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data Analytics Systems and SME type - a Design Science Approach 22

Data Analytics Systems and SME type - a Design Science Appro...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Ulrich, Patrick Becker, Wolfgang Fibitz, Alexandra Reitelshofer, Eva Schuhknecht, Felix Aalen Univ Beethovenstr 1 D-73430 Aalen Germany Univ Bamberg Feldkirchenstr 21 D-96045 Bamberg Germany

Although the current literature on large amounts of data and data analysis is growing rapidly, profound assumptions and practical tasks are still missing in some areas. This paper focuses on SMEs and their approach to data analysis issues and their implementation. With the methodology of design research we use characteristics of SME types and link them with the corresponding requirements of the compatible data analysis system to increase performance. We derive a framework that serves as a starting point for further research The results indicate that there are several requirements that companies need to consider when realigning or restructuring their internal database. Depending on the type of SME, we choose a performance grid analysis based on the characteristics and requirements of data analysis systems. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Design Science Research data Analytics System SME Cycle Big data Decision Making

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Near Real-time Autonomous Quality Control for Streaming Environmental Sensor data 22

Near Real-time Autonomous Quality Control for Streaming Envi...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Scully-Allison, Connor Vinh Le Fritzinger, Eric Strachan, Scotty Harris, Frederick C., Jr. Dascalu, Sergiu M. Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

In this paper, we present a novel and accessible approach to time-series data validation: the Near-Real Time Autonomous Quality Control (NRAQC) system. The design, implementation, and impacts of this software are explored in detail within this paper. This software system, created in close conference with environmental scientists, leverages microservice design patterns employed for high volume web applications to develop a contemporary solution to the problem of data quality control with streaming sensor data. Through a comparative analysis between NRAQC and the GCE Toolbox, we argue that the web based deployment of QC software enhances accessibility to crucial tools required to make a robust and useful data product from raw measurements. Additionally, a key innovation of the NRAQC platform is its positive impact on modern data management practices and quality data dissemination. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Software engineering data Management Microservices Quality Control Web-based Systems Information Quality

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Fintech Model: The Random Neural Network with Genetic Algorithm 22

Fintech Model: The Random Neural Network with Genetic Algori...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems, KES 2018

作者： Serrano, Will Intelligent Systems and Networks Group Imperial College London London United Kingdom

This paper proposes the Random Neural Network with a new learning algorithm based on the genome model. Following genetics, where information is transmitted in the combination of genes rather than the genes themselves, the proposed genetic model transmits information to future generations in the network weights rather than the neuronS. The innovative genetic algorithm is implanted in a complex Deep learning structure that emulates the human brain: Reinforcement learning takes fast local current decisions, Deep learning Clusters provide identity and memory, Deep learning Management Clusters take final strategic decisions and finally Genetic learning transmits the information learned to future generationS. The presented algorithm and Deep learning structure has been applied and validated in a Fintech Model;a Smart Investment application where an intelligent Bankers are specialised in Buy and sell decisions on several Assets, Markets and RiskS. The obtained results are promising;the proposed model combines human brain and genetics with Deep learning structure based on the Random Neural Network model where biology;similar as Artificial Intelligence is learning gradually and continuously while adapting to the environment. © 2018 The Author(s).

关键词： Reinforcement learning

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A distributed framework for information retrieval, processing and presentation of data 22

A distributed framework for information retrieval, processin...

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22nd international conference on System Theory, Control and Computing (ICSTCC)

作者： Alexandrescu, Adrian Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Dept Comp Sci & Engn Iasi Romania

ISBN: (纸本)9781538644447

The Internet is ever growing and the information is highly unstructured. Finding the best priced product when shopping online is a difficult task, but shopping search engines help the user identify the desired product. Starting from this reality, this paper presents a distributed framework for information retrieval, processing and presentation of data, which has three main components: a data server cluster, a distributed crawler with a product extraction feature, and a web server for presenting the processed data. The considered use case consists of extracting information about board and card games from specialized websites and presenting the data in a user-friendly website. Efficiency is determined by means of performance evaluation metrics that include crawl and extraction times, and a discussion in terms of scalability. The high modularization of the framework allows it to be an effective teaching tool in classrooms;each module can be easily swapped with a custom implementation. The proposed framework offers students and researchers an environment for developing and testing a wide range of algorithms and programming solutions, e.g., load balancer, crawler, reverse engineering a web template, product extractor from web pages, data normalization, notification services, web APIs, custom database.

关键词： information retrieval distributed crawler shopping search engine data mining web scraping micro service architecture learning framework

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SCORMAdaptiveQuiz: Implementation of Adaptive e-learning for Moodle 22

SCORMAdaptiveQuiz: Implementation of Adaptive e-Learning for...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Ueda, Hiroshi Furukawa, Masako Yamaji, Kazutsuna Nakamura, Motonori Kyoto Univ Sakyo Ku Yoshida Honmachi Kyoto 6068501 Japan Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

One of the advantages of online learning, which is evolving at a rapid rate, is that it can be standardized. However, it also has a disadvantage in that online content lacks adaptivity. In this study, we aim to improve online content adaptivity, and develop an extension plugin for Sharable Content Object Reference Model (SCORM) for Moodle: SCORMAdaptiveQuiz (https github, com/uedahir4/SCORMAdabtiveQuiz), which can provide the adaptive presentation feature in accordance with the pre-knowledge of the learner as reflected from a pre-test created by the Moodie quiz module. data were collected from the productive operation of the module for a year in the security awareness education online course "Learn with Princess Rin Rin: Cyberethics" provided in Japanese, English, Chinese, and Korean and hosted on GakuNinMoodle (https://***/), which is widely used in universities in Japan. The result of the data analysis revealed the effect of the SCORMAdaptiveQuiz module: An improved access rate was obtained in the 2016 academic year than in 2015 for all four languages. In addition, a significant improvement was seen in the results of the final test of the Korean and Chinese course in comparison with that of the 2015 academic year. On the other hand, a slight decrease in the score of the final test was observed for the English and Japanese courses. We argue that the SCORMAdaptiveQuiz plugin is useful in providing an adaptive online course in accordance with the knowledge of the learner. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： SCORM Moodle e-learning

