2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)最新文献_第5页

A YOLOX-CBAF Traffic Sign Detection Algorithm 一种yox - cbaf交通标志检测算法

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217702

Zhanxin Sun, Huasheng Zhu, Shuyin Tang, Yongjian Li, Mingzhi He

{"title":"A YOLOX-CBAF Traffic Sign Detection Algorithm","authors":"Zhanxin Sun, Huasheng Zhu, Shuyin Tang, Yongjian Li, Mingzhi He","doi":"10.1109/SEAI59139.2023.10217702","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217702","url":null,"abstract":"Traffic sign detection plays a crucial role in autonomous driving tasks. However, in practical applications, the accuracy and real-time performance of general object detection algorithms are often compromised by factors such as scene variations, distance changes, and lighting conditions. To address these challenges, this paper proposes a YOLOX-CBAF algorithm.To improve the detection performance of small traffic sign targets, we propose the CBAM-PIM module, which enhances the channel attention part of the CBAM module. Instead of the original adaptive average pooling layer with forced dimension reduction, different direction pooling layer modules are used to effectively establish the correlation of channel attention modules with features in different directions, thus strengthening the channel representation ability.To address the issue of poor detection performance in complex backgrounds, we propose the ASFF-Dconv module, which replaces the nearest-neighbor interpolation operator in the sampling part of ASFF with a depth-wise separable transpose convolution module. We use a learnable sampling structure to enhance the network’s ability to extract edge information, and use a large convolution kernel’s depth-wise separable structure to reduce the computational complexity of transpose convolution and avoid the aliasing effect. This effectively solves the impact of complex backgrounds on traffic sign detection accuracy.Experimental results show that the proposed algorithm effectively addresses the challenges of detecting small targets and complex backgrounds, and improves the mean average precision (mAP0.5) by 4.33% compared to the YOLOX-tiny algorithm.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133421168","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

Impact of Deep Feature Synthesis on Deep Learning in Electronic Transaction Fraud Detection 深度特征综合对深度学习在电子交易欺诈检测中的影响

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217777

Mohammed Najim Uddin, S. Azad, Md. Rahat Hossain, Ritesh Chugh

{"title":"Impact of Deep Feature Synthesis on Deep Learning in Electronic Transaction Fraud Detection","authors":"Mohammed Najim Uddin, S. Azad, Md. Rahat Hossain, Ritesh Chugh","doi":"10.1109/SEAI59139.2023.10217777","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217777","url":null,"abstract":"Electronic transaction fraud has been a severe threat in recent years, causing substantial financial losses and devaluing the reputation of financial institutions. Various machine learning and deep learning models have been cited in the literature to detect electronic transaction fraud effectively. Feature selection is crucial in enhancing the performance of a machine learning model. Automatic feature engineering techniques outweigh the manual feature selection process regarding the time spent and adapting to the changing nature of the dataset. However, to the best of our knowledge, the impact of state-of-the-art feature selection techniques, such as Deep Feature Synthesis (DFS), on the performance of deep learning-based electronic transaction fraud detection has not been thoroughly studied. Our study uses the DFS algorithm to generate features automatically from the labelled credit card transaction dataset collected from open-source sites. The data is fed into a baseline deep learning model, i.e., Convolutional Neural Networks (CNN), in two separate cases- with DFS and without DFS. A comparison of results from both cases shows that CNN with DFS outperforms the standalone CNN significantly in terms of accuracy, precision, recall, and F1 scores.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114453107","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

Relative Positioning of Multiple Robots Based on Panoramic Vision 基于全景视觉的多机器人相对定位

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217677

Fangmin Zhu, Yuxiu Wu, Xiaokun Guo, Zhichao Hao, Han-dong Zhang

引用次数: 0

A Kind of Paradigm-Based Software Cost Estimation Method Using LSTM-CRF 一种基于范例的LSTM-CRF软件成本估算方法

