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arXiv 2019年

作者： Ju, Fusong Zhu, Jianwei Wei, Guozheng Zhang, Qi Sun, Shiwei Bu, Dongbo Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA) as input and aims to infer structural information from it. Almost all of the existing approaches exploit MSAs in an indirect fashion, i.e., they transform MSAs into position-specific scoring matrices (PSSM) that represent the distribution of amino acid types at each column. PSSM could capture columnwise characteristics of MSA, however, the column-wise characteristics embedded in each individual component sequence were nearly totally neglected. The drawback of PSSM is rooted in the fact that an MSA is essentially an unordered sequence set rather than a matrix. Specifically, the interchange of any two sequences will not affect the whole MSA. In contrast, the pixels in an image essentially form a matrix since any two rows of pixels cannot be interchanged. Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets. Here, we proposed a novel deep neural network framework (called Seq-SetNet) for sequence set processing. By employing a symmetric function module to integrate features calculated from preceding layers, Seq-SetNet are immune to the order of sequences in the input MSA. This advantage enables us to directly and fully exploit MSAs by considering each component protein individually. We evaluated Seq-SetNet by using it to extract structural information from MSA for protein secondary structure prediction. Experimental results on popular benchmark sets suggests that Seq-SetNet outperforms the stateof- the-art approaches by 3.6% in precision. These results clearly suggest the advantages of Seq-SetNet in sequence set processing and it can be readily used in a wide range of fields, say natural language processing. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Social sensing enhanced time estimation for bus service

Social sensing enhanced time estimation for bus service

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作者： Liu, Jin Li, Juan Cui, Xiaohui Niu, Xiaoguang Sun, Xiaoping Zhou, Jing State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. © 2015 John Wiley & Sons, Ltd.

关键词： Buses

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Rate-distortion optimized transform for intra-frame coding

Rate-distortion optimized transform for intra-frame coding

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Zhao Li Zhang Siwei Ma Wen Gao Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Digital Media Peking University Beijing China

In this paper, a novel algorithm is proposed for intra-frame coding, named as rate-distortion optimized transform (RDOT). Unlike existing intra-frame coding schemes where the transform matrices are either fixed or mode dependent, in the proposed algorithm, transform is implemented with multiple candidate transform matrices. With this flexibility, for coding each residual block, the encoder is endowed with the power to select the optimal transform matrix in terms of rate-distortion tradeoff. The proposed algorithm has been implemented in the latest ITU-T VCEG-KTA software. Experimental results show that, over a wide range of test set, the proposed method achieves average 0.43dB coding gain compared with the recent Mode-Dependent Directional Transform (MDDT). The improvement is more significant at high bit-rates, and up to 1dB coding gain can be achieved.

关键词： Transforms Encoding Image coding IP networks Training Decoding Gain

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A Bigtree Index for Resource Space Model

A Bigtree Index for Resource Space Model

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Zhongyi Zeng Hai Zhuge Department of Computer Science and Technology University of Science & Technology of China HefeiChi Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing

This paper presents the Bigtree, which is a hierarchical index for implementing the Resource Space Model.

关键词： Semantics Indexes Data models Humans USA Councils Springs Space technology

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Learning Target-Oriented Dual Attention for Robust RGB-T Tracking

Learning Target-Oriented Dual Attention for Robust RGB-T Tra...

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IEEE International Conference on Image processing

作者： Rui Yang Yabin Zhu Xiao Wang Chenglong Li Jin Tang Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781538662502;9781538662496

RGB-Thermal object tracking attempts to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multimodal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.

