Artificial neural networks are widely applied for prediction, function simulation, and data classification. Among these applications, the wavelet neural network is widely used in image classification problems due to its advantages of high approximati...
Numerous studies have noted that sex and/or menstrual phase influences cognitive performance (in particular, declarative memory), but the effects on motor learning (ML) and procedural memory/consolidation remain unclear. In order to test the hypothes...
Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A dri...
Deep learning techniques for medical image analysis often encounter domain shifts between source and target data. Most existing approaches focus on unsupervised domain adaptation (UDA). However, in practical applications, many source domain data are...
Attention is classically classified according to mode of engagement into voluntary and reflexive, and type of operation into covert and overt. The first distinguishes whether attention is elicited intentionally or by unexpected events; the second, wh...
Findings from recordings of human temporal cortical single neuron activity during several measures of language, including object naming and word reading are reviewed and related to changes in activity in the same neurons during recent verbal memory a...
With the development of ultra-long-range visual sensors, the application of unsupervised person re-identification algorithms to them has become increasingly important. However, these algorithms inevitably generate noisy pseudo-labels, which seriously...
The temporal subtraction technique is a useful tool for computer aided diagnosis (CAD) in visual screening. The technique subtracts the previous image set from the current one for the same subject to emphasize temporal changes and/or new abnormalitie...
This paper proposes an improved multi-agent deep deterministic policy gradient algorithm called the equal-reward and action-enhanced multi-agent deep deterministic policy gradient (EA-MADDPG) algorithm to solve the guidance problem of multiple missil...
Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning...
A unified theory of emotion and motivation is updated in which motivational states are states in which instrumental goal-directed actions are performed to obtain anticipated rewards or avoid punishers, and emotional states are states that are elicite...
In reading, binocular eye movements are required for optimal visual processing and thus, in case of asthenopia or reading problems, standard orthoptic and optometric routines check individual binocular vision by a variety of tests. The present study...
During the vegetation growing season, the forest in the remote sensing image is more distinguishable from other background features, and the forest features are obvious and can show prominent forest area characteristics. However, deep convolutional n...
Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-o...
The COVID crisis has disrupted routine patterns and practices across all spheres of everyday life, rupturing social relations and destabilising our capacity for building coherent selves and communities by recollecting the past and imagining potential...
In this article, we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we...
Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning appro...
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder i...
The global COVID-19 pandemic has disrupted traditional learning methods, leading to a surge in online learning. It has been found that the low course completion and performance are associated with online learning. There has been increasing and urgent...
Machine learning has become indispensable across various domains, yet understanding its theoretical underpinnings remains challenging for many practitioners and researchers. Despite the availability of numerous resources, there is a need for a cohesi...
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that d...
Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is tha...
The rise of omics research, spanning genomics, transcriptomics, proteomics, and epigenomics, has revolutionized our understanding of biological systems [...]
This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learnin...
Learning a deep model from small data is an opening and challenging problem. In high-dimensional spaces, few samples only occupy an extremely small portion of the space, often exhibiting sparsity issues. Classifying in this globally sparse sample spa...
This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of a student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based int...
Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive. As one of the most popular frameworks for dealing with multi-output learning, the perfo...
This article discusses the relationships between drawing and cognition starting from the concept of graphic intelligence, and going beyond the classic approach, widely deepened in the literature from the field of clinical neuropsychology, linked to a...
We consider the learning coefficients in learning theory and give two new methods for obtaining these coefficients in a homogeneous case: a method for finding a deepest singular point and a method to add variables. In application to Vandermonde matri...
First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishe...
StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the...
The use of gamification is garnering attention as a method that promotes sustainable learning during the coronavirus disease 2019 (COVID-19) era. This study investigates the effect that gamified online learning has on student learning and has utilize...
Learning-by-doing is a pedagogical approach that helps in learning skills through practice. An online learning-by-doing tool, CodeLab, has been introduced to students undertaking the digital design and creation bachelor’s degree program at the...
This study attempted to test whether the use of computer-assisted language learning (CALL) and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies....
The past twelve years have seen ubiquitous learning (u-learning) emerging as a new learning paradigm based on ubiquitous technology. By integrating a high level of mobility into the learning environment, u-learning enables learning not only through f...
Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors t...
Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has become a widely used method for building footprint mapping. Recently, DeeplabV3+, an advanced CNN architecture, has shown satisfactory performance for building extrac...
Previous studies have investigated the spatial attributes of Active Learning Classrooms (ALCs) and their impact on students’ learning experiences and learning engagement independently; however, a holistic investigation of the relationship betwe...
The growing prominence of e-learning in education has led to the need for a comprehensive understanding of the factors influencing learning outcomes. This study aims to investigate the combined effects of e-learning orientation, Moodle usage, and lea...
Self-regulated learning (SRL) has been widely recognized as a critical skill for academic success in online and blended learning contexts. However, many students experience difficulty in effectively applying SRL strategies in the absence of structure...
Our paper presents a case for co-learning, a novel hybridization of teacher professional learning and student out-of-school learning wherein students and teachers collaborate and learn together. The benefits of collaborative learning are well documen...
The study aims to compare how discovery learning and collaborative discovery learning affect knowledge acquisition, the development of understanding through phases of self-regulated learning (SRL), and the use of SRL strategies at the individual leve...
At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identifi...
Collaborative problem-solving (CPS) is highly valued in the sustainability of learning to foster the key soft power of talent for the future. In this study, a CPS learning application was built to train and assess individuals with the aim of increasi...
With the growth in popularity of video games in our society many teachers have worked to incorporate gaming into their classroom. It is generally agreed that by adding something fun to the learning process students become more engaged and, consequent...
Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space...
The growing relevance of socio-ecological systems (SESs) thinking reflects both the challenges of an anthropogenic poly crisis and attempts to understand the complexities of societal development in an era of globalisation. The article begins by sugge...
Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the lineari...
The ultimate goal of E-learning environments is to improve students’ learning. To achieve that goal, it is crucial to accurately measure students’ learning. In the field of educational measurement, it is well known that the key issue in the measureme...
With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of...
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