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A data-driven approach for recommending UI element layout

Science China(Information Sciences) 2020年第9期63卷 61-63页

作者： Yonghao LONG Xiangping CHEN Yuting XIE School of Information Science and Technology National Engineering Research Center of Digital LifeSun Yat-sen University Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University

Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps often depends on the user interface (UI)[1]with minor data processing or data processing delegates to the backend component.

关键词： UI development Software mining Layout recommendation data-driven development Intelligent Software Development

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Learning Human-Written Commit Messages to Document Code Changes

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Journal of Computer Science & technology 2020年第6期35卷 1258-1277页

作者： Yuan Huang Nan Jia Hao-Jie Zhou Xiang-Ping Chen Zi-Bin Zheng Ming-Dong Tang National Engineering Research Center of Digital Life School of Data and Computer ScienceSun Yat-sen University Guangzhou 510006China School of Information Engineering Hebei GEO UniversityShijiazhuang 050031China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.

关键词： commit message recommendation code syntax similarity code semantic similarity code change comprehension

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New constructions of q-variable 1-resilient rotation symmetric functions over Fp

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Science China(Information Sciences) 2016年第7期59卷 240-242页

作者： Jiao DU Shaojing FU Longjiang QU Chao LI Shanqi PANG College of Science National University of Defense Technology School of Mathematics and Information Science Henan Normal University Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University College of Computer Science National University of Defense Technology

Dear editor,Motivated by Refs.[1–10],we devote to constructing a class of q-variable 1-resilient rotation symmetric functions（RSFs）over the finite field F;={0,1,...,p-1}in this *** this letter,let p and q be differ... 详细信息

关键词： over New constructions of q-variable 1-resilient rotation symmetric functions over F_p

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Inverse Markov Process Based Constrained Dynamic Graph Layout

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Journal of Computer Science & technology 2021年第3期36卷 707-718页

作者： Shi-Ying Sheng Sheng-Tao Chen Xiao-Ju Dong Chun-Yuan Wu Xiao-Ru Yuan ParisTech Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Machine Perception(Ministry of Education) National Engineering Laboratory for Big Data Analysis and ApplicationPeking UniversityBeijing 100080China

In online dynamic graph drawing,constraints over nodes and node pairs help preserve a coherent mental map in a sequence of *** the constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph *** existing algorithms basically depend on local changes but fail to do proper evaluations on the global propagation when setting *** solve this problem,we introduce a heuristic model derived from PageRank which simulates the node movement as an inverse Markov process hence to give a global analysis of the layout's change,according to which different constraints can be *** constraints,along with stress function,generate layouts maintaining spatial positions and shapes of relatively stable substructures between adjacent *** demonstrate that our method preserves both structure and position similarity to help users track graph changes visually.

关键词： graph drawing data stream dynamic graph layout

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Evaluation of Learning Effect Based on Online data 27th

Evaluation of Learning Effect Based on Online Data

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27th China Conference on Information Retrieval, CCIR 2021

作者： Liu, Zhaohui Yan, Hongfei Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030881887

With the development of Internet technology, online learning has gradually been widely used, which has further enriched the teaching methods. However, the indicators provided by online learning platforms to characterize students’ learning behaviors are still relatively small, it is hard to effectively evaluate students’ learning effects and provide personalized education for students. While students produce a large number of online data records during the learning process, if these data can be used to evaluate the learning effect of students and find indicators that are significantly related to the learning effect, it can help teachers know the learning status of students and provide them with more targeted guidance. The framework of this article includes: firstly collect online data of students’ learning, and determine major types of factors that affect the learning effect. Next, the original data is processed, and new features are constructed from the original data according to the factors that affect the learning effect. Then use several different methods to select the features that have the most significant impact on the learning effect, and use several regression methods to predict students’ academic performance. Finally, this article selects important features that are more relevant to learning effects, studies the relationship between these features. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Hierarchical attention network in stock prediction 26th

Hierarchical attention network in stock prediction

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26th China Conference on Information Retrieval, CCIR 2020

作者： Huang, Liming Yan, Hongfei Ying, Siping Li, Yansong Miao, Rui Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

To solve the stock prediction problem, we propose a deep learning model base on a hierarchical attention network. Our model is divided into two models. The first model is the article selection attention network that transfers the news into a low dimension vector. This model could identify the important factors in the news that affect the stock price. The second model is a time series attention network which combines the output of the first model and the transaction data as input. In this model, we could figure out the potential impact between different dates and summarize the historical data to predict whether the stock price will rise or fall. The most innovative concept in this paper is stock encoding. The model learns the difference between each stock and make predictions more accurate by using the stock encoding. The experimental result shows that the model fully utilize text features and make better predictions than related research papers. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Empirical research on futures trading strategy based on time series algorithm 26th

Empirical research on futures trading strategy based on time...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Yao, Shi Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

This article attempts to establish a trading strategy framework based on deep neural networks for the futures market, which consists of two parts: time series forecasting and trading strategies based on trading signals. In the time series forecasting task, we experimented with three types of methods with different entry points, namely recurrent neural networks with gate structure, networks combining time and frequency domain information, and network structures using attention mechanism. In the trading strategy part, the buying and selling signals and the corresponding trading volume are established according to the prediction results, and trading is conducted with the frequency of hours. In the empirical exploration part, we tested the prediction effect and strategic rate of return of various models on the copper contract. The data shows that in general, the best strategy can obtain a relatively stable income growth that has nothing to do with market fluctuations, but lacks countermeasures for rare external events with greater impact. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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An integrated machine learning framework for stock price prediction 26th

An integrated machine learning framework for stock price pre...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Bi, Quanzhi Yan, Hongfei Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. The framework we use is divided into three modules: Feature engineering, Regressor, and Hyper Optimizer. The Feature engineering module extract multiple features using technical indicators, FinBERT, FFT, ARIMA, stacked auto-encoder, PCA and XGBoost. The Regressor module consists of a generative adversarial network, where the generator is Seq2Seq and the discriminator is GRU. The HyperOptimizer module will tune the parameter in GAN using the Bayesian optimization algorithm. Finally, we conducted numerical experiments on our framework, which shows that the framework implemented in this paper performs better than the benchmark method. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Semi-supervised sentiment analysis for chinese stock texts in scarce labeled data scenario and price prediction 26th

Semi-supervised sentiment analysis for chinese stock texts i...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Zhaoyan, Ji Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

The application of neural network in stock prediction is developing rapidly these years because of its excellency in series data processing. However, as most of research are conducted in English, data sources and labeled data are inadequate in Chinese. Especially for natural language processing tasks in specific domain where specialized labeled data are required to train models to adapt to terminology processing, specialized labeled Chinese in text data are very scarce, such as financial text data. To tackle this challenge, we proposed a semi-supervised learning method to generate well-labeled data and train BERT, a leading natural language processing model, to obtain a trained sentiment machine. Then we got stock-related text data sentiment score based on this machine and further combine the sentiment score and other transaction data as inputs for different neural networks to predict stock price. The experimental results on a large scale of Chinese stock data and texts showed that our proposed method successfully improved prediction accuracy compared to other established methods. Besides, we also examined our method’s applicability combined with different neural networks when predicting different types of stock. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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