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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Keane Ong Rui Mao Deeksha Varshney Ranjan Satapathy Erik Cambria Gianmarco Mengaldo Asian Institute of Digital Finance National University of Singapore College of Computing and Data Science Nanyang Technological University College of Design & Engineering National University of Singapore Institute of High Performance Computing Agency for Science Technology and Research

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

This research involves examining whether sentiment on climate change can be accounted as a systematic risk factor within sustainable finance. Tweets related to climate change from 2014-2022 are collected via the Twitter API. RoBERTa is then deployed to label the collected tweets according to sentiment polarity. Thereafter, DistilBERT is utilised to classify tweets according to their different climate change topics Aggregated, Impact, Politics & Policy, Mitigation, Rootcause. Using these sentiment and topic labels, monthly sentiment scores are aggregated from the frequency of sentiment polarity (number of positive, and negative tweets), and segmented according to the topics that the tweets are classified under. On the other hand, we collect the monthly returns of various green ETFs, alongside ETFs that contain stocks from other sectors (technology, oil and gas), as well as 2 baskets of stocks (sustainable and heavy-polluting). We run Fama-French regressions by conducting time series regression of monthly returns on the traditional 3 factors, with specific addition of sentiment scores.

关键词： Climate change Investment Sustainable development Sentiment analysis Social networking (online) Finance Green products Risk management

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Perceptual Contrastive Generative Adversarial Network Based on Image Warping for Unsupervised Image-to-Image Translation

SSRN

引用

SSRN 2023年

作者： Huang, Lin-Chieh Tsai, Hung-Hsu Institute of Data Science & Information Computing National Chung Hsing University Taichung402 Taiwan

This paper proposes an unsupervised image-to-image (UI2I) translation model, called Perceptual Contrastive Generative Adversarial Network (PCGAN), which can solve the distortion problem to enhance performance of the traditional UI2I methods. The PCGAN is designed with a two-stage UI2I model. In the first stage of the PCGAN, it leverages a novel image warping to transform shapes of objects in input (source) images. In the second stage of the PCGAN, the residual prediction is devised in refinements of the outputs of the first stage of the PCGAN. To promote performance of the image warping, a loss function, called Perceptual Patch-Wise InfoNCE, is developed in the PCGAN to effectively memorize the visual correspondences between warped images and refined images. Experimental results on quantitative evaluation and visualization comparison for UI2I benchmarks show that the PCGAN definitely is superior to other existing methods considered here. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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UPDATE: Mining User-News Engagement Patterns for Dual-Target Cross-Domain Fake News Detection

UPDATE: Mining User-News Engagement Patterns for Dual-Target...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Xuankai Yang Yan Wang Xiuzhen Zhang Shoujin Wang Huaxiong Wang Kwok-Yan Lam School of Computing Macquarie University Sydney Australia School of Computing Technologies RMIT University Melbourne Australia Data Science Institute University of Technology Sydney Sydney Australia School of Physical and Mathematical Sciences Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Transfer of knowledge across domains is the focus for cross-domain and multi-domain fake news detection. However, most of the existing methods based on cross-domain knowledge transfer have two issues: (1) they usually ignore domain-specific features; (2) they are less effective in handling the imbalanced data distribution across domains. Targeting these two issues, we focus on how to effectively leverage user-news engagements in both data-richer and data-sparser domains. This is because not only users' engagement characteristics closely relate to the veracity of the engaged news, but also there are consistent patterns in common users' engagements with news across domains. Considering these two insights, this work aims to perform dual-target cross-domain fake news detection via well modeling users' engagement patterns. In particular, it aims to transfer knowledge based on user-news engagements for handling the imbalanced data distribution across domains, which is novel but challenging. To this end, in this paper, we propose a novel framework to mine User-news engagement Patterns for DuAl-TargEt cross-domain fake news detection (UPDATE). In UPDATE, we first mine user-news engagement patterns as the key auxiliary information for cross-domain knowledge transfer. In such a way, it avoids the necessity to remove the domain-specific news information, and thereby, better preserve useful news information. Then, we combine engagement features of common users in both data-richer and data-sparser domains. By doing so, UPDATE improves the information richness in each of the two domains, thus improving detection performance in both domains when detecting news from domains with imbalanced data distribution. Extensive experiments conducted on real-world datasets demonstrate that UPDATE significantly outperforms state-of-the-art cross-domain and multi-domain methods as well as large language models (LLMs), such as GPT-3.5-turbo in terms of AUC and Fl-score for fake news detection.

关键词： Analytical models Large language models Knowledge based systems data science data models data mining Fake news Knowledge transfer

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SMART UI: Crop Classification in Karnataka Nippani Using Ensemble Random Forest

SMART UI: Crop Classification in Karnataka Nippani Using Ens...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Jagarlamudi Susanth Soubraylu Sivakumar Selvanayaki Kolandapalayam Shanmugam R. Sivakumar S. S. Sridhar S. Ganesh Kumar Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Mathematics and Computer Science Ashland University OH United States of America Earthquake Research Cell Civil Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This study helped in enhancing agricultural management, predicting yields, tracking crop health which are all dependent on accurate crop classification. But the conventional methods are labor intensive and time consuming. The goal of this study is to automate crop classification system that uses machine learning and satellite image to accurately classify the crops. Ground Truth data is gathered from *** website and Random Forest classifier is used to train on data. To be more accurate the algorithm is verified using dataset from specific regions. The Random Forest effectively classified crops with an accuracy of $79 \%$. The outcome shows how well crop classification works when used with machine learning and satellite image together. This study provides scalable algorithm for crop classification, which helps in various agricultural related works like crop health. The future studies should investigate alternative use of real-time data for dynamic classification.

