Background: Customer orders are key in developing logistics processes and in strategic developments of customer orientation. This paper investigates the notion of customer orientation. In the literature, the concept of customer is underdeveloped in t...
Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces hig...
Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance...
Each sparse representation classifier has different classification accuracy for different samples. It is difficult to achieve good performance with a single feature classification model. In order to balance the large-scale information and global feat...
Time-Series-Classification (TSC) is an important machine learning task for many branches of science. Symbolic representations of time series, especially Symbolic Fourier Approximation (SFA), have been proven very effective for this task, given their...
Multiple representations, such as experimental data, schemas, tables, and graphs, are an essential resource in science teaching. However, their use in the classroom typically poses a challenge for preservice teachers. The aim of this research is to e...
Learning physics can be seen by many as a problem, as the standard method of learning tends to focus on remembering and using concepts that fail to construct meaning. To overcome this problem in teaching rectilinear motion, we implemented multiple re...
This article examines representations of multiple sclerosis in direct-to-consumer pharmaceutical advertisements televised during 2021 in the United States. Drawing on and developing Cultural Disability Studies theory, it highlights how advertising pr...
Bioactive peptides play essential roles in various biological processes and hold significant therapeutic potential. However, predicting the functions of these peptides is challenging due to their diversity and complexity. Here, we develop TF-BAPred,...
The Montgomery modular multiplication is an integral operation unit in the public key cryptographic algorithm system. Previous work achieved good performance at low input widths by combining Redundant Binary Representation (RBR) with Montgomery modul...
A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed ei...
Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimens...
In this work, a new algorithm is proposed for a unique representation for simple and multiple joint planar kinematic chains (KCs) having any degree of freedom (DOF). This unique representation of KCs enhances the isomorphism detection during the stru...
Many non-probabilistic approaches have been widely regarded as mathematical tools for the representation of epistemic uncertainties. However, their heavy computational burden and low computational efficiency hinder their applications in practical eng...
Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensi...
Using multiple representations is advocated and emphasized in mathematics and science education. However, many students have difficulty connecting multiple representations of linear functions. Augmented Reality (AR) may affect these teaching and lear...
For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation,...
Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we...
Multimodal medical image fusion is a fundamental, but challenging, problem in the fields of brain science research and brain disease diagnosis, as it is challenging for sparse representation (SR)-based fusion to characterize activity levels with a si...
The representation of multiple acoustic sources in a virtual image of the field of audition based on binaural synthetic-aperture computation (SAC) is described through use of simulated inter-aural time delay (ITD) data. Directions to the acoustic sou...
In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Maki...
In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure...
High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult...
Probabilistic voltage assessment under multiple contingencies involves numerous network topologies and uncertain operating conditions, resulting in substantial surrogate modeling and computational costs. To address this problem, this paper proposes a...
Multiple object tracking (MOT) is a fundamental issue and has attracted considerable attention in the autonomous driving community. This paper presents a novel MOT framework for autonomous driving. The framework consists of two stages of object repre...
Dispensing with the common property of distributivity and replacing classical trigonometric functions with their
Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis. Along with the rapid development of sensor technologies, remotely sensed images can be captured at multiple spatial resol...
Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we...
This study formulates closed-form solution expressions for linear discrete matrix equations that involve several time delays, without requiring the coefficient matrices or the non-homogeneous term to commute. Using a generalized multinomial series an...
Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on uppe...
This paper considers the regional consensus problem for multi-agent systems with actuator saturation. By utilizing the theory of convex set, a novel multiple nonlinear feedback control protocol is presented, which can effectively reduce the conservat...
Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and...
This article is an overview of the SP theory of intelligence, which aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It...
Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorpor...
Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions,...
The presented study analyzes the usage of the didactic efficiency of multiple representations in a computer environment in learning absolute value functions and equations. It is known that the axis of symmetry of the graph of the absolute value funct...
Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to...
It is vital to understand what kind of processes for learning that Geovisual Analytics creates, as certain activities and conditions are produced when employing Geovisual Anlytic tools in education. To understand learning processes created by Geovisu...
Annotated medical imaging data remain scarce, and labels are often weak and noisy: in mammography, an examination comprises several high-resolution views, yet the diagnostic outcome is recorded only at the breast level. Such problems are naturally ca...
In the field of personalized recommendation, user-generated content (UGC) such as videos, images, and product comments are becoming increasingly important, since they implicitly represent the preferences of users. The vectorized representation of a c...
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification syst...
Entity linking, a crucial task in the realm of natural language processing, aims to link entity mentions in a text to their corresponding entities in the knowledge base. While long documents provide abundant contextual information, facilitating featu...
Deep-reinforcement-learning-based multi-UAV collision avoidance and navigation methods have made significant progress. However, the fundamental challenge of those methods is their restricted capability to generalize beyond the specific scenario in wh...
Increasing evidence has suggested that microRNAs (miRNAs) are significant in research on human diseases. Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy....
This paper introduces an HJM-style forward modeling framework for valuing American options. Instead of modeling the dynamics of the underlying asset, we model the maturity-indexed forward drift of the gain process, leading to two no-arbitrage represe...
Gastric cancer is a malignant tumor with high incidence. Computer-aided screening systems for gastric cancer pathological images can contribute to reducing the workload of specialists and improve the efficiency of disease diagnosis. Due to the high r...
The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial ap...
This paper presents an experience in the design and implementation of the condition-monitoring system for the synchronous generators whose primary purpose is to record data for the identification of the capability limits of the P–Q diagram of t...
Reliable cropland, forestland, and grassland maps support resource assessment and ecological management in arid and semi-arid Central Asia. Existing land-cover products often delineate these classes differently, vary in reliability across classes and...
In geographic information systems, the reliability of querying, analysing, or reasoning results depends on the data quality. One central criterion of data quality is consistency, and identifying inconsistencies is crucial for maintaining the integrit...
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