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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

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A survey on game theoretic approaches for privacy preservation in data mining and network security 16

A survey on game theoretic approaches for privacy preservati...

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16th international conference on Mobile Systems and Pervasive Computing (MobiSPC) / 14th international conference on Future Networks and Communications (FNC) / 9th international conference on Sustainable Energy Information Technology

作者： Shah, Hitarth Kakkad, Vishruti Patel, Reema Doshi, Nishant Pandit Deendayal Petr Univ Gandhinagar India

Game theory has found widespread use in various fields like Economics, Biology, Political science, etc. and forms an aegis for logical decision making in these areas. In computer science, due to advancing technologies, there has been a pressing need to use game theory in various problems due to the lack of scalability of traditional solutions. There has been ongoing research in various fields of computer science like security, machine learning, cloud computing, etc. where game theoretic approaches are extensively used. In this paper, we present a review on game theoretical approaches to various fields in computer science such as privacy preservation, network security and intrusion detection and resource optimization In the end, this paper provides a comparative study of various game models used in different applications in a tabular format. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Game Theory Privacy Preservation Network Security Intrusion Detection and Resource Optimization

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Visual explanation of neural network based rotation machinery anomaly detection system

Visual explanation of neural network based rotation machiner...

引用

IEEE international conference on Prognostics and Health Management (ICPHM)

作者： Saeki, Mao Ogata, Jun Murakawa, Masahiro Ogawa, Tetsuji Waseda Univ Dept Commun & Comp Engn Tokyo Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9781538683576

To make a practical anomaly detection system for rotating machinery in large infrastructures, such as wind turbines, providing an explanation along with the detection results is important so that faults can be easily verified by human experts. Therefore, a method for providing a visual explanation of the predictions of a convolutional neural network (CNN)based anomaly detection system is considered in this paper. More specifically, the CNN used takes the monitoring target machine's vibrational data as input and predicts whether the target's state is healthy or anomalous. A CNN visualization technique is applied this network to obtain an explanation of its predictions. In order to evaluate the obtained explanation, it is compared with an expert diagnosis made on the same data set. The results indicate that the frequency used by the experts to detect faults was also included in the network's explanation, indicating that the proposed visualization method can be used to provide useful information to help experts verify faults.

关键词： Condition monitoring data-driven method Anomaly detection machine learning Visualization Vibration signals

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Quantitative and Ontology-Based Comparison of Explanations for Image Classification 5th

Quantitative and Ontology-Based Comparison of Explanations f...

引用

5th international conference on machine learning, Optimization, and data science (LOD)

作者： Ghidini, Valentina Perotti, Alan Schifanella, Rossano ISI Fdn Turin Italy Univ Turin Turin Italy

ISBN: (纸本)9783030375997;9783030375980

Deep learning models have recently achieved incredible performances in the Computer Vision field and are being deployed in an ever-growing range of real-life scenarios. Since they do not intrinsically provide insights of their inner decision processes, the field of eXplainable Artificial Intelligence emerged. Different XAI techniques have already been proposed, but the existing literature lacks methods to quantitatively compare different explanations, and in particular the semantic component is systematically overlooked. In this paper we introduce quantitative and ontology-based techniques and metrics in order to enrich and compare different explanations and XAI algorithms.

关键词： Explainable artificial intelligence Neural networks Deep learning Computer vision

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Industrial load forecasting using machine learning in the context of smart grid 54

Industrial load forecasting using machine learning in the co...

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54th international Universities Power Engineering conference (UPEC)

作者： Ungureanu, Stefan Topa, Vasile Cziker, Andrei Tech Univ Cluj Napoca Dept Elect Power Syst & Management Cluj Napoca Romania Tech Univ Cluj Napoca Dept Elect Engn Cluj Napoca Romania

ISBN: (纸本)9781728133492

Integration of industrial consumers into the smart grid concept can be facilitated by optimizing load forecasting for industrial consumers. Minimizing forecast errors can improve the supplier-consumer relationship by reducing balancing costs and anticipate possible network faults. The present paper aims to research the efficiency of machine learning applied for industrial load. The dataset consists of hourly recorded values for electricity consumption generated by a meat processing facility. In the context of installing complex monitoring systems with high frequency recording intervals, huge amounts of data will be generated that require detalied analysis and real time processing, otherwise the investments in the smart grid are not justified, consequently disfavouring the development and digitization of electrical networks. Integration of the industrial consumer into the smart grid concept can be applied in great detail at large industrial consumers through robust forecasting. Forecasting the energy behaviour of a industrial consumer is a difficult task, high forecasting errors have been obtained due to the unpredictability of the consumer.

关键词： industrial load forecasting machine learning smart grid random forest LSTM

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Solving an Intractable Stochastic Partial Backordering Inventory Problem Using machine learning 2nd

Solving an Intractable Stochastic Partial Backordering Inven...

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2nd international conference on Emerging Technologies in Computer Engineering - Microservices in Big data Analytics (ICETCE)

作者： Srivastav, Nidhi Srivastav, Achin SKIT Dept Comp Sci & Engn Jaipur Rajasthan India SKIT Dept Mech Engn Jaipur Rajasthan India

ISBN: (纸本)9789811383007;9789811382994

This paper addresses the intractability of order crossover in a partial backordering inventory problem. Here, the Artificial Neural Network (ANN), which is a machine leaning algorithm is used to solve a stochastic inventory problem. The results for examining order crossover with the back-propagation ANN shows notable reduction in inventory cost in comparison to linear regression method. A numerical study is taken to demonstrate the findings. This paper further draws insight on effectiveness of machine learning in comparison to regression.

关键词： ANN Order crossover machine learning

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An Accelerated Distributed Conditional Gradient Method for Online Optimization 11

An Accelerated Distributed Conditional Gradient Method for O...

