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arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Comparison of immune profiles between hepatocellular carcinoma subtypes

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Biophysics Reports 2020年第1期6卷 19-32页

作者： Xuemin Pan Ping Lin Fangyoumin Feng Jia Li Yuan-Yuan Li Wentao Dai Bo Hu Xin-Rong Yang Jia Fan Hong Li Yixue Li School of Life Sciences and Biotechnology Shanghai Jiao Tong University Bio-Med Big Data Center Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Shanghai Institutes for Biological Sciences University of Chinese Academy of Sciences Chinese Academy of Sciences University of the Chinese Academy of Sciences School of Life Science and Techonology Shanghai Tech University Shanghai Center for Bioinformation Technology Shanghai Academy of Science & Technology Shanghai Engineering Research Center of Pharmaceutical Translation Department of Liver Surgery Liver Cancer Institute Zhongshan Hospital Fudan University Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education Collaborative Innovation Center of Genetics and Development Fudan University

Immunotherapy, especially immune checkpoint inhibitors, is becoming a promising treatment for hepatocellular carcinoma(HCC). However, the response rate remains limited due to the heterogeneity of HCC samples. Molecular subtypes of HCC vary in genomic background, clinical features, and prognosis. This study aims to compare the immune profiles between HCC subtypes and find subtype-specific immune characteristics that might contribute to the prognosis and potential of immunotherapy. The immune profiles consist of immune-related genes, cytolytic activity, immune pathways, and tumorinfiltrating lymphocytes. HCC-c1 samples showed an overall higher activation level of immune genes and pathways, and this pattern was consistent in validation sets. We associated the difference in immune profiles with the activation level of cancer hallmarks and genomic mutations. There was a negative correlation between most of the metabolism pathway and immune-related pathways in HCC samples. CTNNB1/WNT signaling pathway mutation, one of the common mutations in HCC, appears to be associated with the expression of immune genes as well. These results reveal the difference of immune profiles between HCC subtypes and possible reasons and influence, which may also deepen our understanding of the carcinogenesis process.

关键词： Hepatocellular carcinoma Immune Tumor-infiltrating lymphocytes Metabolism CTNNB1 mutation

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A Study on the Differences of Attitude Learning and Learning Behavior Sequences for University Computer MOOC+ SPOC Course

A Study on the Differences of Attitude Learning and Learning...

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International Conference on Computer Science & Education (ICCSE)

作者： Zhao Changhua Mi Chunqiao School of Computer Science and Engineering Huaihua University Huaihua P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua P. R. China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

The outbreak of COVID-19 make University Computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University Computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University Computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning.

关键词： Interviews Electronic learning data analysis Encoding Correlation

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A Regularized LSTM Network for Short-Term Traffic Flow Prediction

A Regularized LSTM Network for Short-Term Traffic Flow Predi...

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International Conference on Information Science and Control engineering (ICISCE)

作者： Zhan Wang Rui Zhu Ming Zheng Xuebin Jia Runfang Wang Tong Li National Pilot School of software Yunnan University School of Big Data Yunnan Agricultural University The Key Laboratory for Software Engineering of Yunnan Province

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

Short-term traffic flow forecast plays an important role in intelligent transportation systems. Existing traffic flow prediction model used deep layer neural network, which can't prevent over fitting, resulting in performance loss and lack of generalization ability. We propose a regularized LSTM model that fused recurrent dropout and max-norm weight constraint. We apply recurrent dropout to the recurrent connections of LSTM network, and use max-norm weight constraint to arrest the input weights not to grow very large. Simultaneously, we merge ADAM optimizer into our model. We use three datasets from different countries. In order to compare with the other researchers in the field of traffic flow prediction, we introduce same features and perform the same time interval prediction task. The experiment results show that our model has the lowest root mean square error and mean absolute error than the basic LSTM and other machine learning model including BP neural network, RNN, stacked autoencoder.

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A spatial correlation based partial coverage scheduling scheme in wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第2期5卷 34-43页

作者： Zhang, Jing Nian, Hongrui Ye, Xiucai Ji, Xiaorong He, Yao College of Information Science and Engineering Institute of Artificial Intelligence Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33th Xueyuan Road University Town Minhou FuzhouFujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Computer Science University of Tsukuba Tsukuba3058573 Japan

Wireless Sensor Networks are formed by a mass of various sensors, they are adopted in lots of applications. Because of the existence of spatial correlation in WSNs, the information from some geographic independence sensors are more significant than that from high correlation ones. Thus, it is unnecessary to transmit information from every sensor to the sink node. This paper focus on the coverage area for data monitoring in WSNs. To construct the virtual backbone network, we propose an efficient connected dominating set construction method based on clique, by taking advantage of the high spatial correlation characteristics of clique construction. Furthermore, by minimizing the number of sensors to activate, we propose a Spatial Correlation based partial Coverage Scheduling Scheme (SCCS) to prolong the network lifetime. The corresponding experiment indicate the efficiency and the feasibility of the proposed SCCS scheme. Moreover, this algorithm is able to guarantee better performance by comparing to the existing schemes. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Scheduling

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Theoretical Analysis on Index System of Ecological Balanced Fertilization for Three Crops

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Meteorological and Environmental Research 2020年第5期11卷 91-95页

