arXiv subjects

Zhipeng Gao

Publications and source records attributed to Zhipeng Gao.

45 records · Page 3Linked to original sources

arXiv2021

FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework

With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the detection model, a series of techniques have been developed. However, existing work still leaves much to be desired, such as privacy disclosure and communication cost. To address these issues, we propose FedSup, a client-edge-cloud framework for privacy and efficient fatigue detection. Inspired by the federated learning technique, FedSup intelligently utilizes the collaboration between client, edge, and cloud server to realizing dynamic model optimization while protecting edge data privacy. Moreover, to reduce the unnecessary system communication overhead, we further propose a Bayesian convolutional neural network (BCNN) approximation strategy on the clients and an uncertainty weighted aggregation algorithm on the cloud to enhance the central model training efficiency. Extensive experiments demonstrate that the FedSup framework is suitable for IoV scenarios and outperforms other mainstream methods.

cs.LG↗

arXiv2020

When Deep Learning Meets Smart Contracts

Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, many identified bugs and vulnerabilities in smart contracts have led to serious financial losses, which raises serious concerns about smart contract security. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this research: (1) Firstly, we propose an automated deep learning based approach to learn structural code embeddings of smart contracts in Solidity, which is useful for clone detection, bug detection and contract validation on smart contracts. We apply our approach to more than 22K solidity contracts collected from the Ethereum blockchain, results show that the clone ratio of solidity code is at around 90%, much higher than traditional software. We collect a list of 52 known buggy smart contracts belonging to 10 kinds of common vulnerabilities as our bug database. Our approach can identify more than 1000 clone related bugs based on our bug databases efficiently and accurately. (2) Secondly, according to developers' feedback, we have implemented the approach in a web-based tool, named SmartEmbed, to facilitate Solidity developers for using our approach. Our tool can assist Solidity developers to efficiently identify repetitive smart contracts in the existing Ethereum blockchain, as well as checking their contract against a known set of bugs, which can help to improve the users' confidence in the reliability of the contract. We optimize the implementations of SmartEmbed which is sufficient in supporting developers in real-time for practical uses. The Ethereum ecosystem as well as the individual Solidity developer can both benefit from our research.

cs.SE↗

arXiv2020

Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow

Stack Overflow is one of the most popular technical Q&A sites used by software developers. Seeking help from Stack Overflow has become an essential part of software developers' daily work for solving programming-related questions. Although the Stack Overflow community has provided quality assurance guidelines to help users write better questions, we observed that a significant number of questions submitted to Stack Overflow are of low quality. In this paper, we introduce a new web-based tool, Code2Que, which can help developers in writing higher quality questions for a given code snippet. Code2Que consists of two main stages: offline learning and online recommendation. In the offline learning phase, we first collect a set of good quality pairs as training samples. We then train our model on these training samples via a deep sequence-to-sequence approach, enhanced with an attention mechanism, a copy mechanism and a coverage mechanism. In the online recommendation phase, for a given code snippet, we use the offline trained model to generate question titles to assist less experienced developers in writing questions more effectively. At the same time, we embed the given code snippet into a vector and retrieve the related questions with similar problematic code snippets.

cs.SE↗

arXiv2020

Generating Question Titles for Stack Overflow from Mined Code Snippets

Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet. The Stack Overflow community recommends users to provide the related code snippet when they are creating a question to help others better understand it and offer their help. Previous studies have shown that} a significant number of these questions are of low-quality and not attractive to other potential experts in Stack Overflow. These poorly asked questions are less likely to receive useful answers and hinder the overall knowledge generation and sharing process. Considering one of the reasons for introducing low-quality questions in SO is that many developers may not be able to clarify and summarize the key problems behind their presented code snippets due to their lack of knowledge and terminology related to the problem, and/or their poor writing skills, in this study we propose an approach to assist developers in writing high-quality questions by automatically generating question titles for a code snippet using a deep sequence-to-sequence learning approach. Our approach is fully data-driven and uses an attention mechanism to perform better content selection, a copy mechanism to handle the rare-words problem and a coverage mechanism to eliminate word repetition problem. We evaluate our approach on Stack Overflow datasets over a variety of programming languages (e.g., Python, Java, Javascript, C# and SQL) and our experimental results show that our approach significantly outperforms several state-of-the-art baselines in both automatic and human evaluation. We have released our code and datasets to facilitate other researchers to verify their ideas and inspire the follow-up work.

