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Some Topological Properties of the Honeycomb Rhombic Torus Based on Cayley Graph 基于Cayley图的蜂窝菱形环面的若干拓扑性质
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318357
Yue-ying Lin, Sihao Xu, Zhen Zhang
{"title":"Some Topological Properties of the Honeycomb Rhombic Torus Based on Cayley Graph","authors":"Yue-ying Lin, Sihao Xu, Zhen Zhang","doi":"10.1145/3318299.3318357","DOIUrl":"https://doi.org/10.1145/3318299.3318357","url":null,"abstract":"Honeycomb tori are attractive alternatives to torus due to the smaller node degree, leading to lower complexity and lower implementation cost. The honeycomb networks are Cayley graphs with excellent topological properties. However, some topological properties of the honeycomb rhombic tori, such as internode distance, routing algorithm and broadcasting algorithm, are not developed. In this paper, we analyze the distance between any two nodes in the honeycomb rhombic tori and present an optimal routing algorithm for this class of networks. The algorithm is fully distributed, which can construct the shortest path between any pair of vertices. A broadcasting algorithm is also presented.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123800178","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: 1
An Over-sampling Method Based on Margin Theory 基于边际理论的过采样方法
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318337
Zongtang Zhang, Zhe Chen, Weiguo Dai, Yusheng Cheng
{"title":"An Over-sampling Method Based on Margin Theory","authors":"Zongtang Zhang, Zhe Chen, Weiguo Dai, Yusheng Cheng","doi":"10.1145/3318299.3318337","DOIUrl":"https://doi.org/10.1145/3318299.3318337","url":null,"abstract":"Imbalanced data widely exists in real life, while the traditional classification method usually takes accuracy as the classification criterion, which is not suitable for the classification of imbalanced data. Resampling is an important method to deal with imbalanced data classification. In this paper, a margin based random over-sampling (MRO) method is proposed, and then MROBoost algorithm is proposed by combining the AdaBoost algorithm. Experimental results on the UCI dataset show that the MROBoost algorithm is superior to AdaBoost for imbalanced data classification problem.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"11 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123724482","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
Food Item Recognition and Intake Measurement Techniques 食品识别和摄入测量技术
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318379
A. Shehzad, Nauman Zafar, Mir Hassan, Zhidong Shen
{"title":"Food Item Recognition and Intake Measurement Techniques","authors":"A. Shehzad, Nauman Zafar, Mir Hassan, Zhidong Shen","doi":"10.1145/3318299.3318379","DOIUrl":"https://doi.org/10.1145/3318299.3318379","url":null,"abstract":"High-calorie intake can be harmful and result in numerous diseases. Standard intake of a number of calories is fundamental for keeping the right balance of calories in the human body. Currently, some techniques allow users to estimate the calorie count of their food. The latest applications developed to solve under description topic enabled the user to identify calorie part of a food item by taking its photograph. The photograph then passes some pre-processing steps, and after successful segmentation, many physical features are examined such as shape and size etc. Also, dimensions of the food object are determined. The concluding step is then recognition along with calorie estimation. In this paper, different calorie estimation techniques are reviewed. Every method has negative and positive features as well. We also throw light on the deficits of these techniques and some ideas to improve those deficits. The main aim of this review paper is to do a critical analysis of recent studies on accurate calorie estimation and food item recognition. We contribute to building a system that provides tools to monitor calorie intake by estimating calories based on food item recognition and accurate volume calculation.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128805620","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
Out-of-store Object Detection Based on Deep Learning 基于深度学习的库存外目标检测
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318328
Jinyin Chen, Zhen Wang, Kai Cheng, Hai-bin Zheng, An-tao Pan
