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A Metric Framework for Quantifying data Concentration 20th

A Metric Framework for Quantifying Data Concentration

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Mitic, Peter Santander UK Risk London NW1 3AN England UCL Dept Comp Sci Gower St London WC1E 6BT England Lab Excellence Regulat Financiere LabEx ReFi Paris France

ISBN: (纸本)9783030336172;9783030336165

Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. three metrics are developed to rationalise the result with such data. the metrics exploit the distributional properties of the data to rationalise neural net results. they are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. the results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results.

关键词： Copula Hypersphere Cluster Herfindahl-Hirschman HHI Credit Concentration Decision criterion Tensorflow Neural Net

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Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation 20th

Multimodal Web Based Video Annotator with Real-Time Human Po...

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20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Rodrigues, Rui Madeira, Rui Neves Correia, Nuno Fernandes, Carla Ribeiro, Sara Polytech Inst ESTSetubal Sustain RD Ctr Setubal Portugal NOVA Univ Lisboa FCT DI NOVA LINCS Lisbon Portugal NOVA Univ Lisboa FCSH ICNOVA Lisbon Portugal

ISBN: (纸本)9783030336172;9783030336165

this paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. the CultureMoves' "Motion-Notes" Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. this prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.

关键词： Multimodal video annotations Real-time human pose estimation Machine learning for creativity

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Dual learning in the Bachelor's Degree in Automotive engineering at the Faculty of engineering of Vitoria-Gasteiz: Quality Requirements 13th

Dual Learning in the Bachelor's Degree in Automotive Enginee...

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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： Mesanza-Moraza, Amaia Tazo-Herran, Inmaculada Antonio Ramos-Hernanz, Jose Delgado, Ruperta Sancho-Saiz, Javier Manuel Lopez-Guede, Jose Apinaniz, Estibaliz Basque Country Univ UPV EHU Fac Engn Vitoria Gasteiz C Nieves Cano 12 Vitoria 01006 Spain

ISBN: (纸本)9783319941202;9783319941196

Since 2017-2018 academic year, the Bachelor's Degree in Auto-motive engineering has been implemented at the Faculty of engineering of Vitoria-Gasteiz (Basque Country University, UPV/EHU) in dual format, based on the complementarity of the University and company learning environments. With this type of education the aim is to achieve greater motivation of the students and facilitate the work insertion by having greater contact with companies. In this way, companies are fully integrated with the agents who are responsible for the education of university students. the teaching institutions need to accredit Bachelor's degrees or itineraries that are taught using this training format (dual training). In order to get this purpose, the university institutions adapt their studies in dual format to the quality requirements proposed by the state quality agencies of the university systems, based on objective evaluation criteria.

关键词： Quality Internship Company Dual learning Accreditation

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Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT 11

Deep RL-based Trajectory Planning for AoI Minimization in UA...

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11th IEEE international conference on Wireless Communications and Signal Processing (WCSP)

作者： Zhou, Conghao He, Hongli Yang, Peng Lyu, Feng Wu, Wen Cheng, Nan Shen, Xuemin (Sherman) Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Xidian Univ Sch Telecommun Xian Peoples R China

ISBN: (纸本)9781728135557

Due to the flexibility and low deployment cost, unmanned aerial vehicles (UAVs) have been widely used to assist cellular networks in providing extended coverage for Internet of things (IoT) networks. Existing throughput or delay-based UAV trajectory planning methods cannot meet the requirement of collecting fresh information from IoT devices. In this paper, by taking age-of-information (AoI) as a measure of information freshness, we investigate AoI-based UAV trajectory planning for fresh data collection. To model the complicated association and interaction pattern between UAV and IoT devices, the UAV trajectory planning problem is formulated as a Markov decision process (MDP) to capture the dynamics of UAV locations. As network topology and traffic generation pattern are unknown ahead, we propose an AoI-based trajectory planning (A-TP) algorithm using deep reinforcement learning (RL) technique. To accelerate the learning process during online decision making, the off-line pre-training of deep neural networks is performed. Extensive simulation results demonstrate that the proposed algorithm can significantly reduce the AoI of collected IoT data, as compared to other benchmark approaches.

