检索结果-内蒙古大学图书馆

Towards a semantic keyword search over industrial knowledge graphs (extended abstract)

学校读者我要写书评

暂无评论

Towards a semantic keyword search over industrial knowledge ...

IEEE International Conference on Big Data

作者： Gong Cheng Evgeny Kharlamov National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science University of Oxford Oxford United Kingdom

Knowlege graphs have become powerful assets for enhancing search and data integration and are now widely used in both academia and industry. Existing semantics of keyword search over knowledge graphs, especially in industrial context, has limitations. In this extended abstract we discuss these limitations and ingredients for alternative semantics.

关键词： Semantics keyword search Syntactics Ontologies Oils Resource description framework

A privacy-aware two-factor authentication protocol based on elliptic curve cryptography for wireless sensor networks

学校读者我要写书评

暂无评论

A privacy-aware two-factor authentication protocol based on ...

作者： Jiang, Qi Kumar, Neeraj Ma, Jianfeng Shen, Jian He, Debiao Chilamkurti, Naveen School of Cyber Engineering Xidian University Xi'an China Co-innovation Center for Information Supply and Assurance Technology Anhui University Hefei China Department of Computer Science and Engineering Thapar University Patiala India School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Lab of Software Engineering Computer School Wuhan University Wuhan China Department of Computer Systems and Computer Science La Trobe University Melbourne Australia

As one of the enabling components of Internet of things (IoT), wireless sensor networks (WSNs) have found applications in a wide range of fields, in which outside users need to directly interact with sensors to obtain sensed data. However, WSNs are vulnerable to various attacks over wireless links, such as eavesdropping and tampering. How to ensure that sensitive or critical information is only available to legal users becomes a challenging issue. Two-factor authentication combining password and smart card perfectly matches this requirement because of its practicality. However, a dozen of two-factor authentication protocols have been suggested in recent years. Owing to the challenging mission of reconciling efficiency and security requirements, it is still challenging to propose a privacy-aware two-factor protocol that is capable of providing various security features while maintaining acceptable efficiency. In this paper, we put forward a privacy-aware two-factor authentication protocol based on elliptic curve cryptography for WSNs. Our new protocol accomplishes various security features necessary for real-life application environments while maintaining acceptable efficiency. We prove that the new protocol fulfills mutual authentication in the Burrows–Abadi–Needham logic. Additionally, by way of informal security analysis, we show that the new protocol can withstand a variety of attacks and provide desirable security features. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Wireless sensor networks

A Game-Based Approach for PCTL＊ Stochastic Model Checking with Evidence

学校读者我要写书评

暂无评论

Journal of computer science & technology 2016年第1期31卷 198-216页

作者： Yang Liu Xuan-Dong Li Yan Ma State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science School of Computing National University of Singapore Singapore 117417 Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

Stochastic model checking is a recent extension and generalization of the classical model checking, which focuses on quantitatively checking the temporal property of a system model. PCTL＊ is one of the important quantitative property specification languages, which is strictly more expressive than either PCTL （probabilistic computation tree logic） or LTL （linear temporal logic） with probability bounds. At present, PCTL＊ stochastic model checking algorithm is very complicated, and cannot provide any relevant explanation of why a formula does or does not hold in a given model. For dealing with this problem, an intuitive and succinct approach for PCTL＊ stochastic model checking with evidence is put forward in this paper, which includes： presenting the game semantics for PCTL＊ in release-PNF （release-positive normal form）, defining the PCTL＊ stochastic model checking game, using strategy solving in game to achieve the PCTL＊ stochastic model checking, and refining winning strategy as the evidence to certify stochastic model checking result. The soundness and the completeness of game-based PCTL＊ stochastic model checking are proved, and its complexity matches the known lower and upper bounds. The game-based PCTL＊ stochastic model checking algorithm is implemented in a visual prototype tool, and its feasibility is demonstrated by an illustrative example.

关键词： PCTL＊ stochastic model checking game semantics strategy evidence

Feature learning based deep supervised hashing with pairwise labels 25

学校读者我要写书评

暂无评论

Feature learning based deep supervised hashing with pairwise...

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Li, Wu-Jun Wang, Sheng Kang, Wang-Cheng National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on handcrafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand-crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning. In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing (DPSH), to perform simultaneous feature learning and hashcode learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.

