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data Quality Improvements for Internet of Things Using Artificial Neural Networks

Data Quality Improvements for Internet of Things Using Artif...

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international Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Farid Naït-Abdesselam Chafiq Titouna University of Missouri Kansas City USA University of Paris France

ISBN: (数字)9781728192901

ISBN: (纸本)9781728192918

The Internet of Things has gained considerable attention due to its potential applications in multiple domains. However, some deployment environments may be hostile and this may affect the quality of data (QoD) and alter its accuracy. In order to ensure a high level of reliability, an IoT system should be able to clean its own sensed data by discarding those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper suggests a new approach based on Artificial Neural Network (ANN). The proposed scheme can prematurely and efficiently detect outliers before forwarding them to a central processing unit. The performance of this proposed solution is validated through simulations, using a real dataset, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.

关键词： Measurement data integrity Artificial neural networks Telecommunications Internet of Things Telecommunication network reliability Monitoring

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C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

C olumnSGD: A Column-oriented Framework for Distributed Stoc...

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international conference on data Engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： conferences data engineering Radio frequency

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Querying Fuzzy Spatiotemporal RDF data Using R2RML Mappings

Querying Fuzzy Spatiotemporal RDF Data Using R2RML Mappings

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Luyi Bai Jiajia Lu Shuangdi Wang School of Computer and Communication Engineering Northeastern University (Qinhuangdao) Qinhuangdao China

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

With the development of Internet technology, information management has shown a spurt of growth. In real-life applications, information usually includes spatial, temporal, or spatiotemporal features. Spatiotemporal data has temporal and spatial attributes, and these attributes are often fuzzy. Due to the great significance of fuzzy spatiotemporal data management, how to query fuzzy spatiotemporal data efficiently and effectively has become an important research issue. In that case, this paper formally proposes a new implementation method, which is the query processing of fuzzy spatiotemporal data. In view of the advantages of the most advanced mapping method (R2RML) in data transformation, three algorithms are proposed to transform fuzzy spatiotemporal data from relational database into fuzzy RDF data based on R2RML. On this basis, according to the characteristics of fuzzy RDF data, we give three different fuzzy quantifiers (extreme fuzzy quantifier, range fuzzy quantifier, degree fuzzy quantifier) to represent fuzzy spatiotemporal RDF data. Since SPARQL plays an important role in querying RDF data, it is used for the query of fuzzy spatiotemporal RDF data. In addition, three kinds of fuzzy quantifiers are designed, and the experimental results show the superiority of this method by analyzing experiments in the aspects of the recall and precision.

关键词： Resource description framework Spatiotemporal phenomena Arrays Relational databases Containers Semantics

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Electricity Consumption Forecast Based on Empirical Mode Decomposition and Gated Recurrent Unit Hybrid Model

Electricity Consumption Forecast Based on Empirical Mode Dec...

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international conference on Communication Technology (ICCT)

作者： Qi Wang Rongheng Lin Yao Zhao Hua Zou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Electricity is one of the important needs in human production and life. The prediction of user power consumption can help power supply enterprises to analyze users' electricity consumption behavior, provide personalized services for users and formulate effective peak load shifting power supply scheme, which is very important for decision-making and demand response of power management side. As the daily electricity consumption data of users is nonlinear and nonstationary time series data, coupled with its susceptibility to climate change, social activities and other random factors, making electricity consumption forecast is a very challenging demand. At present, many deep learning models, such as recurrent neural network (RNN) and long short-term memory (LSTM) have been applied in electricity consumption forecasting and achieved good results. However, the direct use of these models cannot fully take into account the nonstationary characteristics of electricity data, and there is still room for improvement in the prediction effect. In this paper, a hybrid model of empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict user electricity consumption. First, the original nonstationary electricity consumption time series data is decomposed into multiple stationary component sequences through EMD, then each component is predicted through a multi-layer GRU network, and finally the prediction results of each component are combined to obtain the final Forecast results. Experimental results show that, compared with the direct use of LSTM, the proposed model can effectively reduce the error, achieve a better fitting effect, and improve the training efficiency to a certain extent.

