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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

共 5114 条记录，以下是21-30 订阅

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Crayfish Optimization Algorithm for Enhanced Feature Selection Accuracy for datasets

Crayfish Optimization Algorithm for Enhanced Feature Selecti...

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International conference on Computing and Machine Learning, CML 2024

作者： Rambabu, Bandi Archana, M. Swathi, v.N.v.L.S. Lakshmi, Gundala venkata Rama Reddy, Jakkala Ravichandar CVR College of Engineering Hyderabad India Vijay Rural Engineering College Borgaon India

ISBN: (纸本)9789819778386

Feature selection plays a crucial role in machine learning and data analysis, enabling efficient model training, improved accuracy, and enhanced interpretability. However, traditional methods often struggle to achieve optimal feature subsets when dealing with high-dimensional data, redundant features, or complex interactions. The Crayfish Optimization Algorithm (COA) emerges as a bio-inspired approach inspired by the foraging behavior of crayfish, offering a promising solution to these challenges. This paper presents COA, a novel metaheuristic algorithm for feature selection that mimics the crayfish's exploration–exploitation balance. COA utilizes random walks for global exploration, guided by a fitness function that evaluates feature subset relevance and model performance. Simultaneously, COA employs a unique "pheromone-like" memory mechanism to exploit promising feature neighborhoods, accelerating convergence and enhancing accuracy. We evaluate COA's performance on diverse datasets and benchmark problems, comparing it to established feature selection algorithms. Our results demonstrate that COA significantly outperforms conventional methods in terms of selection accuracy, model performance, and robustness to noise and irrelevant features. Additionally, COA exhibits faster convergence and reduced computational complexity, making it particularly suitable for large-scale datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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Fine-Tuning Large Language Models for Automatic Font Skeleton Generation: exploration and analysis 17th

Fine-Tuning Large Language Models for Automatic Font Skelet...

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17th Asian conference on Computer vision, ACCv 2024

作者： Liu, Yuxuan Fujii, Yasuhisa Zhu, Xinru Nohara, Kayoko School of Environment and Society Institute of Science Tokyo Tokyo Japan Google DeepMind Tokyo Japan

ISBN: (纸本)9789819609079

Despite the pivotal role that font skeletons could play in typeface research and font design, the availability of font skeleton data is sparse and limited. This research explores the possibility of using Large Language Models (LLMs) to generate font skeleton data based on font outline data. Our method represents font skeletons and font outlines as sequences of text tokens derived from SvG commands, and formulates the font skeleton task as a language modeling task predicting the token sequence for a font skeleton given the token sequence of a font outline. As a first attempt, we fine-tuned GPT-3.5 on a dataset of 8,213 Japanese font outlines and corresponding skeletons. Both quantitative and qualitative evaluations show the effectiveness of the approach in terms of rasterized pixel distance, Chamfer distance, and visual analysis. Our proposed method achieved average results of 14.678 for rasterized pixel distance and 1.713 for Chamfer distance, both better than the baseline method (Polyvectorization). In visual analysis, we found better generation results for complex shapes which logograms such as Chinese characters tend to have, than for the simple shapes of syllabograms such as Japanese kana, phonograms such as Latin alphabets, and symbols. Although our fine-tuned model has limitations in generating the skeletons of other font styles, this research establishes a foundation for the automatic generation of font skeletons using LLMs, setting the stage for future work on automatic skeleton generation and the wider application of font skeletons in typography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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The Contribution of Corpus Linguistics and Natural Language Processing Tools in the Development of School Lexicography 8th

The Contribution of Corpus Linguistics and Natural Language ...