关键词： Internet of things age-of-information unmanned aerial vehicle trajectory planning deep reinforcement learning

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Industrial intelligent Optimization System Based on Industrial Internet Platform

Industrial Intelligent Optimization System Based on Industri...

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

作者： Qingchun Wang Ge Ma Sijia He Qiyang Huang Weixi Gu China Academy of Industrial Internet China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

the concept of “Industrial Internet” was first proposed by General Electric in 2012. As a new infrastructure of the fourth industrial revolution, Industrial Internet has become a significant technique to realize digital transformation of industrial economy. the Industrial Internet platform is a service system based on massive data collection, aggregation and analysis, which is oriented to the needs of digitalization, networking and intellectualization of manufacturing industry. In order to solve the problems of current backward industrial data, improve the ability of data acquisition and deal with the industrial interconnection platform, the industrial intelligent optimization system generates the product services required by industrial enterprises and realizes the goal of industrial optimization.

关键词： Manufacturing industries data collection Tools Internet Manufacturing Optimization Software engineering

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A methodology on interpretable novelty detection 11

A methodology on interpretable novelty detection

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11th international conference on Structural Dynamics, EURODYN 2020

作者： Movsessian, A. Garcia Cava, D. Tcherniak, D. Janeliukstis, R. School of Engineering Institute for Infrastructure and Environment University of Edinburgh Alexander Graham Bell Building Thomas Bayes Road EdinburghEH9 3FG United Kingdom Bruel & Kjær Sound and Vibration Measurements Skodsborgvej 307 Nærum2085 Denmark Department of Wind Energy Technical University of Denmark Frederiksborgvej 399 Roskilde4000 Denmark

ISBN: (纸本)9786188507203

Vibration-based Structural Health Monitoring (VSHM) systems continuously gather data from an array of sensors mounted on a structure. Features are constructed from the data measured. the aim is to monitor the vibration responses in the search for changes that may hint to damage. the continuous data acquisition generates high-dimensional feature spaces that require data-Driven approaches to make inferences concerning the integrity of the structure. In recent years, machine learning has played an increasingly important role in VSHM. data-driven algorithms have been successfully used to construct models capable of detecting anomalies such as damage in the features derived from the vibration signals. Mahalanobis Distance based novelty detection is a common used method to detect damage. Yet, the resulting models have been labelled "black box models" given that they lack interpretability. this becomes a relevant challenge in the presence of high-dimensional feature spaces. Using machine learning algorithms that can be interpreted would enable a more reliable novelty detection process, building trust in these methods and easing the decision-making process. Decision Trees (DT) is a widely used interpretable machine learning algorithm. the hierarchical structure of this algorithm enables the prioritisation of features that are used as predictors in the damage detection models. Furthermore, the nature of the algorithm enables the user to track the decisions and understand the classification process in detail. In this paper, we introduce the complementary use of so-called "black box models" and DT for novelty detection. the proposed damage detection approach is tested on an experimental setup with a 14.3m wind turbine blade (WTB) equipped with 24 accelerometers. A pseudo-damage was simulated by adding masses to several locations of the WTB. the pseudo-damage was detected by means of a semisupervised novelty-detection. the novelties were later studied in detail with decision-tr

关键词： Decision trees

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Modeling data Driven Interactions on Property Graph 1

引用

20th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Pongpech, Worapol Alex NIDA Business Analyt & Data Sci Grad Program Fac Appl Stat Bangkok 10240 Thailand

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

data Driven Transformation is a process where an organization transforms its infrastructure, strategies, operational methods, technologies, or organizational culture to facilitate and encourage data driven decision-making behaviors. We defined a data driven network as group/groups of people in an organization network working on common projects where making data driven decisions is necessary in the project. One important problem in data driven transformation is how to understand data driven behaviors in the network. Ability to model and compute on a DDIG is crucial for understanding data driven network behaviors. To this end, we modeled a data driven network as a property graph, in which nodes represent users, projects, or data sources and edges represent interactions between them. We termed this data Driven Interactions Graph and modeled on the Neo4j graph platform. A graph model framework to represent data driven interactions such that a number of network properties can be computed from the proposed graph is introduced. Finally, we discussed data utilization and data collaboration behaviors of the social working network.

