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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

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File Matching Based on Secure Authentication and Proxy Homomorphic Re-encryption 19

File Matching Based on Secure Authentication and Proxy Homom...

引用

11th international conference on machine learning and computing (icmlc)

作者： Yu, Bin Zhang, Chen Li, Weihong Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781450366007

With the rapid development of cloud computing and big data, users often outsource configuration files to the cloud for computing and matching to reduce the burden on the terminals. In order to prevent the leakage of privacy, this paper proposes a new privacy protection scheme based on a dual identity authentication and proxy homomorphic re-encryption private matching, and relies on two non-collusion cloud servers to complete the calculation and matching of attribute configuration files. The plaintext of the configuration file is invisible to both servers from start to finish. Even if the user colludes with one of the servers, they cannot decipher the properties file. The purpose of this is to prevent disguise by attackers and effectively protect personal privacy.

关键词： Cloud computing privacy protection identity authentication proxy homomorphic re-encryption private matching

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MagmaDNN: Towards High-Performance Data Analytics and machine learning for Data-Driven Scientific computing 34th

MagmaDNN: Towards High-Performance Data Analytics and Machin...

引用

34th international conference on High Performance computing (ISC High Performance)

作者： Nichols, Daniel Tomov, Nathalie-Sofia Betancourt, Frank Tomov, Stanimire Wong, Kwai Dongarra, Jack Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab POB 2009 Oak Ridge TN 37831 USA

ISBN: (纸本)9783030343569;9783030343552

In this paper, we present work towards the development of a new data analytics and machine learning (ML) framework, called MagmaDNN. Our main goal is to provide scalable, high-performance data analytics and ML solutions for scientific applications running on current and upcoming heterogeneous many-core GPU-accelerated architectures. To this end, since many of the functionalities needed are based on standard linear algebra (LA) routines, we designed MagmaDNN to derive its performance power from the MAGMA library. The close integration provides the fundamental (scalable high-performance) LA routines available in MAGMA as a backend to MagmaDNN. We present some design issues for performance and scalability that are specific to ML using Deep Neural Networks (DNN), as well as the MagmaDNN designs towards overcoming them. In particular, MagmaDNN uses well established HPC techniques from the area of dense LA, including task-based parallelization, DAG representations, scheduling, mixed-precision algorithms, asynchronous solvers, and autotuned hyperparameter optimization. We illustrate these techniques and their incorporation and use to outperform other frameworks, currently available.

关键词： machine learning High-performance DNN Data-driven scientific computing

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Analytics-Based on Classification and Clustering Methods for Local Community Empowerment in Indonesia 6th

Analytics-Based on Classification and Clustering Methods for...

引用

6th international conference on Soft computing in Data Science (SCDS)

作者： Yuniati, Dyah Sinaga, Kristina Pestaria Dkatalis Jakarta Indonesia Bina Nusantara Univ Dept Master Informat Syst Management Jakarta Indonesia

ISBN: (纸本)9789811673344;9789811673337

West Papua is reportedly the second-most populous province in Indonesia. The United Nations international Children's Emergency Fund (UNICEF) highlights Papua's performance in selecting the Sustainable Development Goals (SDG) indicators compared to other provinces in the country. The data shows that food, nutrition, health, education, housing, water, sanitation, and protection are defined as multidimensional child poverty. Population statistics and poverty figures show that inter-provincial equity in Indonesia needs to be re-measured. In 2008, the Regional Governments of Papua and West Papua Provinces implemented a Community Empowerment Program called "PNPM RESPEK", which provided direct community assistance for IDR 100 million per village. To determine the people's level of understanding and perception towards this program, PNPM RESPEK, in collaboration with the Central Statistics Agency, conducted an integrated PNPM RESPEK Evaluation Survey in July 2009. Based on the survey results, this paper identifies a model (pattern) of understanding the people of Papua and West Papua towards the program and finds the best method to build this model through classification techniques. Then the data model was also tested using unsupervised learning, the clustering method. The experimental results show that the J48 decision tree produces the highest accuracy compared to the others. As for clustering, the clustering hierarchy provides the best accuracy. Decision Tree J48 has the best accuracy with an accuracy of 97.31%. In this case, 97.31% of the people of Papua and West Papua who receive direct community assistance meet the level of understanding and perception of the PNPM RESPEK Program.

