Proceedings of the 2020 5th International Conference on Machine Learning Technologies最新文献_第2页

Solar Power Prediction in IoT Devices using Environmental and Location Factors 使用环境和位置因素的物联网设备中的太阳能预测

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409086

Arnan Mindang, P. Siripongwutikorn

引用次数: 1

Performance Evaluation and Machine Learning based Thermal Modeling of Tilted Active Tiles in Data Centers 数据中心倾斜活动瓷砖的性能评估和基于机器学习的热建模

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409085

Z. Rasheed, Wei Xiong, Gaoxiang Cong, Hongxun Niu, Jianxiong Wan, Yongsheng Wang, Lixiao Li

{"title":"Performance Evaluation and Machine Learning based Thermal Modeling of Tilted Active Tiles in Data Centers","authors":"Z. Rasheed, Wei Xiong, Gaoxiang Cong, Hongxun Niu, Jianxiong Wan, Yongsheng Wang, Lixiao Li","doi":"10.1145/3409073.3409085","DOIUrl":"https://doi.org/10.1145/3409073.3409085","url":null,"abstract":"Thermal management system of data center continuously face a lot of challenges, because data center industry has seen a boom growth in power density. In this paper we proposed the Tilted Active Tiles (TATs) to improve the local cold air supply and prevent the air flow blow over the rack. In traditional active tiles, fans are placed horizontally which cause the airflow blows over the rack, rather than into, the racks. To solve this issue, we adjusted the angle of the active tile to direct the airflow into the rack. We further introduced ANN based thermal models to predict the thermal performance of TATs. To train the ANN models, we adopted the data set obtained from a data center of Inner Mongolia Meteorology Information Center. The prediction accuracy of the model was extensively compared and analyzed, and the prediction accuracy and overhead of different neural network structures, i.e., BP and LSTM, were evaluated. Experimental results show that the rack with blanking panels has better thermal performance, and the temperature distribution at bottom, middle and top of the rack were same under smaller PWM. Thermal efficiency model was established by BP and LSTM, in this experiment single output model and multi output model were analyzed. The single output model can predict the temperature at different heights on the rack. In single output model the predicted effect of BP model is better than LSTM. The average prediction error is 0.57. The multi-output model can only predict the temperature at a fixed height of the rack. In multi output model LSTM model is better than BP. LSTM prediction error is less than BP. The average prediction error is 0.07.","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":"114390994","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

The use of machine learning methods for fast estimation of CO2-brine interfacial tension: A comparative study 使用机器学习方法快速估计co2 -盐水界面张力:比较研究

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409095

Jiyuan Zhang, Q. Feng, Xianmin Zhang

{"title":"The use of machine learning methods for fast estimation of CO2-brine interfacial tension: A comparative study","authors":"Jiyuan Zhang, Q. Feng, Xianmin Zhang","doi":"10.1145/3409073.3409095","DOIUrl":"https://doi.org/10.1145/3409073.3409095","url":null,"abstract":"The CO2-brine interfacial tension (IFT) is key to designing the CO2 injection into underground saline aquifers in order to reduce CO2 and slow global temperature increase. Laboratory measurement of CO2-brine IFT is usually time-consuming and requires an expensive experimental apparatus and sophisticated interpretation procedures. This paper presents comprehensive comparisons of nine state-of-the-art machine learning methods for fast estimation of CO2-brine IFT. Results show that: i) the extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) exhibit the strongest robustness and generalization capability, which can be applied for accurate and fast estimation of CO2-brine IFT in practice; ii) the Gaussian process regression (GPR) is associated with the issue of overfitting and therefore may not be reliable for further application; and iii) the other six methods (support vector machine, multi-player perception, kernel ridge regression, classifier and regression tree, random forest, Adaboost) have comparable accuracies in predicting the \"unseen\" data although noticeable variations in the correlation accuracies are observed for different methods in the training stage.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"78 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":"124064491","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

An artificial immune based dynamic forensics model for distributed anonymous network 基于人工免疫的分布式匿名网络动态取证模型