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11th international conference on Intelligent Human-machine Systems and Cybernetics (IHMSC)

作者： Shen, Xiuyu Li, Dequan Dong, Qiao Xue, Sheng Anhui Univ Sci & Technol Sch Math & Big Data Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Energy & Safety Huainan 232001 Peoples R China

ISBN: (纸本)9781728118598

Based on conditional gradient, this paper present an accelerated distributed online conditional gradient algorithm denoted by ACCDOCG, which can effectively tackle the high time complexity problem of the distributed online optimization algorithm. The proposed ACCDOCG algorithm allows the network optimization objective function to be decomposed into the sum of the local objective function of each node or agent, and it utilizes the local linear optimization Oracle to replace the projection operation to improve convergence rate of the algorithm. We also theoretically analyze the convergence properties of the algorithm by introducing two types of regret bound. Finally, experimental results are implemented on various tasks, which clearly demonstrates that ACCDOCG works well in practice and compares favorably to existed optimization algorithms.

关键词： distributed optimization online learning conditional gradient linear optimization Oracle

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Haptic data Acquisition for Perceived Roughness and Hardness of Texture 2

Haptic data Acquisition for Perceived Roughness and Hardness...

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2nd IEEE international conference on Artificial Intelligence for Industries (AI4I)

作者： Ouyang, Qiangqiang Wu, Juan Cao, Zhiyong Southeast Univ Sch Instrument Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781728140872

In this paper, we presented a method to acquire haptic data of texture and its corresponding roughness and hardness perceptions simultaneously. We carried a psychophysical experiment, in which subjects wearing a haptic ring with pressure and displacement sensors explore fabricated object combined with 5 springs and a texture board. We obtained a dataset labeled with perceived magnitude of roughness and harness, which provides valuable guidance to implementing haptic classifications using machine learning algorithms and improving the realism of haptic devices.

关键词： Haptic data Psychophysical Roughness and hardness perception

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Music Tagging and Similarity Analysis for Recommendation System 1st

Music Tagging and Similarity Analysis for Recommendation Sys...

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1st international conference on Computational Intelligence in Pattern Recognition, CIPR 2019

作者： Kumar, Laxman Mitra, Anirban Mittal, Mamta Sanghvi, Vatsal Roy, Sudipta Setua, Sanjit Kumar Department of Computer Science and Engineering U.V. Patel College of Engineering Ganpat University Mehsana Kherva384012 India Department of Computer Science and Engineering Academy of Technology AdisaptagramWest Bengal712121 India Department of Computer Science and Engineering Calcutta University Technology Campus JD-2 Sector-III Salt Lake Kolkata700098 India Department of Computer Science & Engineering G. B. Pant Government Engineering College Okhla New Delhi110020 India Mallinckrodt Institute of Radiology Department Washington University in St. Louis 510 South Kingshighway Boulevard St. LouisMO63110-1076 United States

ISBN: (纸本)9789811390418

Many of the websites follow the system of retrieving and recommending music based on the metadata. Metadata is generally a text file that attached to the music file has title and genre. Without attached metadata, it is very difficult for such websites to recommend or retrieve music. A regularly utilized rundown of the fundamental components incorporates pitch, timber, surface, volume, span, and frame. In the proposed methodology to process such a vast amount of data, the distributed storage and data processing systems like Hadoop and Spark has been used. Hadoop Distributed File System has been used for storing the music files and extracting feature information. Kafka queues has been used for asynchronous feature extraction in the background and finally Spark has been used for feature analysis using machine learning algorithms. This Proposed automated system for assigning genres for music provides very promising accuracy with a high true positive value. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Metadata

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Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics 28th

Using an Autoencoder for Dimensionality Reduction in Quantum...

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28th international conference on Artificial Neural Networks (ICANN)

作者： Reiter, Sebastian Schnappinger, Thomas de Vivie-Riedle, Regina Ludwig Maximilians Univ Munchen Dept Chem Butenandtstr 5-13 Munich Germany

ISBN: (纸本)9783030304935;9783030304928

A key step in performing quantum dynamics for a chemical system is the reduction of dimensionality to allow a numerical treatment. Here, we introduce a machine learning approach for the (semi)automatic construction of reactive coordinates. After generating a meaningful data set from trajectory calculations, we train an autoencoder to find a low-dimensional set of non-linear coordinates for use in molecular quantum dynamics. We compare the wave packet dynamics of proton transfer reactions in both linear and non-linear coordinate spaces and find significant improvement for physical properties like reaction timescales.

关键词： Quantum dynamics machine learning Autoencoder Dimensionality reduction

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Semi-supervised Multi-task learning with Chest X-Ray Images 1

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10th international Workshop on machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Imran, Abdullah-Al-Zubaer Terzopoulos, Demetri Univ Calif Los Angeles Comp Sci Dept Los Angeles CA 90095 USA

ISBN: (数字)9783030326920

ISBN: (纸本)9783030326920;9783030326913

Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling-i.e., learning data generation and classification-facilitates semi-supervised training with limited labeled data. Moreover, generative modeling can be advantageous in accomplishing multiple objectives for better generalization. We propose a novel multi-task learning model for jointly learning a classifier and a segmentor, from chest X-ray images, through semi-supervised learning. In addition, we propose a new loss function that combines absolute KL divergence with Tversky loss (KLTV) to yield faster convergence and better segmentation performance. Based on our experimental results using a novel segmentation model, an Adversarial Pyramid Progressive Attention U-Net (APPAU-Net), we hypothesize that KLTV can be more effective for generalizing multi-tasking models while being competitive in segmentation-only tasks.

关键词： Semi-supervised Multi-tasking Generative modeling Classification Segmentation KL-Tversky loss Chest X-ray

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