关键词： Accuracy Machine learning algorithms Heuristic algorithms Scalability Crops Real-time systems Satellite images Classification algorithms Random forests Monitoring

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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MVMoE: multi-task vehicle routing solver with mixture-of-experts 24

MVMoE: multi-task vehicle routing solver with mixture-of-exp...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianan Zhou Zhiguang Cao Yaoxin Wu Wen Song Yining Ma Jie Zhang Chi Xu College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology The Netherlands Institute of Marine Science and Technology Shandong University China College of Computing and Data Science Nanyang Technological University Singapore and Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multitask vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark instances. We further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution data. The source code is available at: https://***/RoyalSkye/Routing-MVMoE.

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Application of Generative Artificial Intelligence for Epidemic Modeling

Application of Generative Artificial Intelligence for Epidem...

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Simulation Winter Conference

作者： Hannah Danielle Ladera Villaplana Jaeyoung Kwak Michael H. Lees Hongying Li Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore Singapore Informatics Institute University of Amsterdam Amsterdam The Netherlands Department of Fluid Dynamics Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Epidemic models have become increasingly useful, especially in the wake of the recent COVID-19 pandemic, emphasizing the crucial role of human behavior in the spread of disease. There has been a recent rise in the usage and popularity of generative artificial intelligence (GenAI), such as ChatGPT especially with its ability to mimic human behavior. In this study, we demonstrate a novel application of GenAI for epidemic modeling. We employed GenAI for creating agents living in a hypothetical town in simulations and simulating their behavior within the context of an ongoing pandemic. We performed a series of simulations to quantify the impact of agent traits and the availability of information for health condition, virus, and government guidelines on the disease spread patterns in terms of peak time and epidemic duration. We also characterized the most influential factors in agents' decision-making using random forest model.

关键词： Pandemics Generative AI Computational modeling Government Decision making Behavioral sciences Random forests Diseases Guidelines Context modeling

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Contrastive Bi-Projector for Unsupervised Domain Adaption

arXiv

引用

arXiv 2023年

作者： Huang, Lin-Chieh Tsai, Hung-Hsu Institute of Data Science & Information Computing National Chung Hsing University Taichung402 Taiwan

This paper proposes a novel unsupervised domain adaption (UDA) method based on contrastive bi-projector (CBP), which can improve the existing UDA methods. It is called CBPUDA here, which effectively promotes the feature extractors (FEs) to reduce the generation of ambiguous features for classification and domain adaption. The CBP differs from traditional bi-classifier-based methods at that these two classifiers are replaced with two projectors of performing a mapping from the input feature to two distinct features. These two projectors and the FEs in the CBPUDA can be trained adversarially to obtain more refined decision boundaries so that it can possess powerful classification performance. Two properties of the proposed loss function are analyzed here. The first property is to derive an upper bound of joint prediction entropy, which is used to form the proposed loss function, contrastive discrepancy (CD) loss. The CD loss takes the advantages of the contrastive learning and the bi-classifier. The second property is to analyze the gradient of the CD loss and then overcome the drawback of the CD loss. The result of the second property is utilized in the development of the gradient scaling (GS) scheme in this paper. The GS scheme can be exploited to tackle the unstable problem of the CD loss because training the CBPUDA requires using contrastive learning and adversarial learning at the same time. Therefore, using the CD loss with the GS scheme overcomes the problem mentioned above to make features more compact for intra-class and distinguishable for inter-class. Experimental results express that the CBPUDA is superior to conventional UDA methods under consideration in this paper for UDA and fine-grained UDA tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint

arXiv

引用

arXiv 2025年

作者： Shao, Shuo Zhu, Haozhe Yao, Hongwei Li, Yiming Zhang, Tianwei Qin, Zhan Ren, Kui State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Department of Computer Science City University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models by preventing their unauthorized reuse. It is promising and convenient since it does not necessitate modifying the protected model. In this paper, we revisit existing fingerprinting methods and reveal that they are vulnerable to false claim attacks where adversaries falsely assert ownership of any third-party model. We demonstrate that this vulnerability mostly stems from their untargeted nature, where they generally compare the outputs of given samples on different models instead of the similarities to specific references. Motivated by these findings, we propose a targeted fingerprinting paradigm (i.e., FIT-Print) to counteract false claim attacks. Specifically, FIT-Print transforms the fingerprint into a targeted signature via optimization. Building on the principles of FIT-Print, we develop bit-wise and list-wise black-box model fingerprinting methods, i.e., FIT-ModelDiff and FIT-LIME, which exploit the distance between model outputs and the feature attribution of specific samples as the fingerprint, respectively. Extensive experiments on benchmark models and datasets verify the effectiveness, conferrability, and resistance to false claim attacks of our FIT-Print. © 2025, CC BY.

关键词： Open systems

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