The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learnin...
Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess un...
Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph. Such a technique can facilitate the compu...
Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to conce...
Cultural untranslatability refers to situations in which words, expressions, phrases, or entire conceptual systems encountered in translation cannot be easily transferred from one language to another. Cultural untranslatability reflects differences i...
The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax a...
In this paper, we introduce topological information systems between objects with a distance and attributes with a distance based on a complete co-residuated lattice. We can obtain two distances and Alexandrov topologies for objects and attributes fro...
Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework tha...
The continuous monitoring of physicochemical variables can provide temporally resolved information during fermentation processes while reducing dependence on discrete manual measurements. This work presents the design, implementation, and functional...
While biomedical terminology systems must identify term spans, link mentions to concepts, and generate definitions, retaining additional structural alternatives or contextual evidence may not improve decisions around these elements. We evaluated thre...
Democratic theory encompasses numerous democracy concepts, yet their conceptual relationships are rarely examined systematically at scale. This study develops a reproducible computational framework for grouping 100 democracy concepts from standardize...
An international debate is taking place about embedding sustainability in higher education institutions (HEIs). Separate strands of literature address the importance of sustainability concepts and strategic change approaches. This paper explores conc...
With the rise of online education platforms, the vast number of accumulated data conceal patterns regarding the evolution of students’ knowledge states. Tracking a student’s knowledge state through their historical interaction records is...
Predicting wear in artificial joints requires integrating joint dynamics, contact mechanics and progressive surface evolution, yet these processes are often treated separately. In total hip arthroplasty (THA), finite-element approaches remain the ref...
As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting...
Embedded three-dimensional printing (e-3DP) is an emerging method for additive manufacturing where semi-solid materials are extruded within a solidifying liquid matrix. Here, we present the first example of employing e-3DP in the pharmaceutical field...
Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize elec...
Most text classification systems use machine learning algorithms; among these, naïve Bayes and support vector machine algorithms adapted to handle text data afford reasonable performance. Recently, given developments in deep learning technology, seve...
Classifying social media (SM) messages into relevant or irrelevant categories is challenging due to data sparsity, imbalance, and ambiguity. This study aims to improve Intelligent Transport Systems (ITS) by enhancing short text classification of traf...
With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in...
As the operation of buildings becomes more efficient, the carbon emissions generated by other phases of the building’s life cycle should also be mitigated to address the climate crisis. In this sense, structural systems play an essential role i...
Artificial neural networks (ANNs) make predictions based on patterns learned during training; however, their reliability may deteriorate when the data distribution or the trained model changes. This paper proposes an Embedded One-Class Classification...
Advances in language modeling have provided affordances for term extraction based on the capture of the lexical context and its semantics and encoding this in real-valued vectors (embeddings). Term importance is usually computed using quantitative me...
This paper explores the data hiding schemes which are based on the principle of matrix embedding. Under the same embedding rate, the efficiency of each data hiding scheme is evaluated by the metric of average embedding efficiency. In the literature,...
Word embeddings have been very successful in many natural language processing tasks, but they characterize the meaning of a word/concept by uninterpretable “context signatures”. Such a representation can render results obtained using embe...
To help students choose the knowledge concepts that meet their needs so that they can learn courses in a more personalized way, thus improving the effectiveness of online learning, this paper proposes a knowledge concept recommendation model based on...
The correlation between breath volatilome and health is prompting a growing interest in the development of sensors optimized for breath analysis. On the other hand, the outbreak of COVID-19 evidenced that breath is a vehicle of infection; thus, the i...
The work environments and expectations for the daily activities of primary care physicians are daunting and often include spending a significant amount of time related to chronic care management with complex medication regimens, medication reconcilia...
Systems supporting systematic literature reviews often use machine learning algorithms to create classification models to assess the relevance of articles to study topics. The proper choice of text representation for such algorithms may have a signif...
Ontologies play a central role in structuring knowledge across domains, supporting tasks such as reasoning, data integration, and semantic search. However, their large size and complexity—particularly in fields such as biomedicine, computationa...
Knowledge tracing plays a crucial role in effectively representing learners’ understanding and predicting their future learning progress. However, existing deep knowledge tracing methods, reliant on the forgetting model and Rasch model, often f...
The space of all bounded closed intervals cannot form a vector space because the concept of an additive inverse cannot be considered. Therefore, this paper presents an embedding theorem to show that the space of all bounded closed intervals can be em...
Eulerian and bipartite graph is a dual symmetric concept in Graph theory. It is well-known that a plane graph is Eulerian if and only if its geometric dual is bipartite. In this paper, we generalize the well-known result to embedded graphs and partia...
Data has attributes, and synchronous changes between data and attributes are the basic characteristics of a type of data: under the condition of supplementing (deleting) some attributes, some data elements are deleted (supplemented) from data. The at...
Simple analog devices like manometers, manual valves, etc., have been ignored in the digitization process that has characterized the transition towards Industry 4.0. The reason behind this is that their substitution with the equivalent digital versio...
The model-driven method has recently attracted considerable attention as a means of improving the reliability and efficiency of embedded software design. This paper describes an embedded software course incorporating a model-driven method. In this co...
Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approac...
Understanding heterogeneity in farm development strategies is important in order to design effective policies and support programs to increase the sustainability of agriculture in relation to its socio-material context. Using a unique case study of 1...
Knowledge tracing (KT) models the evolution of students’ knowledge states from historical learning interactions, serving as a fundamental mechanism for intelligent educational systems. The efficacy of KT models heavily relies on the quality of...
This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation man...
With the help of support functions of fuzzy sets, we are able to embed some interesting families of fuzzy sets into Banach spaces. It is better to make the embedded Banach spaces as small as possible, which can be achieved by considering the weak* to...
Subclinical valve thrombosis in heart valve prostheses is characterized by the progressive reduction in leaflet motion detectable with advanced imaging diagnostics. However, without routine imaging surveillance, this subclinical thrombosis may be und...
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machi...
The continuous and injective embeddings of closed curves in Hausdorff topological spaces maintain isometry in subspaces generating components. An embedding of a circle group within a topological space creates isometric subspace with rotational symmet...
A safe Torque Vectoring function is applied to an electric vehicle with a two front motors drivetrain concept. The function has been embedded in a lean and scalable functional architecture adaptable to various drivetrain concepts. The Torque Vectorin...
The concept of “savage thought” in the sense of Claude Lévi-Strauss is being discussed and applied to the magical context within which the discourse of the new right-wing movements is embedded gaining the quality of what we call “populism” for short....
This paper establishes an embedding theorem for solving multiobjective linear set optimization problems. By introducing a partial order through this embedding framework, we formally define three solution concepts, which are optimal solution, Pareto o...
Knowledge graphs (KGs) are widely used in the education domain to offer learners a semantic representation of domain concepts from educational content and their relations, termed as educational knowledge graphs (EduKGs). Previous studies on EduKGs ha...
Knowledge tracing (KT) models learners’ evolving knowledge states to predict future performance, serving as a fundamental component in personalized education systems. However, existing methods suffer from data sparsity challenges, resulting in...
Explainable Artificial Intelligence (XAI) has primarily focused on explaining model predictions, yet a critical gap remains in explaining semantic structure discovery within knowledge graphs derived from concept maps (CMs). This study extends the OBO...
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