Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition最新文献_第3页

An Image Watermarking Scheme Based on Voting Mechanism in Balanced Multiwavelet Domain 一种基于平衡多小波域投票机制的图像水印方案

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430240

Shaobao Wu, Zhihua Wu, Guodong Wang, Dongsheng Shen

引用次数: 0

Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection 网络入侵检测的高效深度CNN-BiLSTM模型

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430224

Jay Sinha, M. Manollas

{"title":"Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection","authors":"Jay Sinha, M. Manollas","doi":"10.1145/3430199.3430224","DOIUrl":"https://doi.org/10.1145/3430199.3430224","url":null,"abstract":"The need for Network Intrusion Detection systems has risen since usage of cloud technologies has become mainstream. With the ever growing network traffic, Network Intrusion Detection is a critical part of network security and a very efficient NIDS is a must, given new variety of attack arises frequently. These Intrusion Detection systems are built on either a pattern matching system or AI/ML based anomaly detection system. Pattern matching methods usually have a high False Positive Rates whereas the AI/ML based method, relies on finding metric/feature or correlation between set of metrics/features to predict the possibility of an attack. The most common of these is KNN, SVM etc., operate on a limited set of features and have less accuracy and still suffer from higher False Positive Rates. In this paper, we propose a deep learning model combining the distinct strengths of a Convolutional Neural Network and a Bi-directional LSTM to incorporate learning of spatial and temporal features of the data. For this paper, we use publicly available datasets NSL-KDD and UNSW-NB15 to train and test the model. The proposed model offers a high detection rate and comparatively lower False Positive Rate. The proposed model performs better than many state-of-the-art Network Intrusion Detection systems leveraging Machine Learning/Deep Learning models.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"68 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130054647","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: 34

An Improved Method of Object Detection Based on Chip 一种改进的基于芯片的目标检测方法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430236

Ji-Xiang Wei, Tongwei Lu, Zhimeng Xin

引用次数: 0

Experimental and Theoretical Scrutiny of the Geometric Derivation of the Fundamental Matrix 基本矩阵几何推导的实验与理论研究

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430227

T. Basta

{"title":"Experimental and Theoretical Scrutiny of the Geometric Derivation of the Fundamental Matrix","authors":"T. Basta","doi":"10.1145/3430199.3430227","DOIUrl":"https://doi.org/10.1145/3430199.3430227","url":null,"abstract":"In this paper, we prove mathematically that the geometric derivation of the fundamental matrix F of the two-view reconstruction problem is flawed. Although the fundamental matrix approach is quite classic, it is still taught in universities around the world. Thus, analyzing the derivation of F now is a non-trivial subject. The geometric derivation of E is based on the cross product of vectors in R3. The cross product (or vector product) of two vectors is x × y where x = ⟨x1, x2, x3⟩ and y = ⟨y1, y2, y3⟩ in R3. The relationship between the skew-matrix of a vector t in R3 and the cross product is [t]×y = t × y for any vector y in R3. In the derivation of the essential matrix we have E = [t]×R which is the result of replacing t × R by [t]×R, the cross product of a vector t and a 3×3 matrix R. This is an undefined operation and therefore the essential matrix derivation is flawed. The derivation of F, is based on the assertion that the set of all points in the first image and their corresponding points in the second image are protectively equivalent and therefore there exists a homography H&pgr; between the two images. An assertion that does not hold for 3D non-planar scenes.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126921033","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: 2

An Improved OFDM Time-Frequency Synchronization Algorithm Based on CAZAC Sequence 基于CAZAC序列的改进OFDM时频同步算法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430232

Xinming Xie, Bowei Wang, Pengfei Han

引用次数: 0

Detection of Key Structure of Auroral Images Based on Weakly Supervised Learning 基于弱监督学习的极光图像关键结构检测

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430216

Qian Wang, Tongxin Xue, Yi Wu, Fan Hu, Pengfei Han

引用次数: 1

CycleGAN Based Data Augmentation For Melanoma images Classification 基于CycleGAN的黑色素瘤图像分类数据增强

