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9th International Symposium on Intelligence Computation and Applications, ISICA 2017

作者： Zhan, Dongming Xie, Chengwang School of Software East China Jiaotong University Nanchang330013 China Guangxi Colleges and Universities Key Laboratory of Scientific Computing and Intelligent Information Processing Guangxi Teachers Education University Nanning530023 China

ISBN: (纸本)9789811316470

In the real world, there exist numerous multi-objective optimization problems (MOPs), and some representative multi-objective optimizers are proposed to meet the challenge. In this paper, an improved multi-objective fireworks algorithm (iMOFA) is presented to solve some complicated continuous MOPs. There are three significant features employed in iMOFA. Firstly, an initialization approach of homogenization with randomization is used to generate an even distributed initial population. Secondly, a method of fine-grained controlling radius is adopted to improve the search efficiency. In addition, a strategy of simplified k-nearest neighbor is utilized to maintain the diversity of external archive. Our iMOFA is compared with the other four multi-objective evolutionary algorithms (MOEAs) using IGD performance indicator on 12 benchmark MOP instances, and the numerical results show that the proposed iMOFA has the overall performance advantages in convergence and diversity. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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Corrigendum to “Discovering and learning sensational episodes of news events” [Inf. Syst. 78 (2018) 68–80]

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information Systems 2020年 93卷

作者： Xiang Ao Ping Luo Chengkai Li Fuzhen Zhuang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Texas at Arlington USA

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Mapping disulfide bonds from sub-micrograms of purified proteins or micrograms of complex protein mixtures

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Biophysics Reports 2018年第2期4卷 68-81页

作者： Shan Lu Yong Cao Sheng-Bo Fan Zhen-Lin Chen Run-Qian Fang Si-Min He Meng-Qiu Dong National Institute of Biological Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CAS Institute of Computing Technology CAS University of Chinese Academy of Sciences

Disulfide bonds are vital for protein functions, but locating the linkage sites has been a challenge in protein chemistry, especially when the quantity of a sample is small or the complexity is high. In 2015,our laboratory developed a sensitive and efficient method for mapping protein disulfide bonds from simple or complex samples(Lu et al. in Nat Methods 12:329, 2015). This method is based on liquid chromatography–mass spectrometry(LC–MS) and a powerful data analysis software tool named p *** facilitate application of this method, we present step-by-step disulfide mapping protocols for three types of samples—purified proteins in solution, proteins in SDS-PAGE gels, and complex protein mixtures in solution. The minimum amount of protein required for this method can be as low as several hundred nanograms for purified proteins, or tens of micrograms for a mixture of hundreds of *** entire workflow—from sample preparation to LC–MS and data analysis—is described in great detail. We believe that this protocol can be easily implemented in any laboratory with access to a fastscanning, high-resolution, and accurate-mass LC–MS system.

关键词： Cross-linking Disulfide Identification of disulfide bonds Mass spectrometry PLink PLink-SS

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Automatic Generation of Survey Paper Based on Template Tree

Automatic Generation of Survey Paper Based on Template Tree

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Xiaoping Sun Hai Zhuge Knowledge Grid Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China KLIIP Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences China Aston University Birmingham UK

ISBN: (数字)9781728158235

ISBN: (纸本)9781728158242

With rapid expansion of scientific papers, making a survey from a large collection of papers on a given research issue or domain becomes more and more important for researchers. This paper proposes a template-based framework for automatically generating survey paper. It allows users to compose a template tree as a syllabus for the required survey. Each tree node corresponds to a section to be composed in the survey therefore the whole tree defines the section structure of the survey. The template consists of two types of nodes, dimension node and topic node, which filter contents of papers. A recursive procedure along the survey generation template tree paths is conducted to process documents, rank sentences, and compose sections. We apply the approach to generating the survey of the reference papers of a survey paper and compare the result with the survey paper. Experiments show improvement over several baseline methods.

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Edge-guided non-local fully convolutional network for salient object detection

arXiv

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arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach

Identifying High Potential Talent: A Neural Network Based Dy...

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IEEE International Conference on Data Mining (ICDM)

作者： Yuyang Ye Hengshu Zhu Tong Xu Fuzhen Zhuang Runlong Yu Hui Xiong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

关键词： Social networking (online) Companies Artificial neural networks Germanium Engineering profession

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Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

Fully Learnable Group Convolution for Acceleration of Deep N...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xijun Wang Meina Kan Shiguang Shan 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 CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (纸本)9781728132945

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.

关键词： acceleration Convolution numerical invariant Group Structure Neural network

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Predicting compositional time series via autoregressive Dirichlet estimation

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Science China(information Sciences) 2018年第9期61卷 300-305页

作者： Ganbin ZHOU Ping LUO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

In recent years,compositional time series(CTS)prediction has become a widely applied data analysis method for modeling tactile sequence data[1],hydrological time series data using a four-

关键词： CTS Predicting compositional time series via autoregressive Dirichlet estimation

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Direct speech-to-image translation

arXiv

引用

arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Video rain/snow removal by transformed online multiscale convolutional sparse coding

arXiv

引用

arXiv 2019年

作者： Li, Minghan Cao, Xiangyong Zhao, Qian Zhang, Lei Gao, Chenqiang Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well encodes their temporally dynamic configurations by real-time ameliorated parameters in the model. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the ADMM algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves better performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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