78,957 research outputs found
A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems
In: Integrated Intelligent Systems for Engineering Design (editors: Zha, X.F. and Howlett, R.J.)Frontiers in Artificial Intelligence and Applications vol. 14
Towards Foundational Semantics - Ontological Semantics Revisited -
Cimiano P, Reyle U. Towards Foundational Semantics - Ontological Semantics Revisited -. In: Bennett B, Fellbaum C, eds. Formal Ontology in Information Systems, Proceedings of the Fourth International Conference, FOIS 2006. Frontiers in Artificial Intelligence and Applications, 150. IOS Press; 2006: 51-62
Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA
Frontiers in Artificial Intelligence and Applications, v. 263 entitled: ECAI 2014: 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014)Analyzing and understanding people's sentiments towards different topics has become an interesting task due to the explosion of opinion-rich resources. In most sentiment analysis applications, sentiment lexicons play a crucial role, to be used as metadata of sentiment polarity. However, most previous works focus on discovering general-purpose sentiment lexicons. They cannot capture domain-specific sentiment words, or implicit and connotative sentiment words that are seemingly objective. In this paper, we propose a supervised sentiment-aware LDA model (ssLDA). The model uses a minimal set of domain-independent seed words and document labels to discover a domain-specific lexicon, learning a lexicon much richer and adaptive to the sentiment of specific document. Experiments on two publicly-available datasets (movie reviews and Obama-McCain debate dataset) show that our model is effective in constructing a comprehensive and high-quality domain-specific sentiment lexicon. Furthermore, the resulting lexicon significantly improves the performance of sentiment classification tasks. © 2014 The Authors and IOS Press.published_or_final_versio
Artificial Intelligence in Concrete Materials: A Scientometric View
Artificial intelligence (AI) has emerged as a transformative and versatile
tool, breaking new frontiers across scientific domains. Among its most
promising applications, AI research is blossoming in concrete science and
engineering, where it has offered new insights towards mixture design
optimization and service life prediction of cementitious systems. This chapter
aims to uncover the main research interests and knowledge structure of the
existing literature on AI for concrete materials. To begin with, a total of 389
journal articles published from 1990 to 2020 were retrieved from the Web of
Science. Scientometric tools such as keyword co-occurrence analysis and
documentation co-citation analysis were adopted to quantify features and
characteristics of the research field. The findings bring to light pressing
questions in data-driven concrete research and suggest future opportunities for
the concrete community to fully utilize the capabilities of AI techniques.Comment: Book chapter in M. Z. Naser (Ed.), Leveraging Artificial Intelligence
in Engineering, Management, and Safety of Infrastructure. CRC Pres
Editorial: New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts
The purpose of the research topic call “New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts “ was to collect the recent developments and undergoing studies in AI in surgery and surgical oncology. More in detail, the aim was to gather contributions on the advancement, deployment, use, and implementation of AI-based applications in surgical practice, understanding their potential contribution to clinical decision making. Moreover, the idea was to assess the potential impacts of such a technology on surgeons, other clinicians, patients, medical institutions, developers, and policy-makers, with an eye open to the organizational and educational consequences and opportunities
Graph Meets LLMs: Towards Large Graph Models
Large models have emerged as the most recent groundbreaking achievements in
artificial intelligence, and particularly machine learning. However, when it
comes to graphs, large models have not achieved the same level of success as in
other fields, such as natural language processing and computer vision. In order
to promote applying large models for graphs forward, we present a perspective
paper to discuss the challenges and opportunities associated with developing
large graph models. First, we discuss the desired characteristics of large
graph models. Then, we present detailed discussions from three key
perspectives: representation basis, graph data, and graph models. In each
category, we provide a brief overview of recent advances and highlight the
remaining challenges together with our visions. Finally, we discuss valuable
applications of large graph models. We believe this perspective can encourage
further investigations into large graph models, ultimately pushing us one step
closer towards artificial general intelligence (AGI). We are the first to
comprehensively study large graph models, to the best of our knowledge.Comment: Accepted by NeurIPS 2023 New Frontiers in Graph Learning Workshop.
Comments are welcom
Opportunities and challenges for deep learning in cell dynamics research
With the growth of artificial intelligence (AI), there has been an increase
in the adoption of computer vision and deep learning (DL) techniques for the
evaluation of microscopy images and movies. This adoption has not only
addressed hurdles in quantitative analysis of dynamic cell biological
processes, but it has also started supporting advances in drug development,
precision medicine and genome-phenome mapping. Here we survey existing AI-based
techniques and tools, and open-source datasets, with a specific focus on the
computational tasks of segmentation, classification, and tracking of cellular
and subcellular structures and dynamics. We summarise long-standing challenges
in microscopy video analysis from the computational perspective and review
emerging research frontiers and innovative applications for deep
learning-guided automation for cell dynamics research
Visual Abstraction and Reasoning through Language
While Artificial Intelligence (AI) models have achieved human or even
superhuman performance in narrowly defined applications, they still struggle to
show signs of broader and more flexible intelligence. The Abstraction and
Reasoning Corpus (ARC), introduced by Fran\c{c}ois Chollet, aims to assess how
close AI systems are to human-like cognitive abilities. Most current approaches
rely on carefully handcrafted domain-specific languages (DSLs), which are used
to brute-force solutions to the tasks present in ARC. In this work, we propose
a general framework for solving ARC based on natural language descriptions of
the tasks. While not yet beating state-of-the-art DSL models on ARC, we
demonstrate the immense potential of our approach hinted at by the ability to
solve previously unsolved tasks.Comment: The first two authors have contributed equally to this work. Accepted
as regular paper at CVPR 2023 Workshop and Challenges for New Frontiers in
Visual Language Reasoning: Compositionality, Prompts and Causality (NFVLR
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