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Smart Data Intelligence (ICSMDI), International Conference on

作者： Elvina Jacia Gabriel Dyan Amelia Ifan Ford Lumban Gaol Tokuro Matsuo Department of Information System Bina Nusantara University Alam Sutera Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

The use of technology nowadays does not feel strange. Everyday people who use technology for their daily needs, starting with each other, seeking knowledge, can even earn income by doing business using technology. Social media is a tool that can be used by people to become with each other by creating, sharing, and exchanging information virtually through a network. However, in the current era, the notion of social media is not only about exchanging information, but also to support business products. The problem is how to compete tightly in this modern era in increasing the selling value of business products. The positive impact of social media for sales in business products are huge such as, increase sales, increase product promotion, greater market distribution. On the other side, the negative impact also takes part such as, the more competitors, the products compete fiercely in the market. This research aims to identify more the impact of social media on sales of business products that focused on business, benefit, and factor. The research method that we used is quantitative which focuses on the number of samples of respondents to support the research results and the results are in the form of a graphic that shows the answers of the respondents to the questionnaire.

关键词： Graphics Freeware Video on demand Social networking (online) Multimedia Web sites Media Internet telephony

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Analysis of Shoping Intention Using E-Commerce during Pandemic 8

Analysis of Shoping Intention Using E-Commerce during Pandem...

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

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Wijaya, Alessandro Bryan Tio, Fendy Malaysiani, Liza Azzahra Sanjaya, Sandi Martin, Yessin Rio Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665416979

This study aims to investigate people's shopping intentions using e-commerce during the pandemic in Indonesia. The method used in this research is the quantitative method in which the results of the study are used as an assumption and research discussion. Data collection was carried out by questionnaires then measure using t-test. The result can be concluded that the intention to shop using e-commerce during the COVID-19 pandemic is influenced using e-commerce features and e-commerce techniques. These results indicate that the intention to shop using e-commerce can increase if the availability of goods in e-commerce is quite complete, the quality of goods sold is very high, normal prices and technical requirement, such as speed, security, and a good e-commerce design system. © 2021 IEEE.

关键词： COVID-19

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INTEGRATED ASSESSMENT OF CORN STOVER AND COB GASIFICATION: ENERGETIC AND ENVIRONMENTAL PERSPECTIVES IN MIDWEST BRAZIL 32nd

INTEGRATED ASSESSMENT OF CORN STOVER AND COB GASIFICATION: E...

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32nd European Biomass Conference and Exhibition, EUBCE 2024

作者： Hack, Marcella Lamas, Giulia Cruz Rodrigues, Thiago Oliveira Rousset, Patrick Ghesti, Grace F. Silveira, Edgar A. University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and 8 Environment DF Brasilia70910-900 Brazil Brazilian Institute of Information in Science and Technology-IBICT DF Brasília70070-912 Brazil CIRAD UPR BioWooEB MontpellierF-34398 France BioWooEB Univ. Montpellier CIRAD Montpellier France University of Brasilia Chemistry Institute Laboratory of Brewing Bioprocesses and Catalysis to Renewable Energy DF Brasilia70910-900 Brazil

This research presents an integrated energy and environmental assessment of the gasification process for residual biomass, specifically corn stover and cobs, in the Midwest Region of Brazil. The study combines numerical modeling of the gasification process (thermodynamic equilibrium) and Life Cycle Assessment (LCA) to evaluate the quality of the gas produced and its associated environmental impacts, including Global Warming Potential (GWP) and Acidification Potential (AP). Corn biomass, often a by-product of industrial processing, is explored for its potential in energy generation, aiming to decentralize energy production and reduce environmental impacts. Preliminary results show variations in Lower Heating Value (LHV), Cold Gas Efficiency (CGE), and gas composition, focusing on syngas production. Results indicate that the highest percentage of H2 (38.5%) and the lowest percentage of CO (7.3%) was obtained for corn cob biomass as feedstock. Considering GWP and AP, the higher presence of steam (S/B = 1) as a gasifying agent generates more CO2-eq and kg SO2-eq emissions for both feedstocks, reporting corn stover with an average of 2.41 kg of CO2-eq. and corn cob with 0.002481 kg of SO2-eq. This research contributes to a better understanding of corn biomass gasification, offering valuable insights for optimizing the process while considering its environmental implications. Therefore, a model of sustainable corn production is aligned with the Sustainable Development Goals (SDGs), green chemistry, and circular economy. © 2024, ETA-Florence Renewable Energies. All rights reserved.

