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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Ying Li Yuling Lu Guangxi Colleges and Universities Key Laboratory of Scientific Computing & Intelligent Information Processing Guangxi Teachers Education University

The existed cloud data models can not well meet the management requirements of structured data including a great deal of relational data,therefore a two-layer cloud data model consisting of a presentation layer and a storage layer is *** the presentation layer,the conception of composite class and composite object are defined to represent the structure and the data of structured data *** the storage layer,a composite class in the presentation layer is transformed into a3-tuple which preserves data structure in mappings between composite attributes and their sub-attributes by using a rule set;and a composite object in the presentation layer is transformed into another 3-tuple which preserves a structured data set in simple objects without embed objects by using a CAO(Component-Attribute-Object) set in which each element mainly consists of a simple object and the identification of its father *** order to store data in key-value model,a method to convert a CAO to a pair of key-value is ***,two algorithms were proposed to convert data between the representation layer and the storage *** experiment shows the two conversion algorithms are *** proposed model is fit for the management requirements of structured data in the cloud because composite object can represent structured data and avoid join operation of relation data and CAO can be stored in key-value model.

关键词： cloud database data model relational data keyvalue structured data

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Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders

Occlusion-free Face Alignment: Deep Regression Networks Coup...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Jie Zhang Meina Kan Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781467388528

Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.

关键词： Occlusion Face Face occluding landmarks Alignment Regression (Psychology) Facial Recognition images computer vision coupling

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Emotion detection from natural walking 1

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2nd International Conference on Human Centered computing, HCC 2016

作者： Cui, Liqing Li, Shun Zhu, Tingshao Beijing100101 China National Computer System Engineering Research Institute of China Beijing100083 China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783319318547

ISBN: (纸本)9783319318530

Emotion identification, which aims to determine a person’s affective state automatically, has immense potential value in many areas, such as action tendency, health care, psychological detection and human-computer (robot) interaction. In this paper, we propose a novel method for identifying emotion from natural walking. After obtaining the three-axis acceleration data of wrist and ankle recorded by smartphone, we run a moving average filter window with different length w, then cut actual data into slices. 114 features are extracted from each slice, and Principal Component Analysis (PCA) is used for feature selection. We train SVM, Decision Tree, Multilayerperception, Random Tree and Random Forest classifiers, and compare the accuracy of emotion identification using different datasets (wrist vs. ankle) on different models. Results show that acceleration data from ankle has better performance in emotion identification than wrist. Among these models, SVM has the highest accuracy of 90.31 % for identifying anger vs. neutral, 89.76 % for happy vs. neutral, and 87.10 % for anger vs. happy. The model for identifying anger/neutral/happy yields the best accuracy of 85 %-78 %-78 %. The results show that we could identify people’s emotional states through the gait of walking with high accuracy. © Springer International Publishing Switzerland 2016.

关键词： Smartphones

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One-shot fine-grained instance retrieval

arXiv

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arXiv 2017年

作者： Yao, Hantao Zhang, Shiliang Zhang, Yongdong Li, Jintao Tian, Qi Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Electronic Engineering and Computer Science Peking University Beijing100871 China Department of Computer Science University of Texas at San Antonio San Antonio United States

Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification. Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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Interactive attention for neural machine translation 26

Interactive attention for neural machine translation

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26th International Conference on Computational Linguistics, COLING 2016

作者： Meng, Fandong Lu, Zhengdong Li, Hang Liu, Qun AI Platform Department Tencent Technology Co. Ltd. China Noah's Ark Lab. Huawei Technologies China ADAPT Centre School of Computing Dublin City University Ireland Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Ireland

ISBN: (纸本)9784879747020

Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets. © 1963-2018 ACL.

