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检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2627 条记录，以下是111-120 订阅

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A framework for Anti-Money Laundering based on AI and SATP in CBDC Transactions

A framework for Anti-Money Laundering based on AI and SATP i...

引用

2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Kim, Soyoung Lee, Harang Kim, Keecheon Computer Science Konkuk University Smart ICT Convergence Dept. IT Convergence and Information Security Seoul Korea Republic of

Central Bank Digital Currency (CBDC) is emerging as a pivotal element of next generation financial innovation, with the digitalization of financial transactions accelerates. Many countries are actively exploring the implementation of CBDCs for retail and wholesale settings and their potential in international financial transactions. Regulatory compliance has become paramount in these evolving payment systems as concerns about money laundering have grown. This paper highlights the need for real-time transaction monitoring and suspicious activity detection systems while proposing an efficient approach to balance privacy protection and regulatory compliance. In this study, we introduce an integrated architecture that combines CBDCs with Secure Asset Transfer Protocol (SATP) systems. It utilizes AI-based prevention and detection techniques. This approach aims to enhance privacy over existing approaches, tailored to international transactions and large financial institutions, while improving the effectiveness of anti-money laundering measures. © 2025 IEEE.

关键词： Bitcoin

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Enhancement Strategies for the AI Platform for Accelerated Materials Development and Innovation

Enhancement Strategies for the AI Platform for Accelerated M...

引用

international conference on big data and smart computing (bigcomp)

作者： Su-Young Chi Hyun Jong Kim Choi Kang Il Yo Han Choi Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

AI Platform aims to accelerate materials development by integrating AI-driven modeling and data management solutions. Key enhancement strategies include expanding materials data through standardized repositories, optimizing AI models for customized material analysis, and implementing a cloud-based infrastructure to support scalable user access and secure data handling. These advancements position AI Platform as a comprehensive platform that streamlines experimental processes, reduces development time, and fosters digital innovation in materials science.

关键词： Industries Analytical models Technological innovation Materials science and technology Computational modeling Digital transformation data handling data models Artificial intelligence Optimization

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Randomizing Hypergraphs Preserving Two-mode Clustering Coefficient

Randomizing Hypergraphs Preserving Two-mode Clustering Coeff...

引用

international conference on big data and smart computing (bigcomp)

作者： Rikuya Miyashita Kazuki Nakajima Mei Fukuda Kazuyuki Shudo Tokyo Institute of Technology Japan

Hypergraphs are data structures that capture the interactions between two or more nodes. By comparing random hypergraphs that preserve particular properties with an original hypergraph, we can analyze the impacts of those properties. In this study, we propose a method for generating random hypergraphs that preserve the pairwise joint degree distribution and the two-mode clustering coefficient. By the proposed method, we generated hypergraphs that preserve the average degree of the node and the degree of each node and approximately preserve the pairwise joint degree distribution and the two-mode clustering coefficient.

关键词： Computational modeling big data data structures data models

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Comparison of Crop Image Classification Model Performance According to Image Augmentation Techniques

Comparison of Crop Image Classification Model Performance Ac...

引用

international conference on big data and smart computing (bigcomp)

作者： Dong Kyu Yun Seong Ju Joe Ki Tae Park Seok Kee Lee Sang Hyun Choi dept. of Bigdata Chungbuk National University Cheongju Republic of Korea dept. of Computer Engineering Hansung University Seoul Republic of Korea dept. of MIS/Bigdata Chungbuk National University Cheongju Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Currently, the agricultural population is rapidly decreasing in Korea, and the aging population is getting worse. Therefore, the development of smart farm-related technologies is more necessary, and image processing technologies using big data and AI are very important. In this study, after collecting crop images directly from farms, three datasets were constructed according to the preprocessing method, and images between crops were classified using a CNN-based image classification model. As a result of the analysis, it was confirmed that the method using dataset B and NASNetLarge with five data augmentation was the best with an average classification accuracy of $\mathbf{9 6 . 7 1 \%}$.

关键词： smart agriculture YOLO Accuracy Crops big data Approximation algorithms Image augmentation Classification algorithms Convolutional neural networks Image classification

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A Modular data-Driven Architecture for Empathetic Conversational Agents

A Modular Data-Driven Architecture for Empathetic Conversati...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Scotti, Vincenzo Tedesco, Roberto Sbattella, Licia Politecn Milan DEIB Milan Italy

ISBN: (纸本)9781728189246

Empathy is a fundamental mechanism of human interactions. As such, it should he an integral part of Human Computer Interaction systems to make them more relatable. With this work, we focused on conversational scenarios where integrating empathy is crucial to perceive the computer like a human. As a result, we derived the high-level architecture of an Empathetic Conversational Agent we are willing to implement. We relied on theories about artificial empathy to derive the function approximating this mechanism and selected the conversational aspects to control for an empathetic interaction. In particular, we designed a core empathetic controller manages the empathetic responses, predicting, at each turn, the high-level content of the response. The derived architecture integrates empathy in a task-agnostic manner;hence we can employ it in multiple scenarios by changing the objective of the controller.