关键词： Target tracking Visualization Training Feature extraction Proposals Object tracking Estimation

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Adaptive Adversarial Patch Attack on Face Recognition Models

Adaptive Adversarial Patch Attack on Face Recognition Models

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bei Yan Jie Zhang Zheng Yuan Shiguang Shan Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

Face recognition models have become widely used for identity authentication in scenarios such as cell phone unlocking and financial payment, but they are vulnerable to adversarial examples. Due to the realizability in the physical world, adversarial patch attack has emerged as a significant security threat. However, most existing adversarial patch attack methods focus on only one aspect of patch generation, such as patch location or shape. To overcome this limitation, we propose a novel unified Adaptive Adversarial Patch (AAP) attack framework for targeted attack on face recognition models. Our method comprehensively considers various factors during patch generation, including location, shape, and number. Our approach adaptively selects patch location and number based on saliency map and clustering, while simultaneously deforming patch shape and optimizing perturbations. Extensive experiments under both white-box and black-box settings demonstrate that our proposed method achieves higher attack success rates compared to SOTA methods.

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongshuai Wang Xiaojun Cai Defeng Li Shiwei Sun Cheng Chen Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features.

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High-through put Identification of Protein Disulfide Bonds from Complex Samples

High-through put Identification of Protein Disulfide Bonds f...

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中国化学会首届全国质谱分析学术研讨会

作者： Shan Lu Sheng-Bo Fan Bing Yang Jia-Ming Meng Long Wu Kun Zhang En-Zhi Shen Chun-Qing Song Rui-Xiang Sun Si-Min He Meng-Qiu Dong College of Life Science Beijing Normal University National Institute of Biological Sciences Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Correct disulfide bond formation is essential for the activity and stability of numerous proteins of essential biological functions such as secreted signaling proteins and cell surface *** protein disulfide bonds typically requires a large amount of highly purified proteins,and much relies on arduous sample preparation and manual data *** we report a robust and sensitive method for identifying disulfide bonds in *** the new method,we successfully mapped all 74 disulfide bonds from a mixture often purified proteins and hundreds of disulfide bonds from an *** periplasmic fraction,a *** mitochondrial fraction,and human whole-cell *** biological experiments verified nine out of nine randomly selected,newly identified disulfide bonds in *** proteins.

关键词： High-through put Identification of Protein Disulfide Bonds from Complex Samples

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Self-Mutual Distillation Learning for Continuous Sign Language Recognition

Self-Mutual Distillation Learning for Continuous Sign Langua...

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International Conference on Computer Vision (ICCV)

作者： Aiming Hao Yuecong Min Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665428132

In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal information, and the Connectionist Temporal Classification (CTC) loss is adopted to train the network. However, due to the limitation of chain rules in back-propagation, the visual module is hard to adjust for seeking optimized visual features. As a result, it enforces that the contextual module focuses on contextual information optimization only rather than balancing efficient visual and contextual information. In this paper, we propose a Self-Mutual Knowledge Distillation (SMKD) method, which enforces the visual and contextual modules to focus on short-term and long-term information and enhances the discriminative power of both modules simultaneously. Specifically, the visual and contextual modules share the weights of their corresponding classifiers, and train with CTC loss simultaneously. Moreover, the spike phenomenon widely exists with CTC loss. Although it can help us choose a few of the key frames of a gloss, it does drop other frames in a gloss and makes the visual feature saturation in the early stage. A gloss segmentation is developed to relieve the spike phenomenon and decrease saturation in the visual module. We conduct experiments on two CSLR bench-marks: PHOENIX14 and PHOENIX14-T. Experimental results demonstrate the effectiveness of the SMKD.

关键词： Training Deep learning Visualization Computer vision Gesture recognition Assistive technologies Benchmark testing

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A Group key assignment scheme based on multi-dimensional space circle properties

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International Journal of Advancements in computing Technology 2012年第16期4卷 508-514页

作者： Ge, Lina School of information science and engineering Guangxi University for nationalities Nanning Guangxi 530006 China Science Computing and Intelligent Information Processing of Guangxi higer education key lab. Guangxi Teacher Education University Nanning 530023 China

A group key assignment scheme based on multi-dimensional space circle properties is proposed, which enable the members of a group to obtain the group key by both the public information published on the notice board and the secret information of their own. The proposed scheme can be divided into three phases: user registration, group key assignment, and group key computation. The proposed scheme has such advantages as easy operations of group key update and member's join/leave, and the security properties of forward secrecy and backward secrecy.

关键词： Geometric approach Group key assignment Multi-dimensional space circle properties

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