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217504

Xiaolu Zhu, Bin Fu, Yang Lu, Tao Lin, Xiaochen Lv

引用次数: 0

Neural Machine Translation for Recovering ASTs from Binaries 从二进制文件中恢复ast的神经机器翻译

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217602

Dharma Kc, Tito Ferra, C. Morrison

{"title":"Neural Machine Translation for Recovering ASTs from Binaries","authors":"Dharma Kc, Tito Ferra, C. Morrison","doi":"10.1109/SEAI59139.2023.10217602","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217602","url":null,"abstract":"Recovering higher-level abstractions of source code from binaries is an important task underlying malware identification, program verification, debugging, program comparison, vulnerability detection, and helping subject matter experts understand compiled code. Existing approaches to extracting higher-level structures from lower-level binary code rely on hand-crafted rules and generally require great time and effort of domain experts to design and implement. We present Binary2AST, a framework for generating a structured representation of binary code in the form of an abstract syntax tree (AST) using neural machine translation (NMT). We use the Ghidra binary analysis tool to extract assembly instructions from binaries. A tokenized version of these instructions are then translated by our NMT system into a sequence of symbols that represent an AST. The NMT framework uses deep neural network models that can require a lot of training examples. To address this, we have developed a C source code generator for a restricted subset of the C language, from which we can sample an arbitrary number of syntactically correct C source code files that in turn can be used to create a parallel data set suitable for NMT training. We evaluate several variant NMT models on their ability to recover AST representations of the original source code from compiled binaries, where the best-performing attention-based model achieves a BLEU score of 0.99 on our corpus.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022511","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

SEAI 2023 Cover Page SEAI 2023封面

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/seai59139.2023.10217529

引用次数: 0

Background Music Removal Using Deep Learning 使用深度学习去除背景音乐

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217480

Shun Ozawa, Tomomi Ogawa

{"title":"Background Music Removal Using Deep Learning","authors":"Shun Ozawa, Tomomi Ogawa","doi":"10.1109/SEAI59139.2023.10217480","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217480","url":null,"abstract":"Due to the increasing popularity of video-sharing websites and social networking services, videos are frequently posted to the Internet. However, users must be careful when posting copyright materials, e.g., music. Thus, the purpose of this paper is to investigate a method to remove music from audio data that include a mixture of various sounds, e.g., driving noises, human conversation, and music. Although speech enhancement techniques can be utilized to remove music, such methods also remove noise other than music, which reduces the realism of the content. Sound source separation can also be utilized for music removal, however, it has to select non-music sounds after separation, which increases the process. Thus, we conducted a preliminary experiment using a deep learning method in order to remove only music from audio data that include a mixture of speech and music sounds in a monaural audio signal, maintaining original noises and without sound source separation. We used Conv-TasNet for the network structure and the parameters were updated such that the RMSE value between the output of the model and the original speech sound was small. The results showed that accuracy tended to improve with longer sample lengths in the range of 50–1,000 ms. The music removal results demonstrated that music components were reduced overall.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134220042","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

Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China 基于改进多变量加权模型的滑坡易感性评价研究——以芦山县为例

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217582

Li Zhao, Gonghao Duan

{"title":"Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China","authors":"Li Zhao, Gonghao Duan","doi":"10.1109/SEAI59139.2023.10217582","DOIUrl":"https://doi.org/10.1109/SEAI59139.2023.10217582","url":null,"abstract":"The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the \"4-20\" Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy.","PeriodicalId":381082,"journal":{"name":"2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114877194","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

Line Quality Evaluation and Service Scheduling Scheme Based on SD-WAN Network 基于SD-WAN网络的线路质量评价与业务调度方案

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217707

Yang Gao

引用次数: 0

A New Convolutional Neural Network Architecture for Mars Landmarks Classification Based on MobileNetV2 基于MobileNetV2的火星地标分类卷积神经网络新架构

2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI) Pub Date : 2023-06-16 DOI: 10.1109/SEAI59139.2023.10217391

Runpeng Xia, Sirui Du, Jiahang Liu, Chenjia Li

引用次数: 0