Frontiers in Artificial Intelligence最新文献_第10页

Low-rank human-like agents are trusted more and blamed less in human-autonomy teaming. 在人类-自主团队合作中，低等级的类人代理受到的信任更多，受到的指责更少。

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Frontiers in Artificial Intelligence Pub Date : 2024-04-29 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1273350

Jody Gall, Christopher J Stanton

{"title":"Low-rank human-like agents are trusted more and blamed less in human-autonomy teaming.","authors":"Jody Gall, Christopher J Stanton","doi":"10.3389/frai.2024.1273350","DOIUrl":"10.3389/frai.2024.1273350","url":null,"abstract":"If humans are to team with artificial teammates, factors that influence trust and shared accountability must be considered when designing agents. This study investigates the influence of anthropomorphism, rank, decision cost, and task difficulty on trust in human-autonomous teams (HAT) and how blame is apportioned if shared tasks fail. Participants (N = 31) completed repeated trials with an artificial teammate using a low-fidelity variation of an air-traffic control game. We manipulated anthropomorphism (human-like or machine-like), military rank of artificial teammates using three-star (superiors), two-star (peers), or one-star (subordinate) agents, the perceived payload of vehicles with people or supplies onboard, and task difficulty with easy or hard missions using a within-subject design. A behavioural measure of trust was inferred when participants accepted agent recommendations, and a measure of no trust when recommendations were rejected or ignored. We analysed the data for trust using binomial logistic regression. After each trial, blame was apportioned using a 2-item scale and analysed using a one-way repeated measures ANOVA. A post-experiment questionnaire obtained participants' power distance orientation using a seven-item scale. Possible power-related effects on trust and blame apportioning are discussed. Our findings suggest that artificial agents with higher levels of anthropomorphism and lower levels of rank increased trust and shared accountability, with human team members accepting more blame for team failures.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image sequence sorting algorithm for commercial tasks. 用于商业任务的图像序列排序算法。

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Frontiers in Artificial Intelligence Pub Date : 2024-04-29 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1382566

Guillaume Grelier, Miguel A Casal, Alvaro Torrente-Patiño, Juan Romero

{"title":"Image sequence sorting algorithm for commercial tasks.","authors":"Guillaume Grelier, Miguel A Casal, Alvaro Torrente-Patiño, Juan Romero","doi":"10.3389/frai.2024.1382566","DOIUrl":"10.3389/frai.2024.1382566","url":null,"abstract":"Introduction: The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings.Methods: This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness.Results: Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate.Conclusion: This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Streamlining event extraction with a simplified annotation framework. 用简化的注释框架简化事件提取。

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Frontiers in Artificial Intelligence Pub Date : 2024-04-29 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1361483

Chanatip Saetia, Areeya Thonglong, Thanpitcha Amornchaiteera, Tawunrat Chalothorn, Supawat Taerungruang, Pakpoom Buabthong

{"title":"Streamlining event extraction with a simplified annotation framework.","authors":"Chanatip Saetia, Areeya Thonglong, Thanpitcha Amornchaiteera, Tawunrat Chalothorn, Supawat Taerungruang, Pakpoom Buabthong","doi":"10.3389/frai.2024.1361483","DOIUrl":"10.3389/frai.2024.1361483","url":null,"abstract":"Event extraction, grounded in semantic relationships, can serve as a simplified relation extraction. In this study, we propose an efficient open-domain event annotation framework tailored for subsequent information extraction, with a specific focus on its applicability to low-resource languages. The proposed event annotation method, which is based on event semantic elements, demonstrates substantial time-efficiency gains over traditional Universal Dependencies (UD) tagging. We show how language-specific pretraining outperforms multilingual counterparts in entity and relation extraction tasks and emphasize the importance of task- and language-specific fine-tuning for optimal model performance. Furthermore, we demonstrate the improvement of model performance upon integrating UD information during pre-training, achieving the F1 score of 71.16 and 60.43% for entity and relation extraction respectively. In addition, we showcase the usage of our extracted event graph for improving node classification in a retail banking domain. This work provides valuable guidance on improving information extraction and outlines a methodology for developing training datasets, particularly for low-resource languages.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Hammer or telescope? Challenges and opportunities of science-oriented AI in legal and sociolegal research. 社论：锤子还是望远镜？以科学为导向的人工智能在法律和社会法律研究中的挑战与机遇。

