Subject-matter expert ≈ Subject-matter expert
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Snorkel Open
Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data…
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The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine Open
Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to uti…
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Development of the Anatomical Quality Assessment (AQUA) Tool for the quality assessment of anatomical studies included in meta‐analyses and systematic reviews Open
Critical appraisal of anatomical studies is essential before the evidence from them undergoes meta‐epidemiological synthesis. However, no instrument for appraising anatomical studies with inherent applicability to different study designs i…
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Virtual Replicas for Remote Assistance in Virtual and Augmented Reality Open
In many complex tasks, a remote subject-matter expert may need to assist a local user to guide actions on objects in the local user's environment. However, effective spatial referencing and action demonstration in a remote physical environ…
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Challenges and opportunities: from big data to knowledge in AI 2.0 Open
In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or impl…
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Moving from Novice to Expertise and Its Implications for Instruction Open
Objective: To address the stages of expertise development, what differentiates a novice from an expert, and how the development and differences impact how we teach our classes or design the curriculum. This paper will also address the down…
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Expert Elicitation: Using the Classical Model to Validate Experts’ Judgments Open
The inclusion of expert judgments along with other forms of data in science, engineering, and decision making is inevitable. Expert elicitation refers to formal procedures for obtaining and combining expert judgments. Expert elicitation is…
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Bayesian Workflow Open
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit…
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Delphi, non-RAND modified Delphi, RAND/UCLA appropriateness method and a novel group awareness and consensus methodology for consensus measurement: a systematic literature review Open
Increasing demand for reliable evidence in patient care and its delivery has necessitated the development of several approaches for generating quality evidence. In particular, the solicitation of expert opinion has been recognised as a rel…
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Almost an Expert Open
Expert feedback is valuable but hard to obtain for many designers. Online crowds can provide fast and affordable feedback, but workers may lack relevant domain knowledge and experience. Can expert rubrics address this issue and help novice…
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The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending Open
Increasingly, artificial intelligence (AI) is being used to assist complex decision-making such as financial investing. However, there are concerns regarding the black-box nature of AI algorithms. The field of explainable AI (XAI) has emer…
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Theory-driven or process-driven prediction? Epistemological challenges of big data analytics Open
Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence …
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Can large language models reason about medical questions? Open
Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether close- and…
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Domains, tasks, and knowledge for clinical informatics subspecialty practice: results of a practice analysis Open
Objective The study sought to develop a comprehensive and current description of what Clinical Informatics Subspecialty (CIS) physician diplomates do and what they need to know. Materials and Methods Three independent subject matter expert…
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Future Prospects for Energy Technologies: Insights from Expert Elicitations Open
Expert elicitation is a structured approach for obtaining judgments from experts about items of interest to decision makers. This method has been increasingly applied in the energy domain to collect information on the future cost, technica…
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BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing Open
Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitatio…
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Can We Automate Scientific Reviewing? Open
The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications. At the same time, the review of each paper is a laborious process that must be carried out by subject m…
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Twelve tips to leverage AI for efficient and effective medical question generation: A guide for educators using Chat GPT Open
Integrating LLM tools like ChatGPT into generating medical assessment questions like MCQs augments but does not replace human expertise. With continual instruction refinement, AI can produce high-standard questions. Yet, the onus of ensuri…
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A brief history and technical review of the expert system research Open
The expert system is a computer system that emulates the decision-making ability of a human expert, which aims to solve complex problems by reasoning knowledge. It is an important branch of artificial intelligence. In this paper, firstly, …
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System safety assessment under epistemic uncertainty: Using imprecise probabilities in Bayesian network Open
System safety and reliability assessment relies on historical data and experts opinion for estimating the required failure probabilities. When data comes from different sources, be it different databases or subject domain experts, the esti…
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Prior Knowledge Elicitation: The Past, Present, and Future Open
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. In principle, prior elicitation transforms domain knowledge …
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Obey validity limits of data-driven models through topological data analysis and one-class classification Open
Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of proce…
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A Review of Building Information Modelling (BIM) for Facility Management (FM): Implementation in Public Organisations Open
Building Information Modelling (BIM) has been extensively studied and applied within the AEC sector, particularly in design and construction. In recent years, Facility Management (FM) processes are becoming more digitalised, thus requiring…
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Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media Open
Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject ma…
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Subject-driven Text-to-Image Generation via Apprenticeship Learning Open
Recent text-to-image generation models like DreamBooth have made remarkable progress in generating highly customized images of a target subject, by fine-tuning an ``expert model'' for a given subject from a few examples. However, this proc…
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Machine learning 2.0 : Engineering Data Driven AI Products Open
ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deploy…
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A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends Open
Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perc…
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Deep Cross-User Models Reduce the Training Burden in Myoelectric Control Open
The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exempla…
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Improving Subject-Area Question Answering with External Knowledge Open
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet ef…
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ProcessAR: An augmented reality-based tool to create in-situ procedural 2D/3D AR Instructions Open
Augmented reality (AR) is an efficient form of delivering spatial information and has great potential for training workers. However, AR is still not widely used for such scenarios due to the technical skills and expertise required to creat…