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Disinformation analysis and Tracking Dashboard

Disinformation Analysis and Tracking Dashboard

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International conference on Military Communications and Information Systems (ICMCIS)

作者： Mandravickait, Justina Songailait, Milita Kankeviciute, Egle Volcok, Anton Krilavicius, Tomas Vytautas Magnus Univ Fac Informat CARD Kaunas Lithuania Lingo Data CARD Vilnius Lithuania

ISBN: (纸本)9798350343854

The importance of disinformation identification and tracking in the information age is not questionable. War in Ukraine, information attacks in Baltic states, and a number of other disinformation attacks worldwide show that it is becoming a conventional tool in information space. In this paper, we present a disinformation analysis and tracking dashboard. It has 3 main components: Disinformation Analyzer, Match disinformation cases and Model Accuracy. The first component consists of a fined-tuned RoBERTa model for classifying textual input into fake and neutral. The second component contains semantic matching functionality for checking the semantic similarity of input text to the set of known disinformation cases. The third component includes the confusion matrix and model accuracy information. All 3 components have been combined into visual analytics platform for easy inspection and exploration of potential and known disinformation cases. The majority of the work was done during TIDE Hackathon 2023 as a part of the Disinformation Analyzer Challenge.

关键词： fake news disinformation deep learning BERT visual analytics

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Estimated Time to Surgical Procedure Completion: An exploration of Video analysis Methods 26th

Estimated Time to Surgical Procedure Completion: An Explorat...

引用

26th International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Ariel, Barak Colbeci, Yariv Ferman, Judith Rapoport Asselmann, Dotan Bar, Omri Theator Inc Palo Alto CA 94301 USA

ISBN: (纸本)9783031439957;9783031439964

An accurate estimation of a surgical procedure's time to completion (ETC) is a valuable capability that has significant impact on operating room efficiency, and yet remains challenging to predict due to significant variability in procedure duration. This paper studies the ETC task in depth;rather than focusing on introducing a novel method or a new application, it provides a methodical exploration of key aspects relevant to training machine learning models to automatically and accurately predict ETC. We study four major elements related to training an ETC model: evaluation metrics, data, model architectures, and loss functions. The analysis was performed on a large-scale dataset of approximately 4,000 surgical videos including three surgical procedures: Cholecystectomy, Appendectomy, and Robotic-Assisted Radical Prostatectomy (RARP). This is the first demonstration of ETC performance using video datasets for Appendectomy and RARP. Even though AI-based applications are ubiquitous in many domains of our lives, some industries are still lagging behind. Specifically, today, ETC is still done by a mere average of a surgeon's past timing data without considering the visual data captured in the surgical video in real time. We hope this work will help bridge the technological gap and provide important information and experience to promote future research in this space. The source code for models and loss functions is available at: https://github. com/theator/etc.

关键词： Surgical Intelligence Operating Room Efficiency ETC RSD Cholecystectomy Appendectomy Radical Prostatectomy

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Composites: A Tangible Interaction Paradigm for visual data analysis in Design Practice 2022

Composites: A Tangible Interaction Paradigm for Visual Data ...

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16th Working conference on Advanced visual Interfaces (ACM AVI)

作者： Subramonyam, Hariharan Adar, Eytan Drucker, Steven M. Stanford Univ Stanford CA 94305 USA Univ Michigan Ann Arbor MI USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450397193

Conventional tools for visual analytics emphasize a linear production workflow and lack organic "work surfaces." A better surface would simultaneously support collaborative visualization construction, data and design exploration, and reasoning. To facilitate datadriven design within existing design tools such as card sorting, we introduce Composites, a tangible, augmented reality interface for constructing visualizations on large surfaces. In response to the placement of physical sticky-notes, Composites projects visualizations and data onto large surfaces. Our spatial grammar allows the designer to flexibly construct visualizations through the use of the notes. Similar to affinity-diagramming, the designer can "connect" the physical notes to data, operations, and visualizations which can then be re-arranged based on creative needs. We develop mechanisms (sticky interactions, visual hinting, etc.) to provide guiding feedback to the end-user. By leveraging low-cost technology, Composites extends a working surface to support a broad range of workflows without limiting creative design thinking.

