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Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative analysis of Small and large Communities 38

Addressing Digital and AI Skills Gaps in European Living Are...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Pham, Long O'Sullivan, Barry Scantamburlo, Teresa Mai, Tai Univ Coll Cork Sch Comp Sci & IT Insight SFI Res Ctr Data Analyt Cork Ireland Ca Foscari Univ Dept Environm Sci Informat & Stat Venice Italy Dublin City Univ ADAPT Res Ctr Sch Comp Dublin Ireland

ISBN: (纸本)1577358872

As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects. this study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy. Drawing from a comprehensive dataset encompassing 4,006 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI Awareness, AI Attitudes and demography variables in both population groups. these insights may refer to the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.

关键词： Population statistics

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A Comprehensive analysis of the Effectiveness of large Language Models as Automatic Dialogue Evaluators 38

A Comprehensive Analysis of the Effectiveness of Large Langu...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Zhang, Chen D'Haro, Luis Fernando Chen, Yiming Zhang, Malu Li, Haizhou Natl Univ Singapore Singapore Singapore Univ Politecn Madrid Speech Technol Grp Madrid Spain Univ Elect Sci & Technol China Chengdu Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China

ISBN: (纸本)1577358872

Automatic evaluation is an integral aspect of dialogue system research. the traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Consequently, recent studies have suggested various unique, reference-free neural metrics that better align with human evaluations. Notably among them, large language models (LLMs), particularly the instruction -tuned variants like ChatGPT, are shown to be promising substitutes for human judges. Yet, existing works on utilizing LLMs for automatic dialogue evaluation are limited in their scope in terms of the number of meta-evaluation datasets, mode of evaluation, coverage of LLMs, etc. Hence, it remains inconclusive how effective these LLMs are. To this end, we conduct a comprehensive study on the application of LLMs for automatic dialogue evaluation. Specifically, we analyze the multi-dimensional evaluation capability of 30 recently emerged LLMs at both turn and dialogue levels, using a comprehensive set of 12 meta-evaluation datasets. Additionally, we probe the robustness of the LLMs in handling various adversarial perturbations at both turn and dialogue levels. Finally, we explore how model -level and dimension -level ensembles impact the evaluation performance. All resources are available at https: //***/e0397123/comp- analysis

关键词： Speech processing

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Exploratory User Study on Graph Temporal Encodings 14

Exploratory User Study on Graph Temporal Encodings

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Filipov, Velitchko Arleo, Alessio Miksch, Silvia TU Wien Vienna Austria

ISBN: (纸本)9781665439312

A temporal graph stores and reflects temporal information associated with its entities and relationships. Such graphs can be utilized to model a broad variety of problems in a multitude of domains. Researchers from different fields of expertise are increasingly applying graph visualization and analysis to explore unknown phenomena, complex emerging structures, and changes occurring over time in their data. While several empirical studies evaluate the benefits and drawbacks of different network representations, visualizing the temporal dimension in graphs still presents an open challenge. In this paper we propose an exploratory user study with the aim of evaluating different combinations of graph representations. namely node-link and adjacency matrix, and temporal encodings, such as superimposition, juxtaposition and animation, on typical temporal tasks. the study participants expressed positive feedback toward matrix representations, with generally quicker and more accurate responses than with the node-link representation.

关键词： Human-centered computing visualization Empirical studies in visualization

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High-quality Approximation of Scientific data using 3D Gaussian Splatting

High-quality Approximation of Scientific Data using 3D Gauss...

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IEEE symposium on large data analysis and visualization (LDAV)

作者： Andres Sewell Landon Dyken Victor A. Mateevitsi Will Usher Jefferson Amstutz thomas Marrinan Khairi Reda Silvio Rizzi Joseph A. lnsley Michael E. Papka Sidharth Kumar Steve Petruzza Utah State university Argonne National Laboratory Universtty of Illinois Chicago Luminary Cloud NVIDIA University of St. Thomas lndiana University Indianapolis Northern Illinois University

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

this work explores the application of recent advancements in radi-ance field rendering, specifically 3D Gaussian splatting, to generate high-quality approximations of scientific data. In this technique, a 3D Gaussian splatting model is built from a 3D point cloud gen-erated using structure-from-motion, or a randomly initialized one when using a NeRF as input. this point cloud serves as the basis for initializing a set of Gaussian primitives, which are then refined through machine learning to minimize differences between ground truth and rendered images. We modified this pipeline to train Gaus-sian models directly from scientific data, eliminating the need for structure-from-motion. We test exporting an isosurface as a point cloud, which is then used to train a Gaussian model representing the dataset's isosurface. We also experimented with using a cinema database to produce a 3D Gaussian model; however, this approach yielded less promising results due to sub-optimal point cloud ini-tialization. Our findings highlight the potential of this technique for scientific datasets, suggesting it could enable efficient post-hoc visualization with reduced computational resources.

