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Conference on New Media Studies (CoNMedia)

作者： Erick Fernando Desman Hidayat Christian Haposan Pengaribuan Yulius Denny Prabowo Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Management Department BINUS Entrepreneurship Center Bina Nusantara University Jakarta Indonesia Management Study Program Faculty of Business Sampoerna University Jakarta Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables.

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Naked-eye observation of water-forming reaction on palladium etalon:transduction of gas-matter reaction into light-matter interaction

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PhotoniX 2023年第1期4卷 349-363页

作者： Jongsu Lee Eui-Sang Yu Taehyun Kim In Soo Kim Seok Chung Seung Jae Kwak Won Bo Lee Yusin Pak Yong-Sang Ryu Sensor System Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Smart Materials Research Section Electronics and Telecommunications Research InstituteDaejeon 34129Republic of Korea Department of Micro/Nano Systems Korea University02841 SeoulRepublic of Korea Nanophotonics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea School of Mechanical Engineering Korea UniversitySeoul 02841Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02481Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02481Republic of Korea

Palladium is the most prominent material in both scientific and industrial research on gas storage,purification,detection,and catalysis due to its unique properties as a catalyst and hydrogen *** the dynamic optical phenomena of palladium reacting with hydrogen,transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction *** simple geometry of the metal-dielectric-metal structure,Fabry-Perot etalon,is employed for a colorimetric reactor,to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium *** adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends:the foggy effect by water bubbles and the whiteout effect by water film *** and experiments demonstrate the robustness of the proposed Fabry-Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

关键词： Palladium Hydrogen Oxygen Fabry-Perot resonators Colorimetric sensors Water-forming reaction

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Link prediction of LOD by multiple label propagation algorithm considering semantic distance

Link prediction of LOD by multiple label propagation algorit...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Toshitaka Maki Kazuki Takahashi Akihiro Yamaguchi Toshihiko Wakahara Toru Kobayashi Akihisa Kodate Noboru Sonehara Intelligent Information System Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Communication & Information Networking Fukuoka Institute of Technology FIT Fukuoka Japan Information and Systems Engineering Fukuoka Institute of Technology FIT Fukuoka Japan Computer and Information Sciences Program Nagasaki University Nagasaki Univ. Nagasaki Japan Department of Computer Science Tsuda College Tsuda Tokyo Japan Information and Society Research Division National Institute of Informatics NII Tokyo Japan

ISBN: (纸本)9781509023349

This paper presents link prediction of Linked Open Data (LOD) by Multiple Label Propagation Algorithm (MLPA). The current LOD do not have enough links. Therefore, the LOD have not been able to exert so much semantic characteristics. In order to solve this problem, we proposed the MLPA considering semantic distance. The MLPA can expand potential links of each data contained in the LOD. The experimental result of the MLPA shows the good performance and the validity of this algorithm is confirmed.

关键词： Prediction algorithms Semantics Metadata Algorithm design and analysis Conferences Consumer electronics Resource description framework

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Continuous Prediction of Leg Kinematics during Walking using Inertial Sensors, Smart Glasses, and Embedded Computing

Continuous Prediction of Leg Kinematics during Walking using...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Oleksii Tsepa Roman Burakov Brokoslaw Laschowski Alex Mihailidis Department of Computer Science AGE-WELL Networks of Centres of Excellence program University of Toronto and the Vector Institute for Artificial Intelligence Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto ON Canada Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mechanical and Industrial Engineering University of Toronto Toronto ON Canada Toronto Rehabilitation Institute University Health Network Toronto ON Canada Institute of Biomedical Engineering University of Toronto Toronto ON Canada

Unlike traditional hierarchical controllers for robotic leg prostheses and exoskeletons, continuous systems could allow persons with mobility impairments to walk more naturally in real-world environments without requiring high-level switching between locomotion modes. To support these next-generation controllers, we developed a new system called KIFNet (Kinematics and Image Fusing Network) that uses lightweight and efficient deep learning models to continuously predict the leg kinematics during walking. We tested different sensor fusion methods to combine kinematics data from inertial sensors and computer vision data from smart glasses and found that adaptive instance normalization achieved the lowest RMSE predictions for knee and ankle joint kinematics. We also deployed our model on an embedded device. Without inference optimization, our model was 20 times faster than the previous state-of-the-art and achieved 20% higher prediction accuracies, and during some locomotor activities like stair descent, decreased RMSE up to 300%. With inference optimization, our best model achieved 125 FPS on an NVIDIA Jetson Nano. These results demonstrate the potential to build fast and accurate deep learning models for continuous prediction of leg kinematics during walking based on sensor fusion and embedded computing, therein providing a foundation for real-time continuous controllers for robotic leg prostheses and exoskeletons.

