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A Comprehensive Study on Earth System Science using Machine Learning and Process Mining 4

A Comprehensive Study on Earth System Science using Machine ...

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4th International conference on Communication, Computing and Industry 6.0, C216 2023

作者： vijayalakshmi, v. Thiyagarajan, Gomathi Department of DSBS SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Applications CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350327328

Machine Learning has emerged as a powerful tool in Earth System Science offering new avenues for data analysis and predictive modeling. This paper deep dive into the application of machine learning techniques to understand and predict air quality a critical aspect of Earth System Science. Process mining techniques are incorporated to explore data patterns and relationships. Our work explores the use of machine learning and process mining techniques in Earth System Science, with a specific focus on air quality assessment. The study involves data collection, preprocessing and the development of machine learning models including Linear Regression, Decision Trees, Random Forest, XGBoost and CatBoost to predict Air Quality Index (AQI). Performance evaluation using Root Mean Squared Error (RMSE) and R-squared (R2) metrics reveals that XGBoost and CatBoost demonstrate superior predictive accuracy. Process mining techniques are applied to datasets containing city-specific NOx (nitrogen oxides) measurements, resulting in the creation of heuristic process models. This research serves as a foundational exploration of the integration of machine learning models and process mining techniques in Earth System Science with a focus on air quality. © 2023 IEEE.

关键词： Decision trees

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Integration of Large Language Models and Computer vision Algorithms in LMS: A Methodology for Automated verification of Software Tasks and Multimodal analysis of Educational data

Integration of Large Language Models and Computer Vision Alg...

引用

International Russian Smart Industry conference (SmartIndustryCon)

作者： E. I. Markin v. v. Zuparova A. I. Martyshkin Penza state technological University PSTU Penza Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents a study on the design and development of a learning management system (LMS) integrated with large language models (LLMs) and image recognition/generation algorithms to automate software task validation and multimodal educational data analysis. The purpose of the development is to improve the quality of feedback for learners, deepen the understanding of errors made and expand the capabilities of the system by synthesizing formal LLM models and visual processing algorithms. The system is based on the use of domestic LLMs such as GigaChat and YandexGPT, Retrieval-Augmented Generation (RAG) technology, and modern computer vision algorithms. The system architecture is described, including Gitverse for code storage, Gitverse Actions for automated testing of solutions in Docker containers, and image processing modules for analyzing and generating visual data in the educational process. The conducted experiment demonstrates the effectiveness of the integrative approach, which allows to improve the quality of feedback and increase the performance of students. Prospects for further research include optimization of computational resources and development of techniques for integrated analysis of multimodal educational data.

关键词： visualization Computer vision Codes Image recognition Costs Large language models Computational modeling Software algorithms Trajectory Testing

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Investigating Ion Conduction Mechanism and Dielectric Characteristics of Sodium-Based PEO + PvDF-HFP Solid Polymer Electrolyte Membranes

Investigating Ion Conduction Mechanism and Dielectric Charac...

引用

2023 International conference on Advances in Condensed Matter Physics, ICACMP 2023

作者： Ravi varma, I.S. Kumar Ganta, Kiran Ramana Jeedi, venkata Ramesh, S. Department of Physics GITAM School of Science GITAM University Telangana Hyderabad502329 India Department of Physics B v Raju Institute of Technology Telangana Medak Narsapur502313 India

This paper focuses on the preparation, characterization and dielectric studies of the flexible and free-standing solid polymer electrolyte membranes based on PEO + PvDF-HFP complexed with NaPF6 by the solution casting technique. The X-ray diffraction, Differential scanning calorimetry techniques were employed to analyse the structural properties and melting temperatures of the prepared solid polymer electrolyte membranes. In this study, we have explored the impact of polymer blending. The weight percentages of PEO and PvDF-HFP were systematically varied, while keeping the NaPF6 weight percentage constant. The impedance spectra of the prepared solid polymer electrolyte membranes are recorded using the N4L-PSM1735 Impedance analysis Interface across a frequency range spanning from 1 Hz to 30 MHz. The obtained impedance spectroscopy data facilitated the determination of bulk resistance (Rb) and enabled an in-depth exploration of the dielectric and electrical modulus characteristics of the prepared solid polymer electrolyte membranes. Ionic conductivity values are derived through the analysis of bulk resistance (Rb) measurements. The most notable ionic conductivity value of 5.418x10-7 S/cm at room temperature was achieved with the solid polymer electrolyte membrane 70wt%PEO+30wt%PvDF-HFP+3wt%NaPF6. In this study, the solid polymer electrolyte membrane comprising 70wt%PEO, 30wt%PvDF-HFP, and 3wt%NaPF6 emerged as the optimum conducting composition. © Published under licence by IOP Publishing Ltd.

