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A Bibliometric analysis of the Current Status and Trends in Regional Cultural and Creative Design 24

A Bibliometric Analysis of the Current Status and Trends in ...

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5th International conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Deng, Tingyin Luo, Jingwen Jiang, Xuemei Wang, Meiling Wei, Liting Sichuan Key Provincial Research Base of Intelligent Tourism Academy of Fine Arts Sichuan University of Science and Engineering Sichuan Zigong China Academy of Fine Arts Sichuan University of Science and Engineering Sichuan Zigong China

ISBN: (纸本)9798400718007

Geographical cultural creativity has disrupted the rigid design and production patterns of traditional products from a market perspective, emerging as a crucial means to enhance industrial economic efficiency and market competitiveness. To delve into the evolutionary trends of the Regional Cultural Creative Design (RCCD) field, this study utilizes the Web of Science core database(WOSCD) as its data source and employs tools such as CiteSpace (6.2R6) and VOSviewer for visual analysis of the retrieved literature. The results reveal that the publication volume in this field has shown an overall upward trend from 2004 to 2024, specifically undergoing three developmental stages: emergence, exploration, and surge. The primary research forces are concentrated in the USA, China, and the UK, with author ZHANG X standing out as a core and influential figure in the field, having published seven papers and thus being recognized as a key contributor. The research hotspots are centered around four themes: Innovative Design, Design Cognition, Cultural Health, and Creative Economy. In conclusion, the rapid development of the RCCD field is poised to bring new growth opportunities to the global cultural and creative industries (CCI) and contribute to sustainable societal development. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Economic efficiency

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Proceedings - 2022 International conference on Innovation, Knowledge, and Management, ICIKM 2022

Proceedings - 2022 International Conference on Innovation, K...

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11th International conference on Innovation, Knowledge, and Management, ICIKM 2022

ISBN: (纸本)9781665461504

The proceedings contain 16 papers. The topics discussed include: research on the development and application of English for specific purposes corpora;study on the characteristics and prevention methods of seeper in mined-out area based on big data analysis and geophysical exploration technology;research on the decision support model of investment and financing based on bp neural network algorithm;research on the influence of visual merchandising on customer value in mobile recommendation system-based on the intermediary effect of brand image;analyzing and comparing national innovation system and regional innovation system literatures: a bibliometric study;research on literature evaluation method based on content analysis;knowledge and networking innovations: a mosaic;and the perspective of digital communication with intangible cultural heritage information under the media technology environment: in case study of Molan culture.

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RouteRover: AI-Enabled Traffic Congestion Prediction and Route Optimization for Indian Urbanites

RouteRover: AI-Enabled Traffic Congestion Prediction and Rou...

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Recent Advances in Science and Engineering Technology (ICRASET), International conference on

作者： S Naveen G Sanketh M G Jayanthi M P Shreya Prashanth Kannadaguli Artificial Intelligence and Machine Learning Cambridge Institute of technology Bengaluru India Computer science and engineering Cambridge Institute of technology Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Here in this paper, a model that has been proposed for predicting and visually representing congestion of traffic using machine learning as well as deep learning. This initiative with the purpose of examining traffic data to obtain insights for traffic regulation and planning. The process initiates with an initial exploration of data through an Exploratory data analysis (EDA) and to perceive the distribution and patterns within the dataset. Descriptive statistics, correlation analysis, and visual aids are employed for this purpose. Following the EDA phase, tactics for manifold learning are utilized to simplify the dataset while retaining crucial information. Identifying significant features influencing the prediction of traffic volume is facilitated by methods for feature importance and selection. The obtained features are rigorously evaluated to comprehend traffic patterns and traffic jam levels. visual representations capture the distribution of traffic volume based on factors like time, day, and month, assisting in pinpointing congestion hotspots and optimizing traffic flow. This project underscores the powerful impact of data analysis and feature extraction in offering practical insights for traffic regulation strategies.

关键词： visualization data analysis Scalability Transportation Traffic control Predictive models Routing Regulation Real-time systems Traffic congestion

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exploration of Cultural IP Image and Common Pattern Gene Extraction in Virtual Reality Design Interaction 16th

Exploration of Cultural IP Image and Common Pattern Gene Ext...