关键词： visual data analysis data-Driven-Design Augmented Reality

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Five Reservoir Fields 4D visualization and Dynamic analysis Based on WebGL and GPU Acceleration 13th

Five Reservoir Fields 4D Visualization and Dynamic Analysis ...

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13th International Field exploration and Development conference, IFEDC 2023

作者： Duan, Jian He, Yi-Ran Bi, Yong-Bin Yu, Cheng-Lin Qu, Li-Li Geng, Rui-Jie Shi, Ya-Hui Min, Chun-Jia Xi, Bo PetroChina Jidong Oilfield Company Tangshan China Kunlun Digital Technology Co. Ltd Beijing China Technology Co. Ltd Beijing China

ISBN: (纸本)9789819702718

Based on the dream cloud platform, and on the Jidong oilfield basis of basin-level regional lake construction, some basic requirements for oilfield production analysis have been realized. However, with the development of informatization and the deepening of research work, it is necessary to develop business characteristic scenarios such as modern oil reservoir visualization and collaborative management. Using WebGL and GPU acceleration technology, an integrated collaborative online work platform is formed that integrates the "five fields" of reservoirs 4D virtual visualization and dynamic analysis results. The results show that: ①Through the standardization of model volume analysis and storage format, online unified management and visual display of 3D reservoir numerical simulation models with a large number of grids based on WebGL and GPU acceleration technology have been realized. ②Based on the simulation results of the "five fields" of four-dimensional geological field, pressure field, saturation field, seepage field and chemical field, by mining the internal data relationship of the reservoir dynamic model, the dynamic analysis and 2D-3D correlation graph could be visualized online, providing support for deep mining of reservoir five-field model data. ③Reservoir research that IFEDC-202315248 2 integrates 2D reservoir dynamic analysis, 3D model visualization, 4D model visualization and five-field quantitative analysis is an internal fluid migration based on macro development situation analysis during the entire life cycle of the reservoir In-depth analysis of the rules. The platform realizes scientific research collaboration among development plate research data, software, and achievements, lies a solid foundation for high-efficiency and high-quality oil and gas exploration business work, and provides strong support for the high-level application of Dream Cloud. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Life cycle

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Exploring Accuracy, Completeness, and Consistency of Student-Created VRA Core 4.0 Paintings Metadata 22

Exploring Accuracy, Completeness, and Consistency of Student...

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22nd International conference on DublinCore and Metadata Applications, DCMI 2024

作者： Zavalin, Vyacheslav Zavalina, Oksana L. Texas Woman's University DentonTX United States University of North Texas DentonTX United States

An important LAM professional competency is the ability to create metadata that effectively facilitates discovery of information resources in various settings. While some metadata courses for information professionals provide training in generating metadata that represents visual resources held by museums and galleries, reports assessing results of this learning are lacking. Our study is the first to evaluate student-created metadata that follows the specialized visual Resources Association Core 4.0 metadata standard for artworks. This paper presents and discusses preliminary results of the exploration of accuracy, completeness, and consistency of metadata records that were created by students enrolled in the introductory graduate metadata course in an ALA-accredited LIS program. Our analysis identified metadata fields which pose challenges to learners in practicing creation of VRA Core 4.0 metadata records to represent paintings. Most common errors are presented and discussed in relation to findings of previous research and future research ideas. © 2024 Copyright for this paper by its authors.

关键词： data consistency

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Vision Mamba Mender 38

Vision Mamba Mender

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Cao, Anda Feng, Zunlei Zhang, Shengxuming Wang, Yi Jia, Lingxiang Song, Mingli College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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analysis of Low-light Image Enhancement Algorithms

Analysis of Low-light Image Enhancement Algorithms

引用

2024 International conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Li, E. School of Intelligent Engineering Shandong Management University Changqing Shandong Jinan250357 China

ISBN: (纸本)9781510680012

Low-light image enhancement is for the sake of improving the quality of visual perception of captured data on the conditions under low-light to obtain more information, and has gradually become a research hotspot in the field of image processing. It has broad application prospects in artificial intelligence related industries such as autonomous driving and security. This article reviews the current research status of low-light image enhancement algorithms, provides a detailed analysis of the circumstances of low-light image enhancement algorithms, and analyzes the current low-light image enhancement algorithms. Based on the exploration of the above current situation and combined with practical applications, this article points out the limitations of the current algorithm and predicts its development trend. © 2024 SPIE.