{"title":"Out-of-store Object Detection Based on Deep Learning","authors":"Jinyin Chen, Zhen Wang, Kai Cheng, Hai-bin Zheng, An-tao Pan","doi":"10.1145/3318299.3318328","DOIUrl":"https://doi.org/10.1145/3318299.3318328","url":null,"abstract":"In the field of urban management, out-of-store operation is one of the key governance objects. Although there are many monitoring probes and large amounts video data, the management process is difficult due to the traditional technology used and the low efficiency of evidence collection. The concept of \"Smart Urban Management\" has introduced technologies such as mobile internet and cloud computing to realize the transformation of urban management into intelligent management. This paper proposed an out-of-store detection method, which combines image processing technology with deep learning model. The Faster R-CNN model is used to detect store locations and identify the out-of-store objects, and Visual Background Extractor (ViBe) method is applied to determine whether there is object outside of the store or not. Finally, a certain data processing method is used to record and collect evidence of the out-of-store operation phenomenon. The method is verified on the test data and the results show that it has a good detection effect which also prove its application value.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130758386","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
Missing Data Processing Based on Deep Neural Network Enhanced by K-Means 基于K-Means增强深度神经网络的缺失数据处理
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318391
Bin Yu, Chen Zhang, Z. Tang
{"title":"Missing Data Processing Based on Deep Neural Network Enhanced by K-Means","authors":"Bin Yu, Chen Zhang, Z. Tang","doi":"10.1145/3318299.3318391","DOIUrl":"https://doi.org/10.1145/3318299.3318391","url":null,"abstract":"This paper proposes a neural network model based on K-means to process the problem of data missing. The method first clusters the samples according to the attributes without missing values to get several clusters, and then puts these clusters into different neural networks to predict the missing values. In this paper, the data can be divided into two types: the continuous numerical type and the discrete numerical type. At the same time, corresponding neural network models are established for these two types. We conduct experiments on the dataset called Human Development Index and Its Components, showing our method to be feasible and superior.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129335954","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: 1
Double-ant Colony Based UAV Path Planning Algorithm 基于双蚁群的无人机路径规划算法
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318376
Y. Guan, Mingsheng Gao, Yufan Bai
{"title":"Double-ant Colony Based UAV Path Planning Algorithm","authors":"Y. Guan, Mingsheng Gao, Yufan Bai","doi":"10.1145/3318299.3318376","DOIUrl":"https://doi.org/10.1145/3318299.3318376","url":null,"abstract":"Path planning plays an important role in the applications of Unmanned Aerial Vehicles (UAVs). It allows the UAV to autonomously compute an optimal path from the initial point to the end by checking some specific control points or fulfill some mission specific constraints (e.g., obstacle avoidance, fuel consumption, etc.). While ant colony optimization (ACO) algorithm has attracted a great deal of attention due to the fact that ants can work cooperatively to find an optimal path. However, ACO converges slowly in finding an optimal path, particularly for the case when the problem domain is large. To solve this problem, a double-ant colony based algorithm is proposed in this paper. More specifically, in the early stage we exploit genetic algorithm to generate pheromones, thus accelerating the convergence of the algorithm. Numerical results validate the effectiveness of the proposed algorithm.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613232","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: 10
Research on Texture Defect Detection Based on Faster-RCNN and Feature Fusion 基于快速rcnn和特征融合的纹理缺陷检测研究
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318341
Zhongkang Lin, Zhiqiang Guo, Jie Yang
{"title":"Research on Texture Defect Detection Based on Faster-RCNN and Feature Fusion","authors":"Zhongkang Lin, Zhiqiang Guo, Jie Yang","doi":"10.1145/3318299.3318341","DOIUrl":"https://doi.org/10.1145/3318299.3318341","url":null,"abstract":"Product texture defect detection is one of the important quality inspection procedures in industrial production. For the traditional defect detection methods, the detection processes are cumbersome, the accuracies are not high, and the generalizations are not strong. This paper proposes a method based on Faster-RCNN and feature fusion. This method uses the ResNet network model to extract the shared convolution feature, and combines the high-level features of the ROI pooling layer output with the low-level features obtained by the direction gradient histogram (HOG) as full connection layer input. Then, optimizing the model by adjusting the training parameters and convolutional neural network structure. Experiments on the German Pattern Recognition Association (GAPR) texture defect dataset show that the proposed model has improved in the mAP index. Through the migration learning strategy, experiments are carried out on several sets of actually collected data sets. The experimental results show that the model has good adaptability and can be applied to the surface defect detection of workpieces under different conditions.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127179788","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: 8
