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Teacher-Oriented data Services for learning Analytics 11th

Teacher-Oriented Data Services for Learning Analytics

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11th international conference on Interactive Mobile Communication Technologies and learning (IMCL)

作者： Miteva, Dafinka Dimov, Aleksandar Stefanova, Eliza Sofia Univ St Kliment Ohridski Fac Math & Informat Sofia Bulgaria

ISBN: (纸本)9783319751757;9783319751740

Modern education is benefitted from using learning Management Systems (LMS) enabling automatic collection of large amount of data about students' activities, progress and results. However, currently there is a lack of enough means for teachers to effectively analyze and assess results from such data. this paper presents a research aiming to design and develop a new learning Analytics (LA) solution which uses the existing LMS as distributed systems and storages of data through user-oriented services. It integrates the best features from LA and data visualization applications, from service composition platforms empower users to build complex business processes. Using teacher-oriented services data is retrieved directly from the relevant LMS as a response to educator's request and are visualized in appropriate way for fast and easy adoption. the paper describes the new approach and functionalities of this solution and ends with some challenges and conclusions.

关键词： learning Analytics Service composition Teacher-oriented

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IoT Based intelligent Agriculture Field Monitoring System 8

IoT Based Intelligent Agriculture Field Monitoring System

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8th international conference Confluence on Cloud Computing, data Science and engineering, Confluence 2018

作者： Ashifuddinmondal, Md Rehena, Zeenat Narula Institute of Technology Department of Computer Science and Engineering Agarpara Kolkata700109 India

ISBN: (纸本)9781538617182

Agriculture is becoming an important growing sector throughout the world due to increasing population. Major challenge in agriculture sector is to improve farm productivity and quality of farming without continuous manual monitoring to meet the rapidly growing demand for food. Apart from increasing population, the climate change is also a big concern in agricultural sector. the purpose of this research work is to propose a smart farming method based on Internet of things (IoT) to deal with the adverse situations. the smart farming can be adopted which offer high precision crop control, collection of useful data and automated farming technique. this work presents an intelligent agriculture field monitoring system which monitors soil humidity and temperature. After processing the sensed data it takes necessary action based on these values without human intervention. Here temperature and moisture of the soil are measured and these sensed values are stored in thingSpeak [11] cloud for future data analysis. © 2018 IEEE.

关键词： Internet of things

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"Developing Interdisciplinary Instructional Design through Creative Problem-Solving by the Pillars of STEAM Methodology" 11th

"Developing Interdisciplinary Instructional Design Through C...

引用

11th international conference on Interactive Mobile Communication Technologies and learning (IMCL)

作者： Eleni, Neofotistou Fotini, Paraskeva Univ Piraeus Dept Digital Syst HAEF Athens Coll Elementary Sch Piraeus Greece Univ Piraeus Dept Digital Syst Learning Psychol Technol Piraeus Greece

ISBN: (纸本)9783319751757;9783319751740

STEAM methodology proposes a holistic view of educational topics and provides the tools and structures so as to combine them in everyday school practice. Science, Technology, engineering, Arts and Mathematics are the five silos, through which each topic is explored. Arts, as the new extension, is considered to be the key in bringing technological topics closer to social and humanity sciences, in order to design real inter-disciplinary teaching and learning experiences. STEAMapT(2)theGalaxy course aims to provide a proper e-learning environment so as to enable participants to manage to use STEAM activities in practice through problem-solving procedures that they design by themselves.

关键词： STEAM Creative Problem Solving Six thinking Hats Creativity Inter-disciplinary

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Unsupervised learning for mental stress detection exploration of self-organizing maps 11

Unsupervised learning for mental stress detection exploratio...

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11th international conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Huysmans, Dorien Smets, Elena De Raedt, Walter Van Hoof, Chris Bogaerts, Katleen Van Diest, Ilse Helic, Denis STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium Leuven Belgium imec Leuven Belgium imec Holst Centre Eindhoven Netherlands REVAL - Rehabilitation Research Center Faculty of Medicine and Life Sciences Hasselt University Diepenbeek Belgium Research Group on Health Psychology Department of Psychology KU Leuven Leuven Belgium Knowledge Technologies Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789897582790

One of the major challenges in the field of ambulant stress detection lies in the model validation. Commonly, different types of questionnaires are used to record perceived stress levels. these only capture stress levels at discrete moments in time and are prone to subjective inaccuracies. Although, many studies have already reported such issues, a solution for these difficulties is still lacking. this paper explores the potential of unsupervised learning with Self-Organizing Maps (SOM) for stress detection. In unsupervised learning settings, the labels from perceived stress levels are not needed anymore. First, a controlled stress experiment was conducted during which relax and stress phases were alternated. the skin conductance (SC) and electrocardiogram (ECG) of test subjects were recorded. then, the structure of the SOM was built based on a training set of SC and ECG features. A Gaussian Mixture Model was used to cluster regions of the SOM with similar characteristics. Finally, by comparison of features values within each cluster, two clusters could be associated to either relax phases or stress phases. A classification performance of 79.0% (±5.16) was reached with a sensitivity of 75.6% (±11.2). In the future, the goal is to transfer these first initial results from a controlled laboratory setting to an ambulant environment. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Electrocardiograms