关键词： Predictive models data models Time series analysis Microprocessors Power demand Deep learning Prediction algorithms

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Degree of Polymerization Evaluation of Field Bushing with Near-Infrared Spectroscopy using Radial Basis Function Neural Network

Degree of Polymerization Evaluation of Field Bushing with Ne...

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international conference on High Voltage Engineering and Application, ICHVE

作者： Xiaolin Chen Wenbo Zhang Xinran Li Ruien Zhang Xiaotao Fu Yuan Li Key Laboratory of Physical and Chemical Analysis for Electric Power of Hainan Province Electric Power Research Institute of Hainan Power Grid Co. Ltd Haikou Hainan PR China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an Shaanxi PR China

ISBN: (数字)9781728155111

ISBN: (纸本)9781728155128

The degree of polymerization (DP) is the most direct parameter to characterize the aging condition of the oilpaper insulation. Recently, near infrared spectroscopy (NIRS) is used to evaluate the DP of oil-paper insulation for it is a rapid and non-destructive detection compared with the traditional viscometric method. However, the real applications of NIRS are constrained by the poor generalization ability of current prediction algorithms. This paper studies the intelligent algorithms for precisely predicting the degree of polymerization (DP) of the oil-paper insulation. The radial basis function (RBF) neural network is adopted due to its fast learning speed and strong nonlinear mapping capability. The basic principles of RBF neural network are introduced and the model for the diagnosis is trained. Especially, considering the noise at the both ends parts of the spectrum produced by the portable near-infrared, these spectral data are removed by the algorithm. A numerical example is presented to demonstrate the capability of the proposed method and the results indicate that the aging state of oil-paper insulation can be diagnosed with a trained RBF neural network of high precision. Our proposed model is applied on the Oil-impregnated paper bushing and the application results show that this method could accurately assess the aging condition of the oil-paper insulation.

关键词： Aging Neural networks Insulation Insulators data models Predictive models Power systems

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Augmented Reality-assisted Cyber-Physical Systems of Smart University Campus

Augmented Reality-assisted Cyber-Physical Systems of Smart U...

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international Computer Sciences and Information Technologies (CSIT)

作者： Oleksandr Kramar Yuriy Drohobytskiy Yuriy Skorenkyy Oleksandr Rokitskyi Nataliia Kunanets Volodymyr Pasichnyk Oleksandr Matsiuk Ternopil Ivan Puluj National Technical University Ternopil Ukraine Ternopil Volodymyr Hnatyuk National Pedagogical University Ternopil Ukraine Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9781728174433

ISBN: (纸本)9781728174440

This paper discusses necessary stages of design and implementation of the smart university campus system. The modular structure of the system encompasses smart mobility module, smart labs, smart library and smart university museum. The mechanisms of distributed and multilevel data analysis is proposed. This approach can improve safety, reduce consumption of energy and interact effectively with other clusters in a smart-city environment. Realization of "smart fast food" project requires the creation of an information technology platform, which comprises information technology for Big data processing, a logistics development for complex structured environment as well as efficient algorithms of the optimal route search for delivery of containers taking into account the courier loads.

