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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

Journal of Physics: Conference Series 2019年第1期1284卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology 710048 Xi'an China Telematics Group the University of Goettingen 37077 Goettingen Germany Shaanxi Key Laboratory of Network Computing and Security 710048 Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University 710048 Xi'an China College of Xi'an Innovation Yan'an University 710100 Xi'an China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase

Implementing an Inference Engine for RDFS/OWL Constructs and...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Zhengbo Liu Wenbin Yao Dongbin Wang National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.

关键词： Engines Ontologies Semantics Cognition Resource description framework OWL Databases

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Multi-task representation learning for demographic prediction 38th

Multi-task representation learning for demographic predictio...

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38th European Conference on Information Retrieval Research, ECIR 2016

作者： Wang, Pengfei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China

ISBN: (纸本)9783319306704

Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require professional knowledge and often suffer from under specification. Meanwhile, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. In this paper, we propose a novel Multi-task Representation Learning (MTRL) model to predict users’ demographic attributes. Comparing with the previous methods, our model conveys the following merits: (1) By using a multitask approach to learn the tasks, our model leverages the large amounts of cross-task data, which is helpful to the task with limited data;(2) MTRL uses a supervised way to learn the shared semantic representation across multiple tasks, thus it can obtain a more general and robust representation by considering the constraints among tasks. Experiments are conducted on a real-world retail dataset where three attributes (gender, marital status, and education background) are predicted. The empirical results show that our MTRL model can improve the performance significantly compared with the state-of-the-art baselines. © Springer International Publishing Switzerland 2016.

关键词： Semantics

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Low-resolution face recognition in the wild via selective knowledge distillation

arXiv

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arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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Anomaly Detection for Application Layer User Browsing Behavior Based on Attributes and Features

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Xiong Luo Xiaoqiang Di Xu Liu Hui Qi Jinqing Li Ligang Cong Huamin Yang School of Computer Science and Technology Changchun University of Science and Technology Changchun 130022 China Jilin Province Key Laboratory of Network and Information Security Changchun 130022 China

Application layer distributed denial of service (App-DDoS) attacks has posed a great threat to the security of the Internet. Since these attacks occur in the application layer, they can easily evade traditional network layer and transport layer detection methods. In this paper, we extract a group of user behavior attributes from our intercept program instead of web server logs and construct a behavior feature matrix based on nine user behavior features to characterize user behavior. Subsequently, principal component analysis (PCA) is applied to profile the user browsing behavior pattern in the feature matrix and outliers from the pattern are used to recognize normal users and attackers. Experiment results show that the proposed method is good to distinguish normal users and attackers. Finally, we implement three machine learning algorithms (K-means, DBSCAN and SVM) to further validate the effectiveness of the proposed attributes and features.

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Yue Wang Xiaoqiang Di Jinqing Li Huamin Yang Lin Bi School of Computer Science and Technology Changchun University of Science and Technology Changchun 130022 China Jilin Province Key Laboratory of Network and Information Security Changchun 130022 China

Sentence similarity learning is a vital task in Natural Language Processing (NLP) such as document summarization and question answering. In this paper, we propose a method to compute semantic similarity between sentences which is based on the attention hybrid model. Our method utilizes Bidirectional Long Short-Term Memory networks (BLSTM) and Convolutional Neural networks (CNN) to extract the semantic features of a sentence. And it learns the representation of each sentence with word-level attention. Then the attentive representations are concatenated and fed into the output layer to compute the score of sentences similarity. Finally, the public datasets of the Quora is used to test the proposed method and experiment results show that our method is effective and outperforms other methods.

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Quantum private comparison based on quantum dense coding

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Science China(Information Sciences) 2016年第11期59卷 195-207页

作者： Feng WANG Mingxing LUO Huiran LI Zhiguo QU Xiaojun WANG College of Mathematical Sciences Dezhou University Information Security and National Computing Grid Laboratory Southwest Jiaotong University Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology School of Electronic Engineering Dublin City University

A serious problem in cloud computing is privacy information protection. This study proposes a new private comparison protocol using Einstein-Podolsky-Rosen(EPR) pairs. This protocol allows two parties to secretly compare their classical information. Quantum dense coding enables the comparison task to be completed with the help of a classical semi-honest center. A one-step transmission scheme and designed decoy photons can be used against various quantum attacks. The new protocol can ensure fairness, efficiency, and security. The classical semi-honest center cannot learn any information about the private inputs of the players. Moreover, this scheme can be easily generalized using the general EPR pairs in order to improve the transmission efficiency.

关键词： private comparison multiparty secure computation classical semi-honesty center quantum dense coding general EPR pair

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Achieve Personalized Anonymity Through Query Blocks Exchanging

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China Communications 2016年第11期13卷 106-118页

作者： Chunguang Ma Lei Zhang Songtao Yang Xiaodong Zheng Pinhui Ke College of Computer Science and Technology Harbin Engineering University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University College of Information and Electronic Technology Jiamusi University College of Applied Technology Qiqihar University

In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.

关键词： cyberspace security location-based services collaborative users personalized anonymity query and location privacy

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Improved quantum ripple-carry addition circuit

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Science China(Information Sciences) 2016年第4期59卷 104-111页

作者： Feng WANG Mingxing LUO Huiran LI Zhiguo QU Xiaojun WANG College of mathematical sciences Dezhou University Information Security and National Computing Grid Laboratory Southwest Jiaotong University Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology School of Electronic Engineering Dublin City University

A serious obstacle to large-scale quantum algorithms is the large number of elementary gates, such as the controlled-NOT gate or Toffoli gate. Herein, we present an improved linear-depth ripple-carry quantum addition circuit, which is an elementary circuit used for quantum computations. Compared with previous addition circuits costing at least two Toffoli gates for each bit of output, the proposed adder uses only a single Toffoli gate. Moreover, our circuit may be used to construct reversible circuits for modular multiplication, Cx mod M with x < M, arising as components of Shor’s algorithm. Our modular-multiplication circuits are simpler than previous constructions, and may be used as primitive circuits for quantum computations.

关键词： quantum addition circuit modular multiplication CNOT gate Toffoli gate circuit complexity

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Survey on Redactable Signatures

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2017年第10期54卷 2144-2152页

作者： Ma, Jinhua Liu, Jianghua Wu, Wei Huang, Xinyi School of Mathematics and Information Fujian Normal University Fuzhou350007 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350007 China School of Information Technology Deakin University Melbourne3125 Australia

Data security issues have become a serious challenge to national economic, political, defence and cultural security. As a core technology in protecting data security, digital signatures have been widely used for the verification of data integrity and source authenticity. The security definition of conventional digital signatures is existentially unforgeable against adaptive chosen-message attacks. Although it meets the basic security requirement of data authentication, it hampers the reasonable operation of authenticated data which is desirable in many practical applications. As a type of malleable homomorphic signatures for editing, redactable signatures allow the signature holder (redactor) to delete sensitive portions of the signed data and generate a valid signature for the disclosed data without any help from the original signer. It has been a research hotspot in the field of cryptography since it was introduced in 2001. In recent years, many researchers have studied redactable signatures from the aspects of formal security definition, redaction control mechanism, computational cost and communication overhead, and there are lots of research results. However, the rapid development of network technology and its applications are putting forward new challenges to redactable signatures. This paper summarizes and analyses redactable signatures in terms of algorithm definition, security model and representative designs. Furthermore, some existing problems worthy of further study are also discussed. © 2017, Science Press. All right reserved.

关键词： Authentication

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