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This article introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of ...

The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person – not a machine – must ultimately be held accountable for the ...

• Propose HEX a deep RL-based human-in-the-loop explainability (HITL) method.
• Propose an explanatory point mode of explanation.
• HEX performs well empirically in both decider-free and human-in-the-loop scenarios.
• HEX increases ...

Defect detection is one of the main areas that Industry 4.0 concepts like automation, IoT, digitization and AI aimed to provide solutions. In this work, a platform that extends the aforementioned concepts with ones coming from Industry 5.0 like ...

• Cutting-edge platform leveraging on human-AI collaboration and eXplainable AI (XAI).
• Advanced defect detection and localization for hard metal industry based on ML/DL.
• Leveraging state-of-the-art XAI techniques to gain valuable ...

Recent advancements in computer vision have led to human-level performance in image recognition tasks, but challenges persist in real-world applications due to differences in data distribution. This paper introduces CLARA, a semi-automatic ...

This study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world ...

Systematic Review (SR) are foundational to influencing policies and decision-making in healthcare and beyond. SRs thoroughly synthesise primary research on a specific topic while maintaining reproducibility and transparency. However, the rigorous ...

• This study explores optimal Active Learning (AL) combinations for systematic reviews (SRs).
• Smaller initial training samples improve performance metrics in datasets.
• TF-IDF consistently outperformed Doc2Vec and S-BERT.
• ...

This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes. ...

Recent studies highlight that deep learning models often learn spurious features mistakenly linked to labels, compromising their reliability in real-world scenarios where such correlations do not hold. Despite the increasing research effort, ...$\mathrm{}$

Computer vision-based re-identification (Re-ID) systems are increasingly being deployed for estimating population size in large image collections. However, the estimated size can be significantly inaccurate when the task is challenging or when ...

This paper discusses the human-in-the-loop (HiTL) time-varying formation (TVF) tracking control problem of multi-agent systems (MASs) with asynchronous edge-based event-triggered communication (ETC). The HiTL framework allows the human operator ...

In the field of fault detection and diagnosis, datasets usually contain a large number of features, which poses a challenge for multi-classification tasks. Feature selection (FS) not only ensures that the model has sufficient generalization ...

In the realm of artificial intelligence-assisted diagnostics, recent advances in foundational models have shown great promise, particularly in medical image computing. However, the current scope of human-computer interaction with these models is ...

In the digital era, the amount of digital documents generated each day have being increasing exponentially with the years, to a point where it is unfeasible to process them manually. Thus, there has been growing interest from different sectors to ...

• We present a new database for DLA in the domain of public affairs, the PALdb.
• In addition to layout info, a large corpus of text data in 5 languages was collected.
• We assess several semantic text labeling strategies based on layout ...

This research explores the integration of Human-in-the-loop (HITL) control within Proactive Intelligent Personalised Agents (PIPAs) that possess the capability to proactively anticipate users’ needs and perform tasks on their behalf. The proactive ...

A key challenge in data mining, machine learning and artificial intelligence concerns data quality. Process mining is not an exception. A range of data quality problems exists in process data, some of which caused by activity labels. While ...

• We investigated the impact of gamification on expert engagement in label repair.
• We examined experts’ motivations to contribute knowledge.
• Two experiments were conducted in the insurance and medical domains.
• We observed ...

Deep neural networks (DNNs) have facilitated the development of computer-aided diagnosis (CAD) systems for fundus diseases, helping ophthalmologists to reduce missed diagnoses and misdiagnosis rates. However, the majority of CAD systems are data-...

• We propose an eye-tracking-based HITL CAD system for fundus disease detection.
• We propose a novel DCAMIL model with the contrast learning regularization.
• We introduce the SA and DAN modules into the DCAMIL model.
• We construct ...

Human–robot cooperative tasks have gained importance with the emergence of robotics and artificial intelligence technology. In interactive reinforcement learning techniques, robots learn target tasks by receiving feedback from an experienced ...

• A robot teaching strategy for cooperative tasks via human–robot interaction is proposed.
• Deep Q-Network is employed with real-time feedback delivered by the trainer’s speech.
• A novel reward function is designed to guide the robot ...

In recent years, one can observe an increasing interest in dynamic models in the personality psychology research. Opposed to the traditional paradigm—in which personality is recognized as a set of several permanent dispositions called traits—...

Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI ...

In this study, we consider a class of human-in-the-loop (HiTL) multi-agent systems that divide agents into two parts: the nonautonomous agents controlled by human operators, and the autonomous agents. First, the human operators’ models are ...