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arXiv 2021年

作者： Wang, Shipeng Li, Xiaorong Sun, Jian Xu, Zongben School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China National Engineering Laboratory of Big Data Algorithms and Analysis Technology Xi’an710049 China Pazhou Lab Guangzhou Guangdong510335 China

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks. We first propose two mathematical conditions respectively for achieving network stability and plasticity in continual learning. Based on them, the network training for sequential tasks can be simply achieved by projecting the candidate parameter update into the approximate null space of all previous tasks in the network training process, where the candidate parameter update can be generated by Adam. The approximate null space can be derived by applying singular value decomposition to the uncentered covariance matrix of all input features of previous tasks for each linear layer. For efficiency, the uncentered covariance matrix can be incrementally computed after learning each task. We also empirically verify the rationality of the approximate null space at each linear layer. We apply our approach to training networks for continual learning on benchmark datasets of CIFAR-100 and TinyImageNet, and the results suggest that the proposed approach outperforms or matches the state-ot-the-art continual learning approaches. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

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Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

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Journal of Geographical Sciences 2019年第9期29卷 1441-1461页

作者： Gao Jianbo FANG Peng YUAN Lihua State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Institute of Complexity Science and Big Data Technology Guangxi UniversityNanning 530004China Wuhan National Laboratory for Optoelectronics Key Laboratory of In formation Storage SystemEngineering Research Center of Data Storage Systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Center for GeoData and Analysis Faculty of Geographical ScieneeBeijing Normal UniversityBeijing 100875China

Since 2005,dozens of geographical observational stations have been established in the Heihe River Basin(HRB),and by now a large amount of meteorological,hydrological,and ecological observations as well as data pertaining to water resources,soil and vegetation have been *** adequately analyze these available data and data to be further collected in future,we present a perspective from complexity *** concrete materials covered include a presentation of adaptive multiscale filter,which can readily determine arbitrary trends,maximally reduce noise,and reliably perform fractal and multifractal analysis,and a presentation of scale-dependent Lyapunov exponent(SDLE),which can reliably distinguish deterministic chaos from random processes,determine the error doubling time for prediction,and obtain the defining parameters of the process *** adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data,and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal *** SDLE is illustrated to determine intermittent chaos from river flow data.

关键词： Heihe River basin geographical observation complexity theory adaptive multiscale filter fractal analysis scale-dependent Lyapunov exponent

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Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM 16th

Study of Advanced Low-Cost Smart Prepaid Electricity Meter U...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Omer, Abdalaziz Altayeb Ibrahim Chang, Kuo-Chi Deng, Hui-Qiong Chu, Kai-Chun Amesimenu, Governor David Kwabena Zhou, Yu-Wen Chang, Fu-Hsiang School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

The use of prepaid electricity meters appeared in many countries a few years ago and has been widely recognized by consumers and managed to find solutions to too many problems faced by distribution companies. But for traditional systems, users must purchase electricity and manually charge the meter. For example, if he is in the office and needs to charge the house electricity meter, then he must go home to do this, which is considered a waste of time and energy. However, the advanced low-cost smart prepaid electricity meter is a solution to the problem mentioned earlier. By sending GSM text messages to the Arduino as a microcontroller through the GSM module, consumers can use their mobile phones to charge the electricity meter anywhere. This is the main reason for the microcontroller. The control process is also connected to an electricity meter and also to a relay module to provide power for consumers or consumers when the balance is equal to zero. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global system for mobile communications

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Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network

arXiv

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arXiv 2024年

作者： Wang, Yuhe National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States

This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. The method leverages the strengths of neural networks in approximating complex, non-linear functions, particularly for tasks involving spatial interpolation. It incorporates a nearest-neighbor algorithm to capture local spatial relationships between data points and introduces randomization to quantify the uncertainty inherent in the interpolation process. This approach addresses the limitations of traditional geostatistical methods, such as Inverse Distance Weighting (IDW) and Kriging, which often fail to model the complex non-linear dependencies in reservoir data. By integrating spatial proximity and uncertainty quantification, the proposed method can improve the accuracy of static property predictions like porosity and permeability. © 2024, CC BY-NC-SA.

关键词： Interpolation

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QCluster: Clustering Packets for Flow Scheduling

arXiv

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arXiv 2020年

作者： Yang, Tong Wang, Hao Li, Jizhou Jiang, Jie Zhao, Yikai Zhang, Yinda Yang, Kaicheng Zhang, Nicholas School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Theory Lab China

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45]. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

引用

18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Science of Traditional Chinese Medicine

引用

Science of Traditional Chinese Medicine 2024年第1期2卷 57-65页

作者： Wei Yang Danhui Yi XiaoHua Zhou Yuanming Leng Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijingChina School of Statistics Renmin University of ChinaBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina Department of Biostatistics School of Public HealthBoston UniversityBostonMAUSA

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical *** study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for *** study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in *** methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational *** goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic *** addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical *** constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized *** causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical *** data science improves efficiency and accuracy for implementing *** proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of *** method supp

关键词： Traditional Chinese medicine Real-world study data science Causal learning Health information system Machine learning Artificial intelligence Treatment effects

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Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

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