cs.SE↗

arXiv2020

Checking Smart Contracts with Structural Code Embedding

Smart contracts have been increasingly used together with blockchains to automate financial and business transactions. However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this paper, we propose an automated approach to learn characteristics of smart contracts in Solidity, which is useful for clone detection, bug detection and contract validation on smart contracts. Our new approach is based on word embeddings and vector space comparison. We parse smart contract code into word streams with code structural information, convert code elements (e.g., statements, functions) into numerical vectors that are supposed to encode the code syntax and semantics, and compare the similarities among the vectors encoding code and known bugs, to identify potential issues. We have implemented the approach in a prototype, named SmartEmbed. Results show that our tool can effectively identify many repetitive instances of Solidity code, where the clone ratio is around 90\%. Code clones such as type-III or even type-IV semantic clones can also be detected accurately. Our tool can identify more than 1000 clone related bugs based on our bug databases efficiently and accurately. Our tool can also help to efficiently validate any given smart contract against a known set of bugs, which can help to improve the users' confidence in the reliability of the contract. The anonymous replication packages can be accessed at: https://drive.google.com/file/d/1kauLT3y2IiHPkUlVx4FSTda-dVAyL4za/view?usp=sharing, and evaluated it with more than 22,000 smart contracts collected from the Ethereum blockchain.

cs.SE↗

arXiv2019

FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems

Channel estimation is of crucial importance in massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) systems. In order to tackle this problem, a fast and flexible denoising convolutional neural network (FFDNet)-based channel estimation scheme for m-MIMO VLC systems was proposed. The channel matrix of the m-MIMO VLC channel is identified as a two-dimensional natural image since the channel has the characteristic of sparsity. A deep learning-enabled image denoising network FFDNet is exploited to learn from a large number of training data and to estimate the m-MIMO VLC channel. Simulation results demonstrate that our proposed channel estimation based on the FFDNet significantly outperforms the benchmark scheme based on minimum mean square error.

eess.SP↗

arXiv2019

SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding

Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, a major concern in Ethereum is the security of its smart contracts. Many identified bugs and vulnerabilities in smart contracts not only present challenges to maintenance of blockchain, but also lead to serious financial loses. There is a significant need to better assist developers in checking smart contracts and ensuring their reliability.In this paper, we propose a web service tool, named SmartEmbed, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts. Our tool is based on code embeddings and similarity checking techniques. By comparing the similarities among the code embedding vectors for existing solidity code in the Ethereum blockchain and known bugs, we are able to efficiently identify code clones and clone-related bugs for any solidity code given by users, which can help to improve the users' confidence in the reliability of their code. In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale. When applied to more than 22K solidity contracts collected from the Ethereum blockchain, we found that the clone ratio of solidity code is close to 90\%, much higher than traditional software, and 194 clone-related bugs can be identified efficiently and accurately based on our small bug database with a precision of 96\%. SmartEmbed can be accessed at \url{http://www.smartembed.net}. A demo video of SmartEmbed is at \url{https://youtu.be/o9ylyOpYFq8}

cs.SE↗

arXiv2017

Origin of the phase change from pyrochlore to perovskite-like layered structure and a new lead free ferroelectric

Materials with formula of A2B2O7 is a famous family with more than 300 compounds, and have abundant properties, like ferroelectric, multiferroic, and photocatalyst properties, etc. Generally, two structures dominate this family, which are pyrochlore and perovskite-like layered (PL) structure. Previously, the structure and properties design of these materials are usually complex, and solid solutions, which complicates the manufacture, as well as introducing complexity in the study of the microscopic origins of the properties. Here, we report that the pyrochlore-PL structure change happened in pure Eu2Ti2O7 under high pressure and temperature, and the formed PL structure will transfer back by heating. These results reveal that the PL structure formed in PL-pyrochlore solid solutions, is due to tuning of the high-pressure formed PL structure in pure pyrochlore compounds to ambient pressure. These results indicate the high pressure and high temperature can be used to manipulate the crystal structures from pyrochlore to PL structure, or vice versa. Furthermore, the PL Eu2Ti2O7 was confirmed as a lead free ferroelectric material for the first time.

cond-mat.mtrl-sci↗

arXiv2016

A Time-constraint Satisfying and Cost-reducing node evaluation metric for Message Routing in Mobile Crowd Sensing Networks

In mobile crowd sensing networks data forwarding through opportunistic contacts between participants. Data is replicated to encountered participants. For optimizing data delivery ratio and reducing redundant data a lot of data forwarding schemes, which selectively replicate data to encountered participants through node's data forwarding metric are proposed. However most of them neglect a kind of redundant data whose Time-To-Live is expired. For reducing this kind of redundant data we proposed a new method to evaluate node's data forwarding metric, which is used to measure the node's probability of forwarding data to destination within data's constraint time. The method is divided into two parts. The first is evaluating nodes whether have possibility to contact destination within time constraint based on transient cluster. We propose a method to detect node's transient cluster, which is based on node's contact rate. Only node, which has possibility to contact destination, has chances to the second step. It effectively reduces the computational complexity. The second is calculating data forwarding probability of node to destination according to individual ICT (inter contact time) distribution. Evaluation results show that our proposed transient cluster detection method is more simple and quick. And from two aspects of data delivery ratio and network overhead our approach outperforms other existing data forwarding approach.

cs.NI↗