In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Deep learning models typically accept video input without prior processing and train them to achieve recognition. However, conducting prelim...
In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize traini...
We propose a violin bowing action recognition system that can accurately recognize distinct bowing actions in classical violin performance. This system can recognize bowing actions by analyzing signals from a depth camera and from inertial sensors th...
Traditional multi-agent reinforcement learning (MARL) algorithms typically implement global parameter sharing across various types of heterogeneous agents without meticulously differentiating between different action semantics. This approach results...
The safety and efficiency of assembly lines are critical to manufacturing, but human supervisors cannot oversee all activities simultaneously. This study addresses this challenge by performing a comparative study to construct an initial real-time, se...
Games are considered a suitable and standard benchmark for checking the performance of artificial intelligence-based algorithms in terms of training, evaluating, and comparing the performance of AI agents. In this research, an application of the Intr...
In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to...
The goal to ensure sustainable development in the education process obliges to create such practices of teaching and learning which would create conditions for individuals to act in complex situations in a sustainable manner. Personalized, perceived...
This study examines students’ learning for action towards sustainability when addressing a local environmental problem related to mining through an Inquiry-Based Science Education (IBSE) approach. A total of 54 eighth-grade students (ages 13&nd...
In this paper, we propose a contrastive mask learning (CML) method for self-supervised 3D skeleton-based action recognition. Specifically, the mask modeling mechanism is integrated into multi-level contrastive learning with the aim of forming a mutua...
Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such...
An important outcome of social learning in the context of natural resource management is the potential for collective action—actions taken by a group of people that are the result of finding shared or common interest. Evidence of the relationsh...
This paper critically reviews the body of literature on affordances relating to the design and inhabitation of school buildings. Focusing on the influence of learning spaces on pedagogical practices, we argue that links between affordances, architect...
Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This pa...
Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Lear...
Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that...
Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognit...
This paper introduces a fusion convolutional architecture for efficient learning of spatio-temporal features in video action recognition. Unlike 2D convolutional neural networks (CNNs), 3D CNNs can be applied directly on consecutive frames to extract...
Facial Action Units (AUs) correspond to the deformation/contraction of individual facial muscles or their combinations. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Generating and ana...
RGB and depth modalities contain more abundant and interactive information, and convolutional neural networks (ConvNets) based on multi-modal data have achieved successful progress in action recognition. Due to the limitation of a single stream, it i...
Intellectual disability (ID) compromises intellectual and adaptive functioning. People with an ID show difficulty with procedural skills, with loss of autonomy in daily life. From an embodiment perspective, observation of action promotes motor skill...
Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial...
Action recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segme...
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Exi...
A key determinant and outcome of successful environmental education is ‘pro-environmental behavior’, i.e., behavior that involves conscious action to mitigate adverse environmental impacts at personal or community level, e.g., reducing re...
Human action recognition (HAR) is growing in machine learning with a wide range of applications. One challenging aspect of HAR is recognizing human actions while playing music, further complicated by the need to recognize the musical notes being play...
Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been dev...
Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields...
Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detecti...
Action recognition based on 3D heatmap volumes has received increasing attention recently because it is suitable for application to 3D CNNs to improve the recognition performance of deep networks. However, it is difficult for models to capture global...
Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approac...
Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as “IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed”. As the number of IoT devices grows,...
Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human com...
Recently, people’s demand for action recognition has extended from the initial high classification accuracy to the high accuracy of the temporal action detection. It is challenging to meet the two requirements simultaneously. The key to behavior reco...
The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selecte...
Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations...
Video surveillance and image acquisition systems represent one of the most active research topics in computer vision and smart city domains. The growing concern for public and workers’ safety has led to a significant increase in the use of surv...
In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use bas...
Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variati...
Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line...
Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that...
The aim of the study is to examine educators’ reflections on their practices and views regarding outdoor play and learning (OPL) and unveil the impact and sustainability of participating in participatory action research (PAR). The study draws b...
This study investigates the experiences of a professional learning community (PLC) composed of six secondary math teachers enrolled in a graduate math methods course. Through the discussion of educational texts and collaborative inquiry, the teachers...
Thermal conductivity is a crucial factor for the soil, which is significantly affected by environmental conditions. Based on the variation in the thermal conductivity and the influencing factors of silty clay obtained by the freeze–thaw cycle t...
In the quest for industrial efficiency, human performance within manufacturing systems remains pivotal. Traditional time study methods, reliant on direct observation and manual video analysis, are increasingly inadequate, given technological advancem...
Human fault detection plays an important role in the industrial assembly process. In the current unstructured industrial workspace, the definition of human faults may vary over a long sequence, and this vagueness introduces multiple issues when using...
This study builds on a research-practitioner partnership embedded within an education for sustainable development (ESD) project and aims to explore the major potential challenges (i.e., disciplinary boundaries set by subject specialization, especiall...
Various action recognition approaches have recently been proposed with the aid of three-dimensional (3D) convolution and a multiple stream structure. However, existing methods are sensitive to background and optical flow noise, which prevents from le...
Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perfor...
Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions b...
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