作者： Shuojin WANG Changhong MI Zhiping HUANG Shutian LIU Xianda HOU Hongyan ZHENG Jian DING Ling LU Yanlin HOU Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology Nanning Normal UniversityNanning 530001China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal UniversityNanning 530001China Key Laboratory of Environment Change and Resources Use in Beibu Gulf(Nanning Normal University) Nanning 530001China Agro-Environmental Protection Institute Ministry of Agriculture and Rural AffairsTianjin 300191China

This paper analyzed theoretical basis of ecological balanced fertilization by data mining method based on the relationships among relative abundance-deficiency values of soil nutrients,optimum fertilization amounts and nutrient conversion rates of N,P and K for rice,wheat and maize in column *** results and conclusions were as follows:①the scientific basis of guiding fertilization by the relative abundance-deficiency value of soil nutrient was insufficient.②The scientific basis for predicting fertilizer amount according to nutrient conversion rate was sufficient.③There was consistency among the relative abundance-deficiency values of soil nutrients,indicating that the soil nutrient balance was important to the production.④There was some consistency among the optimum fertilizer amounts,indicating that N,P and K fertilizer should be balanced.⑤There was some consistency among nutrient conversion rates,indicating that nutrient uptake was consistent,which required the consistency among relative abundance-deficiency values of soil *** there was inconsistency,it needed to be supplemented by means of fertilization.⑥The relative abundance-deficiency value of soil nutrient and optimum fertilizer amounts of other nutrients were negatively correlated mostly,indicating that if the relative abundance-deficiency value of soil nutrient was high,the others also should be high,resulting in optimum fertilizer amounts of other nutrients decreased.⑦The nutrient conversion rate was negatively correlated mostly with the optimal fertilizer amounts of other nutrients,indicating that more fertilizer amount of one nutrient lowered its own conversion rate,mean while also reduced the conversion rates of other *** embodied the unity and balance of nutrient absorption by the crops.

关键词： Optimum yield Optimum fertilization amount Nutrient conversion rate Fertilizer utilization ratio Relative abundance-deficiency value of soil nutrient

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GL-RF： a reconciliation framework for label-free entity resolution

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Frontiers of Computer Science 2018年第5期12卷 1035-1037页

作者： Yaoli XU Zhanhuai LI Qun CHEN Fengfeng FAN School of Computer Science and Engineering Northwestern Polytechnical University Xi'an 710072 China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information Technology Xi'an 710129 China

1 Introduction Entity resolution （ER）, also referred to as record linkage and entity matching [1], is a long-standing challenge, existing in various data management systems, particularly data integration and cleaning systems. As so often happens, there exist multiple data sources which store duplicate real-world en- tity information in different descriptions, incurred by mis- spellings, typos, diverse name conventions, random usage of the abbreviation or full name, ongoing changes such as in DBpedia, and so forth. The purpose of ER is to determine whether two data records describe the same real-world entity.

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Research on Government Information Sharing Model Using Blockchain Technology

Research on Government Information Sharing Model Using Block...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yiwei Zhang Sanhong Deng Yue Zhang Jia Kong School of Information Management Nanjing University Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China Nanjing University Nanjing China

Research Purpose: The distributed, traceable and security of blockchain technology are applicable to the construction of new government information resource models, which could eliminate the barn effect and trust in government information sharing, as well as promoting the transformation of government affairs from management to service, it is also of great significance to the sharing of government information and construction of service-oriented e-government. Propose Methods: By analyzing the current problems of government information sharing, combined with literature research, this paper proposes the theoretical framework and advantages of blockchain technology applied to government information management and sharing, expounds the blockchain-based solution, it also constructs a government information sharing model based on blockchain, and gives implementation strategies at the technical and management levels. Results and Conclusion: The government information sharing model based on the blockchain solution and the transparency of government information can be used as a research framework for information interaction analysis between the government and users. It can also promote the construction and development of information sharing for Chinese government, as well as providing unified information sharing solution at the departmental and regional levels for e-government.

关键词： Blockchain Information management Security Electronic government Information services data engineering

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VFL: A Verifiable Federated Learning with Privacy-Preserving for big data in Industrial IoT

arXiv

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A Spatial and Temporal Combination Model for Traffic Flow: A Case Study of Beijing Expressway

A Spatial and Temporal Combination Model for Traffic Flow: A...

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IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Wan Liu Yuanli Gu Ying Ding Wenqi Lu Xiaoping Rui Lu Tao Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China Auto Talents Committee of China Talents Society Shanghai China School of Transportation Southeast University Nanjing China School of Earth Sciences and Engineering Hohai University Nanjing China

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

Short-term traffic flow prediction is playing an important role in the intelligent transportation system. However, exploring high-precision and efficient prediction methods is still a challenge. To capture the spatiotemporal characteristics of traffic flow and accurately perceive the traffic state, a spatial and temporal combination (STC) model was proposed. The radial basis function neural network (RBFNN) was used to capture the spatial characteristics of traffic flow, while the clockwork recurrent neural network (CWRNN) was utilized to predict the temporal characteristics. The prediction accuracy of the model can be further improved by the result fusion based on the spatiotemporal feature prediction model. To verify the accuracy and robustness of the algorithm, the Beijing 3rd Ring Road speed data are used to compare with other models. The results show that the accuracy of the STC algorithm is better than benchmark prediction models at different service levels.

关键词： Predictive models data models Adaptation models Roads Analytical models Recurrent neural networks Training

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