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Generalized Low-Computational Cost Laplacian Eigenmaps 19th

Generalized Low-Computational Cost Laplacian Eigenmaps

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Salazar-Castro, J. A. Pena, D. F. Basante, C. Ortega, C. Cruz-Cruz, L. Revelo-Fuelagan, J. Blanco-Valencia, X. P. Castellanos-Dominguez, G. Peluffo-Ordonez, D. H. Univ Nacl Colombia Sede Manizales Manizales Colombia Corp Univ Autonoma Narino Pasto Colombia Univ Narino Pasto Colombia Yachay Tech SDAS Res Grp Urcuqui Ecuador

ISBN: (纸本)9783030034931;9783030034924

Dimensionality reduction (DR) is a methodology used in many fields linked to data processing, and may represent a preprocessing stage or be an essential element for the representation and classification of data. the main objective of DR is to obtain a new representation of the original data in a space of smaller dimension, such that more refined information is produced, as well as the time of the subsequent processing is decreased and/or visual representations more intelligible for human beings are generated. the spectral DR methods involve the calculation of an eigenvalue and eigenvector decomposition, which is usually high-computational-cost demanding, and, therefore, the task of obtaining a more dynamic and interactive user-machine integration is difficult. therefore, for the design of an interactive IV system based on DR spectral methods, it is necessary to propose a strategy to reduce the computational cost required in the calculation of eigenvectors and eigenvalues. For this purpose, it is proposed to use locally linear submatrices and spectral embedding. this allows integrating natural intelligence with computational intelligence for the representation of data interactively, dynamically and at low computational cost. Additionally, an interactive model is proposed that allows the user to dynamically visualize the data through a weighted mixture.

关键词： Dimensionality reduction Generalized methodology Kernel approximations Low-computational cost Multiple kernel learning Spectral methods

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Anomaly-Based Network Intrusion Detection Using SVM

Anomaly-Based Network Intrusion Detection Using SVM

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Yuan Zhang Qinghai Yang Sangarapillai Lambotharan Konstantinos Kyriakopoulos Ibrahim Ghafir Basil AsSadhan School of Telecom. Engineering Xidian University Shaanxi China Wolfson School of Engineering Loughborough University Leicestershire UK King Saud University Riyadh Saudi Arabia

With the rapid development of new technologies and applications of Internet, much attention has been paid to the detection of anomalies in cyberspace traffic. A series of intrusion detection techniques based on machine learning have been developed. Support vector machine (SVM), as an essential approach, has been paid close attention in this filed. Nevertheless, the existing SVM-based techniques with the training features can not efficiently detect short duration intrusions and attacks in the traffic. To tackle this issue, we propose an anomaly-based SVM detection scheme by extracting and optimizing the training features. It trains the SVM with Kullback-Leibler (KL) divergence and cross-correlation calculated by the control and data planes traffic. Following this way, the novel training method can effectively enhance the detection accuracy. And the performance of the presented scheme is validated and evaluated based on a recent realistic Internet traffic dataset. Finally, relevant results indicate that the developed method establishes the relationship between Transmission Control Protocol (TCP) traffic and intrusions. It can efficiently detect short duration intrusions and attacks in the network traffic.

关键词： Support vector machines Internet Feature extraction Intrusion detection Training Kernel data mining

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11th international conference on Interactive Mobile Communication Technologies and learning, IMCL2017

11th International Conference on Interactive Mobile Communic...

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11th international conference on Interactive Mobile Communication Technologies and learning, IMCL2017

ISBN: (纸本)9783319751740

the proceedings contain 94 papers. the special focus in this conference is on Interactive Mobile Communication Technologies and learning. the topics include: Design of lifelong learning content using the mobile E-Time capsule system;céos: A collaborative web-based application for improving teaching-learning strategies;An iOS knowledge app to support the course “Developing applications in a programming environment” of the Greek Lyceum;WIP: Design, development and implementation of a “Web technologies” android application for higher education;design of communications system for idea creation in team activity;employing theatrical interactions and audience engagement to enable creative learning experiences in formal and informal learning: Enriching social and community theatre practices with digital technologies;digital mobile-based behaviour change interventions to assess and promote critical thinking and research skills among undergraduate students;the design of a mobile system for voice e-assessment and vocal hygiene e-training;Mobilizing the semantic interpreter Pythia – Teaching engineering students to integrate GIS and soil data during in situ measurements;designing an augmented reality smartphone application for the enhancement of asthma care education;a game-based learning platform for vocational education and training;an Overview of location-based game authoring tools for education;conceptual framework of microlearning-based training mobile application for improving programming skills;the game as a way to train the mind games of reasoning;pattern-based game apps for collaborative learning about sustainable management of public space;serious games and motivation;trials of the acropolis: Teaching greek mythology using virtual reality and game based learning;design and evaluation of a virtual-reality braille writer-simulator.