关键词： SDG Poverty Equality machine learning J48 Decision Tree Hierarchical clustering

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Quantifying Customer Review by Integrating Multiple Source of Knowledge 19

Quantifying Customer Review by Integrating Multiple Source o...

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11th international conference on machine learning and computing (icmlc)

作者： Liu, Yuanchao Li, Xinping Wang, Mingjiang Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China Northeast Forestry Univ Coll Informat & Comp Engn Harbin Heilongjiang Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781450366007

The recent emergence of a large volume of customer reviews on e-commerce web sites has raised concerns on the provision of intuitive and comprehensive reputation comparisons of feature dimensions. In this paper, we propose and implement a product reputation mining prototype system. A multiple-knowledge based F-O pair extraction model, which is the center piece of our work, is presented for conducting analyses toward deeper sentence-level comprehension of sentiments in customer reviews. Experimental results demonstrate the effectiveness of the proposed method.

关键词： Sentiment quantification reputation mining feature-opinion extraction machine learning

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Vehicle Detection Based on Drone Images with the Improved Faster R-CNN 19

Vehicle Detection Based on Drone Images with the Improved Fa...

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11th international conference on machine learning and computing (icmlc)

作者： Wang, Lixin Liao, Junguo Xu, Chaoqian Hunan Univ Sci & Technol Xiangtan Hunan Peoples R China

ISBN: (纸本)9781450366007

With the increasing number of vehicles, traffic management has put forward higher requirements for vehicle monitoring, thus the technology of vehicle detection based on drone images has received increasing attention. Firstly, we construct a new vehicle detection data set of 600 drone images so that to solve the vehicle detection tasks in real world. Secondly, aiming at the problem of false detection and missed detection in vehicle detection, the Faster R-CNN is improved by using ResNet and constructing Feature Pyramid Networks (FPN) to extract the image features. Finally, based on the vehicle detection data set, the improved Faster R-CNN can be used to detect vehicle targets. The experiment results show that the accuracy of improved method is 96.83%, which is 3.86% higher than that of the original Faster R-CNN method.

关键词： Drone image vehicle detection deep learning faster R-CNN

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An Automated Vehicle Tracking System Using Haar-Cascade Classifiers and Optical Character Recognition Engine

An Automated Vehicle Tracking System Using Haar-Cascade Clas...

引用

international conference on Computational Science and Technology (ICCST)

作者： N. Ramya M. Annamalai K. Santhosh Dept of CSE Sri Sairam Engineering College Chennai India

ISBN: (纸本)9781665476560

A secure lifestyle and travel are in high demand among individuals in this period of rapidly developing technologies. The number of automobiles on the road has grown over the last ten years. Being statistical, in 2009 the number of vehicles on the road was 115 million in India. After a decade, in 2019,the number increased to 295.77 million. As the automotive industry continues to develop dramatically on a daily basis, tracking individual vehicles becomes a very difficult undertaking. With the assistance of the security cameras on the side of the road, we propose an automated vehicle tracking system. For object detection, machine learning techniques like Haar-cascade classifiers are used. The text extraction is done by an Optical Character Recognition Engine called Easy OCR. The proposed work is divided into three main phases. The car is identified from each frame of the video in the first stage, which involves turning the video footage into pictures. The following stage is identifying the licence plate from the identified autos. From the observed number plates in the last step, the number plate characters are recognised.

关键词： Scientific computing Roads Urban areas Object detection Turning Automobiles Security

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AN ACCELERATION METHOD FOR computing DOMINACE CLASSES IN ORDERED INFORMATION SYSTEM 18

AN ACCELERATION METHOD FOR COMPUTING DOMINACE CLASSES IN ORD...

