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Retrieval-based Neural Source Code Summarization

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Retrieval-based Neural Source Code Summarization

International Conference on Software Engineering (ICSE)

作者： Jian Zhang Xu Wang Hongyu Zhang Hailong Sun Xudong Liu SKLSDE Lab Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China The University of Newcastle Australia

ISBN: (数字)9781450371216

ISBN: (纸本)9781728165196

Source code summarization aims to automatically generate concise summaries of source code in natural language texts, in order to help developers better understand and maintain source code. Traditional work generates a source code summary by utilizing information retrieval techniques, which select terms from original source code or adapt summaries of similar code snippets. Recent studies adopt Neural Machine Translation techniques and generate summaries from code snippets using encoder-decoder neural networks. The neural-based approaches prefer the high-frequency words in the corpus and have trouble with the low-frequency ones. In this paper, we propose a retrieval-based neural source code summarization approach where we enhance the neural model with the most similar code snippets retrieved from the training set. Our approach can take advantages of both neural and retrieval-based techniques. Specifically, we first train an attentional encoder-decoder model based on the code snippets and the summaries in the training set; Second, given one input code snippet for testing, we retrieve its two most similar code snippets in the training set from the aspects of syntax and semantics, respectively; Third, we encode the input and two retrieved code snippets, and predict the summary by fusing them during decoding. We conduct extensive experiments to evaluate our approach and the experimental results show that our proposed approach can improve the state-of-the-art methods.

关键词： Training Semantics Neural networks Syntactics Predictive models Testing Software engineering

Robust Optimization for Quantum Reinforcement Learning Control using Partial Observations

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

Fast Subspace Clustering Based on the Kronecker Product

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Fast Subspace Clustering Based on the Kronecker Product

International Conference on Pattern Recognition

作者： Lei Zhou Xiao Bai Liang Zhang Jun Zhou Edwin Hancock School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Nathan Australia University of York York U.K.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N³) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN³/^k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

关键词： Adaptation models Computational modeling Scalability Clustering methods Face recognition Memory management data models

Approximate computation for big data analytics

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arXiv 2019年

作者： Ma, Shuai Huai, Jinpeng Sklsde Lab and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or not required or has a price to high to pay, it is reasonable to sacrifice optimality with a "good" feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e., query approximation and data approximation, for efficiency and effectiveness big data analytics. We first explain the idea and rationale of query approximation, and show efficiency can be obtained with high effectiveness in practice with three analytic tasks: Graph pattern matching, trajectory compression and dense subgraph computation. We then explain the idea and rationale of data approximation, and show efficiency can be obtained even without sacrificing for effectiveness in practice with three analytic tasks: Shortest paths/distances, network anomaly detection and link prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： big data

Federated multi-view learning for private medical data integration and analysis

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arXiv 2021年

作者： Che, Sicong Peng, Hao Sun, Lichao Chen, Yong He, Lifang College of Information and Electrical Engineering China Agricultural University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making it difficult to collectively train machine learning models without data leakage. Secondly, in medical applications, data are often collected from different sources and views, resulting in heterogeneity and complexity that requires reconciliation. This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We experimented with real-world keyboard data collected from BiAffect study. The results demonstrated that the proposed FedMV approach can make full use of multi-view data in a privacy-preserving way, and both V-FedMV and H-FedMV methods perform better than their single-view and pairwise counterparts. Besides, the proposed model can be easily adapted to deal with multi-view sequential data in a federated environment, which has been modeled and experimentally studied. To the best of our knowledge, this framework is the first to consider both vertical and horizontal diversification in the multi-view setting as well as their sequential federated learning. Copyright © 2021, The Authors. All rights reserved.

关键词： data integration

Why we go where we go: Profiling user decisions on choosing POIs 29

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Why we go where we go: Profiling user decisions on choosing ...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Hu, Renjun Lu, Xinjiang Liu, Chuanren Li, Yanyan Liu, Hao Gu, Jingjing Ma, Shuai Xiong, Hui SKLSDE Lab Beihang University and Beijing Adv. Inno. Center for Big Data and Brain Computing China Business Intelligence Lab Baidu Research Business Analytics and Statistics University of Tennessee Knoxville United States Nanjing University of Aeronautics and Astronautics China Rutgers University United States

ISBN: (纸本)9780999241165

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is nontrivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： User profile

MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series

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MERITS: Medication Recommendation for Chronic Disease with I...

IEEE International Conference on data Mining (ICDM)

作者： Shuai Zhang Jianxin Li Haoyi Zhou Qishan Zhu Shanghang Zhang Danding Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Computer Science and Engineering Beihang University China Shenyuan Honors College Beihang University China School of Software Beihang University China National University of Singapore >Singapore

ISBN: (纸本)9781665423991

Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.

关键词： Correlation data visualization Ordinary differential equations data models Diabetes History Electronic medical records

A quantum convolutional neural network for image classification

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arXiv 2021年

作者： Lü, Yanxuan Gao, Qing Lü, Jinhu Ogorzalek, MacIej Zheng, Jin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Information Technologies Jagiellonian University Kraków30-348 Poland

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

Distributed adaptive finite-time time-varying group formation tracking for high-order multi-agent systems with directed topologies 39

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Distributed adaptive finite-time time-varying group formatio...

39th Chinese Control Conference, CCC 2020

作者： Tian, Lei Hua, Yongzhao Dong, Xiwang Li, Qingdong Ren, Zhang Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Aerospace Engineering University of Bristol BristolBS8 1TR United Kingdom

ISBN: (纸本)9789881563903

This paper investigates finite-time time-varying group formation (TVGF) tracking control problems for high-order multi-agent systems (MASs) with directed topologies. Firstly, the agents are classified into three types, i.e., the virtual leader, the group leader and the follower. A virtual leader is used to control the macroscopic movement of MASs. Subgroups can be interacted and coordinated with each other by the communication among group leaders. Secondly, a novel finite-time group formation tracking protocol using only the neighboring interaction is constructed based on adaptive integral sliding mode control. Then sufficient conditions for MASs with unknown external disturbances and directed topologies to realize finite-time TVGF tracking are derived using Lyapunov theory. Finally, a numerical simulation example is performed to demonstrate the effectiveness of theoretical results. © 2020 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems