Fifth International Conference on Computer Information Science and Artificial Intelligence最新文献_第8页

Cointegration identification with metric learning 与度量学习的协整辨识

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667621

Zeyu Xia, Changle Lin

引用次数: 0

An improved deep-learning monocular visual SLAM method based on local features 一种改进的基于局部特征的深度学习单目SLAM方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667711

Rui Yu, Chenhai Long, Guoliang Ma, Jianpo Guo, Lisong Xu, Zhaoli Guo

引用次数: 0

JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning JSVulExplorer:基于迁移学习的JavaScript漏洞检测模型

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667324

S. Chen, Nan Jiang, Zheng Wu, Zichen Wang

{"title":"JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning","authors":"S. Chen, Nan Jiang, Zheng Wu, Zichen Wang","doi":"10.1117/12.2667324","DOIUrl":"https://doi.org/10.1117/12.2667324","url":null,"abstract":"Software vulnerabilities will make the system vulnerable to attack, affect the reliability of the software and cause information leakage, which will have a huge impact on enterprises or individuals. Vulnerabilities are inevitable in software development engineering. Therefore, relying on some methods or tools for continuous vulnerability analysis of code is the solution to minimize software vulnerabilities. We propose a neural network model, JSVulExplorer, for static vulnerability analysis of the dynamic programming language JavaScript. The JSVulExplorer focuses on feature enhancement of data. We use pre-training to learn the semantic similarity between code slices, utilize abstract syntax trees to generate path information, and design positional encoding to use the path information. Based on transfer learning, we combine the pre-trained model with path information to improve vulnerability detection performance. Experiments show that JSVulExplorer has significantly improved precision and recall compared to previous models. It is verified that the dynamic event-based programming language can also use the static analysis method for vulnerability detection.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134083338","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

Airfoil inverse design based on particle swarm optimization algorithm 基于粒子群优化算法的翼型反设计

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667289

Qinghe Zhao

引用次数: 0

A reinforcement learning-based network load balancing mechanism 基于强化学习的网络负载均衡机制

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667915

Jiawei Wang

{"title":"A reinforcement learning-based network load balancing mechanism","authors":"Jiawei Wang","doi":"10.1117/12.2667915","DOIUrl":"https://doi.org/10.1117/12.2667915","url":null,"abstract":"With the exponential growth of cloud computing demands, load balancing gradually becomes the infrastructure of high concurrency applications. The increased demand makes load balancing the cornerstone of large-scale systems’ stability and efficiency. Due to the diversity of modern client-server architecture, irregular fluctuations in cluster system resource allocation gradually become significant. The existing load balancing algorithms are rule-based, leading to a gap between operational and practical scenarios. The phenomenon causes noticeable tribulations in cluster resource utilization optimization. This research proposes a reinforcement learning-based load balancing model optimization approach. This paper modelled the load balancing problem as a Markov Decision Process and implemented the conjecture with the Q-learning algorithm. This paper performs a load balancer that can efficiently utilize the resources in the cluster system by observing the association between the packet body and node resource utilization rate in the cluster system. The experiment demonstrated that the author’s mechanism substantially improves the average cluster system resource utilization efficiency and reduces the Round-Trip Time performance in real network environments","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":" 454","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131977357","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 high performance bitcoin trading strategy prediction model 一种高性能比特币交易策略预测模型

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667409

Yunjing Zhang, Jiaxu Sun, Hengbin Liu, Shaoqing Shi, Qiufeng Wang

引用次数: 0

Maize pests identification based on improved YOLOv4-Tiny 基于改良YOLOv4-Tiny的玉米害虫鉴定

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667932

Haiying Lin, Yuyue Zhang, Yukun Zhang, Dexue Zhang

{"title":"Maize pests identification based on improved YOLOv4-Tiny","authors":"Haiying Lin, Yuyue Zhang, Yukun Zhang, Dexue Zhang","doi":"10.1117/12.2667932","DOIUrl":"https://doi.org/10.1117/12.2667932","url":null,"abstract":"Agricultural pest identification occupies a key position in agricultural economy and development. The accurate identification of pests is the premise of agricultural pest control. In recent years, image processing technology and deep learning technology have rapidly developed. Some researches have been applied to the field of insect recognition. Thus, some insect recognition deep learning models with good recognition accuracy and speed have been established. However, there is still much room for improvement when they were applied to the insect monitoring system deployed in the field. Considering the target recognition accuracy and speed, this paper selects the target detection algorithm YOLOv4-Tiny as the base model for insect recognition. The major advances are the attention mechanism and Spatial Pyramidal Pooling (SPP) structure as shown in: applying Convolutional Block Attention Module (CBAM) reduce computation and number of parameters; adopting SPP structure multi-scale pooling of input feature layers which increases the perceptual field and improves the robustness of the model. The experimental results show that the improved YOLOv4-Tiny model can significantly enhance the insect recognition accuracy.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132719578","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 fault detection method for AUV based on multi-scale spatiotemporal feature fusion 基于多尺度时空特征融合的水下航行器故障检测方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667304

Shaoxuan Xia, Xiaofeng Zhou, H. Shi, Shuai Li

{"title":"A fault detection method for AUV based on multi-scale spatiotemporal feature fusion","authors":"Shaoxuan Xia, Xiaofeng Zhou, H. Shi, Shuai Li","doi":"10.1117/12.2667304","DOIUrl":"https://doi.org/10.1117/12.2667304","url":null,"abstract":"Autonomous Underwater Vehicles (AUVs) are important equipment for ocean development and exploration. To ensure the task implementation of AUV, faults shall be detected in time. We propose a fault detection method based on Multiscale Spatiotemporal Feature fusion (MSF) for the time-varying characteristics and multiple correlation characteristics of AUV monitoring data. First, we apply a variety of sampling and data processing methods to generate monitoring windows with different scales along the time axis. Then, a composite feature extraction method is proposed to obtain temporal and spatial features simultaneously, and a feature pyramid of temporal and spatial information is formed. We use Bidirectional Long Short-Term Memory (BiLSTM) to obtain the time-series characteristics of a single monitoring variable, and Convolutional Neural Networks (CNN) to obtain the implicit spatial relationship characteristics among multiple monitoring variables. Next, we use an adaptive feature fusion method to solve the inconsistency in different feature scales, which can adaptively suppress the possible conflict information of different scale features. Finally, we use a fully connected network to detect the fault of the fused features. The fault detection experiment of Haizhe AUV shows the effectiveness and superiority of the proposed method.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132893340","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

MFCSA-CAT: a multimodal fusion method for cancer survival analysis based on cross-attention transformer MFCSA-CAT:一种基于交叉注意转换器的癌症生存分析多模态融合方法

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2668986

Shenyang Deng, Yuanchi Suo, Shicong Liu, Xin Ma, Hao Chen, Xiaoqi Liao, Jianjun Zhang, Wing W. Y. Ng

{"title":"MFCSA-CAT: a multimodal fusion method for cancer survival analysis based on cross-attention transformer","authors":"Shenyang Deng, Yuanchi Suo, Shicong Liu, Xin Ma, Hao Chen, Xiaoqi Liao, Jianjun Zhang, Wing W. Y. Ng","doi":"10.1117/12.2668986","DOIUrl":"https://doi.org/10.1117/12.2668986","url":null,"abstract":"Cancer diagnosis, prognosis, and therapeutic response predictions are based on data from various modalities, such as histology slides and molecular profiles from genomic data. In cancer clinical treatment, the technology of intelligent diagnosis for cancer patients has become an essential research domain with the rapid growth of various pathological data. In this work, we propose a multimodal fusion method for cancer survival analysis based on Cross-Attention Transformer. Compared to similar bimodal work, our work greatly reduces the number of parameters in the feature fusion model (our fusion model has 7625 parameters), and achieves the State-of-the-Art effect (81.85%) in bimodal cancer survival analysis task with histology images and genomic features data of Glioma cancer from TCGA database. （Previous bimodal Sota work in this task is Kronecker Product which achieves 81.40% with 170130 parameters）In addition, our experiments show that Cross-Attention can not only increase the correlation between the two modalities but also offer a better bimodal feature representation for the final fusion.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124655519","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

Hybrid forecasting model in used car price forecasting based on stochastic algorithm 基于随机算法的二手车价格混合预测模型

Fifth International Conference on Computer Information Science and Artificial Intelligence Pub Date : 2023-03-28 DOI: 10.1117/12.2667745

Lili Xu, Cong Tian, Linhui Liu

引用次数: 0