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430217

Yixin Chen, Yifan Zhu, Yanfeng Chang

{"title":"CycleGAN Based Data Augmentation For Melanoma images Classification","authors":"Yixin Chen, Yifan Zhu, Yanfeng Chang","doi":"10.1145/3430199.3430217","DOIUrl":"https://doi.org/10.1145/3430199.3430217","url":null,"abstract":"It is widely-known that melanoma is one of the deadliest skin cancers with a very high mortality rate, while it is curable with early identification. Therefore, early detection of melanoma is extremely necessary for the treatment of this disease. In recent decades, Convolutional Neural Networks (CNN) have achieved state-of-the-art performance in many different visual classification tasks, so they have also been employed in melanoma recognition tasks. Due to the complexity of the deep learning model and huge numbers of parameters, a large amount of labelled data is required to achieve a better training performance. However, in practical settings, it is difficult for many applications to obtain enough labelled sample data. This paper explore to solve this problems based on data augmentation strategy. In the experiment conducted in our paper, the training data is augmented through CycleGAN-based approaches to generate more training samples with detailed information, and then the CNN model can be trained using the artificially enlarged dataset. The experimental results show that the combination of CycleGAN data augmentation method and EfficientNet B1 can effectively saves the cost of manual annotation, while dramatically improves classification accuracy.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381248","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: 5

Real-time Efficient Facial Landmark Detection Algorithms 实时高效的人脸地标检测算法

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430200

Hanying Xiong, Tongwei Lu, Hongzhi Zhang

引用次数: 1

Ensembling Learning Based Melanoma Classification Using Gradient Boosting Decision Trees 基于集成学习的基于梯度增强决策树的黑色素瘤分类

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430215

Yipeng Han, Xiaolu Zheng

{"title":"Ensembling Learning Based Melanoma Classification Using Gradient Boosting Decision Trees","authors":"Yipeng Han, Xiaolu Zheng","doi":"10.1145/3430199.3430215","DOIUrl":"https://doi.org/10.1145/3430199.3430215","url":null,"abstract":"Melanoma has been regarded as one of the fatal skin cancer diseases all around the world. Early detection on melanoma can be quite helpful in the clinical treatment, to prevent the deterioration of the deadly diseases. Handcrafted-feature extraction and shallow architecture-based classifier (such as k-nearest neighbors algorithm, random forest, support vector machine) worked as the basis of the previous attempts in detecting process. During the recent years, the new approach named deep convolutional neural network (CNN) was used for the detecting task. Although the persistent progress and efforts have been achieved, the classification methods desire to go a further step in pursuing further improvement on its performance. The goal of this paper is to improve the detection performance using an ensemble learning framework. Both the personal information (such as the age, gender information of the patients) and latest deep learning approaches are applied in this paper. The two approaches have provided the mutual complements for each other, which demonstrated enormous advantages for the ensemble learning framework in detecting task. We conducted extensive experiments that provide a large dataset for detecting melanoma, which illustrates that our ensemble learning can provide superior performance with high accuracy.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123648050","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: 2

Annotating Documents using Active Learning Methods for a Maintenance Analysis Application 使用主动学习方法为维护分析应用程序注释文档

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2020-06-26 DOI: 10.1145/3430199.3430214

James Pope, Mark G. Terwilliger, J. A. Connell, Gabriel Talley, Nicholas Blozik, David Taylor

{"title":"Annotating Documents using Active Learning Methods for a Maintenance Analysis Application","authors":"James Pope, Mark G. Terwilliger, J. A. Connell, Gabriel Talley, Nicholas Blozik, David Taylor","doi":"10.1145/3430199.3430214","DOIUrl":"https://doi.org/10.1145/3430199.3430214","url":null,"abstract":"The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognising these documents for further information extraction. A well known deficiency of supervised learning approaches is that annotating sufficient documents to create an effective model requires valuable human effort. This paper first shows how to obtain a representative sample from a supplier's corpus. Given this sample of unlabelled documents an active learning approach is used to select which documents to annotate first using a normalised certainty measure derived from a soft classifier's prediction distribution. Finally the accuracy of various selection approaches using this certainty measure are compared along each iteration of the active learning cycle. The experiments show that a greedy selection method using the uncertainty measure can significantly reduce the number of annotations required for a certain accuracy. The results provide valuable information for users and more generally illustrate an effective deployment of a machine learning application.","PeriodicalId":371055,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125217533","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