关键词： Sodium compounds

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A Unified Framework and dataset for Assessing Societal Bias in vision-Language Models

A Unified Framework and Dataset for Assessing Societal Bias ...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sathe, Ashutosh Jain, Prachi Sitaram, Sunayana Microsoft Research India MSR

ISBN: (纸本)9798891761681

vision-language models (vLMs) have gained widespread adoption in both industry and academia. In this study, we propose a unified framework for systematically evaluating gender, race, and age biases in vLMs with respect to professions. Our evaluation encompasses all supported inference modes of the recent vLMs, including image-to-text, text-to-text, text-to-image, and image-to-image. Additionally, we propose an automated pipeline to generate high-quality synthetic datasets that intentionally conceal gender, race, and age information across different professional domains, both in generated text and images. The dataset includes action-based descriptions of each profession and serves as a benchmark for evaluating societal biases in vision-language models (vLMs). In our comparative analysis of widely used vLMs, we have identified that varying input-output modalities lead to discernible differences in bias magnitudes and directions. Additionally, we find that vLM models exhibit distinct biases across different bias attributes we investigated. We hope our work will help guide future progress in improving vLMs to learn socially unbiased representations. We will release our data and code. © 2024 Association for Computational Linguistics.

关键词： visual languages

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vision Transformers and CNN-Based Knowledge-Distillation for Histopathological Image Classification 10

Vision Transformers and CNN-Based Knowledge-Distillation for...

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10th International conference on Fuzzy Systems and data Mining, FSDM 2024

作者： Boudissa, Seddik Debsarkar, Shyam Sundar Kawanaka, Hiroharu Aronow, Bruce Prasath, v.B. Surya Graduate School of Engineering Mie University 1577 Kurima-machiya Mie Tsu514-8507 Japan Department of Computer Science University of Cincinnati OH45221 United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati OH45267 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States

ISBN: (纸本)9781643685694

Histopathological image analysis remains at the forefront of computational pathology presenting numerous challenges and demanding tasks, primarily due to the complex nature of tissue structures and the extensive scale of whole slide images (WSIs). Deep learning models have been widely used in histopathology image analysis, especially convolutional neural network (CNN)-based models for classification. However, CNNs have certain limitations due to their small receptive field. Recent works employed adaptations of the classical transformer architecture to visual data [1] [2]. Models such as vision Transformer (viT) and Swin Transformer leverage the powerful multi-head self-attention mechanism and have demonstrated comparable or superior performance to state-of-the-art CNN-based classification models. Despite their successes, these models require huge amounts of training data to effectively learn representations as they lack the inherent inductive biases of CNNs. This work compares vision Transformers with baseline CNN models using a breast cancer histopathological dataset. Further, we employ a novel knowledge-distillation approach to enhance the learning efficiency of vits, When trained with a limited amount of data, Unlike previous works, we aimed to minimize convolution operations when generating patch embeddings to preserve spatial information before reaching the transformer attention layers, we achieved an accuracy of 87.7% for the viT-base trained as a student of ResNet50, which represents a 1.2% improvement in accuracy over the standalone viT-base. [3]. © 2024 The Authors.

关键词： Convolutional neural networks

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23rd International conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International Symposium on Emerging Technologies for Education, SETE 2024

23rd International Conference on Web-Based Learning, ICWL 20...