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Frontiers in Artificial Intelligence Pub Date : 2024-04-26 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1333219

Nicola Lettieri, Alessandro Pluchino

引用次数: 0

Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience. Xputer：利用 NMF、XGBoost 和简化的图形用户界面体验缩小数据差距。

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Frontiers in Artificial Intelligence Pub Date : 2024-04-24 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1345179

Saleena Younus, Lars Rönnstrand, Julhash U Kazi

{"title":"Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience.","authors":"Saleena Younus, Lars Rönnstrand, Julhash U Kazi","doi":"10.3389/frai.2024.1345179","DOIUrl":"10.3389/frai.2024.1345179","url":null,"abstract":"The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer often outperforms IterativeImputer in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11076752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140891728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discriminative context-aware network for camouflaged object detection 用于伪装物体检测的判别式情境感知网络

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Frontiers in Artificial Intelligence Pub Date : 2024-03-27 DOI: 10.3389/frai.2024.1347898

Chidiebere Somadina Ike, Nazeer Muhammad, N. Bibi, Samah Alhazmi, Furey Eoghan

{"title":"Discriminative context-aware network for camouflaged object detection","authors":"Chidiebere Somadina Ike, Nazeer Muhammad, N. Bibi, Samah Alhazmi, Furey Eoghan","doi":"10.3389/frai.2024.1347898","DOIUrl":"https://doi.org/10.3389/frai.2024.1347898","url":null,"abstract":"Animals use camouflage (background matching, disruptive coloration, etc.) for protection, confusing predators and making detection difficult. Camouflage Object Detection (COD) tackles this challenge by identifying objects seamlessly blended into their surroundings. Existing COD techniques struggle with hidden objects due to noisy inferences inherent in natural environments. To address this, we propose the Discriminative Context-aware Network (DiCANet) for improved COD performance.DiCANet addresses camouflage challenges through a two-stage approach. First, an adaptive restoration block intelligently learns feature weights, prioritizing informative channels and pixels. This enhances convolutional neural networks’ ability to represent diverse data and handle complex camouflage. Second, a cascaded detection module with an enlarged receptive field refines the object prediction map, achieving clear boundaries without post-processing.Without post-processing, DiCANet achieves state-of-the-art performance on challenging COD datasets (CAMO, CHAMELEON, COD10K) by generating accurate saliency maps with rich contextual details and precise boundaries.DiCANet tackles the challenge of identifying camouflaged objects in noisy environments with its two-stage restoration and cascaded detection approach. This innovative architecture surpasses existing methods in COD tasks, as proven by benchmark dataset experiments.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ethics and responsible AI deployment 伦理与负责任的人工智能部署

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Frontiers in Artificial Intelligence Pub Date : 2024-03-27 DOI: 10.3389/frai.2024.1377011

P. Radanliev, Omar Santos, Alistair Brandon-Jones, Adam Joinson

引用次数: 0

Knowledge sharing in manufacturing using LLM-powered tools: user study and model benchmarking 使用 LLM 驱动的工具共享制造业知识：用户研究和模型基准测试

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Frontiers in Artificial Intelligence Pub Date : 2024-03-27 DOI: 10.3389/frai.2024.1293084

Samuel Kernan Freire, Chaofan Wang, Mina Foosherian, Stefan Wellsandt, Santiago Ruiz-Arenas, Evangelos Niforatos

{"title":"Knowledge sharing in manufacturing using LLM-powered tools: user study and model benchmarking","authors":"Samuel Kernan Freire, Chaofan Wang, Mina Foosherian, Stefan Wellsandt, Santiago Ruiz-Arenas, Evangelos Niforatos","doi":"10.3389/frai.2024.1293084","DOIUrl":"https://doi.org/10.3389/frai.2024.1293084","url":null,"abstract":"Recent advances in natural language processing enable more intelligent ways to support knowledge sharing in factories. In manufacturing, operating production lines has become increasingly knowledge-intensive, putting strain on a factory's capacity to train and support new operators. This paper introduces a Large Language Model (LLM)-based system designed to retrieve information from the extensive knowledge contained in factory documentation and knowledge shared by expert operators. The system aims to efficiently answer queries from operators and facilitate the sharing of new knowledge. We conducted a user study at a factory to assess its potential impact and adoption, eliciting several perceived benefits, namely, enabling quicker information retrieval and more efficient resolution of issues. However, the study also highlighted a preference for learning from a human expert when such an option is available. Furthermore, we benchmarked several commercial and open-sourced LLMs for this system. The current state-of-the-art model, GPT-4, consistently outperformed its counterparts, with open-source models trailing closely, presenting an attractive option given their data privacy and customization benefits. In summary, this work offers preliminary insights and a system design for factories considering using LLM tools for knowledge management.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From service design thinking to the third generation of activity theory: a new model for designing AI-based decision-support systems 从服务设计思维到第三代活动理论：设计基于人工智能的决策支持系统的新模型