关键词： data Driven Transformation Collaboration Utilization Property graph Social network analysis Clustering Communities Neo4j

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Generating education in-game data: the case of an ancient theatre serious game 11

Generating education in-game data: The case of an ancient th...

引用

11th international conference on Computer Supported Education, CSEDU 2019

作者： Vidakis, Nikolas Barianos, Anastasios Kristofer Trampas, Apostolos Marios Papadakis, Stamatios Kalogiannakis, Michail Vassilakis, Kostas Department of Informatics Engineering Technological Educational Institute of Crete Heraklion Crete Greece Department of Preschool Education Faculty of Education University of Crete Greece Department of Electronic Engineering Technological Educational Institute of Crete Heraklion Crete Greece

ISBN: (纸本)9789897583674

learning Analytics have become an indispensable element of education, as digital mediums are increasingly used within formal and informal education. Integrating specifications for learning analytics in non-traditional educational mediums, such as serious games, has not yet reached the level of development necessary to fulfil their potential. though much research has been conducted on the issue of managing and extracting value from learning analytics, the importance of specifications, methods and decisions for the initial creation of such data has been somewhat overlooked. To this end, we have developed a custom library that implements the Experience API specification within the Unity 3D game engine. In this paper, we present this library, as well as a representative scenario illustrating the procedure of generating and recording data. through this work we aim to expand the reach of learning analytics into serious games, facilitate the generation of such data in commercially popular development tools and identify significant events, with educational value, to be recorded. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Serious games

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Where Does LDA Sit for Github? 34

Where Does LDA Sit for GitHub?

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34th IEEE/ACM international conference on automated Software engineering (ASE)

作者： Wang, Xukun Lee, Matthias Pinchbeck, Angie Fard, Fatemeh H. Univ British Columbia Okanagan Dept Comp Sci Kelowna BC Canada

ISBN: (纸本)9781728141367

A lot of research in Software engineering (SE) automatically extract topics of the text data and use the results directly or as a feature for a machine learning method. Research has shown that the majority of studies in SE use Latent Dirichlet Allocation (LDA) as the topic modeling approach. Similarly, there is a lot of work that apply LDA on Github data. However, there is no study that explores whether LDA is a good choice compared to other algorithms, nor is there any to investigate the effects of specific pre-processing steps on its performance. In this paper, we explore a large dataset of Github repositories and apply two main topic modeling algorithms, LDA (3 variants) and Non-Negative Matrix Factorization (NMF), in several experiments with different experimental settings. the results show that LDA results in a higher coherence score compared to NMF. However, care should be taken in the choice of LDA algorithm, setting its parameters, and the text pre- processing steps. the results of this paper benefit SE researchers who apply intelligent techniques using LDA.

关键词： Software engineering Topic modeling Software analysis

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On the Design of Solfeggio Audio Machine Assessment System 11

On the Design of Solfeggio Audio Machine Assessment System

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IEEE 11th international conference on Communication Software and Networks (ICCSN)

作者： Yang, Xiaojuan Liu, Kunhong Chen, Bin Wu, Qingqiang Xu, Minhong Li, Changchun Xiamen Univ Software Sch Xiamen Fujian Peoples R China Little Knowledge & Big Data Info Tech Co Fuzhou Fujian Peoples R China

ISBN: (纸本)9781728121840

this paper presents an approach of solfeggio audio machine assessment based on multiple audio spectrum features. In this machine assessment system, we analyze 13 spectrum audio features of solfeggio audios, and build a machine assessment model based on the scoring results of music experts, aiming to score the solfeggio audio system at the professional level based on machine learning techniques. An effective way is designed to implement the system with high generalization ability in this paper, with deploying 150 practice audio files marked with expert scores for training and testing the model. Various features are extracted to train predictors, which access the distances between practice audio files and their target audio files. In this way, these predictors can mark unknown audio files reliably. the training and testing process are described in detail, and the test results testify that our system can provide expert level scoring.

关键词： machine learning solfeggio audio audio features intelligent assessment

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