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IEEJ Transactions on Electronics, Information and Systems 2022年第7期142卷 771-780页

作者： Furukawa, Shoya Sakuma, Takuto Kato, Shohei Creative Engineering Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Computer Science Program Deptartment of Engineering Graduate School of Engineering Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan Frontier Research Institute for Information Science Nagoya Institute of Technology Gokiso-cho Showa-ku Aichi Nagoya466-8555 Japan

In recent years, making computers understand the emotions of users is necessary because emotions are an important factor in human communication. Among many methods of recognizing emotions, EEG is widely used because it has high temporal resolution and it is impossible to disguise intentionally. However, it is necessary to acquire new user data and construct the personal Emotion recognition model since EEG varies widely among individuals. The conventional methods lack practicality because it builds a different model for every new user data. Our method builds a model in a single training using new user data. To reduce the number of new user data for training and to relieve the burden of EEG measurement, we adapt existing user data. We absorb the individual differences in EEG between a new user and existing users by domain adaptation using a small number of new user data and construct a model by deep neural networks. From the experiments, we confirmed that the proposed method performs as well as the conventional methods, even though it is built with single training using new user data. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Deep neural networks

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Potential Impact of Social Media to Support Government Services in Jakarta Smart City 3

Potential Impact of Social Media to Support Government Servi...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Didik Madyatmadja, Evaristus Abdurachman, Edi Gaol, Ford Lumban Pudjianto, Bonifasius W. Hapsara, Manik Information Systems Department Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Indonesian Creative Economy Agency Jakarta Indonesia University of New South Wales School of Engineering and Information Technology Australia

ISBN: (纸本)9781538658215

The purpose of this initiative is to ensure that citizens use social media to access information about the state of the city. Social media application offers benefits in the form of a channel of communication between the community and the government or the group that has public interest. Through this application, the public could channel complaints about public services. Perception becomes the important key for this condition because user action is determined by their perception toward the government e-services. The result of standard measurement for customer satisfaction on government organization is used to help find the weakness of the service and correct it. The interface is made to be more user-friendly to ensure the effective service. So, it is necessary to develop the system continuously. Perceived quality is one of the important determinants of the success of social media application, therefore needs identification of the user's perception and expectations and involving becomes the key to success besides the good quality that will increase the trust and satisfaction of users. Evaluation of social media applications is needed so that it can provide recommendations to the provincial government of DKI Jakarta related to what things can be done to improve community service. © 2018 IEEE.

关键词： Smart city

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Dynamic Sampling-Based Meta-Learning Using Multilingual Acoustic Data for Under-Resourced Speech Recognition

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IEEE Access 2024年 12卷 106070-106083页

作者： Hsieh, I-Ting Wu, Chung-Hsien Zhao, Zhe-Hong National Cheng Kung University Graduate Program of Multimedia Systems and Intelligent Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Under-resourced automatic speech recognition (ASR) has become an active field of research and has experienced significant progress during the past decade. However, the performance of under-resourced ASR trained by existing methods is still far inferior to high-resourced ASR for practical applications. In this paper, speech data from languages that share the most phonemes with the under-resourced language are selected as supplementary resources for meta-training based on the Model-Agnostic Meta-Learning (MAML) strategy. Besides supplementary language selection, this paper proposes a dynamic sampling method instead of the original random sampling method to select support and query sets for each task in MAML to improve meta-training performance. In this study, Taiwanese is selected as the under-resourced language, and the speech corpus of five languages, including Mandarin, English, Japanese, Cantonese, and Thai, are chosen as supplementary training data for acoustic model training. The proposed dynamic sampling approach uses phonemes, pronunciation, and speech recognition models as the basis to determine the proportion of each supplementary language to select helpful utterances for MAML. For evaluation, with the selected utterances from each supplementary language for meta-training, we obtained a Word Error Rate of 20.24% and a Syllable Error Rate of 8.35% for Taiwanese ASR, which were better than the baseline model (26.18% and 13.99%) using only the Taiwanese corpus and other methods. © 2013 IEEE.

关键词： Speech recognition

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Data-driven analysis of kinaesthetic and tactile information for shape classification

Data-driven analysis of kinaesthetic and tactile information...

