Proceedings of the 2020 5th International Conference on Machine Learning Technologies最新文献

Comparative Analysis Using Supervised Learning Methods for Anti-Money Laundering in Bitcoin 基于监督学习方法的比特币反洗钱比较分析

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409078

Ismail Alarab, S. Prakoonwit, Mohamed Ikbal Nacer

{"title":"Comparative Analysis Using Supervised Learning Methods for Anti-Money Laundering in Bitcoin","authors":"Ismail Alarab, S. Prakoonwit, Mohamed Ikbal Nacer","doi":"10.1145/3409073.3409078","DOIUrl":"https://doi.org/10.1145/3409073.3409078","url":null,"abstract":"With the advance of Bitcoin technology, money laundering has been incentivised as a den of Bitcoin blockchain, in which the user's identity is hidden behind a pseudonym known as address. Although this trait permits concealing in the plain sight, the public ledger of Bitcoin blockchain provides more power for investigators and allows collective intelligence for anti-money laundering and forensic analysis. This fascinating paradox arises in the strength of Bitcoin technology. Machine learning techniques have attained promising results in forensic analysis, in order to spot suspicious behaviour in Bitcoin blockchain. This paper presents a comparative analysis of the performance of classical supervised learning methods using a recently published data set derived from Bitcoin blockchain, to predict licit and illicit transactions in the network. Besides, an ensemble learning method is utilised using a combination of the given supervised learning models, which outperforms the given classical methods. This experiment is performed using a newly published data set derived from Bitcoin blockchain. Our main contribution points out that using ensemble learning approach outperforms the performance of the classical learning models used in the original paper, using Elliptic data set, a time series of Bitcoin transaction graph with node transactions and directed payments flow edges. Using the same data set, we show that we are able to predict licit/illicit transactions with an accuracy of 98.13% and F1 score equals to 83.36% using the proposed method. We discuss the variety of supervised learning methods, and their capabilities of assisting forensic analysis, and propose future work directions.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114979794","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: 32

Byte Visualization Method for Malware Classification 恶意软件分类的字节可视化方法

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409093

Zhuojun Ren, Guang Chen, Wenke Lu

引用次数: 0

YU-net Lung Segment Image Preprocess Methods Used for Common Chest Diseases Prediction 用于常见胸部疾病预测的YU-net肺段图像预处理方法

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409074

Haoxiong Yu, Xianbo Xu, Ziqi Zhao, Dancheng Li

引用次数: 2

Application of Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation in Public Transport of Ulaanbaatar City, Mongolia 层次分析法-模糊综合评价在蒙古乌兰巴托市公共交通中的应用

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409075

Khaliun Nasanjargal, Jing Lu

引用次数: 0

Efficient Logistic Regression with L2 Regularization using ADMM on Spark 基于Spark的ADMM高效L2正则化逻辑回归

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409077

Xiao Su

引用次数: 0

A Short Text Classification Approach with Event Detection and Conceptual Information 基于事件检测和概念信息的短文本分类方法

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409091

Wei Yin, Liping Shen

{"title":"A Short Text Classification Approach with Event Detection and Conceptual Information","authors":"Wei Yin, Liping Shen","doi":"10.1145/3409073.3409091","DOIUrl":"https://doi.org/10.1145/3409073.3409091","url":null,"abstract":"Text classification is an elementary task in Natural Language Processing (NLP). Existing methods, such as Long Short-Term Memory Networks (LSTM) and Attention Mechanism have recently achieved strong performance on multiple NLP related tasks. However, in the field of text classification, their results are often limited by the quality of feature extraction. This phenomenon is particularly prominent in short text classification tasks, since short text does not have enough contextual information compared to paragraphs and documents. To address this challenge, in this article, we propose a method to enhance the semantic information of short text with two aspects: event-level information extracted from text and conceptual information retrieved from external knowledge base. We take event and conceptual information as a type of supplementary knowledge and incorporate it into deep neural networks. Attention mechanism is utilized to measure the importance of the supplementary knowledge. Meanwhile, we have discussed the granularity selection for Chinese word segmentation, and select char-based models. Finally, we classify a short text with the help of event and conceptual information. The experimental results show that the proposed method outperforms the state-of-the-art methods.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133343455","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

Data Assimilation by Artificial Neural Network using Conventional Observation for WRF Model WRF模型常规观测数据的人工神经网络同化

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409097

Shijin Yuan, Bo Shi, Bin Mu

引用次数: 0

Prediction of Soybean Yield using Self-normalizing Neural Networks 利用自归一化神经网络预测大豆产量

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409092

Kaki Shu

引用次数: 2

Domain Adaptation Based Person-Specific Face Anti-spoofing Using Color Texture Features 基于领域自适应的人脸颜色纹理特征防欺骗

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409087

Junwei Zhou, Ke Shu, Dongdong Zhao, Zhe Xia

{"title":"Domain Adaptation Based Person-Specific Face Anti-spoofing Using Color Texture Features","authors":"Junwei Zhou, Ke Shu, Dongdong Zhao, Zhe Xia","doi":"10.1145/3409073.3409087","DOIUrl":"https://doi.org/10.1145/3409073.3409087","url":null,"abstract":"Face anti-spoofing technology is indispensable for the face recognition system, which is vulnerable to malicious spoofing attacks such as printed attacks and replayed video attacks. In this paper, we focus on more challenging cross-database generalization, which can reflect the performance of face anti-spoofing methods in the practical application scenario. In general, the discrepancy between the sample distribution of different databases can be reduced by domain adaptation algorithms. For texture-based face anti-spoofing methods, the identity of subjects can significantly influence the effectiveness of domain adaptation. Thus, in this paper, we propose a domain adaptation based person-specific face anti-spoofing method to improve cross-database generalization. The feature used here is the local directional number pattern (LDN) extracted from HSV and YCbCr color spaces. For each target subject, we synthesize virtual fake samples for training using subject domain adaptation method. To further improve the generalization performance, we use the domain adaptation method to reduce the discrepancy between sample distribution of training and testing samples. To evaluate the performance of the proposed method, we perform cross-database experiments on CASIA and Replay-Attack database. Our method can realize promising generalization performance, outperforming most of the recently proposed methods.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129814720","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: 4

Top-down Feature Aggregation Block Fusion Network for Salient Object Detection 基于自顶向下特征聚合块融合网络的显著目标检测

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409076

Meiyi Li, Lide Zhou

{"title":"Top-down Feature Aggregation Block Fusion Network for Salient Object Detection","authors":"Meiyi Li, Lide Zhou","doi":"10.1145/3409073.3409076","DOIUrl":"https://doi.org/10.1145/3409073.3409076","url":null,"abstract":"The emergence of deep neural networks and full convolutional neural networks has brought great progress to salient object detection. In this paper, we propose a new type of deep full convolutional neural network structure, named top-down feature aggregation block fusion network, which aims to fuse the rich features of feature aggregation blocks at each layer. In addition to the features of this layer, the feature aggregation blocks have other layer features, that is, each layer of feature aggregation blocks has both strong semantic information of the deep network and detailed features of the shallow network. In the top-down fusion process, the residual information of each layer can be learned like ResNet. At the same time, a non-local attention mechanism is introduced to improve the relevance of the context, and multiple auxiliary supervision connections are added to the intermediate layers, so that the network can more easily optimize and accelerate convergence. We have performed experiments on six benchmark datasets, and the results of the experiments show that our model is superior to the state-of-the-art methods both quantitatively and qualitatively.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127938201","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