Model-based reinforcement learning (MBRL) is a promising approach for achieving high sample efficiency in learning control policies. The existing world models in MBRL typically represent the environment’s state as a single global latent vector....
Vision–language–action (VLA) models have shown strong potential for enabling robots to interpret goals and perform complex manipulation tasks by integrating perception, language, and control. However, existing VLAs rely heavily on large-s...
In this paper, we propose a novel approach, COMPAS (COMPose Actions and Slots), which leverages the strengths of state-of-the-art object-centric approaches for modeling the dynamics of an environment. Our method encodes the environment’s state...
Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-worl...
In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development...
Electric Vehicle (EV) charging infrastructure plays a critical role in modern energy systems, affecting energy load distribution, demand-response programs, and grid stability. As EV adoption accelerates globally, the varied charging habits and concur...
The direct effect of global warming on viticulture is already apparent, with unexpected pests and diseases as one of the most concerning consequences. Deploying sticky traps on grape plantations to attract key insects has been the backbone of convent...
Detecting dynamic and amorphous objects like fire and smoke poses significant challenges in object detection. To address this, we propose Dual-Path Cascade Stochastic DETR (Dual-Path CSDETR). Unlike Cascade DETR, our model introduces cascade stochast...
Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new h...
Humanoid object transport couples high-dimensional locomotion with non-smooth contact interactions. Retargeted demonstration priors can fail under contact mismatches, payload changes, and dynamic perturbations. We present residual learning for humano...
Selective algorithmic auditing requires deciding which process evidence should receive attention when exhaustive review is infeasible. This Review introduces a four-layer framework that connects process representation, learning, inspection allocation...
Learning from Demonstration (LfD) enables robots to acquire manipulation skills by observing human actions. However, existing methods often face challenges such as high computational cost, limited generalizability, and a loss of key interaction detai...
Surveillance cameras are extensively deployed across public and private environments, driving the need for intelligent video monitoring systems. However, a major challenge arises in Weakly Supervised Video Anomaly Detection (WSVAD), where supervision...
In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluati...
Neural radiance fields (NeRF) have become an effective method for encoding scenes into neural representations, allowing for the synthesis of photorealistic views of unseen views from given input images. However, the applicability of traditional NeRF...
Modern neural networks have achieved remarkable success in visual recognition; however, due to their sensitivity to domain shifts, Unsupervised Domain Adaptation (UDA) remains an open research problem. A key reason for this limitation is that source-...
World models represent environments and predict how their states evolve under interventions. Their use in generative simulation and interactive environments, autonomous driving, and robotics and embodied control is growing, yet application validation...
Video Anomaly Detection (VAD) is commonly formulated as a one-class classification task. Global motion, with temporal variations across most pixels within an object-centric region, e.g., walking, is typically regular, whereas localized motion, e.g.,...
Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading...
Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essent...
In Deep Learning Remote Sensing, data quantity is rarely the limiting factor. A single high-resolution satellite image can yield thousands of training patches. What determines model performance, yet remains largely overlooked, is the quality of those...
Recently, most state-of-the-art anomaly detection methods are based on apparent motion and appearance reconstruction networks and use error estimation between generated and real information as detection features. These approaches achieve promising re...
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (si...
Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project dev...
The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a chal...
The objective of portfolio diversification is to reduce risk and potentially enhance returns by spreading investments across different asset classes. Existing portfolio diversification models have traditionally been trained on historical financial ti...
This study explores how the sequence and timing of Industry 4.0 technology adoption affect sustainable innovation in manufacturing firms. Using longitudinal data from the State Society of Industrial Participations, we track the adoption patterns of e...
Focusing on the deficiencies of traditional tourism attraction survey methods in terms of accuracy, efficiency, and large-scale visual representation, this study selects Qingdao as the research case, collects tourism image data from the Weibo platfor...
Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the l...
The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitiv...
Deep learning has become increasingly common in aerial imagery analysis. As its use continues to grow, it is crucial that we understand and can explain its behavior. One eXplainable AI (XAI) approach is to generate linguistic summarizations of data a...
Interactive science galleries have transformed how the public engages with science, shifting from object-centred displays to immersive, design-led experiences. This study situates these changes within broader cultural and economic contexts, exploring...
Flexible Learning Environments (FLEs) arose as enablers for implementation of student-centric pedagogical approaches. Interior design is the key to the success of FLEs, providing the physical infrastructure needed for students to engage on several le...
Video-centric applications have seen significant growth in recent years with HTTP Adaptive Streaming (HAS) becoming a widely adopted method for video delivery. Recently, low-latency (LL) adaptive bitrate (ABR) algorithms have recently been proposed t...
Th metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the chal...
We argue that the core methods of modern machine learning—conformal prediction, large language models and in-context learning, and generative/diffusion models—are not rivals to Bayesian inference but implementations of it, almost always o...
3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots...
Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading....
Robot navigation has transitioned from avoiding static obstacles to adopting socially aware navigation strategies for coexisting with humans. Consequently, socially aware navigation in dynamic, human-centric environments has gained prominence in the...
To enhance online dispatch decision support capabilities for power grid outage planning, this study proposes a Universal Information Extraction (UIE)-based method for enhanced named entity recognition and event extraction from outage documents. First...
The rapid expansion of global e-commerce platforms has led to unprecedented volumes of heterogeneous, multimodal, and continuously evolving data, creating significant challenges for prediction, personalization, trust, and operational decision-making....
Learning procedural knowledge from human egocentric videos offers a source of demonstrations for long-horizon biomedical laboratory automation. Such workflows span functional zones, depend on persistent object and workspace states, and include recove...
Chemical formulation design is a challenging multi-objective optimization problem involving mixed continuous and discrete variables, feasibility constraints, and limited experimental data. Existing virtual formulation generation methods are often res...
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed superv...
The Metaverse is an end-users-oriented integration of various layers of Information Technology (IT), where Human–Computer Interaction (HCI) would be the core technology. With the rapid development of IT, the Metaverse would allow users to conne...
Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints i...
The rapid aging of bridges has increased interest in real-time, data-driven monitoring for predictive maintenance and safety management; however, practical deployment on in-service bridges remains limited. This paper presents lessons learned from lon...
The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning e...
When flexible job shop scheduling faces concurrent disturbances such as machine failures and rush orders, worker-centric constraints emphasized under Industry 5.0 must also be satisfied. Existing deep reinforcement learning methods for the Dynamic Fl...
Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpin...
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