Camera-based visual sensing for classroom behavior detection remains challenging because fine-grained behaviors often differ only in subtle posture and hand–object cues, while dense seating, occlusion, scale variation, and background interferen...
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets tha...
The increasing sophistication of malware and the use of evasive techniques such as obfuscation pose significant challenges to traditional detection methods. This paper presents a deep convolutional neural network (CNN) framework that integrates stati...
This paper proposes a novel method capable of both detecting OOD data and generating in-distribution data samples. To achieve this, a VAE model is adopted and augmented with a memory module, providing capacities for identifying OOD data and synthesis...
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...
The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods...
Generating continuous mechanical response fields under small-sample conditions remains a critical challenge in civil engineering. This paper proposes MMA-CGAN, a memory-augmented multi-head attention conditional generative adversarial network. A lear...
Due to the inherent complexity of urban road networks and the irregular periodic fluctuations of traffic flow, traffic forecasting remains a challenging spatiotemporal modeling task.Existing studies predominantly focus on capturing spatial dependenci...
Satellites are critical components of modern space information systems. During long-term on-orbit operation, satellite telemetry often exhibits multi-scale temporal dynamics, heterogeneous channel behavior, and time-varying inter-variable dependencie...
This paper presents a study about augmented-reality-based chemistry learning in a university lecture. Organic chemistry is often perceived as particularly difficult by students because spatial information must be processed in order to understand subj...
Standard ensemble classifiers assign weights based on performance over a single feature space, leaving multi-scale fractional representations unexplored as a tabular augmentation strategy. This study proposes the FRAE (Fractional-order Recalibrated A...
Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsin...
Wideband power amplifiers (PAs) operating with high-order modulation signals exhibit strong nonlinear distortion and dynamic memory effects, making real-time digital predistortion (DPD) increasingly challenging under strict computational constraints....
Despite the increasing amount of spaceborne synthetic aperture radar (SAR) images and optical images, only a few annotated data can be used directly for scene classification tasks based on convolution neural networks (CNNs). For this situation, self-...
Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental f...
This paper introduces VERA (Vision Expert Real Analysis), a privacy-supporting cyber-physical framework designed for real-time data collection and visual analysis in healthcare environments. VERA limits exposure to identifiable RGB content by ensurin...
We develop a fast Chebyshev spectral collocation method for a coupled system of nonlinear Klein–Gordon equations augmented by Caputo-type fractional memory integrals. The governing equations retain the classical second-order time derivative as...
Immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as powerful tools in cultural heritage education and preservation. Building on prior work that demonstrated the effectiveness of gamified XR applications in e...
Few-shot instance segmentation (FSIS) is devised to address the intricate challenge of instance segmentation when labeled data for novel classes is scant. Nevertheless, existing methodologies encounter notable constraints in the agile expansion of no...
This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on...
Oriented object detection in remote-sensing imagery benefits from a geometric representation that remains well-defined across localization, label assignment, and post-processing. We present Ellipse-ABC, a YOLOv8-based detector that directly predicts...
High-quality code representations are fundamental to code intelligence. Achieving such representations with parameter-efficient fine-tuning (PEFT) remains a key challenge. While code pre-trained models (CodePTMs) offer a robust foundation for general...
With the recent intensification of geopolitical tensions, cyber-attacks have become increasingly sophisticated and dynamic. Traditional machine learning-based anomaly detection techniques, which rely on pre-trained data, often suffer from performance...
With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. However, several challenges remain unresolved, including the inability to fully capture the nuanced se...
Convolutional Neural Network (CNNs) models’ size reduction has recently gained interest due to several advantages: energy cost reduction, embedded devices, and multi-core interfaces. One possible way to achieve model reduction is the usage of R...
To address the dual challenges of scarce historical time-series data and limited representational capacity of standalone models in pavement performance prediction, this study proposes an Engineering-heuristic-constrained Perturbation Data Augmentatio...
With the advent of generative AI, medical imaging has been revolutionized, allowing for unprecedented capabilities in data generation, volumetric reconstruction, and clinical decision support. Although significant advances have been achieved in two-d...
We propose a Transformer-based data augmentation framework with a time-series dual-stream architecture to address performance degradation in encrypted network traffic classification caused by class imbalance between attack and benign traffic. The pro...
Cluster computing has attracted much attention as an effective way of solving large-scale problems. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. To...
This paper is related to the field of digital-informed modelling of heritage assets (HBIM). The scientific literature has been addressing this topic for years with many different facets: focusing digitalization on the analysis and modelling of herita...
Retrieval-augmented generation and structured agent memory are commonly treated as competing ways to ground large language models. Our results show that their relative value changes with the task and that neither retrieved similarity nor graph associ...
Credit card fraud detection remains a major challenge due to extreme class imbalance and evolving attack patterns. This paper proposes a practical hybrid pipeline that combines conditional tabular generative adversarial networks (CTGANs) for targeted...
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in s...
Photoplethysmography (PPG)-based blood glucose estimation is attractive for non-invasive monitoring, but its development is constrained by limited paired PPG–glucose data and the weak representation of glucose-related waveform variations. This...
This study addresses the need for applied sentiment analysis in engineering decision-support systems by presenting a hybrid framework for domain-specific engineering text. This study presents a hybrid sentiment classification framework by integrating...
Alzheimer’s disease (AD) is a neurodegenerative disorder that causes progressive damage to brain neurons, leading to declines in cognitive and behavioral abilities. This deterioration often results in changes in personality and increasing diffi...
Since the late 2000s, numerous studies have focused on the application of Heritage Building Information Modelling (HBIM) processes and technologies for the documentation of the historic built environment. Many of these studies have focused on the use...
To address the lack of effective risk-identification methods during the commissioning of new power grid equipment, we propose a knowledge graph construction approach for both scheme generation and risk identification. First, a gated attention mechani...
The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that en...
When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-co...
We propose SGA-DCAT (Sentiment-Gated Dual-stream Cross-Attention Temporal Network), an architecture for stock price prediction that treats news sentiment not as a passive input feature but as a control signal that governs the model’s memory dyn...
Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the...
Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces...
Industrial low-altitude multi-object tracking is challenged by overhead viewpoints, dense small targets, mutual occlusion, homogeneous appearances, and domain-shifted backgrounds. Vision-language pre-trained models provide useful semantic priors, but...
This paper presents a Liquid-Augmented Model Predictive Control (LA-MPC) framework for robust and adaptive motion control of quadrupedal robots operating under dynamic disturbances. The proposed approach integrates liquid neural dynamics into the pre...
The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outp...
This paper presents a modulation signal classification and recognition algorithm based on data augmentation and time–frequency joint attention (DA-TFJA) for underwater acoustic (UWA) communication systems. UWA communication, as an important mea...
Perceptual aliasing occurs when distinct environmental states generate the same observation even though different actions may be appropriate. This paper studies whether the observable presence of other agents can mitigate such ambiguity without addin...
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of su...
Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision...
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