion algebra, can provide more efficient and compact expression. This paper constructs a general framework of deep octonion networks (DONs) and provides the main building blocks of DONs such as octonion convolution, octonion batch normalization and octonion weight initialization;DONs are then used in image classification tasks for CIFAR-10 and CIFAR-100 data sets. Compared with the DRNs, the DCNs, and the DQNs, the proposed DONs have better convergence and higher classification accuracy. The success of DONs is also explained by multi-task learning. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

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Question-answering aspect classification with hierarchical attention network 17th

Question-answering aspect classification with hierarchical a...

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Wu, Hanqian Liu, Mumu Wang, Jingjing Xie, Jue Shen, Chenlin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing China NLP Lab School of Computer Science and Technology Soochow University Suzhou China Southeast University-Monash University Joint Graduate School Suzhou China

ISBN: (纸本)9783030017156

In e-commerce websites, user-generated question-answering text pairs generally contain rich aspect information of products. In this paper, we address a new task, namely Question-answering (QA) aspect classification, which aims to automatically classify the aspect category of a given QA text pair. In particular, we build a high-quality annotated corpus with specifically designed annotation guidelines for QA aspect classification. On this basis, we propose a hierarchical attention network to address the specific challenges in this new task in three stages. Specifically, we firstly segment both question text and answer text into sentences, and then construct (sentence, sentence) units for each QA text pair. Second, we leverage a QA matching attention layer to encode these (sentence, sentence) units in order to capture the aspect matching information between the sentence inside question text and the sentence inside answer text. Finally, we leverage a self-matching attention layer to capture different importance degrees of different (sentence, sentence) units in each QA text pair. Experimental results demonstrate that our proposed hierarchical attention network outperforms some strong baselines for QA aspect classification. © Springer Nature Switzerland AG 2018.

关键词： Text processing

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A Semi-Supervised Deep network Embedding Approach Based on the Neighborhood Structure

A Semi-Supervised Deep Network Embedding Approach Based on t...

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第六届中国计算机学会大数据学术会议

作者： Wenmao Wu Zhizhou Yu Jieyue He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information IntegrationSoutheast UniversityNanjing 210096China

network embedding is a very important task to represent the high-dimensional network in a low-dimensional vector space,which aims to capture and preserve the network *** existing network embedding methods are based on shallow ***,the actual network structure is so complicated that the shallow model can not obtain the high-dimensional nonlinear features of the network *** recently proposed unsupervised deep learning model of network representation learning ignores the labels *** overcome these problems,in this paper,we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding(SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using SVM classifier as a supervised component to improve the ability of ***,we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural *** experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.

关键词： network embedding deep learning semi-supervised network analysis reconstruction

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A Construction of Quantum Error-Locating Codes

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Communications in Theoretical Physics 2017年第1期67卷 37-40页

作者：樊继豪陈汉武 School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 211189China

We present the construction of quantum error-locating(QEL) codes based on classical error-locating(EL)codes. Similar to classical EL codes, QEL codes lie midway between quantum error-correcting codes and quantum errordetecting codes. Then QEL codes can locate qubit errors within one sub-block of the received qubit symbols but do not need to determine the exact locations of the erroneous qubits. We show that, an e-error-locating code derived from an arbitrary binary cyclic code with generator polynomial g(x), can lead to a QEL code with e error-locating abilities, only if g(x) does not contain the(1 + x)-factor.

关键词： quantum error-correcting code error-locating code cyclic code

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