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8th International conference on Arabic Language Processing

作者： Maachi, Hafid Khamar, Hakima Mohammed V Univ Rabat ATLAS Res Team FLHS Rabat Morocco

ISBN: (纸本)9783031804373;9783031804380

Corpus linguistics contributes significantly to numerous fields, including lexicography, it has a crucial and effective role in providing valuable linguistic data, and empirical evidence of language use, enabling the construction of more accurate and comprehensive dictionaries, and reflecting how language is used in real-world contexts. Corpus linguistics has revolutionized the way dictionaries are constructed, compiled, and revised. Corpus linguistics has benefited from developments in computational linguistics, particularly in Natural Language Processing (NLP), providing a set of tools that greatly contribute to the processing, analysis, and exploration of language data. The primary objective of the paper is to demonstrate and highlight the effectiveness of using a textbook-based corpus in the development of school dictionaries. The research aims to explore how this corpus linguistics methodology, coupled with NLP tools, contributes to creating contextually relevant and pedagogically oriented dictionaries for educational purposes. This paper introduces a novel model for building corpora specifically designed for school dictionaries. The model's effectiveness is demonstrated by creating a corpus from a single Arabic language textbook through a detailed methodology and a real-world use case.

关键词： Corpus Linguistic Lexicography Corpus School Dictionary Corpus School Dictionary Corpora Natural Language Processing POS tagging

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Interactive visual exploration of Latent Spaces for Explainable AI: Bridging Concepts and Features

Interactive Visual Exploration of Latent Spaces for Explaina...

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Joint of the ACM Workshops at the International conference on Intelligent User Interfaces 2025, IUI-WS 2025

作者： Metta, Carlo Cappuccio, Eleonora Rinzivillo, Salvatore CNR-ISTI Italy Università di Pisa Pisa Italy Università degli studi di Bari Aldo Moro Bari Italy

Latent space exploration offers a powerful lens for interpreting and improving the explainability of black-box AI models. This paper introduces a visual interface based on β-variational Autoencoders that enables users to navigate latent spaces interactively. By employing tools like visual latent sliders and transformation pathways, the interface demonstrates how latent dimensions can influence image representations, uncovering semantic structure and disentangled features. Using the MedMNIST medical image dataset, we illustrate the potential of this approach to bridge the gap between technical latent space analysis and intuitive understanding. Although the focus is on presenting the methodology, this work sets the stage for integrating user interaction and metrics, particularly in high-stakes domains such as medical imaging. © 2025 Copyright © 2025 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Healthcare Interactive Interfaces Latent Space XAI

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Context-Based Annotation visualisation in virtual Reality: A Use Case in Archaeological data exploration 21st

Context-Based Annotation Visualisation in Virtual Reality: A...

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21st EuroXR International conference, EuroXR 2024

作者： De Bonis, Michele Nguyen, Huyen Bourdot, Patrick Université Paris-Saclay CNRS LISN Gif-sur-Yvette France

ISBN: (纸本)9783031785924

In this paper we present a virtual Reality (vR) tool that facilitates the visualisation and exploration of context-based, multi-level annotations in archaeology. Indeed, thanks to photogrammetry and laser scanning techniques, archaeologists can capture and reconstruct almost faithfully ruins and excavations in 3D. Thus, they can maximise their work performance on the field within their limited excavation time and leave deep, but often slow, analysis tasks at later stages offsite. Using vR, we can enrich 3D captured and reconstructed environments by adding extra information for ex situ analysis of archaeological data. This approach can also help the archaeologists speed up their collaboration with other experts or easily share the information with the public. We aim to assist the exploration of numerous annotations and information at disposal that can be densely clustered in a 3D reconstructed world. We collaborated closely with archaeologists at AOROC in the process of designing annotations that convey information with increasing Levels of Detail (LoDs), reflecting the complexity of the contained data. We then developed different interaction techniques that allow users to explore the annotations by switching between LoDs, including active selection by clicking, proximity approach, and the combination of both. Additionally, we proposed and evaluated the effectiveness of annotation grouping mechanism to enhance visual clarity by aggregating annotations in close spatial proximity. The results of our user study with non-experts, aggregated with a qualitative study with the archaeologists, showed user preference for the combination of the interaction techniques, but evidenced some limitations for the grouping of annotations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： virtual reality

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Knowledge-based Graph exploration analysis on Customer's Preference of Insurance Services 10

Knowledge-based Graph Exploration Analysis on Customer's Pre...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Chen, Yingfan Kongjit, Chalermpon Media and Technology Chiang Mai University College of Arts Chiang Mai Thailand

ISBN: (纸本)9798331543273

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics. © 2025 IEEE.

