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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hapnes Toba Elizabeth Wianto Maya Malinda Agung Wijaya Al Halim Chien-Hsu Chen Master Program in Computer Science Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Management Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Informatics Universitas Kristen Maranatha Bandung Indonesia Industrial Design Department National Cheng Kung University Tainan Taiwan

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Improved accuracy of long short term memory (LSTM) algorithm in chili price prediction

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AIP Conference Proceedings 2024年第1期3222卷

作者： Rizki Ramadhansyah Benny Benyamin Nasution Ade Candra Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Computer Engineering Program Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

The unstable price of chili is still a serious problem in society. Many previous studies have created systems that can predict chili prices. However, there are no chili price prediction results that are considered quite accurate and consistent. So the problem is overcome by processing chili price data using interpolation and dropna on missing values in order to maintain data quality and using the Long Short Term Memory (LSTM) algorithm development in predicting chili prices. This LSTM development was carried out to improve the inconsistent chili price prediction results due to the high error value. LSTM development is carried out in the cell state (ct) and hidden state (ht) with the aim of obtaining better prediction results. Then the accuracy of the error value obtained from the LSTM development in Labuhanbatu district is MAE = 2.589, RMSE = 3.419, and MSE = 11.695.900. This value is lower than using the original LSTM and only using dropna in processing missing value data, namely MAE = 5.517, RMSE = 7.930, and MSE = 62.900.289. Then the difference in percentage error value from the comparison is MAE = 53.07%, RMSE = 56.88%, and MSE = 81.41%. Therefore, it is expected that the low error value results from the development of LSTM can be an indicator of increasing the accuracy of chili price prediction results and making the results more consistent.

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Hypersaline Tidal Flats Detection Using Deep Learning Over 37 Years of Landsat Data

Hypersaline Tidal Flats Detection Using Deep Learning Over 3...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Maria Luize Pinheiro Luiz Cortinhas Cesar Diniz Raian V Maretto Mateus Grellert Graduate Program in Computer Science Federal University of Santa Catarina (UFSC) Brazil Solved - Solutions in Geoinformation Brazil Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente The Netherlands Federal University of Rio Grande do Sul (UFRGS) Informatics Institute Brazil

Hypersaline tidal flats are plane areas usually related to mangrove forests, acting as guard and buffer against rising sea levels, and as maintainer of regional biodiversity. Such areas are primarily impacted by anthropogenic and natural activities, such as sea-salt extraction and pollution, so identifying and monitoring them is an important and challenging task. The present work uses a U-shaped Convolutional Neural Network architecture to systematically classify such formations over Landsat imagery. A large dataset containing data from 1985 to 2021 of the Brazilian Coastal Zone is used to train and evaluate our model. Experimental results show that the total area increased by 58.6 km 2 from 1985 to 2001, and decreased by approximately 92 km 2 from 2001 to 2021, representing a total reduction of ≈ 33.34 km 2 for the entire period. We also show that our model outperforms a related solution trained with the same dataset, achieving 70% and 86% for 1985 and 2020 respectively, against 69% and 82%.

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Ensemble Learning in Predicting Financial Distress of Indonesian Public Company 8

Ensemble Learning in Predicting Financial Distress of Indone...

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8th International Conference on Information and Communication Technology, ICoICT 2020

作者： Rahayu, Dyah Sulistyowati Suhartanto, Heru Pancasila University Informatics Department Jakarta Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Doctorate Program Computer Science University of Indonesia Depok Indonesia

ISBN: (纸本)9781728161426

Predicting financial distress can avoid firm bankruptcy. That is an important issue in matters of company sustainability and the economic growth in general. Indonesia as a developing country needs a reliable system that is able to predict the bankruptcy of a company because it can affect the overall economic condition at different levels. The ensemble learning which is built to achieve better performance of prediction can be implemented to forecast the unhealthy company conditions. Random forest ensemble learning and AdaBoost have been proven superior to the single one. Both methods are applied to Indonesia Public Company data with 6 variables based on Altman Z-Score and one additional variable. The accuracy, precision, recall, and f1-score have an average of 91% regardless of the data imbalance. The ensemble score determines its superiority to the single machine learning. © 2020 IEEE.

关键词： Forecasting

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D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

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SN computer science 2021年第1期2卷 48页

作者： Demiray, Bekir Z. Sit, Muhammed Demir, Ibrahim Department of Computer Science University of Iowa Iowa City United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City United States Department of Civil and Environmental Engineering University of Iowa Iowa City United States Department of Electrical and Computer Engineering University of Iowa Iowa City United States

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network *** study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Deep learning DEM DEM reconstruction Generative adversarial networks (GANs) Super-resolution

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GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs

arXiv

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arXiv 2023年

作者： Wang, Ruijie Rossetto, Luca Cochez, Michael Bernstein, Abraham The Department of Informatics University of Zurich Zurich Switzerland The University Research Priority Program "Dynamics of Healthy Aging" University of Zurich Zurich Switzerland The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Discovery Lab Elsevier Amsterdam Netherlands

Most current methods for multi-hop question answering (QA) over knowledge graphs (KGs) only provide final conclusive answers without explanations, such as a set of KG entities that is difficult for normal users to review and comprehend. This issue severely limits the application of KG-based QA in real-world scenarios. However, it is non-trivial to solve due to two challenges: First, annotations of reasoning chains of multi-hop questions, which could serve as supervision for explanation generation, are usually lacking. Second, it is difficult to maintain high efficiency when explicit KG triples need to be retrieved to generate explanations. In this paper, we propose a novel Graph Neural Network-based Two-Step Reasoning model (GNN2R) to solve this issue. GNN2R can provide both final answers and reasoning subgraphs as a rationale behind final answers efficiently with only weak supervision that is available through question-final answer pairs. We extensively evaluated GNN2R with detailed analyses in experiments. The results demonstrate that, in terms of effectiveness, efficiency, and quality of generated explanations, GNN2R outperforms existing state-of-the-art methods that are applicable to this task. Our code and pre-trained models are available at https://***/ruijie-wang-uzh/GNN2R. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) Anywhere

Post-Covid-19 Digital Nomadism: Beyond Work from (Almost) An...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Marcos Antonio De Almeida Jano Moreira De Souza António Correia Daniel Schneider Systems Engineering and Computer Science Program (PESCICOPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Tras-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska Omaha USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

In this paper, we continue our investigations on digital nomadism and the impact of COVID-19 pandemic on the work-related aspects and lifestyle of digital nomads (DN). The findings presented in this empirical study reflect the analysis of the impact of COVID-19 outbreak (and its waves) on the market economy and work-life boundaries of DNs as perceived from posts and comments gathered from a Reddit community during the period of early March 2020 until the end of 2022. From this point, our results indicate that the massification of remote work among formal workers in response to COVID-19 pandemic has impacted both the formal labor market and the DN ecosystem. As a consequence, we argue that digital nomadism tends to play a critical role beyond work from (almost) anywhere (WFA) in a post-COVID-19 era taking into account the novel facets of nomadic work-lifestyle.

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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