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Inventory Satisfaction Discrimination Method Utilizing Images and Deep learning 22

Inventory Satisfaction Discrimination Method Utilizing Image...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Kawanaka, Tenma Kudo, Tsukasa Shizuoka Inst Sci & Technol 22200-2 Toyosawa Fukuroi Shizuoka 4378555 Japan

In factories, since various kinds of parts are stocked and used to manufacture products, stocktaking is important in order to keep the appropriate inventory quantity of parts. However, since the number of types of parts is often enormous, their stocktaking workload becomes a heavy burden in such a case. Especially, parts such as bolts and nuts are often stored in bulk containers, and they cannot be counted visually from the outside. So, they must be counted by taking out, and it increases the workload more. On the other hand, image recognition accuracy has been rapidly improved by the deep learning, and its applications to various fields are spreading in recent years. This suggests the automatic stocktaking by using images is possible, especially even for the bulk containers. In this study, we evaluated the feasibility of automatic inventory satisfaction discrimination of order point for the parts stocked in the bulk containers. Furthermore, we investigated to apply it to the inventory management system. As a result, we found the precision of this discrimination was higher than a human being when there were many parts in a bulk container;also, we found the more effective inventory management would be possible by using this discrimination together with the data of production management system such as logical inventory. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： inventory management system stocktaking bulk container deep learning convolutional neural network order point

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Enhancing the Attractiveness of learning through Augmented Reality 22

Enhancing the Attractiveness of Learning through Augmented R...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Iftene, Adrian Trandabat, Diana Alexandru Ioan Cuza Univ Fac Comp Sci Gen Berthelot 16 Iasi 700483 Romania

Over the last years, augmented reality was used in various domains, from medical, industrial design, modeling and production, robot teleoperation, military, entertainment, leisure activities to translation, facial recognition, assistance while driving, interior and exterior design, virtual friends, internet of things and elearning. In elearning, the combination between classical and augmented content (the later coming with 3D models, images, sounds, animations, Internet browsing, etc.) can help the teacher to better explain the content of the courses. In this paper, we present four augmented reality applications, created with the aim to improve communication and collaboration skills (two of them) and to ease the learning of biology and geography (the other two). The motivation behind these applications is to enhance the attractiveness of the classes, allow students to retrain new information more easily and reduce the stress behind tests when presented as games. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Augmented Reality elearning Autism Usability Testing

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Hardcore Gamer Profiling: Results from an unsupervised learning approach to playing behavior on the Steam platform 22

Hardcore Gamer Profiling: Results from an unsupervised learn...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Baumann, Florian Emmert, Dominik Baumgartl, Hermann Buettner, Ricardo Aalen Univ Aalen Germany

Based on a very large dataset of over 100 million Steam platform users we present the first comprehensive analysis of hardcore gamer profiles by over 700,000 hardcore players (users playing more than 20 hours per week) covering more than 3,300 games. Using an unsupervised machine learning approach we reveal the specific behavioral categories of hardcore players, i.e. First Person Shooter, Team Fortress 2 player, Action game player, Dota 2 player, Strategy and action combiner, Genre-switching player. Subsequently we derive individual patterns of hardcore gamers in the categories found, such as strategy-action games combiner or game switching players. Our results are useful for computer science and information systems scholars interested in individual differences in user behavior as well as practitioners interested in game-designing. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： computer gaming behavior hardcore gamer player profiling k-means cluster analysis

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Using unsupervised learning methods for enhancing protein structure insight 22

Using unsupervised learning methods for enhancing protein st...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Teletin, Mihai Czibula, Gabriela Albert, Silvana Bocicor, Maria-Iuliana Babes Bolyai Univ Dept Comp Sci 1M Kogalniceanu St Cluj Napoca 400084 Romania

Proteins are complex macromolecules which contribute to maintaining cellular environments and thus have fundamental roles in biological processes of living organisms. Understanding the conformational transitions of proteins represents an important stage towards comprehending protein function and would help to identify situations when mutations can occur. In this paper we use clustering as an unsupervised classification method in order to study the relevance of the residues' relative solvent accessibility (RSA) values to analyze protein internal transitions. With the main goal of studying the evolution of RSA values between conformational transitions, we experimentally show that RSA values are slowly modifying as the protein undergoes conformational changes. The study conducted in this paper is aimed to provide a better apprehension of how proteins' conformational transitions are evolving in time, with the broader goal of better understanding protein internal dynamics. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Protein analysis Unsupervised learning Clustering

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Pilot considerations of brain activity detection based on difference of English words difficulty levels at recognition of English words 22

Pilot considerations of brain activity detection based on di...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Sasaki, Tatsuya Akimoto, Yoritaka Nakahira, Katsuko T. Nagaoka Univ Technol Nagaoka Niigata 9402188 Japan

We performed pilot electroencephalography experiments to examine whether brain activity would reflect the difficulty levels of English words for Japanese English learner. During experiment, "easy" and "difficult" English words were visually presented and the participant responded whether he understood the meaning of the presented English word. We found that the most characteristic brain activity was a power decrease in the 10-20 Hz frequency band at 400-800 ms after stimulus presentation, which seemed to reflect the differences in the difficulty of the words. Future studies will focus on applying machine-learning techniques so as to classify brain activities for each word. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： English word learning visual word recognition electroencephalography event-related spectral perturbations frontal lobe

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