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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是81-90 订阅

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Challenges of Teaching Programming in StackOverflow Era 23rd

Challenges of Teaching Programming in StackOverflow Era

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23rd international conference on Interactive Collaborative learning (ICL) / 49th IGIP international conference on engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Poial, Jaanus Tallinn Univ Technol Sch Informat Technol Raja 4C EE-12616 Tallinn Estonia

ISBN: (纸本)9783030681982;9783030681975

Teaching computer programming has changed a lot during the last 15 years. Instead of visiting lectures, reading books, documentation and manuals, solving individual programming tasks, etc., students prefer to "find" solutions to all problems in Google, StackOverflow and similar forums, You-Tube videos and other helpful tools. It could be enjoyed, if there were no negative side effects: programs are created without proper understanding using "copy and paste" from different sources and brute force hacks to comply them to the tests. Often these internet resources are incomplete, contain errors and do not fit to solve the given problem. This article covers some techniques used by the author in the following areas: 1. Choice of assignments - full scale projects vs. small (but meaningful and complex enough) exercises. 2. Using version control to track students' progress. 3. Using learning environments, e.g. Moodle. 4. Role of automated tests. 5. Cheating issues. 6. Providing enough feedback to massive amount of homework. 7. Using "flipped classroom" and "work in pairs" approaches. Daily teaching in a rapidly changing environment necessitates constant updating of teaching methods using peer experience, continuous student feedback, professional publications and common sense. Author has tried "flipped classroom" and "work in pairs" approaches combined with automated assessment and version control for a medium level programming course ("Algorithms and data Structures"). The results were impressive - dropout decreased from 54% to 17% in two years.

关键词： Teaching best practices Collaborative learning Flipped classrooms

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Grasp Control of a Multi-fingered Robot Hand for Cups of Different Sizes using a Visuomotor Integration Model

Grasp Control of a Multi-fingered Robot Hand for Cups of Dif...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Motoi Matsuda Satoshi Katayama Mai Ohashi Naohiro Fukumura Department of Computer Science and Engineering Toyohashi University of Technology Aichi Japan

Herein, we focused on a robot hand control technology based on image recognition. We conducted experiments on a model that calculates the finger joint angle information of the robot hand from an image of a target object. A model of human visuomotor integration and translation has been proposed, and this model consists of modular auto-encoders corresponding to visual and motor information. To verify the effectiveness of this model, we conducted learning experiments with a real four-fingered robot hand with 16 degrees of freedom. We used cups with different bottom diameters and handle lengths as target objects. We grasped the cups with a robot hand and obtained the finger joint angle when grasping the sides of the cup and when holding the top of the cup as motor information. We conducted a learning experiment using depth images as visual information. As a result, we confirmed that the information on the diameter of the cups could be extracted. Even when images with different cup sizes and handle directions, which were excluded in the training data, were input as test data, the system could represent the cup size. In the hand posture calculation experiment, we confirmed that the posture of the robot hand changes according to the size of these untrained cups.

关键词： Visualization Image recognition Training data Grasping data mining Robots

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Handling of Missing Values in FCM Clustering-based ANFIS with Partial Distance Strategy

Handling of Missing Values in FCM Clustering-based ANFIS wit...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Katsuhiro Honda Satoshi Hyakutake Seiki Ubukata Akira Notsu Graduate School of Informatics Osaka Metropolitan University Sakai Osaka Japan Graduate School of Engineering Osaka Prefecture University Sakai Osaka Japan Graduate School of Sustainable System Sciences Osaka Metropolitan University Sakai Osaka Japan

Adaptive Network-based Fuzzy Inference System (ANFIS) is a promising model of explainable neural networks, which constructs Takagi-Sugeno fuzzy inference system based on neuro learning, but cannot work well with incomplete datasets including missing values. In this research, a novel approach of making ANFIS robust against missing values is proposed by modifying the premise part with Fuzzy c-Means (FCM) utilizing the partial distance strategy. By calculating the premise fuzzy memberships of incomplete objects having missing values with FCM criterion using partial distances, the proposed ANFIS variant can produce ANFIS outputs even if some objects include missing values in their input observation. The characteristics of the proposed model are demonstrated through a numerical experiment with a time series data.

关键词： Analytical models Time series analysis Neural networks Prototypes Collaboration data models Takagi-Sugeno model

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Estimation Method of Visual Prognosis in Branch Retinal Vein Occlusion with Macular Edema

Estimation Method of Visual Prognosis in Branch Retinal Vein...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Kazuya Imamura Hiroharu Kawanaka Yoshitsugu Matsui Division of Electrical and Electronic Engineering Graduate School of Engineering Mie University Japan Depertment of Ophthalmology Mie University Graduate School of Medicine Japan

