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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Novel Network Traffic Anomaly Detection Approach Using the Optimal $\varphi$-DTW

A Novel Network Traffic Anomaly Detection Approach Using the...

引用

IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Peng Zhan Haoran Xu Wei Luo Xueqing Li School of Software Shandong University Jinan Shandong China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Under the current severe situation of cyber security, it is of great significance to propose an effective anomaly detection approach for ensuring the stability of network. It is generally known that the network traffic data is a kind of typical streaming time series data, which are recorded by network equipments usually accompanied by time instants. In order to detect the anomalous sections in network traffic data effectively, we propose an unsupervised anomaly detection approach based on anomaly definition in time series by utilizing the optimal φ-DTW and the corresponding similarity matrix, which is called ADOPD. Comprehensive experiments have demonstrated that our proposed approach achieves satisfying performance on detecting anomalous in real world data sets.

关键词： Deep learning conferences Time series analysis Telecommunication traffic Stability analysis Anomaly detection Software engineering

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Stochastic Neighbor Embedding Algorithm Based on Quantum Genetic Algorithm with Gaussian Parameters

Stochastic Neighbor Embedding Algorithm Based on Quantum Gen...

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IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Jiayi Song Dan Li Haoyang Li Lanfei Ma Yidan Xing Zan Yang Tongji Zhejiang College Jiaxing Zhejiang China Faculty of Science Tongji Zhejiang College Jiaxing Zhejiang China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Stochastic neighbor embedding algorithm is an important nonlinear dimension reduction manifold learning algorithm in the field of big data and machine learning. In the stochastic neighborhood embedding algorithm, it changes the idea of constant distance based on the medium in and, while mapping high-dimensional to low-dimensional, trying to ensure that the distribution probability of each other is consistent. the gradient descent method is often used to solve the problem of minimum divergence, but because the gradient descent method has the disadvantage of easily falling into local optimal values, this article combines SNE with a quantum genetic algorithm and uses the strong uncertainty of the quantum genetic algorithm and high convergence to solve the problem.

关键词： Machine learning algorithms Uncertainty Search methods Optimization Genetic algorithms Convergence Software engineering

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Students learning results prediction with usage of mixed diagnostic tests and 2-simplex prism 11th

Students learning results prediction with usage of mixed dia...

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11th international conference on intelligent data Processing, IDP 2016

作者： Yankovskaya, Anna Dementyev, Yury Yamshanov, Artem Lyapunov, Danil Tomsk State University of Architecture and Building Tomsk Russia National Research Tomsk State University Tomsk Russia Tomsk State University of Control Systems and Radioelectronics Tomsk Russia National Research Tomsk Polytechnic University Tomsk Russia

ISBN: (纸本)9783030353995

Students learning results prediction is one of the "hottest" problems in modern learning process. We describe mathematical framework of students learning results assessment on the basis of mixed diagnostic tests (MDT). this framework includes cognitive graphic tools 2-simplex and 2-simplex prism, being the powerful means of data visualization for learning outcomes evaluation and efficient goal-setting. A new approach to the prediction of students’ learning results based on MDT and cognitive graphic tools is proposed. Students test results for the e-learning course "Selected Chapters of Electronics" and examples of their learning outcomes cognitive visualization are given. the proposed approach to prediction and cognitive visualization is discussed. Cross-platform software implementation specificity of cognitive graphic tools invariant to problem areas is described. © Springer Nature Switzerland AG 2019.

关键词： data visualization

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Design of intelligent IP Address Management System for Communication Networks

Design of Intelligent IP Address Management System for Commu...

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international conference on Computing and Networking Technology (ICCNT)

作者： N. Sahana Usha Rani K.R. RV College of Engineering Bengaluru India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

IP addresses play a crucial role in the current digital world which deals lakhs of devices and networks. With the advent of cloud computing and the Internet of things, the number of devices connected to a network has tripled hence increasing the complexity of networks. this has led to an ever-growing demand for IP addresses. thus Organizations all around the world have to handle the IP address space with care using tools such as IP Address management systems (IPAM). the existing IP Address management systems have several drawbacks such as dependencies on external packages and lack of data harmonization. this paper aims at designing a robust and intelligent IP address management system which overcomes the drawbacks of the existing system. It also comments on the performance of the design based on size, cost, time, and figure of merit. the implemented system reduced the time consumed to manage IP addresses by 98.61% and also reduced the errors in IP Asset data by 36.3%.

关键词： IP networks Servers Protocols Monitoring Testing Task analysis databases

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Stacking-based ensemble learning on low dimensional features for fake news detection 21

Stacking-based ensemble learning on low dimensional features...

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21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Songqian Ma, Kun Niu, Xuewei Wang, Yufeng Ji, Ke Yu, Ziqiang Chen, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9781728120584

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. this has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches. © 2019 IEEE.