关键词： Empathetic computing Conversational Agents Deep Learning

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Entropy-based Sentence-level Hallucination Score in Large Language Models

Entropy-based Sentence-level Hallucination Score in Large La...

引用

international conference on big data and smart computing (bigcomp)

作者： Eojin Joo Young-Jun Lee Ho-Jin Choi School of Computing KAIST Daejeon South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Large Language Models (LLMs) excel at generating human-like text but are prone to hallucinations-fabricated outputs that deviate from reality. Addressing this is critical for ensuring trustworthy AI, especially in fields like medicine, law, and education. This paper introduces an entropy-based metric for detecting sentence-level hallucinations by assessing token prediction uncertainty. High entropy correlates with hallucinatory outputs, and we validate this metric’s effectiveness across different LLMs. Applications include hallucination flagging and improving user trust through uncertainty-driven feedback. This approach advances the reliability and transparency of LLMs, contributing to safer AI systems.

关键词： Measurement Deep learning Uncertainty Accuracy Large language models Natural language generation Education big data Entropy Reliability

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DefectGRANDE: Hybrid Approach for Class Imbalance in Software Defect Prediction

DefectGRANDE: Hybrid Approach for Class Imbalance in Softwar...

引用

international conference on big data and smart computing (bigcomp)

作者： Eunjeong Ju Jeonghwa Lee Duksan Ryu Department of Software Engineering Jeonbuk National University Jeonju South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Software Defect Prediction (SDP) ensures software quality by identifying defects early in development. However, a major challenge is class imbalance, where defect data is vastly outnumbered by non-defect data, reducing prediction accuracy. To address this, we propose DefectGRANDE, a hybrid model leveraging SMOTE for data balancing, RandomForest for feature importance, and ensemble learning for stability. DefectGRANDE outperforms existing methods in metrics like Positive Detection (PD), Balance, and AUC. Through this, defects can be predicted more effectively in the early stages of development, enhancing software quality and reliability while reducing development costs and time.

关键词： Measurement Costs Accuracy Software quality big data Stability analysis data models Software reliability Ensemble learning Optimization

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An Automated Scenario Generation Model for Anti-phishing using Generative AI

An Automated Scenario Generation Model for Anti-phishing usi...

引用

international conference on big data and smart computing (bigcomp)

作者： Jin Yong Park Tae-Sung Kim Dept. of Big Data Chungbuk National University Cheong-ju South Korea Dept of Management Information System Chungbuk National University Cheong-ju South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Generative AI could be used not only for classification or prediction, but also to generate images, music, etc. based on the input data. Generative AI was abused to generate deepfake and spear-phishing to cause social and economic problems. To prevent these attacks, simulation training is necessary. Simulation training was necessary because it allowed us to effectively respond to attacks and reduce damage. We proposed an ensemble model that automated scenarios generation by utilizing multiple generative AIs. This study was expected to develop an automated training scenario generation model using generative AI that could be used in cybersecurity training, anti-phishing training, and security consulting to automatically generate realistic and customized training scenarios.

关键词： Training Measurement Economics Generative AI databases Phishing Computational modeling Organizations Vectors Scenario generation

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An Object Detection Model Robust to Out-of-Distribution data

An Object Detection Model Robust to Out-of-Distribution Data

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Park, Ho-rim Hwang, Kyu-hong Ha, Young-guk Konkuk Univ Dept Comp Sci & Engn 120 Neungdong Ro Seoul South Korea

ISBN: (纸本)9781728189246

Most of the studies of the existing object detection models are studies to better detect the objects to be detected. The problem of false detection of objects that should not be detected is not considered. When an object detection model that does not take this problem into account is applied to an industrial field close to humans, false detection can lead to a dangerous situation that greatly interferes with human life. To solve this false detection problem, this paper proposes a method of fine-tuning the backbone neural network model of the object detection model using the Outlier Exposure method and applying the class-specific uncertainty constant to the confidence score to detect the object.

关键词： deep learning object detection out-of-distribution data

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Proposing a business model analysis framework for IoT bigdata

Proposing a business model analysis framework for IoT Bigdat...

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2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Lee, Junghwan Lee, Yeonseok Chungbuk National University Department of Management Information Systems Cheongju Korea Republic of Chungbuk National University Graduate School of Big data Collaboration Courser Cheongju Korea Republic of

This study emphasizes the importance of business model analysis in the IoT big data field and proposes a general business model framework for this purpose. The analysis focuses on the exchange of various types of value among stakeholders and customers, playing a crucial role in enabling companies to gain a competitive edge and create more value in the IoT sector. Furthermore, the paper conducts a case analysis of Samsung Electronics, with a specific focus on their IoT big data platform, smartThings. smartThings is integrated with the company's range of products (such as home appliances and mobile devices), allowing Samsung to deliver diverse value to customers. This analysis demonstrates how companies can secure a competitive advantage and generate more value in the IoT industry. By providing this analysis, the paper offers a general framework for analyzing business models in the IoT sector, aiding companies in formulating successful competitive strategies in the IoT market. This support aims to enable companies to compete more effectively in the IoT bigdata industry. © 2025 IEEE.

关键词： big data Business Model Ecosystem Framework Internet of Things(IoT) Value Chain

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