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2023 International Conference on Cyber Management and Engineering, CyMaEn 2023

作者： Anom, Rahmat Indra Pratama Yasirandi, Rahmat Ladkoom, Kobthong Dewantara, Yudiet Fajar Gandhiwati, Haililah Tri Doctoral Program of Tourism Trisakti School of Tourism Jakarta Indonesia School of Computing Telkom University Bandung Indonesia Faculty of Science Chandrakasem Rajabhat University Bangkok Thailand Bunda Mulia University Departement of Hospitality Jakarta Indonesia

ISBN: (纸本)9781665493291

This study aims to determine the effect of the level of Digital Literacy and the quality of Information Technology on Tourists' Visit Decisions to Lampung marine tourism during the COVID-19 Pandemic. From the results of questionnaires distributed to 106 tourists. All the questions in the given questionnaire were declared 100% valid by comparing the rcount value of each question item, which was more than the rtable value 0.195. The calculation results of the reliability test, all variables X1, X2, and Y have Cronbach's alpha coefficient of more than 0.6. So that all variables are declared reliable. Furthermore, based on the hypotheses testing of H1, H2, and H3, the results can be concluded that Digital Literacy and IT Service Quality individually or simultaneously positively and significantly influence the Tourist Decision to Visit Lampung Marine Attraction during the Covid-19 pandemic. Moreover, the R2 value of 0.631 indicates that the level of Digital Literacy and the IT Service Quality can explain Interest in Decisions to Visit Marine Tourism in Lampung during the COVID-19 pandemic at a percentage of 63.1%. © 2023 IEEE.

关键词： COVID-19

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Analysis of News Sentiment for Stock Price Prediction Using VADER Sentiment

Analysis of News Sentiment for Stock Price Prediction Using ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Michael Siek Vic Declan Chandra Information Systems Department Business Information Systems Program School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Stock market trading is often thought to be a lucrative subject and in it, is predominantly occupied by the discussion of stock price prediction. Within it comes a multitude of theories pertaining to stock movement, while they seem irreconcilable an integrated model can be created to improve the accuracy of stock prediction as well as form a synthesis of said multitude. The paper focuses on finding the correlation that lies between the historical stock price of Bank Jago alongside economic indicators and news sentiments through implementing VADER sentiment analysis and regression models. The analysis and modelling data were collected in real-time through Yahoo Finance API and GDELT for the stock news. The analysis results indicate that Pearson and Spearman correlation coefficients between stock prices and news sentiments have low values or relationships due the fact that these methods only describe general statistical relationship on average. However, there are reasonably improvements in correlation coefficients when using rolling techniques and economic indicators. In other hand, the stock traders and investors prefer to obtain real-time predictions or indicators of low or high stock prices and the dynamical stock price directions. The latter is denoted by reasonably good dynamical alignments between stock prices and news sentiments through visual inspection of the chart. This research is highly recommended to continue for the next steps by integrating sentiment analysis and machine learning models for predicting stock prices.

关键词： Correlation coefficient Hands Sentiment analysis Analytical models Visualization Economic indicators Machine learning Predictive models Inspection Real-time systems

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Alternative Reference Samples to Improve Coding Efficiency for Parallel Intra Prediction Solutions

Alternative Reference Samples to Improve Coding Efficiency f...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Iago Storch Nuno Roma Daniel Palomino Sergio Bampi Graduate Program in Computing Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil INESC-ID Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Video Technology Research Group Graduate Program in Computing Federal University of Pelotas Pelotas Brazil

Exploring massive parallelism is a common strategy to mitigate the processing time of modern video encoding standards. Nonetheless, the existing data dependencies in some encoding tools pose difficult challenges to exploit such parallelism, especially during intra prediction, where the reconstructed adjacent blocks are used as references. Although some works made use of different reference samples to allow block-level parallelism in intra prediction, their proposals do not consider the variations caused by different bitrates, leading to some degradation in the output sequence. To deal with multiple bitrates more properly, this work proposes the application of image smoothing techniques to generate alternative reference samples that better represent the nuances of different bitrates. Experimental validations demonstrate that these improved references provide coding efficiency gains while still offering an equivalent parallelization opportunity.

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Quantum-Chiplet: A Novel Python-Based Efficient and Scalable Design Methodology for Quantum Circuit Verification and Implementation

arXiv

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arXiv 2025年

作者： Kao, Yu-Ting Lu, Hao-Yu Chang, Yeong-Jar Lu, Darsen Industrial Technology Research Institute Taiwan Intelligent Computing Industrial Doctorate Program Miin Wu School of Computing National Cheng Kung University Taiwan Institute of Microelectronics Department of Electrical Engineering National Cheng Kung University Taiwan

Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Score-based Diffusion Model based Synthetic CT Generation from MR Images and Post-hoc Uncertainty Analysis

Score-based Diffusion Model based Synthetic CT Generation fr...