关键词： Big data Libraries Information technology Containers Laboratories Cyber-physical systems Education

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Credit Card Fraud Detection Using Lightgbm Model

Credit Card Fraud Detection Using Lightgbm Model

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E-Commerce and Internet Technology (ECIT), international conference on

作者： Dingling Ge Jianyang Gu Shunyu Chang JingHui Cai Northeastern University Boston United States Nankai University Tianjin China Changchun University of Science and Technology Jilin China JiNan University Guangzhou China

ISBN: (数字)9781728159027

ISBN: (纸本)9781728159034

With the rapid growth of online banking and shopping, many companies deploy their online transaction system and thus raising many issues of fraud online credit card transactions. In recent years, several studies have developed some data mining based method to overcome this problem. However, most of the studies used a small amount of data and feature engineering methods. Moreover, the learning models used by them are too weak to fit the large scale of data. This paper expands fraud detection strategy and proposed a detection algorithm using lightgbm. The dataset is IEEE-CIS Fraud Detection dataset provided by Vesta Corporation. The experiments indicated that our method outperformed the other classical methods like Support Vector Machine, Random Forest and Xgboost. Moreover, it also shows the feature importance of our feature engineering, which is valuable for feature selection and performance tuning.

关键词： Fraud detection Lightgbm Feature selection data Mining data mining Feature extraction vandalism forgery Fraud Credit cards Detection algorithms dataset Support Vector Network

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Communication Network Modeling Taking into Account Different Kinds of Inhomogeneities

Communication Network Modeling Taking into Account Different...

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international Multi-conference on Industrial Engineering and Modern Technologies (FarEastCon)

作者： E.V. Vershennik E.G. Balenko P.V. Zakalkin V.H. Fedorov FGKVOU VPO “Military Academy of communications named after Marshal of the Soviet Union S. M. Budyonny” St. Petersburg Russian Federation Federal State Budgetary Educational Institution of Higher Education DON STATE AGRARIAN UNIVERSITY Russian Federation

ISBN: (数字)9781728169514

ISBN: (纸本)9781728169521

The application efficiency of communication networks, which use the resources of cyberspace, as well as organizational needs and technical capabilities, predetermine various types of inhomogeneities that emerge in the entire set of network characteristics and properties. The applied communication network modeling methods do not take into account the inhomogeneities arising during the communication network development, which reduces modeling adequacy. The proposed method allows simulating sections of communication networks taking into account structural and topological inhomogeneities.

关键词： Nonhomogeneous media Communication networks Artificial neural networks Industrial engineering Generators data models Clustering algorithms

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Audio Melody Extraction from Monophonic Turkish Maqam Music

Audio Melody Extraction from Monophonic Turkish Maqam Music

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IEEE Signal processing and Communications Applications (SIU)

作者： Berrak Öztürk Şimşek Aydın Akan İstanbul Kültür Üniversitesi İstanbul Türkiye İzmir Ekonomi Üniversitesi İzmir Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

In this study, a new method is proposed for predominant audio melody extraction in monophonic Turkish maqam music works. The music signals are decomposed using the Improved Variable Mode Decomposition Method and the fundamental frequencies are obtained by calculation of center frequencies on each mode. In order to estimate and selection of predominant melody line some parameters are determined from the different number of frequency information in each window. The obtained results are compared to YIN and MELODIA which are the signal processing algorithms used for western music. MIREX criterias are used in the evaluation step, adhering to international standards. Simulation results have shown that Improved Variable Mode Decomposition Method gives more successful results than other methods used in comparison.

关键词： Music information retrieval Multiple signal classification Frequency modulation Signal processing data mining Streaming media Standards

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HeAT – a Distributed and GPU-accelerated Tensor Framework for data Analytics

HeAT – a Distributed and GPU-accelerated Tensor Framework f...

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IEEE international conference on Big data

作者： Markus Götz Charlotte Debus Daniel Coquelin Kai Krajsek Claudia Comito Philipp Knechtges Björn Hagemeier Michael Tarnawa Simon Hanselmann Martin Siggel Achim Basermann Achim Streit Steinbuch Centre for Computing (SCC) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute for Software Technology (SC) German Aerospace Center (DLR) Cologne Germany Institute of Bio- and Geosciences Agrosphere (IBG-3) Forschungszentrum Jülich (FZJ) Jülich Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich (FZJ) Jülich Germany

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.

关键词： Heating systems Industries data analysis Big data Parallel processing Libraries Arrays

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