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16th International conference on Virtual, Augmented and Mixed Reality (VAMR)

作者： Zhou, Junyu Chen, Ruiyang Nanjing Normal Univ Dept Visual Commun Nanjing 210098 Peoples R China Nanjing Normal Univ Art Educ Major Nanjing 210098 Peoples R China

ISBN: (纸本)9783031610400;9783031610417

This paper first studies the origin and flow of IP images from the interactive perspective of design, explores the definition and rules of IP in the form of knowledge graph based on semiotics, and puts this problem into the background of the current wave of AI and virtual reality, and raises the following questions: - The definition of IP in virtual reality and the original IP in the network? What is the way forward? Based on the AI data era background of IP business image for induction automation design processor, to achieve better IP cultural communication and exchange. The author selected the female ethnic characteristic clothing of Hui 'an area of Fujian Province as the representative object of design. In this realization part, the article first takes Hui 'an women's ethnic clothing as an example to explore the characteristics of ethnic clothing induction methods - structure multi-modal form and cross-comparison analysis of horizontal multi-cultural pattern design and extract the elements. Then, according to the formula points summarized, the virtual IP image is designed with the characteristic ethnic dress of women in Hui 'an area of Fujian Province, China. According to the cultural gene and regional characteristics contained in the regional ethnic image, the pattern factors are decomposed reconstructed, and applied to the IP image design under the meta-structure virtual reality technology. On this basis, the VR system of the minority cultural IP generator is built. It allows users to generate IP images of featured nationalities in a more immersive environment through shapes, colors, lines, etc., summarized by patterns. Finally, the team tested the VR system. According to the five aspects of "whether the generated IP image is more vivid/whether it can integrate national characteristics/whether it is more immersive/whether it is more memorable/whether it is easier to understand", a total of 20 members participated in the evaluation. In the answer, we finally fin

关键词： IP image design National characteristic pattern finishing Virtual reality interaction

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36th International Florida Artificial Intelligence Research Society conference, FLAIRS-36 2023

36th International Florida Artificial Intelligence Research ...

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36th International Florida Artificial Intelligence Research Society conference, FLAIRS-36 2023

The proceedings contain 132 papers. The special focus in this conference is on Florida Artificial Intelligence Research Society. The topics include: Causanom: Anomaly Detection With Flexible Causal Graphs;answering Student Queries with a Supervised Memory Conversational Agent;topological data analysis in Natural Language Processing – A Tutorial;k-medianoids Clustering Algorithm;automatic Generation of Paragraph Templates Using a Textual Energy Approach: Applications in Literary Text Production;on Bounding the Behavior of a Neuron;semantics-based Framework for Incentivized Research data Sharing;considerations in the Design of Realistic Agents for Serious Games;impact of Artificial Intelligence Regulations on Organizational Risks;an Interactive Interpretation Viewer for Typed First-order Logic;security of Text to Icon/Image conversions;Code Generation for Collectible Card Games with Complex APIs;a Study of Compressed Language Models in Social Media Domain;biogeography-based optimization for feature selection;malicious or Benign? Towards Effective Content Moderation for Children’s Videos;a Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution;bayesian Network Conflict Detection for Normative Monitoring of Black-Box Systems;relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets;relative Effects of Positive and Negative Explanations on Satisfaction and Performance in Human-Agent Teams;ARCHIMEDES: Active Reasoning Conducted with Heterogeneous Information Monitors for Evading Dangers and Extended Surveillance;increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization;visual Episodic Memory-based exploration;A Comparison of AutoML Hyperparameter Optimization Tools For Tabular data;DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM;unsupervised Keyword Extraction for Hashtag Recommendation in Social Media;deep Separable Hypercomplex Networks.

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Self-Supervised visual Representation Learning Framework for Road Sign Classification 1

Self-Supervised Visual Representation Learning Framework for...

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2023 1st DMIHER International conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Dsilva, Godson Michael Ashok, Vinayak Finolex Academy of Management and Technology Information Technology Department Ratnagiri India

ISBN: (纸本)9798350338423

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCoV3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms. © 2023 IEEE.

关键词： Supervised learning

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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...

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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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Combining Static analysis Techniques for Program Comprehension Using Slicito

Combining Static Analysis Techniques for Program Comprehensi...

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IEEE/ACM International conference on Program Comprehension (ICPC)

作者： Robert Husák Jan Kofroň Filip Zavoral Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (数字)9798331502232

ISBN: (纸本)9798331502249

While program comprehension tools often use static program analysis techniques to obtain useful information, they usually work only with sufficiently scalable techniques with limited precision. A possible improvement of this approach is to let the developer interactively reduce the scope of the code being analyzed and then apply a more precise analysis technique to the reduced scope. This paper presents a new version of the tool Slicito that allows developers to perform this kind of exploration on C\# code in visual Studio. A common usage of Slicito is to use interprocedural data-flow analysis to identify the parts of the code most relevant for the given task and then apply symbolic execution to reason about the precise behavior of these parts. Inspired by Moldable Development, Slicito provides a set of program analysis and visualization building blocks that can be used to create specialized program comprehension tools directly in visual Studio. We demonstrate the full scope of features on a real industrial example both in the text and in the following video: https://***/***4

关键词： visualization Codes Operating systems Prototypes Static analysis C# languages Windows Software measurement Reliability Software development management

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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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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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