关键词： Neural machine translation

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Learning label-Specific Features and Class-Dependent labels for Multi-label Classification

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IEEE Transactions on Knowledge and Data Engineering 2016年第99期PP卷 3309-3323页

作者： Huang, Jun Li, Guorong Huang, Qingming Wu, Xindong School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101480 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer Science University of Vermont 33 Colchester Avenue BurlingtonVT05405 United States

Binary Relevance is a well-known framework for multi-label classification, which considers each class label as a binary classification problem. Many existing multi-label algorithms are constructed within this framework and utilize identical data representation in the discrimination of all the class labels. In multi-label classification, however, each class label might be determined by some specific characteristics of its own. In this paper, we seek to learn label-specific data representation for each class label, which is composed of label-specific features. Our proposed method LLSF can not only be utilized for multi-label classification directly, but also be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Inspired by the research works on modeling high-order label correlations, we further extend LLSF to learn class-Dependent labels in a sparse stacking way, denoted as LLSFDL. It incorporates both second-order and high-order label correlations. A comparative study with the state-of-the-art approaches manifests the effectiveness and efficiency of our proposed methods. © 2016 IEEE.

关键词： Classification (of information)

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Online Bayesian multiple kernel bipartite ranking 32

Online Bayesian multiple kernel bipartite ranking

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32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016

作者： Du, Changying Du, Changde Long, Guoping He, Qing Li, Yucheng Beijing100190 China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China Key Laboratory of Optical Astronomy National Astronomical Observatories CAS Beijing100012 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510827806

Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.

关键词： Budget control

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Cross-Layer Power Allocation Scheme for Two-Way Amplify-and-Forward Relaying System

Cross-Layer Power Allocation Scheme for Two-Way Amplify-and-...

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Quan Yuan Yanjun Hu Hui Zhi Mengmeng He Key Lab of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University

Two-way relaying can considerably improve spectral efficiency in relay-assisted bidirectional ***,most existing works focus on physical layer to exploit its resource *** benefits and flexible structures of two-way relaying on datalink layer are much less *** in this paper,the cross-layer optimization for amplify-and-forward(AF)two-way relaying system is studied and an optimal power allocation scheme is *** goal is to find the optimal power allocation factors to maximize the weighted sum effective capacity of two sources in the physical layer while guaranteeing the statistical delay quality-of-service(QoS)requirement for each source in the datalink *** statistical delay Qo S requirement is characterized by the QoS exponentq,which is the only requested information exchanged between the physical layer and the datalink layer in our cross-layer design based *** integrating the concept of effective capacity,the cross-layer optimization problem is equivalent to a weighted sum effective capacity maximization *** problem is formulated into a convex optimization problem,and an algorithm is presented to solve this *** results show that proposed optimal power allocation scheme can achieve better weighted sum effective capacity than equal power allocation scheme and direct transmission scheme,and the relay transmission combined with direct links can get the max weighted sum effective capacity.

关键词： two-way relaying quality-of-service(Qo S) cross-layer optimization power allocation effective capacity

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Cross-Layer Power Allocation Scheme for Wireless Full Duplex Relaying System

Cross-Layer Power Allocation Scheme for Wireless Full Duplex...

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Mengmeng He Yanjun Hu Hui Zhi Quan Yuan Key Lab of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University

In this paper,we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(Qo S)*** formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective *** our scheme,we focus on full duplex mode and amplify-and-forward(AF)*** particular,our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying *** comparison purpose,we also give the analysis of half duplex relaying *** results show that our proposed power allocation scheme can support diverse Qo S guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission *** analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.

关键词： power allocation scheme effective capacity full duplex quality-of-service(Qo S) amplify-and-forward(AF)

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GLAD: Global-local-alignment descriptor for pedestrian retrieval

arXiv

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arXiv 2017年

作者： Wei, Longhui Zhang, Shiliang Yao, Hantao Gao, Wen Tian, Qi School of Electronic Engineering and Computer Science Peking University Beijing100871 China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID systems are required to cope with the massive visual data being produced by video surveillance systems. Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively. GLAD explicitly leverages the local and global cues in human body to generate a discriminative and robust representation. It consists of part extraction and descriptor learning modules, where several part regions are first detected and then deep neural networks are designed for representation learning on both the local and global regions. A hierarchical indexing and retrieval framework is designed to eliminate the huge redundancy in the gallery set, and accelerate the online Re-ID procedure. Extensive experimental results show GLAD achieves competitive accuracy compared to the state-of-the-art methods. Our retrieval framework significantly accelerates the online Re-ID procedure without loss of accuracy. Therefore, this work has potential to work better on person Re-ID tasks in real scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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