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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Cui, Jianzhong Yin, Zhixiang Yang, Jing Geng, Xianya School of Electronic and Information Engineering AnHui University of Science and Technology Huainan AnHui232001 China School of Mathematics and Big Data AnHui University of Science and Technology Huainan AnHui232001 China Department of Computer Huainan Union University Huainan AnHui232001 China

ISBN: (纸本)9781538680971

Scaffolded DNA origami, a long single-stranded DNA is folded into pre-designed pattern with the guidance of short staple strands, enables the fabrication of sophisticated nanodevice. Inspired by this nanotechnology, we presented searching the maximum clique in an undirected graph with six vertices and eleven edges. We initially folded a unique type of scaffolds into a pool of strands with n hairpin-like structures, or hairpin for convenience, which corresponded to the total ensemble of six vertices cliques. Followed by, re-folded scaffolds by staples to perform a series of selection processes according to edges in the complementary graph. Scaffolds encoding correct cliques were selected via gel electrophoresis. When this selection processes terminated, all cliques were found. Scaffold with the maximum number of in-clique hairpins was selected by unfolding simultaneously all in-clique hairpins, followed by gel electrophoresis. Then a series of unfolding out-of-clique hairpins one by one via unfolding strands, each vertex in the given graph was decided whether it is in the maximum clique or not. When deciding processes terminated, the maximum clique in the given graph was eventually found. The proposed method employed relatively reliable biological operations. Gel electrophoresis guarantees the accuracy of each step of the proposed method. Therefore, the method proposed is reliable, error tolerant, and feasible. Our work demonstrates, with simple designs for scaffold, staples, and unfolding strands, NP-complete searching problem can be readily and reliably solved by using DNA origami. The work extends application fields of DNA origami a step further. © 2018 IEEE.

关键词： DNA

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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Meta mask correction for nuclei segmentation in histopathological image

arXiv

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arXiv 2021年

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao Tong University Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'an Jiao tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710061 China

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021, CC BY.

关键词： Deep learning

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Explainable fashion recommendation: A semantic attribute region guided approach

arXiv

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arXiv 2019年

作者： Hou, Min Wu, Le Chen, Enhong Li, Zhi Zheng, Vincent W. Liu, Qi Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China School of Data Science University of S&T of China Hefei University of Technology WeBank

In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on realworld datasets clearly demonstrate the effectiveness of our approach compared with the state-ofthe-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Relation-Guided Representation Learning

arXiv

引用

arXiv 2020年

作者： Kang, Zhao Lu, Xiao Liang, Jian Bai, Kun Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Cloud and Smart Industries Group Tencent Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Center for Artificial Intelligence Peng Cheng Lab Shenzhen China

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most dominant structures which are able to reconstruct the data from a latent space and neglect rich latent structural information. In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning. Different from previous work, our framework well preserves the relations between samples. Since the prediction of pairwise relations themselves is a fundamental problem, our model adaptively learns them from data. This provides much flexibility to encode real data manifold. The important role of relation and representation learning is evaluated on the clustering task. Extensive experiments on benchmark data sets demonstrate the superiority of our approach. By seeking to embed samples into subspace, we further show that our method can address the large-scale and out-of-sample problem. Our source code is publicly available at: https://***/nbShawnLu/RGRL. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV

arXiv

引用

arXiv 2020年

作者： Zhou, Tao Liu, Quanhui Yang, Zimo Liao, Jingyi Yang, Kexin Bai, Wei Lu, Xin Zhang, Wei Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science Sichuan University Chengdu610065 China Beijing AiQiYi Science & Technology Co. Ltd. Beijing100080 China Shenzhen International Graduate School Tsinghua University Shenzhen518055 China CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China College of Systems Engineering National University of Defense Technology Changsha410073 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610047 China

Objectives: To estimate the basic reproduction number of the Wuhan novel coronavirus (2019-nCoV). Methods: Based on the susceptible-exposed-infected-removed (SEIR) compartment model and the assumption that the infectious cases with symptoms occurred before 26 January, 2020 are resulted from free propagation without intervention, we estimate the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as the theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome. Results: The basic reproduction number fall between 2.8 to 3.3 by using the real-time reports on the number of 2019-nCoV infected cases from People's Daily in China and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues. Using a newly reported epidemiological determinants for early 2019-nCoV, the estimated basic reproduction number is in the range [2.2,3.0]. Conclusions: The early transmission ability of 2019-nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate-high transmissibility. Timely and effective control measures are needed to suppress the further transmissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent.

arXiv

引用

arXiv 2019年

作者： Hu, Wenqing Zhu, Zhanxing Xiong, Haoyi Huan, Jun Department of Mathematics and Statistics Missouri University of Science and Technology University of Missouri Rolla Peking University Beijing Institute of Big Data Research Beijing China Big Data Lab Baidu Inc. National Engineering Laboratory of Deep Learning Application and Technology

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the quasi-potential. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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A simple image encryption algorithm based on logistic map 5th

A simple image encryption algorithm based on logistic map

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5th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2018

作者： Wu, Tsu-Yang Wang, King-Hang Chen, Chien-Ming Wu, Jimmy Ming-Tai Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shenzhen518055 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783030037659

Based on the better properties of chaotic maps, the studies of chaotic-based image encryption have received much attentions by researchers in recent years. In this paper, an image encryption algorithm based on logistic map with substitution approach is proposed. Meanwhile, we make the line chart, histogram, and pixel loss analysis to show the performance of our algorithm. © Springer Nature Switzerland AG 2019.

关键词： Cryptography

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Hierarchical semantic approximate multi-keyword ranked search over encrypted data 2nd

Hierarchical semantic approximate multi-keyword ranked searc...

引用

2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Chen, Chien-Ming Zhang, Wenhao Wu, Tsu-Yang Wang, King-Hang Wu, Jimmy Ming-Tai Pan, Jeng-Shyang Shenzhen518055 China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783030045845

With the rapid development of Cloud Computing, how to improve users search experience over encrypted data just like plaintext search has become a challenging task. In this paper, we propose a hierarchical semantic approximate multi-keyword ranked search (HSAMS) scheme over encrypted data. Performance analysis is demonstrated that our proposed scheme is efficient and effective while supporting semantic approximate match. © Springer Nature Switzerland AG 2019.

关键词： Semantics

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