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Medical Imaging 2025: Image Processing

作者： Yie, Si Young Yoon, Siyeop Yang, Jaewon Kim, Kyungsang Lee, Jae Sung Li, Quanzheng Interdisciplinary Program in Bioengineering Seoul National University Seoul03080 Korea Republic of Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Havard Medical School BostonMA02114 United States Department of Radiology University of Texas Southwestern DallasTX75390 United States

ISBN: (纸本)9781510685901

PET/MRI and MR-guided radiotherapy have significantly advanced clinical diagnostics and treatment planning by integrating anatomy and function, while synthetic CT generation from MRI has emerged as a tool for extracting tissue density and attenuation properties. Despite these advances, generating synthetic CT from MRI remains challenging in the pelvic region due to substantial differences in tissue contrast and intensity between the two modalities. Traditional methods using dedicated MR sequences often fail to accurately represent bone structure. Recent deep learning models, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), have improved the quality of synthetic CT generation, they typically lack uncertainty estimation, which limits their clinical utility. In this study, we propose a conditional diffusion model for generating pelvic CT from T1-weigted MR, enhanced by tissue segmentation extracted from TotalSegmentator-MR. We also investigated the self-explanatory characteristics of the diffusion model through uncertainty mapping from multiple realizations without requiring ground truth to enhance interpretability. Our results demonstrate that the diffusion model with tissue segmentation priors, produces higher-quality synthetic CTs and improved tissue differentiation compared to deterministic generative model and diffusion models conditioned only on MR data. Notably, the uncertainty map showed a strong spatial correlation with the ground truth-based error map. Furthermore, the inclusion of segmentation priors also reduces uncertainty, thereby improving the model's reliability. © 2025 SPIE.

关键词： Convolutional neural networks

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Introduction of Applied Aerodynamics Surrogate Modeling Benchmark Cases

Introduction of Applied Aerodynamics Surrogate Modeling Benc...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Bekemeyer, Philipp Hariharan, Nathan Wissink, Andrew Cornelius, Jason Lilienthalplatz 7 Braunschweig38108 Germany DoD High Performance Computing Modernization Program AlexandriaVA22315 United States US Army DEVCOM AvMC Moffett FieldCA94035 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

From aircraft design to certification, a significant volume of aerodynamic data is required to ensure optimal performance, meet regulatory standards, and maintain structural integrity. These data must span the entire flight envelope, encompassing pressure and shear stress distributions, global coefficients, and derivatives. Traditionally sourced from flight tests, wind tunnel experiments, or numerical simulations, the data are often of varying fidelity, ranging from handbook methods to high-resolution simulations. In recent years, the demand for efficient use of these data has grown, driven by advancements in artificial intelligence and machine learning, enabling the development of fast-running surrogate models. Unlike traditional high-fidelity simulations or experimental setups, which can be resource-intensive, surrogate models trained on these data sets deliver rapid predictions comparable to database queries. The AIAA Applied Aerodynamics Surrogate Modeling (AASM) group was formed to bring focus to data-driven and AI modeling in aerospace sciences, uniting experts from academia, industry, and government agencies worldwide. The AASM group prioritizes the development, accuracy, and applicability of surrogate modeling for aerospace applications, including design optimization, uncertainty quantification, systems engineering, and mission analysis—all critical to a digital engineering ecosystem. To support evaluation and comparison of methodologies, this paper introduces four benchmark cases: an aerodynamic database of integrated airfoil performance coefficients, a missile case for 6DOF generation, and two data sets focusing on surface pressure distributions. These benchmarks highlight associated surrogate modeling challenges and will be made publicly available through AIAA, offering valuable resources for the aerospace community. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shear stress

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Evaluation of Enrichment in Ontology-based Knowledge Management System

Evaluation of Enrichment in Ontology-based Knowledge Managem...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Dessy Hariyanti, Ni Kadek Linawati, Linawati Made Oka Widyantara, I. Putra Sastra, Nyoman Adisusilo, Anang Kukuh Wayan Budi Sentana, I. Dewa Made Bayu Atmaja Darmawan, I. Universitas Udayana Study Program of Doctoral Engineering Science Indonesia Universitas Udayana Electrical Engineering Department Indonesia Universitas Wijaya Kusuma Surabaya Engineering Department Surabaya Indonesia Macquarie University School of Computing SydneyNSW Australia

