2022 14th International Conference on Advanced Computational Intelligence (ICACI)最新文献_第2页

Sentiment Analysis of Developers’ Comments on GitHub Repository: A Study 开发者对GitHub Repository评论的情感分析研究

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837754

Aman Kumar, Manish Khare, Saurabh Tiwari

{"title":"Sentiment Analysis of Developers’ Comments on GitHub Repository: A Study","authors":"Aman Kumar, Manish Khare, Saurabh Tiwari","doi":"10.1109/icaci55529.2022.9837754","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837754","url":null,"abstract":"The sentiments of developers play a major role in productivity, code quality, and satisfaction. The workload of the developers and their interest in a specific programming language affect the overall quality of the development process. Open source projects, where developers (or contributors) work based on their interest in contributing to the project apart of their routine work. In this paper, we are analysing the sentiments of the developers on GitHub while working on different open source projects. Our study mainly focuses on three aspects: (1) analysing the day of the week in which the comment was made by the developer, (2) emotions of the developer throughout the course of a project, and (3) emotions with different programming languages. The analysis was done by looking into the developer comments on issues, pull requests, and comments for the repository. Our results show that projects developed on Monday’s tend to more negative emotion. Additionally, comments written in issues have higher negative polarity in their sentimental content, and projects developed in Java and Python have more positive comments as compared to C and C++.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115340799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stock Price Forecast Based on LSTM and DDQN 基于LSTM和DDQN的股票价格预测

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837594

Na Wu, Zongwu Ke, Lei Feng

{"title":"Stock Price Forecast Based on LSTM and DDQN","authors":"Na Wu, Zongwu Ke, Lei Feng","doi":"10.1109/icaci55529.2022.9837594","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837594","url":null,"abstract":"The prediction of time series data is very difficult. For example, the price of stocks belongs to time series. Small fluctuations in society, politics, economy and culture may affect the stocks in the stock market. In the stock market, it is very important for people to have a general judgment on stocks. Therefore, the study of stocks has practical significance. This experiment confirms that the results are affected by the data set and statesize. Statesize is predicted by the closing price of several days.On the premise that the appropriate size of statesize makes the final profit the highest, and on the premise that improved algorithm of Q value based on DQN adds regularization (DDQN), it is proved that under different data sets, adding Long Short-Term Memory (LSTM) and full connection layer are better than only full connection layer. DQN is composed of neural network and Q-learning. Q-learning is a basic algorithm in reinforcement learning. And it is proved that DDQN algorithm is better than DQN on the premise that the appropriate statesize makes the final profit the highest, and on the premise of adding regularization and LSTM. Finally, it is also proved that under certain preconditions, the combination of LSTM and DDQN is better than only DQN and full connection layer. The only indicator of this experiment is the total profit. At the same time, this paper uses the closing price to predict.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

New Results on Finite-Time Synchronization of Delayed Fuzzy Neural Networks 延迟模糊神经网络有限时间同步的新结果

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837603

Changqing Long, Houping Dai, Guodong Zhang, Junhao Hu

引用次数: 0

Recurrent Neural Networks with Fractional Order Gradient Method 分数阶梯度法递归神经网络

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837518

Honggang Yang, Rui Fan, Jiejie Chen, Mengfei Xu

引用次数: 3

Asymptotic Bipartite Synchronization of Coupled Neural Networks Via Quantized Control 基于量化控制的耦合神经网络渐近二部同步

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837729

Ting Liu, Junhong Zhao, Peng Liu, Jian Yong, Shulong Fan, Junwei Sun

引用次数: 0

An Improved Graph Neural Network Method Using Relative Position Information for Session-based Recommendation 基于相对位置信息的改进图神经网络会话推荐方法

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837599

Shuai Zhang, Yujie Xiao, Mingze Li, Xiaowei Li, Benhui Chen

引用次数: 0

Finite-time Anti-synchronization of Memristor Oscillation System 忆阻振荡系统的有限时间反同步

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837669

Fei Wei, Guici Chen, Lei Yu

引用次数: 0

Boosting Adversarial Attack Transferability via Random Block Shuffle 通过随机分组洗牌提高对抗性攻击的可转移性

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837674

Hanwen Liu, Bingrong Xu, Yin Sheng, Zhigang Zeng

{"title":"Boosting Adversarial Attack Transferability via Random Block Shuffle","authors":"Hanwen Liu, Bingrong Xu, Yin Sheng, Zhigang Zeng","doi":"10.1109/icaci55529.2022.9837674","DOIUrl":"https://doi.org/10.1109/icaci55529.2022.9837674","url":null,"abstract":"An interesting property of deep convolutional neural networks is their weakness to adversarial examples, which can deceive the models with subtle perturbations. Though adversarial attack algorithms have accomplished excellent performance in the white-box scenario, they frequently display a low attack success rate in the black-box scenario. Various transformation-based attack methods are shown to be powerful to enhance the transferability of adversarial examples. In this work, several novel transformation-based attack methods that integrate with the Random Block Shuffle (RBS) and Ensemble Random Block Shuffle (ERBS) mechanisms are come up with to boost adversarial transferability. First of all, the RBS calculates the gradient of the shuffled input instead of the original input. It increases the diversity of adversarial perturbation’s gradient and makes the original input’s information more invisible for the model. Based on the RBS, the ERBS is proposed to decrease gradient variance and stabilize the update direction further, which integrates the gradient of transformed inputs. Moreover, by incorporating various gradient-based attack methods with transformation-based methods, the adversarial transferability could be additionally improved fundamentally and relieve the overfitting problem. Our best attack method arrives an average success rate of 85.5% on two normally trained models and two adversarially trained models, which outperforms existing baselines.","PeriodicalId":412347,"journal":{"name":"2022 14th International Conference on Advanced Computational Intelligence (ICACI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Unified Weighted MMD For Unsupervised Domain Adaptation 无监督域自适应的统一加权MMD

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837581

Zhansong Ma, Bingrong Xu, Lei Wang, Hanwen Liu, Zhigang Zeng

引用次数: 1

Automatic Pantograph Health Status Report Generation Based on Dense Captioning 基于密集标题的受电弓运行状况自动生成报告

2022 14th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2022-07-15 DOI: 10.1109/icaci55529.2022.9837656

Xinqiang Yin, Xiukun Wei, Zhaoxin Li, Dehua Wei, Qingfeng Tang

引用次数: 0