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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhang, Lingyu Bian, Wenjie Qu, Wenyi Tuo, Liheng Wang, Yunhai School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm

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International Journal of Agricultural and Biological engineering 2020年第2期13卷 182-188页

作者： Linsheng Huang Hansu Zhang Chao Ruan Wenjiang Huang Tingguang Hu Jinling Zhao National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefeiChina Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

A new method was proposed to extract sensitive features and to construct a monitoring model for wheat scab based on in situ hyperspectral data of wheat ears to achieve effective prevention and control and provide theoretical support for its large-scale *** sensitive features were selected through correlation analysis and wavelet *** features were as follows:three original bands of 350-400 nm,500-600 nm,and 720-1000 nm;three vegetation indices of modified simple ratio(MSR),normalized difference vegetation index,and structural independent pigment index;and two wavelet features of WF01 and *** combining the selected sensitive features with support vector machine(SVM)and SVM optimized by genetic algorithm(GASVM),a total of 16 monitoring models were built,and the monitoring accuracies of the two types of models were *** ability of the monitoring models built by GASVM to identify scab was better than that of SVM algorithm under the same characteristic *** the 16 models,MSR combined with GASVM had an overall accuracy of 75%and a Kappa coefficient of *** can be used to monitor wheat scab and its application can improve the accuracy of disease monitoring.

关键词： wheat scab hyperspectral data correlation analysis genetic algorithm wavelet transform support vector machine

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Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation 40

Accelerating Scalable Graph Neural Network Inference with No...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Xinyi Zhang, Wentao Yu, Junliang Shao, Yingxia Nguyen, Quoc Viet Hung Cui, Bin Yin, Hongzhi The University of Queensland Brisbane Australia Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Beijing University of Posts and Telecommunications Beijing China Griffith University Gold Coast Australia

ISBN: (纸本)9798350317152

Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. Although existing Scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these methods still suffer from scalability issues when making inferences on unseen nodes, as the feature preprocessing requires the graph to be known and fixed. To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation. The trade-off between accuracy and latency can be flexibly managed through simple hyper-parameters to accommodate various latency constraints. Moreover, to compensate for the inference accuracy loss caused by the potential early termination of propagation, we further propose Inception Distillation to exploit the multi-scale receptive field information within graphs. The rigorous and comprehensive experimental study on public datasets with varying scales and characteristics demonstrates that the proposed inference acceleration framework outperforms existing state-of-the-art graph inference acceleration methods in terms of accuracy and efficiency. Particularly, the superiority of our approach is notable on datasets with larger scales, yielding a 75× inference speedup on the largest Ogbn-products dataset. © 2024 IEEE.

关键词： Graph neural networks

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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RIM: Reliable influence-based active learning on graphs

arXiv

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arXiv 2021年

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Cao, Meng Huang, Ping Shan, Jiulong Yang, Zhi Cui, Bin School of CS Peking University Apple National Engineering Laboratory for Big Data Analysis and Applications China

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph. However, the labeling process can be tedious, costly, and error-prone in practice. In this paper, we propose to unify active learning (AL) and message passing towards minimizing labeling costs, e.g., making use of few and unreliable labels that can be obtained cheaply. We make two contributions towards that end. First, we open up a perspective by drawing a connection between AL enforcing message passing and social influence maximization, ensuring that the selected samples effectively improve the model performance. Second, we propose an extension to the influence model that incorporates an explicit quality factor to model label noise. In this way, we derive a fundamentally new AL selection criterion for GCN and LP–reliable influence maximization (RIM)–by considering quantity and quality of influence simultaneously. Empirical studies on public datasets show that RIM significantly outperforms current AL methods in terms of accuracy and efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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A Digital Twin-based Framework of Motion Control and State Monitoring for Pneumatic Muscle

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Recent Patents on Mechanical engineering 2023年第3期16卷 203-213页

作者： Xie, Shenglong Liu, Wenyuan Duan, Huiru Chen, Dijian Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Ji-liang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma'anshan243032 China

