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Knowledge based machine reading comprehension

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arXiv 2018年

作者： Sun, Yibo Guo, Daya Tang, Duyu Duan, Nan Yan, Zhao Feng, Xiaocheng Qin, Bing Harbin Institute of Technology Harbin China Microsoft Research Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Beihang University Beijing China

Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world. However, existing datasets are typically dominated by questions that can be well solved by context matching, which fail to test this capability. To encourage the progress on knowledge-based reasoning in MRC, we present knowledge-based MRC in this paper, and build a new dataset consisting of 40,047 question-answer pairs. The annotation of this dataset is designed so that successfully answering the questions requires understanding and the knowledge involved in a document. We implement a framework consisting of both a question answering model and a question generation model, both of which take the knowledge extracted from the document as well as relevant facts from an external knowledge base such as Freebase/ProBase/Reverb/NELL. Results show that incorporating side information from external KB improves the accuracy of the baseline question answer system. We compare it with a standard MRC model BiDAF, and also provide the difficulty of the dataset and lay out remaining challenges. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge based systems

AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： data acquisition

MIXGAN: Learning concepts from different domains for mixture generation

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arXiv 2018年

作者： Hao, Guang-Yuan Yu, Hong-Xing Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing NUDT China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-theshelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

A data tamper protection method based on block chain 5

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A data tamper protection method based on block chain

5th International Conference on Information Science and Cloud Computing, ISCC 2017

作者： Zhou, Fuken Yu, Zepeng Xie, Yongjie Chen, Tian'en Liao, Yongfa Department of Computer Science Guangdong Neusoft Institute Foshan 528000 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

data centric storage with network as the carrier is vulnerable to external attacks, resulting in data loss and tampering. We propose a method of tamper proofing for block chaining data without centralization and centralization. According to the technology principle of bitcoin block chain, the network data information is protected by block chain encryption technology, and the security of high information tamper resistant is realized. A block chain data storage algorithm based on hash table is hereby proposed, which can get enough address space to save all the data, and make block chain form a time sequence to achieve the safe transmission of data. The research results show that the data protection method has very high tamper resistance ability because of the high security of encrypted digital block chaining technology. The block chain implements the value transfer without relying on centralized organization. © Copyright owned by the author(s).

关键词： Digital storage

Learning fine-grained expressions to solve math word problems

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Learning fine-grained expressions to solve math word problem...

2017 Conference on Empirical Methods in Natural Language processing, EMNLP 2017

作者： Huang, Danqing Shi, Shuming Yin, Jian Lin, Chin-Yew Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Tencent AI Lab United States Microsoft Research United States

ISBN: (纸本)9781945626838

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset. © 2017 Association for Computational Linguistics.

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Soft-gated warping-GAN for pose-guided person image synthesis

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arXiv 2018年

作者： Dong, Haoye Liang, Xiaodan Gong, Ke Lai, Hanjiang Zhu, Jia Yin, Jian School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Intelligent Systems Engineering Sun Yat-sen University School of Computer Science South China Normal University

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints;2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our Warping-GAN that significantly outperforms all existing methods on two large datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

Triangle extension: Efficient localizability detection in wireless sensor networks

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arXiv 2018年

作者： Wu, Hejun Ding, Ao Liu, Weiwei Li, Lvzhou Yang, Zheng Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China Horizon Robotics Beijing China School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability *** Codes 68T40 Copyright © 2018, The Authors. All rights reserved.

关键词： Sensor nodes

P3-359: EARLY DETECTION OF ALZHEIMER'S DISEASE BASED ON DEEP LEARNING

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Alzheimer's & Dementia 2019年第7S_PART_21期15卷

作者： Dan Pan An Zeng Longfei Jia Xiaowei Song Guangdong Construction Polytechnic Guangzhou China Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Simon Fraser University Surrey BC Canada

EARLY DETECTION OF ALZHEIMER'S DISEASE BASED ON DEEP LEARNING

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Alzheimer's & Dementia 2019年第7期15卷 P1082-P1083页