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Technical debt and agile software development practices and processes: An industry practitioner survey

arXiv

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arXiv 2021年

作者： Holvitie, Johannes Licorish, Sherlock A. Spínola, Rodrigo O. Hyrynsalmi, Sami MacDonell, Stephen G. Mendes, Thiago S. Buchan, Jim Leppänen, Ville Turku Centre for Computer Science Software Development Laboratory Turku Finland University of Turku Department of Information Technology Turku Finland University of Otago Department of Information Science Otago Dunedin New Zealand Salvador University Graduate Program in Systems and Computer Bahia Salvador Brazil Federal University of Bahia Fraunhofer Project Center for Software and System Engineering Salvador Brazil Tampere University of Technology Pori Campus Pori Finland Federal Institute of Bahia Information Technology Department Bahia Santo Amaro Brazil Auckland University of Technology School of Engineering Computer and Mathematical Sciences Auckland New Zealand

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management, given the popularity and perceived success of agile methods. We explore the breadth of practitioners' knowledge about technical debt;how technical debt is manifested across the software process;and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt;in particular, techniques that verify and maintain the structure and clarity of implemented artifacts (e.g., Coding standards and Refactoring) positively affect technical debt management. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, comp

关键词： Legacy systems

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Blockchain Technology Based Credibility Verification in E-Journal Entities

Blockchain Technology Based Credibility Verification in E-Jo...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Sri Watini Zulkarnain Kedah Chandra Lukita Petrus Sokibi Gadis Mustika Reymund Rahardja Arsi Yulianjani Husni Teja Sukmana Early Childhood Teacher Education University of Panca Sakti Bekasi Bekasi Indonesia Vice Chancellor University of Malaysian Computer Science Selangor Malaysia Information System University of Catur Insan Cendekia Cirebon Indonesia Information System University of Raharja Tangerang Indonesia Digital Business University of Raharja Tangerang Indonesia Information Management Study Program University of Raharja Tangerang Indonesia Lecture of Information Engineering Syarif Hidayatullah Islamic University Jakarta Indonesia

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era.

关键词： COVID-19 Economics Epidemics Soft sensors Focusing Forgery Blockchains

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Improving debris flow evacuation alerts in Taiwan using machine learning

arXiv

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arXiv 2022年

作者： Tsai, Yi-Lin Irvin, Jeremy Chundi, Suhas Ng, Andrew Y. Field, Christopher B. Kitanidis, Peter K. Department of Civil and Environmental Engineering Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Interdisciplinary Environmental Studies Program Stanford University StanfordCA United States Department of Earth System Science Stanford University StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts. Copyright © 2022, The Authors. All rights reserved.

关键词： Rain

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Real-Time Music Tracking Based on a Weightless Neural Network

Real-Time Music Tracking Based on a Weightless Neural Networ...

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International Conference on Complex, Intelligent and Software Intensive systems, CISIS

作者： Diego F. P. De Souza Felipe M.G. Franca Priscila M.V. Lima Syst. Eng. & Comput. Sci. Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro RJ BR System Engineering and Computer Science Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Inst. Tercio Pacitti (NCE) Univ. Fed. do Rio de Janeiro Janeiro Brazil

Music tracking is a useful technique for many music related tasks. Some applications include evaluating musicians performance, automatic score page turning and syncing music lyrics. In this work, we describe a WiSARD-based system for real-time music tracking and evaluate the performance of the corresponding implementation. Given any audio example, the system is capable of recognizing which point of the original signal is currently playing and of dynamically tracking it. In other words, if the music restarts or jumps to another position, the system is capable of following it. This is accomplished thanks to the low complexity training and classifying of the models used, which continually keep track of all possible points of the music, and not only of the current neighboring region. Experiments are provided in order to analyze the performance of the system in three test scenarios: tracking continuous playing, tracking multiple jumps and self predicting errors. The final results demonstrate that the system is efficient even when applied to musics with some level of repetitions and short periods of silence.

关键词： Training Random access memory Music Robot sensing systems Hidden Markov models Neurons

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