Introduction: The current digital twin systems usually have the drawback of high cost and complex technology, and it is necessary to develop a simple solution to reduce the cost and cycle for the development of digital twin systems, especially for small projects or systems with simple structures. Objective: A low-cost patent technology of digital twin system was proposed by taking the motion control and state monitoring system (MCSMS) of pneumatic muscle as an example. Methods: The MCSMS is developed based on the browser/server architecture. The software of 3ds Max and SolidWorks are used to make the virtual model, *** and JavaScript are applied to build the browser side. data of the physical world is collected and processed on the server side firstly, and then is sent to the browser side through HTTP communication protocol to realize data interchange between the browser and server. Results: In the roaming experiment and the experiment of motion control and state monitoring of pneumatic muscle, the MCSMS can work smoothly without obvious delay and has good real-time performance, which can realize the 3D visual monitoring of the pneumatic muscle very well. Conclusion: The experimental results indicate that the proposed method possesses the ability of good feasibility and effectiveness. © 2023 Bentham Science Publishers.

关键词： Muscle

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HyperCalm Sketch: One-Pass Mining Periodic Batches in data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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Investigate the relationship between learners' social characteristics and academic achievements 5

Investigate the relationship between learners' social charac...

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5th International Workshop on Statistical Physics and Mathematics for Complex Systems, SPMCS 2017

作者： Liu, Zhi Kang, Lingyun Su, Zhu Liu, Sannyuya Sun, Jianwen National Engineering Research Center for E-Learning Central China Normal University Wuhan China National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan China

The social interaction behaviors of group learning have obtained a lot of attention from researches, and social network analysis (SNA) plays a critical role in exploring collective interactive patterns. In this paper, we investigated the experimental data from the discussion forum of "Discourse analysis" course in a university learning platform. By calculating learners social characteristics, we explored the relationship between social characteristics and academic achievements, as well as the differences between various achieving learners. The results show that there exist strong positive correlations between most social characteristics and academic achievements, and the majority of high-achieving learners are located at the core position of the network. In addition, the high-achieving learners exhibited more active participations in discussions and received more posts compared with middle- and low-achieving learners. © Published under licence by IOP Publishing Ltd.

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Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

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Tsinghua Science and Technology 2020年第6期25卷 712-720页

作者： Lei Zhang Chenbo Xu Yihua Gao Yi Han Xiaojiang Du Zhihong Tian Henan Key Laboratory of Big Data Analysis and Processing Kaifeng 457004China Institute of Data and Knowledge Engineering Henan UniversityKaifeng 475004.China National Internet Emergency Center Zhengzhou 450000China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6008USA Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 510006China

In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be *** to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win *** one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to *** solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup *** model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero *** CBOW model can predict the context of a word in a ***,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.

关键词： Word2vec mutiplayer online battle arena games Continuous Bag Of Words(CBOW)model Long Short-Term Memory(LSTM)

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Design and implementation of a Cesium-based 3D visualization platform for the Yellow River dam bank

Design and implementation of a Cesium-based 3D visualization...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Sun, Huaiyuan Zhang, Lei Dang, Lanxue Liu, Yang Xie, Yi Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan Universfiity Kaifeng475001 China Zhong Ke Langfang Institute of Spatial Information Applications Langfang065000 China

ISBN: (纸本)9781510664616

Flood control is one of the most important operations of Yellow River control. The dam bank danger in flood season every year has the characteristics of time-varying, multi-dimension and complex situation, which poses a certain threat to people's life and property safety. For timely, flood control engineering data quickly and implement management interface of information visualization, effectively reduce the labor intensity of artificial working risk, this paper puts forward a kind of based on three-dimensional library of Cesium Huang Heba shore the realization method of 3D visualization platform adopted the aerial drones to create dam shore entity 3D model, Combined with WebGIS technology and front-end technology, the system's 3D expression is realized. Through interface design and system function development, the system has three-dimensional model display,with the functions of dam bank cruise and measurement tools, the platform has strong sense of reality and good interaction effect, which realizes the three-dimensional visual display of the Madu control project in Zhengzhou, Henan Province. © 2023 SPIE.

关键词： Flood control

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