关键词： Knowledge graph

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Fire Detection and Autonomous Evacuation Guidance System 3

Fire Detection and Autonomous Evacuation Guidance System

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3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： vadlamudi, Sandyarani Roja Ramani, D. Preetham, S. Dagadi, Ganesh Goyal, Nipun Mantri, Harshavardhan v. Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception v3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system’s ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system’s capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use. © 2025 IEEE.

关键词： video streaming

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational Modeling Approach;transient Milling Dynamics in Collective Motion with visual Occlusions;extended Swarming with Embodied Neural Computation for Human Control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg vehicles;neural Chaotic Dynamics for Adaptive exploration Control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback Controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.

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An Effectual Forecasting of Oil Production Performance Using Advanced Adaptive Recurrent Neural Network Classifier 14

An Effectual Forecasting of Oil Production Performance Using...

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14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, K. Siva Nethi, v.R. Nagalakshmi Kumar, Korupula Siva Chanikhya, Yagati Sai Basha, Mohammad Yousf Harshavardhan, B.P.M. Andhra Pradesh Rajahmundry533296 India

ISBN: (纸本)9798331531935

Oil is the primary source of energy worldwide, accounting for a large percentage of global energy consumption, with significant impacts on economic, industrial, geopolitical, and social dimensions. It enables the movement of transport, driving globalization and economic integration because oil is a backbone of international trade. However, various reasons, including political, technical, natural, economic, and strategic ones, lead to the reduction of oil production. Hence, this paper proposed a comprehensive analysis and prediction of oil production using an advanced adaptive Recurrent Neural Network (RNN). Initially, diverse datasets are collected to ensure a robust foundation for analysis. Following this, preprocessing techniques, including data cleaning and filtering, are employed to enhance data quality and relevance. The exploration phase focuses on evaluating the data, identifying trends, and uncovering underlying patterns. Feature selection is critical to optimize the predictive model's performance. The Adaptive RNN is then utilized to model the relationships within the dataset, leading to the generation of predicted values. Overall, this work is implemented by python language. Finally, predicted the error value, such as the Root Mean Square Error (RMSE) of 72.9% and the Mean Absolute Error (MAE) of 26.1%, are attained. This structured approach facilitates effective data analysis and contributes to the advancement of predictive analytics in various domains. © 2025 IEEE.

关键词： Recurrent neural networks

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Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts visual data Preparation and analysis 25

Guidance Source Matters: How Guidance from AI, Expert, or a ...

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30th International conference on Intelligent User Interfaces, IUI 2025

作者： Narechania, Arpit Endert, Alex Sinha, Atanu R Georgia Institute of Technology AtlantaGA United States Adobe Research Bangalore India

ISBN: (纸本)9798400713064

The progress in generative Artificial Intelligence (AI) has fueled AI-powered tools like co-pilots and assistants to provision better guidance, particularly during data analysis. However, research on guidance has not yet examined the perceived efficacy of the source from which guidance is offered and the impact of this source on the user's perception and usage of guidance. We ask whether users perceive all guidance sources as equal, with particular interest in three sources: (i) "AI,"(ii) "human expert,"and (iii) "a group of human analysts."As a benchmark, we consider a fourth source, (iv) "unattributed guidance,"where guidance is provided without attribution to any source, enabling isolation of and comparison with the effects of source-specific guidance. We design a five-condition between-subjects study, with one condition for each of the four guidance sources and an additional (v) "no-guidance"condition, which serves as a baseline to evaluate the influence of any kind of guidance. We situate our study in a custom data preparation and analysis tool wherein we task users to select relevant attributes from an unfamiliar dataset to inform a business report. Depending on the assigned condition, users can request guidance, which the system then provides in the form of attribute suggestions. To ensure internal validity, we control for the quality of guidance across source-conditions. Through several metrics of usage and perception, we statistically test five preregistered hypotheses and report on additional analysis. We find that the source of guidance matters to users, but not in a manner that matches received wisdom. For instance, users utilize guidance differently at various stages of analysis, including expressing varying levels of regret, despite receiving guidance of similar quality. Notably, users in the AI condition reported both higher post-task benefit and regret. These findings strongly indicate the need to further understand how different guidance sources imp

关键词： visual analytics

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