ISBN: (纸本)9798350382822

Ontology is an organization of knowledge that can represent knowledge in a structured manner. An ontology-based Knowledge Management System is a system that combines elements of knowledge management with the application of ontology as a knowledge base. Meanwhile, knowledge is dynamic and continues to develop all the time, giving rise to new knowledge. Therefore, the ontology must be updated regularly through ontology enrichment to meet adequate knowledge requirements. Ontology enrichment is carried out to ensure that the ontology remains relevant and responsive to developments in its knowledge domain. However, there are no standard stages in implementing enrichment in ontology-based Knowledge Management systems. Therefore, this research conducted a literature review to determine when and how to apply enrichment in ontology development. Data was obtained from related journals in 2018 - 2023. The method used in this research is the Systematic Literature Review method to systematically identify, review, evaluate, and interpret all available research in the area of interest with specific relevant research questions. The research results show that the ontology enrichment procedure generally includes preprocessing stages, relation extraction, and enrichment processes. Ontology enrichment is generally applied during the development of a new ontology as part of the ontology development cycle and on existing ontologies, which are a separate stage from the initial ontology development cycle. Based on these findings, the researcher provides recommendations for researchers who want to use or develop new ontology enrichment methods. © 2023 IEEE.

关键词： Ontology

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Cross-Platform Neural Video Coding: A Case Study

arXiv

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arXiv 2024年

作者： Conceição, Ruhan Porto, Marcelo Peng, Wen-Hsiao Agostini, Luciano Graduate Program in Computing Federal University of Pelotas Brazil Departament of Computer Science National Yang Ming Chiao Tung University Taiwan

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters are determined through an exhaustive search of all possible combinations of observers and quantization schemes from PyTorch. For the SSF codec, when encoding and decoding on different machines, the proposed solution effectively mitigates the mismatch issue and enhances compression efficiency results by preventing severe image quality degradation. When encoding and decoding are performed on the same machine, it constrains the average BD-rate increase to 9.93% and 9.02% for UVG and HEVC-B sequences, respectively. © 2024, CC BY-NC-SA.

关键词： Encoding (symbols)

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Exploring Drug Repurposing Success Stories Through a Network-based Approach: Insights from a Case Study

Exploring Drug Repurposing Success Stories Through a Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pierini, Elisa Mazza, Ludovica Petti, Manuela Tieri, Paolo Sapienza University of Rome Data Science MSc Program Rome Italy Sapienza University of Rome Diag Dept. Comp. Contr. Manag. Eng. Rome Italy Cnr National Research Council Iac Institute for Applied Computing Rome Italy

ISBN: (纸本)9798350386226

Drug repositioning is a promising strategy to discover new therapeutic applications for existing drugs, significantly reducing the time and costs associated with traditional drug development. This study employs a network medicine approach to analyze successful cases of drug repositioning, focusing on the exploratory hypothesis that the efficacy of repositioning may be determined by functional similarity between between diseases for which the drug was originally designed and diseases for which the same drug is reused. Network medicine tools were employed to investigate the connections between disease-associated genes, proteins, and approved drugs. Biological networks, including protein-protein interactions and functional interactions networks, as well as gene- and drug-disease association data are analyzed to identify functional similarities and possible molecular connections between diseases and treatments. Using clustering techniques and topological analysis, the results reveal a suggestive overlap of involved genes and functional interactions, emphasizing the value of computational methods in accelerating drug repositioning efforts and improving understanding of drug repositioning dynamics for more efficient therapeutic interventions. © 2024 IEEE.

关键词： Network theory (graphs)

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A FBMC based ISDB-Tb Digital TV Frame Proposal 4

A FBMC based ISDB-Tb Digital TV Frame Proposal

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4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021

作者： Almeida, Jefferson Jesus Hengles Lopes, Paulo Batista Akamine, Cristiano Mackenzie Presbyterian University Graduate Program in Electrical Engineering and Computing São Paulo Brazil

ISBN: (纸本)9781665414807

Nowadays, one of the challenges of communications systems is to enhance spectrum utilization and data rate without robustness loses. This situation is more perceptive in digital television applications, since there is a constant need of providing better experiences to the users with the transmission of higher resolution videos. In this context, novel approaches for the physical layer of such systems are subject of intense research, including investigation on the effect of replacing the Orthogonal Frequency Division Multiplexing (OFDM) technique currently used by other modulation schemes such as Filter Bank Multi Carrier (FBMC). This article presents a novel FBMC based frame implementation for the Integrated Services Digital Broadcasting Terrestrial Version B (ISDB-TB), developed as a software defined radio (SDR). The results show the robustness and rate gains of FBMC and prove that ISDB-TB frame can be modified to enhance the performance in Digital TV applications. © 2021 IEEE.

关键词： Digital television

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