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Frontiers in Artificial Intelligence Pub Date : 2024-03-21 DOI: 10.3389/frai.2024.1303691

Silvia Marocco, Alessandra Talamo, Francesca Quintiliani

{"title":"From service design thinking to the third generation of activity theory: a new model for designing AI-based decision-support systems","authors":"Silvia Marocco, Alessandra Talamo, Francesca Quintiliani","doi":"10.3389/frai.2024.1303691","DOIUrl":"https://doi.org/10.3389/frai.2024.1303691","url":null,"abstract":"The rise of Artificial Intelligence (AI), particularly machine learning, has brought a significant transformation in decision-making (DM) processes within organizations, with AI gradually assuming responsibilities that were traditionally performed by humans. However, as shown by recent findings, the acceptance of AI-based solutions in DM remains a concern as individuals still strongly prefer human intervention. This resistance can be attributed to psychological factors and other trust-related issues. To address these challenges, recent studies show that practical guidelines for user-centered design of AI are needed to promote justified trust in AI-based systems.To this aim, our study bridges Service Design Thinking and the third generation of Activity Theory to create a model which serves as a set of practical guidelines for the user centered design of Multi-Actor AI-based DSS. This model is created through the qualitative study of human activity as a unit of analysis. Nevertheless, it holds the potential for further enhancement through the application of quantitative methods to explore its diverse dimensions more extensively. As an illustrative example, we used a case study in the field of human capital investments, with a particular focus on organizational development, which involves managers, professionals, coaches and other significant actors. As a result, the qualitative methodology employed in our study can be characterized as a “pre-quantitative” investigation.This framework aims at locating the contribution of AI in complex human activity and identifying the potential role of quantitative data in it.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised learning and natural language processing highlight research trends in a superbug 无监督学习和自然语言处理凸显超级细菌的研究趋势

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Frontiers in Artificial Intelligence Pub Date : 2024-03-21 DOI: 10.3389/frai.2024.1336071

Carlos-Francisco Méndez-Cruz, Joel Rodríguez-Herrera, A. Varela-Vega, Valeria Mateo-Estrada, Santiago Castillo-Ramírez

{"title":"Unsupervised learning and natural language processing highlight research trends in a superbug","authors":"Carlos-Francisco Méndez-Cruz, Joel Rodríguez-Herrera, A. Varela-Vega, Valeria Mateo-Estrada, Santiago Castillo-Ramírez","doi":"10.3389/frai.2024.1336071","DOIUrl":"https://doi.org/10.3389/frai.2024.1336071","url":null,"abstract":"Antibiotic-resistance Acinetobacter baumannii is a very important nosocomial pathogen worldwide. Thousands of studies have been conducted about this pathogen. However, there has not been any attempt to use all this information to highlight the research trends concerning this pathogen. Here we use unsupervised learning and natural language processing (NLP), two areas of Artificial Intelligence, to analyse the most extensive database of articles created (5,500+ articles, from 851 different journals, published over 3 decades). K-means clustering found 113 theme clusters and these were defined with representative terms automatically obtained with topic modelling, summarising different research areas. The biggest clusters, all with over 100 articles, are biased toward multidrug resistance, carbapenem resistance, clinical treatment, and nosocomial infections. However, we also found that some research areas, such as ecology and non-human infections, have received very little attention. This approach allowed us to study research themes over time unveiling those of recent interest, such as the use of cefiderocol (a recently approved antibiotic) against A. baumannii. In a broader context, our results show that unsupervised learning, NLP and topic modelling can be used to describe and analyse the research themes for important infectious diseases. This strategy should be very useful to analyse other ESKAPE pathogens or any other pathogens relevant to Public Health.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0