关键词： Point cloud compression Solid modeling Technological innovation three-dimensional displays Pipelines Rendering (computer graphics) data models Real-time systems Image reconstruction Isosurfaces

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Bibliometric visualization analysis of Sustainable Design from 2001 to 2021 14

Bibliometric Visualization Analysis of Sustainable Design fr...

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14th International symposium on Computational Intelligence and Design, ISCID 2021

作者： Cai, Ruiyi Xiang, Yudi Modern Industrial Design Institute Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665438568

Based on the data of 1,430 sustainable design literature included in the Web of Science database from 2001 to 2021, this paper integrates the analytical advantages of CiteSpace and VOSviewer, and conducts field-specific bibliometric analysis and visualization presentation of results. the article analyzes the distribution and sources of the literature at the micro level, combs the research hotspots, branches and development frontiers at the macro level, providing a comprehensive and clear context combing of the sustainable design field. © 2021 IEEE.

关键词： visualization

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visualization Support for Multi-criteria Decision Making in Software Issue Propagation 14

Visualization Support for Multi-criteria Decision Making in ...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Kiml, Youngtaek Jeon, Hyeon Kim, Young-Ho Ki, Yuhoon Song, Hyunjoo Seo, Jinwook Seoul Natl Univ Seoul South Korea Univ Maryland College Pk MD 20742 USA Samsung Elect Suwon Gyunggi Do South Korea Soongsil Univ Seoul South Korea

ISBN: (纸本)9781665439312

Finding the propagation scope for various types of issues in Software Product Lines (SPLs) is a complicated Multi-Criteria Decision Making (MCDM) problem. this task often requires human-in-the-loop data analysis, which covers not only multiple product attributes but also contextual information (e.g., internal policy, customer requirements, exceptional cases, cost efficiency). We propose an interactive visualization tool to support MCDM tasks in software issue propagation based on the user's mental model. Our tool enables users to explore multiple criteria with their insight intuitively and find the appropriate propagation scope.

关键词： Human-centered computing visualization visualization systems and tools visualization toolkits Software and its engineering Software notations and tools Software maintenance tools Issue Propagation

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Visual analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes 14

Visual Analysis on Machine Learning Assisted Prediction of I...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Shao, Hui Pu, Jiansu Zhu, Yanlin Gao, Boyang Zhu, Zhengguo Rao, Yunbo UESTC Chengdu Peoples R China Shenzhen Clean Energy Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781665439312

Lithium ion batteries (LIBs) are widely used as the important energy sources in our daily life such as mobile phones. electric vehicles. and drones etc. Due to the potential safety risks caused by liquid electrolytes. the experts have tried to replace liquid electrolytes with solid ones. However, it is very difficult to find suitable alternatives materials in traditional ways for its incredible high cost in searching. Machine learning (ML) based methods are currently introduced and used for material prediction. But there is rarely an assisting learning tools designed for domain experts for institutive performance comparison and analysis of ML model. In this case, we propose an interactive visualization system for experts to select suitable ML models, understand and explore the predication results comprehensively. Our system employs a multi-faceted visualization scheme designed to support analysis from the perspective of feature composition, data similarity, model performance. and results presentation. A case study with real experiments in lab has been taken by the expert and the results of confirmed the effectiveness and helpfulness of our system.