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409088

H. Deng, Tao Yang

引用次数: 1

On the Application of Computer War Chess Technology in the Support of Military Supplies 论计算机战棋技术在军品保障中的应用

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409081

Chenggong Zhai, Yong Kang, Min Luo

引用次数: 0

Meta Learning for Few-Shot Joint Intent Detection and Slot-Filling 基于元学习的少射联合意图检测和缝隙填充

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409090

H. S. Bhathiya, Uthayasanker Thayasivam

{"title":"Meta Learning for Few-Shot Joint Intent Detection and Slot-Filling","authors":"H. S. Bhathiya, Uthayasanker Thayasivam","doi":"10.1145/3409073.3409090","DOIUrl":"https://doi.org/10.1145/3409073.3409090","url":null,"abstract":"Intent detection and slot filling are the two main tasks in natural language understanding module in goal oriented conversational agents. Models which optimize these two objectives simultaneously within a single network (joint models) have proven themselves to be superior to mono-objective networks. However, these data-intensive deep learning approaches have not been successful in catering the demand of the industry for adaptable, multilingual dialogue systems. To this end, we cast joint intent detection as an n-way k-shot classification problem and establish it within meta learning setup. Our approach is motivated by the success of meta learning on few-shot image classification tasks. We empirically demonstrate that, our approach can meta-learn a prior from similar tasks under highly resource constrained settings which enable rapid inference on target tasks. First, we show the adaptability of proposed approach by meta learning n-way k-shot joint intent detection using set of intents and evaluating on a completely new set of intents. Second, we exemplify the cross-lingual adaptability by learning a prior, utilizing English utterances and evaluating on Spanish and Thai utterances. Compared to random initialization, our method significantly improves the accuracy in both intent detection and slot-filling.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"150 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":"125771251","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: 15

Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain 图卷积网络在比特币区块链反洗钱中的能力

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409080

Ismail Alarab, S. Prakoonwit, Mohamed Ikbal Nacer

{"title":"Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain","authors":"Ismail Alarab, S. Prakoonwit, Mohamed Ikbal Nacer","doi":"10.1145/3409073.3409080","DOIUrl":"https://doi.org/10.1145/3409073.3409080","url":null,"abstract":"Graph networks are extensively used as an essential framework to analyse the interconnections between transactions and capture illicit behaviour in Bitcoin blockchain. Due to the complexity of Bitcoin transaction graph, the prediction of illicit transactions has become a challenging problem to unveil illicit services over the network. Graph Convolutional Network, a graph neural network based spectral approach, has recently emerged and gained much attention regarding graph-structured data. Previous research has highlighted the degraded performance of the latter approach to predict illicit transactions using, a Bitcoin transaction graph, so-called Elliptic data derived from Bitcoin blockchain. Motivated by the previous work, we seek to explore graph convolutions in a novel way. For this purpose, we present a novel approach that is modelled using the existing Graph Convolutional Network intertwined with linear layers. Concisely, we concatenate node embeddings obtained from graph convolutional layers with a single hidden layer derived from the linear transformation of the node feature matrix and followed by Multi-layer Perceptron. Our approach is evaluated using Elliptic data, wherein efficient accuracy is yielded. The proposed approach outperforms the original work of same data set.","PeriodicalId":229746,"journal":{"name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","volume":"16 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":"122080849","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: 47

Application of virtual machine in Quartermaster training 虚拟机在军需官训练中的应用

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409082

Chenggong Zhai, Zhaofan Yang, X. Lin

引用次数: 0

Method for Detecting Android Malware Based on Ensemble Learning 基于集成学习的Android恶意软件检测方法

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409084

Deng Congyi, S. Guangshun

引用次数: 3

A Trajectory-based Deep Sequential Method for Customer Churn Prediction 基于轨迹的客户流失深度序列预测方法

Proceedings of the 2020 5th International Conference on Machine Learning Technologies Pub Date : 2020-06-19 DOI: 10.1145/3409073.3409083

B. Zhu, Cheng Qian, Xin Pan, Hao Chen

引用次数: 1