关键词： Corn Environmental Impact Gasification Life Cycle Assessment Modeling

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Effects of interactive unplugged programming activities on computational thinking skills and student engagement in elementary education

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Education and information Technologies 2023年第9期28卷 12293-12318页

作者： Li, Qi Jiang, Qiang Liang, Jyh-Chong Xiong, Weiyan Liang, Yu Zhao, Wei School of Information Science and Technology Northeast Normal University No. 2555 Jingyue Street Jilin Changchun 130117 China Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University #162 Section 1 Heping East Road Taipei 10610 Taiwan Institute of Policy Studies School of Graduate Studies Lingnan University #8 Castle Peak Road Tuen Mun New Territories SAR Hong Kong

Different instructional strategies have been drawn to assist elementary school students in improving computational thinking (CT) skills and student engagement (SE) in unplugged programming activities. This paper aimed to explore how the interactive strategies of unplugged programming affect CT skills and SE. The study was conducted based on a quasi-experimental research method. The sample was composed of 104 sixth-grade students from northern China. The students in the experimental group learned with interactive unplugged programming, whereas those in the control group learned with non-interactive unplugged programming. Analysis of Covariance statistics and Partial Least Squares Structural Equation Modeling were adopted for data analysis. Results showed that the performance of the experimental group in CT skills and SE was considerably better than that of the control group. Moreover, the predictive relationships were more complex between CT skills and SE in the experimental group. In general, these findings confirmed the teaching value of interactive unplugged programming, and further emphasized the important role of students’ CT skills in programming engagement. Therefore, through the application of interaction in instruction design, each student can be assigned a positive role to create a programming classroom that is more conducive to students. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Computational thinking skills Interactive unplugged programming Partial least squares structural equation modeling (PLS-SEM) Student engagement Unplugged programming

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on information Management and technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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On using Euler's Factorization Algorithm to Factor RSA Modulus 4

On using Euler's Factorization Algorithm to Factor RSA Modul...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Budiman, M.A. Zarlis, M. Sitompul, O.S. Mawengkang, H. Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The RSA public key cryptosystem was among the first algorithms to implement the Diffie-Hellman key exchange protocol. At the core of RSA's security is the problem of factoring its modulus, a very large integer, into its prime factors. In this study, we show a step-by-step tutorial on how to factor the RSA modulus using Euler's factorization algorithm, an algorithm that belongs to the class of exact algorithms. The Euler's factorization algorithm is implemented in Python programming language. In this experiment, we also record the relation between the length of the RSA moduli and its factorization time. As a result, this study shows that the Euler's factorization algorithm can be used to factor small modulus of RSA, the correlation between the factoring time and the size of RSA modulus is directly proportional, and better selection of some Euler's parameters may lead to lower factoring time. © Published under licence by IOP Publishing Ltd.

关键词： Factorization

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University Students’ Attitudes toward Adoption of Mobile Health Application for Serving Older People

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Sensors and Materials 2023年第7期35卷 2581-2601页

作者： Chao, Pei-Ju Cheng, Yu-Huei Liu, Yi-Hui Hsu, Tsu-Hsuan Travel & Tourism Program International College Ming Chuan University 5 De Ming Rd. Gui Shan District Taoyuan333 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung413 Taiwan Department of Senior Citizen Service Management National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung404 Taiwan Graduate Institute of Rehabilitation Counseling National Changhua University of Education 1 Jin-De Rd. Changhua City500 Taiwan

In this quantitative study, we examined the awareness of mobile health (mHealth) applications among Taiwan’s university students, particularly the government-operated My Health Bank application. The My Health Bank application allows users to access their personal health information simply through their smartphone’s sensor-assisted facial recognition camera and fingerprint sensor. Whether these university students might help older adults to use this application if they perceive it to be helpful was also explored. Two instruments were used in this study, namely, the Perceptions toward My Health Bank (PMHB) and Geriatric Attitudes Scale (GAS). Two hundred twenty-five (225) university students from the Departments of Senior Citizen Service Management (n = 136) and Nursing (n = 89) at two universities participated in this study. The findings indicated low adoption of My Health Bank and related mHealth applications among students even though they had high adoption rates of smartphones. However, they perceived that the functions of this application are useful and should be promoted to older adults. Thus, building a basic foundation of mHealth and related technology courses is recommended to assist healthcare students in being well-prepared for working with older adults in the digital society. © MYU K.K.

关键词： mHealth

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer

arXiv

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arXiv 2023年

作者： Sengar, Neha Kumari, Indra Lee, Jihui Har, Dongsoo The Robotics Program Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Korea Institute of Science and Technology Information Daejeon34141 Korea Republic of The Division of Future Vehicle Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The CCS Graduate School of Green Transportation Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Driver distraction is a principal cause of traffic accidents. In a study conducted by the National Highway Traffic Safety Administration, engaging in activities such as interacting with in-car menus, consuming food or beverages, or engaging in telephonic conversations while operating a vehicle can be significant sources of driver distraction. From this viewpoint, this paper introduces a novel method for detection of driver distraction using multi-view driver action images. The proposed method is a vision transformer-based framework with pose estimation and action inference, namely PoseViNet. The motivation for adding posture information is to enable the transformer to focus more on key features. As a result, the framework is more adept at identifying critical actions. The proposed framework is compared with various state-of-the-art models using SFD3 dataset representing 10 behaviors of drivers. It is found from the comparison that the PoseViNet outperforms these models. The proposed framework is also evaluated with the SynDD1 dataset representing 16 behaviors of driver. As a result, the PoseViNet achieves 97.55% validation accuracy and 90.92% testing accuracy with the challenging dataset. © 2023, CC BY.

关键词： Deep learning

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