2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献_第6页

A Framework of Vulnerable Code Dataset Generation by Open-Source Injection 基于开源注入的漏洞代码数据集生成框架

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497888

Shasha Zhang

{"title":"A Framework of Vulnerable Code Dataset Generation by Open-Source Injection","authors":"Shasha Zhang","doi":"10.1109/ICAICA52286.2021.9497888","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497888","url":null,"abstract":"Evaluation benchmark plays an important role in the design of defect detection algorithms and tools. Especially with the development of deep learning techniques, code defect detection models based on deep neural network requires a large number of training and testing cases. Existing test cases are far from meeting the requirements of new algorithm design and verification. On the one hand, the number of test cases designed manually or collected from open source projects is small. On the other hand, the test cases generated automatically according to rules have similar pattern, high redundancy and simple structure. This paper proposes an algorithm of code defect injection and test case generation based on open source projects. The basic idea is to find reaching definitions in open source projects, and modify the source code according to the analysis results, so as to generate defect dataset with a large number of test cases that have similar feature to open source codes. This paper selects 8 open source projects to verify the proposed method and generates more than 6,000 null pointer dereference test cases in total. We use existing tools to evaluate the injected test cases and the results show that the proposed method can generate a large number of high-quality test cases.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505808","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: 3

Load Balancing Scheduling Algorithm Based on Improved Particle Swarm Optimization 基于改进粒子群优化的负载均衡调度算法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498058

Yongming Cui, Zhaohua Long

{"title":"Load Balancing Scheduling Algorithm Based on Improved Particle Swarm Optimization","authors":"Yongming Cui, Zhaohua Long","doi":"10.1109/ICAICA52286.2021.9498058","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498058","url":null,"abstract":"Aiming at the defects of the traditional particle swarm optimization algorithm, as the number of iterations increases, the flight speed of the particles in the space becomes faster, and the particles are easy to gather and close to the local position, resulting in the algorithm falling into the local optimal situation, and the situation that it cannot continue to explore the optimal solution in a larger space. Therefore, the inertial center of gravity is introduced to determine the position and state of particles. If the inertial center of gravity of particle swarm is smaller in the current iteration, the distribution of particles will be more uniform, so as to avoid the algorithm falling into local solution. The larger the inertia barycenter of particle swarm is, the looser the particle distribution is. When it reaches a certain degree, the algorithm will converge prematurely and may miss the optimal solution. Therefore, the inertia center of gravity is allowed to float within a certain range during optimization to solve the problem that the inertia weight fluctuates greatly and the algorithm converges in the early stage. In this paper, the inertia weight is analyzed and improved, so that the performance of the algorithm is further improved.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133459056","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

Analysis on the Dynamic Response of an EDS Maglev Train Based on Pacejka Similarity Tire Model 基于Pacejka相似轮胎模型的EDS磁浮列车动力响应分析

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498233

Nan Shao, Min Wang, Qi Hou

{"title":"Analysis on the Dynamic Response of an EDS Maglev Train Based on Pacejka Similarity Tire Model","authors":"Nan Shao, Min Wang, Qi Hou","doi":"10.1109/ICAICA52286.2021.9498233","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498233","url":null,"abstract":"To research into the key technologies of the super-conducting electro-dynamic suspension (EDS) maglev train, an experimental system which contains all the key elements including a 200-meter test-bed, a propulsion power supply system, an operation control system, a data collecting system and a prototype of a maglev train is established. In this study, we mainly focus on the stability of the train during the accelerating maneuver and analyze the dynamic response of the train through simulation. In this paper, a dynamic model of an EDS maglev train is established in Simpack to analyze the influences of various aspects on the stability of the train during the accelerating maneuver. Specifically, we analyzed the influences of the railway irregularity and vertical tire stiffness on the lateral displacement and acceleration based on the Pacejka Similarity tire model. The simulation results demonstrates that the lateral displacement and acceleration decrease as the railway irregularity or tire vertical stiffness decrease, which means that reducing irregularity or the tire stiffness helps to improve the stability of the train. This conclusion provides effective theoretical guidance to the design of the railway and tire of an EDS maglev train.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133616978","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

Research on optimization of energy consumption monitoring point layout on user side 用户侧能耗监测点布置优化研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498073