关键词： Interactive visualization Machine learning High-dimensional data Materials discovery Ionic conductivity Solid electrolyte

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RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing 38

RL-SeqISP: Reinforcement Learning-Based Sequential Optimizat...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Sun, Xinyu Zhao, Zhikun Wei, Lili Long, Congyan Cai, Mingxuan Han, Longfei Wang, Juan Li, Bing Guo, Yuxuan Beijing Jiaotong Univ Sch Comp & Informat Technol Minist Educ Key Lab Big Data & Artificial Intelligence Trans Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Beijing Technol & Business Univ Beijing Peoples R China PeopleAI Inc Beijing Beijing Peoples R China Shenzhen Heytap Technology Corp Ltd Shenzhen Peoples R China

ISBN: (纸本)1577358872

Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images, consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP parameters are manually tuned in isolation by imaging experts according to application-specific quality and performance metrics, which is time-consuming and biased towards human perception due to complex interaction with the output image. Since the relationship between any single parameter's variation and the output performance metric is a complex, non-linear function, optimizing such a large number of ISP parameters is challenging. To address this challenge, we propose a novel Sequential ISP parameter optimization model, called the RL-SeqISP model, which utilizes deep reinforcement learning to optimize all ISP parameters for different imaging applications. Concretely, inspired by the sequential tuning process of human experts, the proposed model can progressively enhance image quality by seamlessly integrating information from both the image feature space and the parameter space. Furthermore, a dynamic parameter optimization module is introduced to avoid ISP parameters getting stuck into local optima, which is able to more effectively guarantee the optimal parameters resulting from the sequential learning strategy. these merits of the RL-SeqISP model as well as its high efficiency are substantiated by comprehensive experiments on a wide range of downstream tasks, including two visual analysis tasks (instance segmentation and object detection), and image quality assessment (IQA), as compared with representative methods both quantitatively and qualitatively. In particular, even using only 10% of the training data, our model outperforms other SOTA methods by an average of 7% mAP on two visual analysis tasks.

关键词： Reinforcement learning

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Visual Analytics Methods for Interactively Exploring the Campus Lifestyle 14

Visual Analytics Methods for Interactively Exploring the Cam...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Liu, Liang Wang, Song Cai, Ting Li, Hanglin Zhao, Weixin Wu, Yadong Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China Sichuan Univ Sci & Engn Sch Comp Sci & Engn Zigong Peoples R China

ISBN: (纸本)9781665439312

Exploring campus lifestyle is conducive to innovating education management, optimizing campus resources allocation, and providing personalized services, but little attention had been paid to the exploration campus lifestyle. A novel interactive system based on behavioral data of campus cards is presented in this paper to provide new ideas and technical support for campus management. Interactive visualization techniques are utilized to help users analyze campus lifestyle via intelligible diagrams. the system contains three functional modules: providing a decision-making reference to educators on students' poverty subsidies, predicting students' academic performance by quantitative analysis, and scheduling cafeteria repast based on the scheduling model during the outbreak of COVID-19. Finally, three exploratory case studies are presented to demonstrate the effectiveness of the system.

关键词： Human-centered computing visualization visualization application domains Visual analytics

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Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images 38

Point Transformer with Federated Learning for Predicting Bre...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Li, Bao Liu, Zhenyu Shao, Lizhi Qiu, Bensheng Bu, Hong Tian, Jie Univ Sci & Technol China Ctr Biomed Imaging Hefei Peoples R China Chinese Acad Sci Beijing Key Lab Mol Imaging Inst Automat CAS Key Lab Mol Imaging Beijing Peoples R China Sichuan Univ West China Hosp Dept Pathol Chengdu Peoples R China Beihang Univ Sch Engn Med Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing Peoples R China

ISBN: (纸本)1577358872

Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs without gigabyte-size WSIs transportation and data privacy concerns. However, federated learning encounters challenges in addressing label imbalance in multi-site WSIs from the real world. Moreover, existing WSI classification methods cannot simultaneously exploit local context information and long-range dependencies in the site-end feature representation of federated learning. To address these issues, we present a point transformer with federated learning for multi-site HER2 status prediction from HE-stained WSIs. Our approach incorporates two novel designs. We propose a dynamic label distribution strategy and an auxiliary classifier, which helps to establish a well-initialized model and mitigate label distribution variations across sites. Additionally, we propose a farthest cosine sampling based on cosine distance. It can sample the most distinctive features and capture the long-range dependencies. Extensive experiments and analysis show that our method achieves state-of-the-art performance at four sites with a total of 2687 WSIs. Furthermore, we demonstrate that our model can generalize to two unseen sites with 229 WSIs. Code is available at: https://***/boyden/PointTransformerFL

关键词： data privacy

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