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23rd International conference on Web-Based Learning, ICWL 2024 was held in conjunction with the 9th International Symposium on Emerging Technologies for Education, SETE 2024

ISBN: (纸本)9789819644063

The proceedings contain 21 papers. The special focus in this conference is on Web-Based Learning. The topics include: Towards Effective Collaborative Learning in Edu-Metaverse: A Study on Learners’ Anxiety, Perception, and Behaviour;an Empirical Study of Code Pattern Reuse and Pattern Preference Consistency in Novice Programmers;self-attentive Knowledge Tracing with Relative Position Encoding;students’ Pre-concepts of Robot Control Before Their First Robotics Lesson: A Pilot Study;evaluating verbs of Chatting in Large Language Models;mapping the Landscape of Digital and Intelligent Technologies in Intercultural Learning: A Bibliometric analysis (2014–2024);AI-Enhanced Intercultural Teaching: Investigating Chinese EFL University Teachers’ Technology Readiness and Acceptance of Large Language Models;leveraging data visualization with ggplot2 in Translation Pedagogy: Enhancing Learning Through visual Insights;AI-Assisted Writing on News Report: A Case Study on AI-Generated Content and Human-Generated Content;analysis of Interpreter Performance on Numbers, Proper Nouns, and Complex Sentences in AI-Assisted English-Chinese Simultaneous Interpreting: An Eye-Tracking Experimental Study;Human v.s. AI: A Comparative Study Between MTI Student Translators and AI Engines;knowledge Graph-Based Advance Preparation in Medical Interpreting;Quality Comparison of MT in Translating Chinese Music Historical Texts and Inspiration for Translation Pedagogy;Human-in-the-Loop: A Conceptual Framework for Business English Teachers’ AI-Empowered Assessment;an Exploratory Research on Reading Comprehension Question Generation Based on Annotated Clause Complex Corpus;ChatGPT Prompt Engineering Methods for Financial News Translation;A Robust and Secure Framework for an AI-Assisted Academic Writing Platform;an Automatic Model for Lesson Plans Generation Based on Logical Chains and Prompt Tuning.

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative analysis

Faces in the Fog: A Deep Dive into Face De-Identification Te...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Shaonti Goswami Sudipty Konica Paul Sanyam Jawlia Anurag Goel Department of CSE Delhi Technological University New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks.

关键词： Deep learning data privacy Privacy visualization Systematics Reviews Focusing Diffusion models Protection Faces

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Enabling Activity Based Intelligence with visual Analytics

Enabling Activity Based Intelligence with Visual Analytics

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International conference on Military Communications and Information Systems (ICMCIS)

作者： valérie Lavigne Alexandre Bergeron-Guyard Valcartier Research Centre Defence Research and Development Canada Québec QC Canada

Thornviz is a visual analytics tool presenting data along multiple perspectives for discovering the unexpected and performing Activity Based Intelligence (ABI) analysis. It leverages a flexible data model along with a graph database to support ABI principles through interactive visual exploration of intelligence data including dynamic filtering and social network analysis techniques. Thornviz was applied to explore adversarial communication network data, to investigate crypto-currency transactions for suspicious financial activity, as well as to look for potential collaborations between NATO scientific activities, which demonstrated its multi-domain support. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16—;17 May 2023

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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A Personalized AI Assistant For Intuition-Driven visual explorations

A Personalized AI Assistant For Intuition-Driven Visual Expl...

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IEEE International conference on e-Science and Grid Computing

作者： James Hammer Tanner Hobson David Pugmire Scott Klasky Kenneth Moreland Jian Huang Dept. of EECS University of Tennessee Knoxville Knoxville USA CSMD Division Oak Ridge National Laboratory Oak Ridge USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Understanding the time-varying magnetic field within a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the analysis and visualization of magnetic field lines in fusion devices. Current state-of-the-art relies on manually and iteratively generating Poincaré plots from simulation data. Using Poincaré plots in deep analysis is very time consuming because Poincaré plots can be very computationally expensive, especially for a time-varying simulation with thousands of time steps. Further, the visualization results are so complex that only expert users know how to explore, interpret, and control. In addition, collaboration is hampered due to the high barrier to entry. To this end, we contribute Fugent, a reinforcement learning-based agent capable of recommending and evaluating the importance of exploration regions based on training data captured from historic expert user usage. Using Fugent, we show that important regions can be identified and recommended for further exploration. Fugent is open source.

关键词： Training visualization data visualization Training data Prototypes User experience Robustness

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