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2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

作者： De Oliveira, Thiago Eustaquio Alves Prado Da Fonseca, Vinicius Huluta, Emanuil Rosa, Paulo F. F. Petriu, Emil M. School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781479960910

Humans sense of touch consists in a complexity of sensors and nervous system. The information inferred by this system enables the daily dexterous manipulation tasks. In biological systems, there is no conscious prioritization of sensors while performing tactile exploration and the selection of exploratory movements is driven by learning instincts and data gathered by previous movements. The development of artificial systems tries to mimic such systems with engineered sensors and strategies for movement selection. This paper presents a data-driven analysis to the problem of sensor selection in the contour following for shape discrimination task. This task consists of a 4-DOF robotic finger exploring a set of 7 synthetic shapes. The data collected from the motors, inertial measurement unit, and magnetometer was analyzed applying principal component analysis and a multilayer perceptron neural network. Results show the variation of classification rate depending on the fingertip material and sensor considered. It is worth to observe that the magnetometer was the most robust in both cases. © 2015 IEEE.

关键词： Magnetometers

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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Spinal functional Magnetic Resonance Imaging (fMRI) on Human Studies: A Literature Review

Spinal functional Magnetic Resonance Imaging (fMRI) on Human...

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2019 Advances in science and engineering Technology International Conferences, ASET 2019

作者： Al-Momani, Sajedah Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering Biomedical Engineering Graduate Program Biosciences and Bioengineering Research Institute American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781538682715

Spinal functional Magnetic Resonance Imaging (fMRI) is a newly emerging non-invasive imaging technique that has been introduced to study the Blood Oxygenation Level Dependent (BOLD) changes in the spinal cord associated with a certain activity or even at rest. Spinal fMRI is also used to assess the treatment of many dysfunctions affect the spinal cord such as spinal cord injury (SCI) and Multiple sclerosis (MS). The purpose of this paper is to propose a literature review of spinal fMRI studies performed on healthy human volunteers. The studies reviewed are divided based on the paradigm being performed during image acquisition into (1) studies under activities and stimulus which are further classified according to the type of the stimuli, and (2) studies at resting state. Moreover, the limitations of spinal fMRI and how to tackle them are discussed. © 2019 IEEE.

关键词： Magnetic resonance imaging

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Review of Image-Guided Percutaneous Coronary Interventions

Review of Image-Guided Percutaneous Coronary Interventions

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2019 Advances in science and engineering Technology International Conferences, ASET 2019

作者： Alatoom, Aseel Farhat, Jumana Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering Biomedical Engineering Graduate Program Biosciences and Bioengineering Research Institute American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781538682715

Image-guided surgery (IGS) involves using surgical instruments tracking system to guide the surgeon during invasive surgical procedures. The use of preoperative or intraoperative images is very essential in providing navigation for different medical applications. In this paper, a literature review of different imaging modalities that aid in the diagnosis and treatment of coronary artery disease (CAD) is proposed. The imaging modalities discussed include x-ray fluoroscopy, coronary computed tomography angiography (CCTA), fractional flow reserve (FFR), cardiac single-photon emission computed tomography (SPECT), cardiovascular magnetic resonance imaging (CMRI) and ultrasound. Additionally, pseudoaneurysms, which can result from percutaneous coronary interventions (PCI) procedure, are reviewed as well as the treatment procedure. Finally, a comparison between the different imaging modalities is carried out, based on the number of papers that have been published, in addition to the sensitivity and specificity for each modality according to PubMed and Web of science databases. © 2019 IEEE.

关键词： Ultrasonics

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Vibration-based classification of road damages: Gyroscope data and a simple neural network model 2

Vibration-based classification of road damages: Gyroscope da...

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2nd International Conference on Eco engineering Development 2018, ICEED 2018

作者： Gunawan, Fergyanto E. Herriyandi Soewito, Benfano Mauritsius, Tuga Surantha, Nico Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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Foreign Exchange Prediction Using Machine Learning Approach: A Pilot Study 4

Foreign Exchange Prediction Using Machine Learning Approach:...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Sudimanto Heryadi, Yaya Lukas Wibowo, Antoni Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia

ISBN: (纸本)9781665433945

Foreign Exchange or FOREX trading is not only done on foreign currencies but, FOREX also can be done on several commodities such as Gold, Silver, Oil. Gold is one of the most valuable commodities in the world. Investors began to offer gold as a trading material against foreign currencies. Machine Learning (ML) in the FOREX trading world is usually used to predict future FOREX values. This pilot study aims to see a model from machine learning that has a fairly high level of accuracy in making FOREX predictions. This pilot study using historical data taken from the *** database where the FOREX data taken is FOREX XAU/USD data, with a period year from 2019 until 2021, and the indicator used is Moving Average Convergence/Divergence (MACD) technical analysis. The average accuracy obtained after training on the Tree model is 86.3%, the Support Vector Machine (SVM) model is 86.6% and the Ensemble model is 86.55%. Testing conducted using machine learning models for Tree, SVM and Ensemble models have the same level of accuracy, which is 88.3%. © 2021 IEEE

关键词： Forecasting

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