关键词： Image enhancement

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Multimodal Image Clustering via Textual Descriptions: An exploration of Contextual Topic Modeling 24

Multimodal Image Clustering via Textual Descriptions: An Exp...

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10th International conference on Computing and data Engineering, ICCDE 2024

作者： Tsujioka, Suguru Watanabe, Kojiro Tsukamoto, Akihiro Shikoku University Japan Tokushima University Japan

ISBN: (纸本)9798400709319

In an era where digital imagery is proliferating at an unprecedented rate on social media platforms, organizing and understanding these visual resources has become a challenge of significant importance. This study introduces a novel approach to image clustering, leveraging the synergy between multimodal image-to-text transformation and advanced topic modeling techniques. Utilizing the Large Language and Vision Assistant (LLaVA) to generate detailed textual descriptions and BERTopic for subsequent clustering, we have analyzed and categorized images from Flickr into eleven distinct topics. Our methodology transcends traditional visual feature-based clustering by incorporating the contextual richness of textual data, thereby aiming to enhance the granularity and relevance of the clustering results. The performance of this approach was quantitatively evaluated using precision, recall, and F1 scores, revealing overall promising results with some variability across different topics. The findings indicated that while our approach is generally effective, it also encounters challenges when dealing with images that encompass overlapping thematic elements. Despite this, certain topics were clustered with high accuracy, showcasing the method's potential. The study also identified areas for improvement, particularly in refining the text generation and clustering algorithms to better handle the complexity of multiple themes within single images. In conclusion, this research contributes to the field of image data organization, providing insights into the use of textual descriptions for more contextually nuanced image clustering. It also opens avenues for future work to further improve the accuracy and interpretability of unsupervised learning methods in multimedia content analysis. © 2024 ACM.

关键词： Clustering algorithms

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Short Video E-commerce Marketing of Convolutional Neural Network based Transfer Learning with visual Geometry Group16 2

Short Video E-commerce Marketing of Convolutional Neural Net...

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2nd IEEE International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Wen, Jia School of Business Administration Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (纸本)9798331504960

In Recent years, E-commerce stands as a prominent testament to the transformative power of information and communications technologies in driving economic growth and development. Traditional approaches from short video ecommerce marketing had faced several issues which include large data size, required more computational resources. Therefore, this research proposes Convolutional Neural Network (CNN) based Transfer Learning with visual Geometry Group (VGG)16 for Short Video E-commerce Marketing. Initially, the data is taken from Buyer-generated Fashion Video (BFV) dataset and consists of fashion videos generated by users, showcasing their purchases and experiences with fashion products. The data is preprocessed by using frame extraction and then the features are extracted by using CNN based Transfer Learning with VGG16 which effectively reduces the training time and computational resources. Next, the You Only Look Once (YOLO) is used for feature engineering where YOLO enabled the analysis of object based features. Finally, the evaluation and optimization is done by Multi armed bandit where it provides efficient way to balance exploration and exploitation which enabled real time optimization. The proposed CNN based Transfer Learning with VGG16 achieved accuracy (0.955), precision (0.977), recall (0.968) and F1 Score (0.970) when compared with existing Hierarchical Oversampling Word Embedding Network (HOWNET). © 2024 IEEE.

关键词： Convolution

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Research on Deep Learning-based Art Creation Method and Its Application in Art Space Design

Research on Deep Learning-based Art Creation Method and Its ...

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2023 International conference on Educational Knowledge and Informatization, EKI 2023

作者： Liu, Siqi Hunan Normal University Fine Arts Academy Hunan Changsha410081 China

ISBN: (纸本)9798350331264

Multimedia art is a comprehensive audio-visual art type, which utilises a variety of elements such as graphics, text, sound and image to support the spatial representation of the work. Therefore, the virtual space form of multimedia art is formed and expressed by the spatial structure of the elements, and in the creation of specific multimedia art works, we must implement the spatial theory into the spatial expression of specific elements. In this paper, based on the deep learning background of big data on the creation of new research and analysis of art, the creation of art and deep learning big data on the combination of research, research not only on the creation of new breakthroughs in art, but also on the deep learning technology for in-depth exploration and research. © 2023 IEEE.

关键词： Big data

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