关键词： Pipelines

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Meta-Relation Networks for Few Shot learning 11

Meta-Relation Networks for Few Shot Learning

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11th international conference on Advanced Computational Intelligence (ICACI)

作者： Ye, An Wang, Ruomei Luo, Xiaonan Lang, Rushi Guilin Univ Elect Technol Guangxi Key Lab Intelligent Proc Comp Images & Gr Guilin Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781538677322

We propose a meta-relation network to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each. Meta-relation networks is based on relation networks and Model-Agnostic Meta-learning (MAML) training methods, which can be trained end-to-end. After training with MAML algorithm, the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step. It can also classify new classes of images by calculating the scores between query images and few examples of each new class. In experiments, we will demonstrate that the proposed approach leads to good performance on two few-shot image classification benchmarks.

关键词： Deep learning Mela learning Few Shot Classification

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Machine learning Method for Spinning Cyber-Physical Production System Subject to Condition Monitoring 16th

Machine Learning Method for Spinning Cyber-Physical Producti...

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16th international conference on Cooperative Design, Visualization, and engineering (CDVE)

作者： Farooq, Basit Bao, Jinsong Donghua Univ Coll Mech Engn Shanghai Peoples R China

ISBN: (纸本)9783030309480;9783030309497

Digitalization encapsulates the importance of machine condition monitoring which is subjected to predictive analytics for realizing significant improvements in the performance and reliability of rotating equipment i.e., spinning. this paper presents a machine learning approach for condition monitoring, based on a regularized deep neural network using automated diagnostics for spinning manufacturing. this article contributes a solution to find disturbances in a running system through real-time data sensing and signal to process via industrial internet of things. Because this controlled sensor network may comprise on different critical components of the same type of machines, therefore back propagation neural network based multi-sensor performance assessment and prediction strategy were developed for our system which worked as intelligent maintenance and diagnostic system. It is completely automatic requiring no manual extraction of handcrafted features.

关键词： Cyber-physical production system Condition monitoring Machine learning Prognostics and health management Spinning

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Intra and Intergroup Cooperative learning in Industrial Informatics Area 13th

Intra and Intergroup Cooperative Learning in Industrial Info...

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Int Joint Conf of the 13th Int Conf on Soft Comp Models in Ind and Environm Applicat (SOCO) / 11th Int Conf on Computat Intelligence in Secur for Informat Syst (CISIS) / 9th Int Conf on European Transnat Educ (ICEUTE)

作者： Manuel Lopez-Guede, Jose Antonio Ramos-Hernanz, Jose Apinaniz, Estibaliz Mesanza, Amaia Delgado, Ruperta Grana, Manuel Basque Country Univ UPV EHU Fac Engn Vitoria Gasteiz C Nieves Cano 12 Vitoria 01006 Spain Basque Country Univ UPV EHU Computat Intelligence Grp Leioa Spain

ISBN: (纸本)9783319941202;9783319941196

this paper describes an educational experiment carried out by an Educational Innovation Project (EIP), developed in the field of Industrial Informatics during the biennium 2011/2013 at the Faculty of engineering of Vitoria-Gasteiz (University of the Basque Country, UPV/EHU, Spain). In this paper the general situation regarding the European Higher Education Area (EHEA) at the start of that biennium, as well as the situation and specific problems that occurred in the field of Industrial Informatics at the Faculty are described. It was proposed to rectify the situation by the explicit formulation of ambitious objectives and the use of active learning methods, specifically by intragroup (between members of the same group) and intergroup (between members of different groups) cooperative learning. the paper includes the designing details of the proposed innovation carried out, the designed assessment, and the steps taken for the implementation in each one of the two years of the project. the results have been successful in the academic field since the specific and generic competences have been achieved and even from the point of view of the evaluation of teachers by students.

关键词： learning systems

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Terminal bulb segmentation of caenorhabditis elegans under small samples based on two-stage u-net network 3

Terminal bulb segmentation of caenorhabditis elegans under s...

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3rd international conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - the 11th international conference on Information and Multimedia Technology, ICIMT 2019

作者： Cao, Si Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. the method solves the problem that traditional singlestage network cannot be implemented in small samples. the Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method. © 2019 Association for Computing Machinery.

关键词： Computer vision

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Visualizing Industrial Development Distance to Better Understand internationalization of Spanish Companies 13th

Visualizing Industrial Development Distance to Better Unders...

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作者： Jimenez, Alfredo Herrero, Alvaro KEDGE Business Sch Dept Management Bordeaux France Univ Burgos Dept Ingn Civil Escuela Politecn Super Grp Inteligencia Computac Aplicada GICAP Av Cantabria S-N Burgos 09006 Spain

ISBN: (纸本)9783319941202;9783319941196

the analysis of bilateral distance between home and host countries is a key issue in the internationalization strategy of companies. As a multi-faceted concept, distance encompasses multiple dimensions, with psychic distance being one of the most critical ones for the overseas investments of firms. Among all the psychic distance stimuli that have been proposed until now, the present paper focuses on Industrial Development Distance (IDD). Together with data from both the countries and the companies, IDD is analysed by means of neural projection models based on unsupervised learning, to gain deep knowledge about the internationalization strategy of Spanish large companies. Informative projections are obtained from a real-life dataset, leading to useful conclusions and the identification of those destinations attracting large flows of investment but with a particular idiosyncrasy.

关键词： Artificial neural networks Unsupervised learning Exploratory projection Industrial development distance internationalization

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