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international conference on machine learning and Cybernetics (IMLC)

作者： Li, Yan Zhang, Jung He, Qiang Liu, Siyuan Huo, Lujing Hebei Univ Coll Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intelli Baoding 071002 Hebei Peoples R China Beijing Normal Univ Zhuhai Sch Appl Math Zhuhai City 519087 Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 102616 Peoples R China

ISBN: (纸本)9781728128160

In rough set theory, two crisp sets (i.e., the lower and upper approximates of a target concept) is used to describe uncertainties in given information systems. Howevei;the traditional rough set models are built based on equivalence relations which do not consider the preference relationship of attribute values. Dominance relation-based rough set approach effectively solve this problem which uses dominance relations to substitute equivalence relations to deal with ordered data. In this kind of approach, the computing of dominance class is a necessary step to attribute reduction which is very time-consuming. In order to reduce the computational cost in calculating dominance classes, this paper presents a method to compute dominance classes by gradually reducing the search space in the domain. The corresponding algorithm is proposed. In each step of the algorithm, the inferior classes of the objects in a given information system are removed in the universe with the increase of the attributes. Experiments using six UCI data show that the proposed method improves the efficiency of computing dominance classes with the increasing of attributes and objects.

关键词： Rough set Dominance relation Dominance class

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Collaborative Multi-Auxiliary Information Variational Autoencoder for Recommender Systems 19

Collaborative Multi-Auxiliary Information Variational Autoen...

引用

11th international conference on machine learning and computing (icmlc)

作者： Bai, Jinxin Ban, Zhijie Inner Mongolian Univ Coll Comp Sci Hohhot Inner Mongolia Peoples R China

ISBN: (纸本)9781450366007

Collaborative filtering is widely used in recommendation systems. Hybrid approach has been proposed since the collaborative-based method is susceptible to problems such as sparsity and cold start. Recently, the related methods have pointed out that it is very effective to alleviate the above problem by inferring the stochastic distribution of the latent variables for item's auxiliary information. Usually, item boasts more than one kind of auxiliary information. How do we infer the stochastic distribution of the latent variables with multiple auxiliary information? In this paper, we proposed a collaborative multi-auxiliary information autoencoder that can simultaneously consider multiple types of auxiliary information correspondingly. On the one hand, we can successfully accomplish the above issues via the improvement of variational autoencoder. On the other hand, we demonstrated the effectiveness of our method through experiments on real datasets.

关键词： Recommendation system information retrieval deep learning autoencoder

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An Abnormal Phone Identification Model with Meta learning Two-layer Framework Based on PCA Dimension Reduction 19

An Abnormal Phone Identification Model with Meta learning Tw...

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11th international conference on machine learning and computing (icmlc)

作者： Yuan, Yahan Ji, Ke Sun, Runyuan Ma, Kun Chen, Zhenxiang Wang, Lin Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China

ISBN: (纸本)9781450366007

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.

关键词： Ensemble algorithm classification heterogeneous meta-learning two-layer framework

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Model Loss and Distribution Analysis of Regression Problems in machine learning 19

Model Loss and Distribution Analysis of Regression Problems ...

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11th international conference on machine learning and computing (icmlc)

作者： Yang, Nan Zheng, Zeyu Wang, Tianran Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 1000049 Peoples R China

ISBN: (纸本)9781450366007

The machine learning regression model is based on the assumption of normal distribution. In this paper, we mainly study the probability distribution of the machine learning model and the effect of the convergence values of different loss functions on the probability distribution model. Based on the idea of robust regression and the assumption of homogeneous variance of the model, we solved the statistical solution of two-dimensional regression problem by using least square method. The maximum likelihood estimation parameters of the probabilistic model are obtained by using the maximum likelihood estimation method. In order to compare the solving parameters of the two methods, the convergence values of L1 loss function and L2 loss function are used for the regression verification. Through the mathematical and statistical rigorous derivation, obtained two important conclusions;First, under the condition that the data satisfies normal distribution and is based on the assumption of homogeneous variance, the probability model conforms to the multivariate gaussian distribution. Secondly, the model satisfying the multi-gaussian distribution has little influence on the parameter estimation under the condition of the large number theorem, that is, the multi-gaussian distribution model has good tolerance to the loss function.

关键词： Statistics machine learning regression model maximum likelihood

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