Survey on Crowd-based Mobile App Testing 基于人群的手机应用测试调查
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318312
Ying Liu, Tao Zhang, Jing Cheng
{"title":"Survey on Crowd-based Mobile App Testing","authors":"Ying Liu, Tao Zhang, Jing Cheng","doi":"10.1145/3318299.3318312","DOIUrl":"https://doi.org/10.1145/3318299.3318312","url":null,"abstract":"In recent years, mobile applications market develop rapidly, especially in China. A large number of mobile applications are developed and applied in various fields, forming a huge market. The crowd-based mobile app testing provides a new method for mobile application quality assurance and testing services, which will also obtain huge market and development opportunities. In this paper, a survey is provided on existing research of crowd-based mobile app testing. First of all, the component, testing type and definition of mobile application crowdsourced test are introduced, and the advantages and disadvantages of crowd-based mobile app testing are summarized. Then, the framework and work flow of crowdsourcing for mobile applications are explained. Furthermore, three core research problems are discussed. Finally, possible research directions of crowd-based mobile app testing and related references are given.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126368385","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: 1
An Abnormal Phone Identification Model with Meta-learning Two-layer Framework Based on PCA Dimension Reduction 基于PCA降维的元学习双层框架异常手机识别模型
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318350
Y. Yuan, Ke Ji, R. Sun, Kun Ma, Zhenxiang Chen, Lin Wang
{"title":"An Abnormal Phone Identification Model with Meta-learning Two-layer Framework Based on PCA Dimension Reduction","authors":"Y. Yuan, Ke Ji, R. Sun, Kun Ma, Zhenxiang Chen, Lin Wang","doi":"10.1145/3318299.3318350","DOIUrl":"https://doi.org/10.1145/3318299.3318350","url":null,"abstract":"In the telecommunications industry, it is a critical and challenging problem that identify fraudulent calls in time. In the traditional abnormal phone identification method, there are generally cases where the initiative is weak and the recognition accuracy is low. In order to solve the problem of data sample imbalance and dirty data in the sample set, we use ensemble algorithms to improve the recognition accuracy of abnormal phones. Specially, we design a meta-learning two-layer framework (MTF) algorithm by integrating heterogeneous learners based on PCA dimension reduction. The experiment demonstrates that the MTF model has a great improvement in the abnormal phone identification compared with traditional classification method.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"55 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124866787","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
Abnormal Phone Analysis Based on Learning to Rank and Ensemble Learning in Environment of Telecom Big Data 电信大数据环境下基于排序学习和集成学习的异常电话分析
International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318349
Jian Liu, Ke Ji, R. Sun, Kun Ma, Zhenxiang Chen, Lin Wang
{"title":"Abnormal Phone Analysis Based on Learning to Rank and Ensemble Learning in Environment of Telecom Big Data","authors":"Jian Liu, Ke Ji, R. Sun, Kun Ma, Zhenxiang Chen, Lin Wang","doi":"10.1145/3318299.3318349","DOIUrl":"https://doi.org/10.1145/3318299.3318349","url":null,"abstract":"With the rapid development of Telecom era, the number of telecom users has increased dramatically. User phone have been widely recognized as user identities and are registered in a large number of Internet applications. Due to the leakage of user information, a series of social problems have arisen. Abnormal telephone has become a social problem to be solved. Current methods are mostly passive detection methods, and some of them are extremely expensive and do not meet the requirements of practical application. Our current situation of lack of effective control measures and active detection methods for abnormal phones. Based on the existing telecommunication big data, an abnormal phone active detection method is designed based on learning to rank and ensemble learning algorithm. The experimental results on the real dataset show that the proposed method has higher accuracy than the experimental results obtained by a single learning algorithm.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328705","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: 1
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