This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visualanalysis of graphs. Inspired by the concept of federated learnin...
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ISBN:
(纸本)9798350393811;9798350393804
This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visualanalysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.
In sports, the generation and change of momentum are always accompanied, and tennis is no exception. It is of great significance for winning to analyze how the momentum generates, changes and its impact on the score i...
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Investor sentiment significantly influences stock market dynamics, often displaying pronounced volatility. Concurrently, objective financial news remains a crucial determinant of market shifts. This study sourced an e...
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ISBN:
(纸本)9798350366396;9798350366389
Investor sentiment significantly influences stock market dynamics, often displaying pronounced volatility. Concurrently, objective financial news remains a crucial determinant of market shifts. This study sourced an extensive collection of textual comments from the StockTwits trading platform. We then implemented a semi-supervised GAN-Bert methodology to perform sentiment analysis on these unlabeled remarks. Following this, the Inception v3 model facilitated sentiment evaluation of financial visuals from The Wall Street Journal (WSJ). Upon validation and integration of sentiment scores from these visuals, we amalgamated sentiments derived from both textual and visualdata into a Bi-LSTM framework. This integrated model underwent training for predictive analytics, underpinning trading simulations based on anticipated returns. The experimental results demonstrated a notable return of 30.41% during our evaluation period, showcasing an improvement over single sentiment feature analysis.
This paper discusses the innovative application and challenges of generative artificial intelligence technology (AIGC) in brand visual communication and creative design of advertisements. AIGC has subverted the tradit...
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Study BackgroundMechanical alignment has always been considered as the gold standard in total knee arthroplasty (TKA), but various other coronal alignment strategies have been proposed to enhance native knee kinematic...
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Study BackgroundMechanical alignment has always been considered as the gold standard in total knee arthroplasty (TKA), but various other coronal alignment strategies have been proposed to enhance native knee kinematics and thus elevate patient satisfaction levels. Coronal plane alignment of the knee (CPAK) classification introduced by MacDessi is a simple yet comprehensive system to classify knees based on their coronal plane alignment. It categorizes knees into nine phenotypes based on medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA).Materials and MethodsThis study investigates the distribution of classification of primary arthritic knees (CPAK) types among arthritic knees in the South Indian population and compares the functional outcomes following total knee arthroplasty (TKA) using traditional mechanical alignment among various CPAK types. The research, spanning from September 2021 to August 2023, encompasses a comprehensive analysis of 324 patients with 352 knees in the first part and 48 patients with 72 knees in the second part of the study who underwent TKA, incorporating demographic data and radiological *** indicate a predominant distribution of CPAK type 1, followed by type 2 and type 4 among the South Indian population. In the functional outcomes analysis, regardless of CPAK type, patients exhibited significant improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS), Oxford Knee Score (OKS), and visual analog scale (vAS) scores *** distribution among the South Indian population is comparable to other Indian study and studies with an Asian population, but varies with studies among the White population. Significant improvement of functional outcome among all CPAK types signifies the robust nature of conventional mechanical alignment strategy. Thus, our study serves as an initial exploration into the knee phenotype of the South Indian population and findings contribute
In the rapidly evolving landscape of computer vision, image captioning has emerged as a challenging task. This report explores the techniques involved in image captioning using deep learning techniques, especially the...
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This paper introduces MISTA, a novel dataset for visual instruction tuning on aerial imagery, designed to enhance large multi-modal model applications in remote sensing. Originating from the renowned DOTA-v2.0 aerial ...
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ISBN:
(纸本)9798350390155;9798350390162
This paper introduces MISTA, a novel dataset for visual instruction tuning on aerial imagery, designed to enhance large multi-modal model applications in remote sensing. Originating from the renowned DOTA-v2.0 aerial object detection benchmark, MISTA uniformly processes high-resolution images into 2048.2048 pixels, creating a detailed and complex dataset tailored for remote sensing analysis. To craft this dataset, we design an automated annotation pipeline, employing advanced language models such as GPT-4 and LLavA1.5, to generate diverse and specialized instruction-following data. The annotations include various instruction types like multi-turn conversation, detailed description, and complex reasoning, each reflecting the intricacies inherent in remote sensing tasks. The innovative approach of subdividing aerial images into individually annotated sub-patches significantly enhances the richness of the dataset and allows for a more granular analysis of visual content. As a robust foundation for multi-modal model development in remote sensing, MISTA represents a significant advancement, setting the stage for future research and further applications in the field.
Citizen Observatories are a promising instrument to drive societal behaviour change towards greener more sustainable practices. However, assembling Citizen Observatories is not easy, since apart from the continuous en...
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ISBN:
(纸本)9798350390797;9789532901351
Citizen Observatories are a promising instrument to drive societal behaviour change towards greener more sustainable practices. However, assembling Citizen Observatories is not easy, since apart from the continuous engagement of their participants, there is the need to have some specialized domain and technical knowledge. data quality, continuous engagement and retention and factual impact into decision making are three usual roadblocks which impend a wider adoption of this practice. This paper explains how GREENGAGE project aims to democratize the co-production of thematic co-explorations and overcome those barriers.
With the progression of artificial intelligence, there has been substantial advancement in autonomous driving technology. However, even the most advanced systems may confront failures in certain corner cases, necessit...
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ISBN:
(纸本)9798350393811;9798350393804
With the progression of artificial intelligence, there has been substantial advancement in autonomous driving technology. However, even the most advanced systems may confront failures in certain corner cases, necessitating enhanced analytical approaches. Traditional approaches focused on the numerical analysis of isolated sensor data, are often insufficient for deriving meaningful insights in such situations. To address this inadequacy, we propose a visual analytics approach, crafted to aid domain experts in performing analyses and extracting system improvements from cases with unexpected behaviors. This approach intricately integrates extensive driving scenarios and low-level module behaviors into the autonomous driving decision-making process, utilizing rich visualizations and an interface for interactive exploration and systematic synthesis of findings. Uniquely, our system opens the "black box" of modules in the decision-making pipeline during corner cases, taking into account both the overall decision-making pipeline and the fine-grained behaviors of the modules in the pipeline, setting our approach apart from previous works. To validate our system's effectiveness, we perform two case studies, inviting domain experts for evaluation, and the results confirm our system's efficacy in allowing experts to obtain crucial insights into autonomous driving systems.
The increase in data availability and the popularization of data science brought a continuously growing need to use visualizations to explore complex data. Creating new visualizations, however, is a difficult task tha...
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ISBN:
(纸本)9798350338737;9798350338720
The increase in data availability and the popularization of data science brought a continuously growing need to use visualizations to explore complex data. Creating new visualizations, however, is a difficult task that often requires extensive programming expertise. High-level grammars for authoring visualizations provide a systematic and flexible way to specify visualizations in a declarative manner, lowering the entry barrier for dataanalysis. Such grammars effectively assist in the exploration of the visualization design space and of complex datasets. The goal of this tutorial paper is to then provide a summary of recently proposed grammars, so that a broad audience (e.g., students, researchers, practitioners) can better understand how they are designed and used in practice for visualization and visual analytics tasks.
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