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Performance Evaluation Of Lazy And Decision Tree Classifier: A data Mining Approach for Global Celebrity's Death Analysis 3

Performance Evaluation Of Lazy And Decision Tree Classifier:...

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3rd IEEE international conference on Research in intelligent and Computing in engineering (RICE)

作者： Kumar, Vivek Zinovyev, Roman Verma, Abhishek Tiwari, Prayag Natl Univ Sci & Technol MiSiS Dept Automated Control Syst Moscow Russia Natl Univ Sci & Technol MiSiS Dept Semicond Elect Moscow Russia Malaviya Natl Inst Technol Dept CSE Jaipur Rajasthan India Univ PaPadova Dept Informat Engn Padua Italy

ISBN: (纸本)9781538625996

In present world data is a valuable asset. the best utilization of this asset by means of technology gives an upper hand to an organization. Technologies like machine learning, data mining, and artificial intelligence are no exception to this either. Celebrities influence common people behaviors through biological, psychological and social processes to a great extent. Simultaneously there is vast disparity amongst their demise over the cause, region, and age. therefore, it is a challenging and interesting endorsement to work upon. the objective of this work is to come up with the comprehensive result to understand the celebrity deaths by investigating the incidence happened over the decade. the database for training is created from the public and open access databases for years 2006-2016 comprising of 11,200 reported deaths over the globe. Findings of the work are year by year extraction of death, the cause behind it, age, gender, and place. Lazy and decision tree classifier model of data mining is being used for the analysis based on the profession as evaluation class.

关键词： machine learning artificial Intelligence data mining celebrity lazy classifier decision tree

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BIOINFORMATICS 2018 - 9th international conference on Bioinformatics Models, Methods and Algorithms, Proceedings;Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

BIOINFORMATICS 2018 - 9th International Conference on Bioinf...

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9th international conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

ISBN: (纸本)9789897582806

the proceedings contain 26 papers. the topics discussed include: parameter learning for spiking neural networks modelled as timed automata;loop-loop interaction metrics on RNA secondary structures with pseudoknots;protein disorder prediction using jumping motifs from torsion angles dynamics in Ramachandran plots;computer-aided formal proofs about dendritic integration within a neuron;bicluster detection by hyperplane projection and evolutionary optimization;supervised classification of metatranscriptomic reads reveals the existence of light-dark oscillations during infection of phytoplankton by viruses;selective covariance-based human localization, classification and tracking in video streams from multiple cameras;a model-checking approach to reduce spiking neural networks;a novel computer vision methodology for intelligent molecular modeling and simulation;grammar-based compression for directed and undirected generalized series-parallel graphs using integer linear programming;species categorization via microRNAs - based on 3'UTR target sites using sequence features;apoptotic regulatory module as switched control system - analysis of asymptotic properties;classification of Helitron's types in the *** genome based on features extracted from wavelet transform and SVM methods;study on the fidelity of biodevice T7 DNA polymerase;and a new dimension of breast cancer epigenetics - applications of variational autoencoders with DNA methylation.

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PET-Train: Automatic Ground Truth Generation from PET Acquisitions for Urinary Bladder Segmentation in CT Images using Deep learning 11

PET-Train: Automatic Ground Truth Generation from PET Acquis...

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11th Biomedical engineering international conference, BMEiCON 2018

作者： Gsaxner, Christina Pfarrkirchner, Birgit Lindner, Lydia Pepe, Antonio Roth, Peter M. Egger, Jan Wallner, Jurgen Inst. of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Maxillofacial Surgery Medical University of Graz Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria

ISBN: (纸本)9781538657249

In this contribution, we propose an automatic ground truth generation approach that utilizes Positron Emission Tomography (PET) acquisitions to train neural networks for automatic urinary bladder segmentation in Computed Tomography (CT) images. We evaluated different deep learning architectures to segment the urinary bladder. However, deep neural networks require a large amount of training data, which is currently the main bottleneck in the medical field, because ground truth labels have to be created by medical experts on a time-consuming slice-by-slice basis. To overcome this problem, we generate the training data set from the PET data of combined PET/CT acquisitions. this can be achieved by applying simple thresholding to the PET data, where the radiotracer accumulates very distinct in the urinary bladder. However, the ultimate goal is to entirely skip PET imaging and its additional radiation exposure in the future, and only use CT images for segmentation. © 2018 IEEE.

关键词： Positron emission tomography

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