Branch Retinal Vein Occlusion (BRVO) with Macular Edema (ME) is a common retinal vascular disease and needs a remedy for an extended period. There is a high possibility that patients lose motivation for treatments. Therefore, visual prognostic evaluation is essential to reduce anxiety and increase motivation. However, in present clinical practice, it is challenging for ophthalmologists to predict the prognosis individually. On this clinical background, this study aims to establish a method for estimating visual prognosis in BRVO with ME. In this paper, we attempted binary classification using clinical data and Machine learning techniques, in which patients are classified into the good or poor visual prognostic group. Specifically, we designed the classifier to predict the good prognostic group perfectly. That is because the most important thing is improving the patient’s motivation. In this experiment, we targeted 66 patients who were followed up for 12 months at Mie University Hospital. Logistic Regression was employed as a learning algorithm. After extracting the features from clinical data, we performed 5-fold stratified cross-validation. As a result, we achieved a mean precision for the good group of 0.95 and 0.80 for the poor group. In addition, the mean precision for the poor group in training data was 0.86 when the classifier satisfied the precision for the good group in training data of 1.0. This result suggests that the performance for unknown data does not significantly decline relative to the performance on known data. Furthermore, obtaining this performance using a linear classifier has an additional advantage because the prediction process is interpretable. We expect that this method would provide a better treatment experience if patients receive the models’ performance, the number of training samples, and the prediction process, in addition to the diagnosis.

关键词： Training Visualization Veins Training data Machine learning Predictive models Feature extraction

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Classification of finger movement based on EEG phase using deep learning

Classification of finger movement based on EEG phase using d...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Han Wenhao Ma Lei Kosuke Hashimoto Tadanori Fukami Graduate School of Science and Engineering Yamagata University Yonezawa Japan

In recent years, the availability of a huge number of different electroencephalogram (EEG) datasets and the development of deep learning systems have enabled many studies about motor imagery-related EEG classification. However, most of these studies have focused on the movement of relatively large body parts such as the left/right hand, and few have considered the classification of finger movement. Some classification methods have been based on frequency components such as power and amplitude spectra. However, these spectra are less likely to show unique spatial distributions of EEGs on the scalp for different fingers. This is because the regions that control the fingers are close to one another in the motor cortex, leading to similar distributions. We predicted that the arrival timing at electrodes on the scalp of the signal reflecting neural activity would differ depending on the source in the motor cortex. Based on this assumption, we attempted classification using the phase distribution of each EEG frequency component. We used deep learning to classify movement of the thumb and index finger, which are controlled by adjacent regions in the motor area. We collected data during finger movement. Our proposed EEG-phase-based method enabled classification of the movement of two fingers with an accuracy of 70.0%. This is superior to the result of amplitude-based classification, i.e., 62.3%.

关键词： Deep learning Electrodes Graphical models Scalp Neural activity Thumb Electroencephalography

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Toward Kinesthetic Empathy in Developmental Disorder Prediction: A key to raise the Dance/Movement Therapy to a medical test

Toward Kinesthetic Empathy in Developmental Disorder Predict...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yukari Sakiyama Hiroharu Kawanaka Haruhiko Takase Yoshihiko Hirabayashi Atsushi Inoue Dep. Of Early Childhood Education Mukogawa Women’s University Nishinomiya Japan Graduate School of Engineering Mie University Tus Japan Graduate School of Regional Innovation Mie University Tus Japan

This paper introduces the concept of Kinesthetic Empathy (KE) as a key to attribute critical states of emotion throughout observing movement and dancing of a patient in Dance/Movement Therapy. While this concept of KE has been well recognized and indeed played important roles in profiling activities, its quantitative aspects still remain open. We are now looking into the quantitative aspects of KE so that this may be a key to make the current profiling activities more consistently measurable for its further advancement, such as a medical testing procedure to predict developmental disorder and digital transformation – the IT assisted profiling and beyond. We attempt this introduction by (1) describing the current qualitative aspects of KE through examples, and (2) introducing our current digitization project that aims at some data Sciences and Machine learning, and (3) raising DMT to a medical test for developmental disorder prediction at an early stage such as infants and toddlers.

关键词： Pediatrics Digital transformation Current measurement Medical treatment Machine learning Medical tests Testing

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How Can Machine learning Models Be Used for Subjective Assessment of Safety and Comfort: Application on Free Lane Change Maneuver

How Can Machine Learning Models Be Used for Subjective Asses...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Batuhan Durukal Mert Eren Ömer Çetin Egemen Karabıyık Namık Zengin Sarp Kaya Yetkin AVL Research and Engineering Istanbul Turkey

Subjective evaluation plays a key role in autonomous driving feature validation for safety, comfort, and driving quality. As for being objective, it is also important to evaluate the autonomous features with key performance indicators (KPI) depending on physical parameters before stepping into the delivery phase. To provide better driving experience for autonomous features, calibration parameters need to be tuned carefully while considering safety and comfort. Calibration parameters can be evaluated in terms of safe, unsafe, comfortable, or uncomfortable states through questions that allow the evaluation of the passenger’s feelings during real-world testing which includes predefined scenarios and environments. In this paper, we proposed a method that performs the rating of the free lane change maneuver in terms of safety and comfort by employing the machine learning algorithms to model the passenger feedback according to the questionnaire for the subjective evaluation of the test maneuver execution. After trying several machine and deep learning regression techniques, we have shown that Extreme Gradient Boosting (XGB) regressor can be used to model drive feeling accurately for validation and calibration purposes. The constituted evaluation model can be utilized to improve quality of the autonomous driving, optimize calibration parameters and achieve user acceptance.