关键词： Image retrieval

Improved neural machine translation with a syntax-aware encoder and decoder

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Chen, Huadong Huang, Shujian Chiang, David Chen, Jiajun State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Engineering University of Notre Dame

Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations;(2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural machine translation

Frequency synchronization for uplink massive MIMO with adaptive MUI suppression in angle-domain

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Ge, Yinghao Zhang, Weile Gao, Feifei Li, Geoffrey Ye MOE Key Lab for Intelligent Networks and Network Security School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) multiuser network, which is applicable for users with either separate or overlapped angle-of-arrival (AoA) regions. For each user, we first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-filter (MF) beamformers pointing to the AoAs of the interested user. Then, the adaptive beamformer can be acquired by appropriately designing the ADAF vectors. Such beamformer can achieve two-stage adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing the MUI from non-overlapping users by ACM in the first stage and substantially mitigating the MUI from adjacent overlapping users by ADAF vector in the second stage. For both separate and mutually overlapping users, the carrier frequency offset (CFO) estimation and subsequent data detection can be performed independently for each user, which significantly reduces computational complexity. Moreover, ADAF is rather robust to imperfect AoA knowledge and side cluster of scatterers, making itself promising for multiuser uplink transmission. We also analyze the performance of CFO estimation and obtain an approximated closed-form expression of mean-squared error (MSE). The effectiveness of the proposed method and its superiority over the existing methods are demonstrated by numerical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Adaptive filters

Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Zhang, Yangsong Yin, Erwei Li, Fali Zhang, Yu Guo, Daqing Yao, Dezhong Xu, Peng School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China National Institute of Defense Technology Innovation Academy of Military Sciences China Beijing100081 China Tianjin300450 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu610054 China Department of Psychiatry and Behavior Sciences Stanford University StanfordCA94305 United States School of life Science and technology Center for information in medicine University of Electronic Science and Technology of China Chengdu611731 China

Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary technique for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects and indicate that the extended CORRCA method used within the framework significantly outperforms the original CORCCA method. Accordingly, the proposed framework holds promise to enhance the performance of frequency recognition methods in SSVEP-based BCIs. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

Top-rank enhanced listwise optimization for statistical machine translation

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Chen, Huadong Huang, Shujian Chiang, David Dai, Xinyu Chen, Jiajun State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Engineering University of Notre Dame

Pairwise ranking methods are the basis of many widely used discriminative training approaches for structure prediction problems in natural language processing (NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list's ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality. Copyright © 2017, The Authors. All rights reserved.

关键词： computer aided language translation

Midterm periodicity analysis of the mount wilson magnetic indices using the Synchrosqueezing transform

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Feng, Song Yu, Lan Wang, Feng Deng, Hui Yang, Yunfei Yunnan Key Lab. of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Cas Key Laboratory of Solar Activity National Astronomical Observatories Beijing100012 China Department of Mechanical and Electrical Engineering Yunnan Land and Resources Vocational College Kunming650217 China

A novel time-frequency technique, called the synchrosqueezing transform (SST), is used to investigate the midterm periodic variations of magnetic fields on the solar surface. The Magnetic Plage Strength Index (MPSI) and the Mount Wilson Sunspot Index (MWSI), measured daily by the Mount Wilson Observatory between 1970 January 19 and 2012 January 22, are selected. The short-, mid, and longer-term periodicities are represented and decomposed by the SST with hardly any mode mixing. This demonstrates that the SST is a useful time-frequency analysis technique to characterize the periodic modes of helioseismic data. Apart from the fundamental modes of the annual periodicity, ∼27 day rotational cycle and ∼11 year solar cycle, the SST reveals several midterm periodicities in the two magnetic activity indices, specifically, ∼157 day (i.e., Rieger-type periodicity), and ∼1.3 and 1.7 years. The periodic modes, with 116.4 and 276.2 day periodicity in the MPSI, with 108.5 and 251.6 day periodicity in the MWSI, and the 157.7 day periodicity in the two indices, are in better accord with those significant periodicities derived from the Rossby waves theoretical model. This study suggests that the modes are caused by the Rossby waves. For the 1.30 and 1.71 year periodicity of the MPSI, and the 1.33 and 1.67 year periodicity of the MWSI, our analysis infers that they are related to those periodicity with the same timescale in the interior of the Sun and in the high atmospheric layers. Copyright © 2018, The Authors. All rights reserved.

关键词： Mechanical waves