Jiajian Zheng, Guo-qiang Han, Shouyong Yang, Shujuan Tan

引用次数: 1

Radar Signal Sorting System Based on Multi-domain Feature Extraction and Decision Tree Algorithm 基于多域特征提取和决策树算法的雷达信号分选系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498227

Zhang Huaidong, Ma Xiaowen, L. Jianing

引用次数: 0

The Outage Probability Analysis of Network-Centric Clustering in C-RAN C-RAN中以网络为中心的聚类中断概率分析

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497983

Yidi Shao, Yixin Du, Yi Jiang, W. Huang, Kai Sun

{"title":"The Outage Probability Analysis of Network-Centric Clustering in C-RAN","authors":"Yidi Shao, Yixin Du, Yi Jiang, W. Huang, Kai Sun","doi":"10.1109/ICAICA52286.2021.9497983","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497983","url":null,"abstract":"The baseband unit (BBU) pool in the cloud radio access network (C-RAN) realizes the centralized deployment of baseband processing units, and the centralized signal processing promotes the implementation of coordination multi- point (CoMP) between remote radio heads (RRHs). Network- centric clustering mode is considered to be an effective solution to improve network coverage probability. As an adjustable parameter in the network, cluster size and frequency reuse distance play an important role in improving the quality of service. However, due to the uncertainty of wireless networks and the resulting aggregated interference distribution, it is a severe challenge to evaluate the performance of wireless networks. The goal of this paper is to study the impact of cluster size and frequency reuse distance on the outage probability of users. The network-centric hexagonal clustering method is adopted, in which each user is associated with the nearest RRH in the cluster. In order to mitigate the intra- cluster interference, we assume that the channel state information (CSI) can be perfectly obtained by the RRHs in the same cluster, and the intra-cluster interference can be eliminated by beamforming. By modeling RRHs and users as binomial point process (BPP), and the outage probability is obtained in integral form. The accuracy of the derived outage probability expression is verified by Monte Carlo simulation.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895144","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

Design of Intelligent Socket Based on Cloud Platform of IoT 基于物联网云平台的智能插座设计

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497943

Juhua Guan, Kewang Zhang, Xuebin Zhong

引用次数: 0

Research on Image Caption of Children's Image Based on Attention Mechanism 基于注意机制的儿童图像标题研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498013

Haibing Li, Xiang Li, Wenyon Wang

引用次数: 0

U-net gland cell image segmentation method combined with spatial attention 结合空间注意力的U-net腺体细胞图像分割方法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498180

Mingmin Gong, Aijun Chen, Hao Feng

{"title":"U-net gland cell image segmentation method combined with spatial attention","authors":"Mingmin Gong, Aijun Chen, Hao Feng","doi":"10.1109/ICAICA52286.2021.9498180","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498180","url":null,"abstract":"Glandular cell image segmentation is an important auxiliary analysis method for judging whether glandular cells are diseased. The segmentation of gland cell images helps doctors make reliable disease diagnosis and improve diagnosis efficiency. U-net is a convolutional neural network commonly used in the field of medical image segmentation. It surpasses traditional image segmentation methods in performance of a variety of medical image segmentation tasks. However, U-net still has certain limitations. Because U-net is a symmetrical convolutional neural network model, while increasing the input image resolution, the number of convolutional layers in the network will double, which will lead to the network The deepening of the level makes the training of the network more difficult. Although U-net uses layer jump connections to combine low-level features and high-level features to improve network performance, since low-level features contain a large number of redundant features and background noise, direct splicing of low-level features and high-level features will bring a lot of redundancy. Excess information, which easily leads to a decrease in the accuracy and robustness of the network model. In order to solve these problems, this paper proposes a U-net model based on spatial attention. This model uses a new lightweight spatial attention module in the layer jump connection, which can effectively eliminate low-level features. Redundant information and highlighting the key features in low-level features will ultimately enable the improved spatial attention U-net to have higher segmentation accuracy and robustness. The method proposed in this paper has been experimentally verified on the Warwick-QU dataset. The experimental results show that compared with other improved U-net and traditional segmentation methods, the U-net based on spatial attention proposed in this paper has higher segmentation accuracy with only a small increase in training parameters.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420393","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

A Non-Autoregressivee Network for Chinese Text to Speech and Voice Cloning 中文文本到语音和语音克隆的非自回归网络

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497934

Chun Zhang, Yueqing Cai, Wenbi Rao

引用次数: 1