关键词： Deep learning Machine learning algorithms Key performance indicator Boosting data models Safety Calibration

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Modelling the relationships between local landscape features and habitat suitability of Genji-firefly Luciola cruciata using Random Forests

Modelling the relationships between local landscape features...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Tomoya Ogami Shinji Fukuda Hirofumi Kakudo Graduate School of Agriculture Tokyo University of Agriculture and Technology Tokyo Japan Division of Environmental and Agricultural Engineering Tokyo University of Agriculture and Technology Tokyo Japan Program in Architecture Civil and Environmental Engineering Faculty of Engineering and Design Kagawa University Kagawa Japan

Genji-firefly has been one of the key symbols of rural environmental conservation. The species lives underwater as larvae, in the soil when they pupate, and emerges on the ground as adults. Because the habitat conditions of larvae and adults differ greatly, the habitat requirement of Genji-firefly must be clarified over a wide range from instream to the surrounding riparian environment. In this study, we developed a habitat suitability model for Genji-firefly by relating the number of individuals to local landscape features surveyed in a small river in an agricultural watershed. For habitat modelling, Random Forests, a well-known highly predictive machine learning method, was used to model species distributions and to extract quantitative information on their ecological characteristics. As a result, a good agreement was found between observed and modelled number of individuals, although some issues still remain for the test data. We were able to extract local landscape features that characterize the habitat suitability of adult firefly. In future, we plan to deepen our habitat suitability assessment by taking into account uncertainties such as noise originated from absent data.

关键词： Uncertainty Biological system modeling Computational modeling Vegetation Forestry Predictive models Feature extraction

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Automatic Generation of Road Feature Identification Models from Point Cloud data Using HD Maps

Automatic Generation of Road Feature Identification Models f...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Ryuichi Imai Kenji Nakamura Yoshinori Tsukada Noriko Aso Jin Yamamoto Faculty of Engineering and Design Hosei University Shinjuku City Tokyo Japan Faculty of Information Technology and Social Sciences Osaka University of Economics Osaka City Osaka Japan Faculty of Business Administration Setsunan University Neyagawa City Osaka Japan Dynamic Map Platform Chuo City Tokyo Japan Faculty of Engineering and Design Hosei University Graduate School Shinjuku City Tokyo Japan

A large amount of point cloud data has been measured and accumulated at various locations for Japanese public works by introducing mobile mapping systems and terrestrial laser scanners. However, since point cloud data are a huge set of points that hold position coordinates, their use is limited in the unprocessed state. For this reason, we have been proposing a method for constructing a product model that structures point cloud data by assigning the meaning of a planimetric feature to the points using a plan of completion drawing that shows the completed shape of a construction object and 3D map data with high precision for autonomous driving. However, the applicable scope of the existing method is limited to the plan of completion drawing and the already developed sections of road maps. Therefore, a method of extracting road features from point cloud data using deep learning has been proposed. However, it requires manual preparation of a huge amount and highly qualified training data. In this study, we propose a method to automatically generate training data for constructing a road feature identification model from point cloud data automatically extracted using road maps. The usefulness of the proposed method has been confirmed through demonstration experiments.

关键词： Point cloud compression Solid modeling Three-dimensional displays Shape Roads Training data Measurement by laser beam

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Specific Energy Consumption Prediction Framework in Hydrogen Liquefaction Process Using Deep Neural Network Based on Quantum Mechanics

Specific Energy Consumption Prediction Framework in Hydrogen...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Eunseo Oh Jaeuk Jang Sangmin Park Hyunsoo Lee Industrial Engineering Kumoh National Institute of Technology Gumi South Korea

Energy supply methods are being diversified due to competition for resources due to depletion of fossil fuels and continued high oil prices. Hydrogen energy has the advantage of low carbon dioxide emission and the ability to generate a lot of energy per weight. However, gaseous hydrogen has a low hydrogen storage density per unit volume, so a liquefaction process is required. Liquid hydrogen has a lower pressure than hydrogen in gaseous state, so the risk of explosion is low and transportation cost is reduced because it is easy to transport in large quantities. Therefore, the hydrogen liquefaction process is important and can improve energy efficiency. Specific Energy Consumption (SEC), commonly used as a performance indicator in the hydrogen liquefaction process, is the sum of energy consumed in the entire process to liquefy 1 kg of hydrogen. However, since the uncertainty of the attribute values lowers the accuracy of prediction, in order to overcome this issue, this study proposes a framework for predicting the SEC value according to the attribute value using a Deep Neural Network (DNN) based on quantum mechanics. The attribute values corrected through quantum mechanics more closely simulate the attribute of the data and contribute to improving the prediction accuracy.

关键词： Deep learning Energy consumption Uncertainty Oils Hydrogen Neural networks Quantum mechanics

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