Integrating wav2vec 2.0 with Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) often involves a trade-off between capturing global semantic consistency and maintaining local feature discriminability. This study propos...
Convolutional neural networks (CNNs) are widely utilized in image classification. Nevertheless, CNNs typically require substantial computational resources, posing challenges for deployment on resource-constrained edge devices and limiting the spread...
Greedy schedulers are widely used for low-Earth-orbit satellite task scheduling for their computational efficiency and determinism. However, they select candidate arcs by primary criteria alone, so near-equivalent alternatives are separated arbitrari...
Multi-task learning (MTL) is essential for satellite internet systems requiring simultaneous optimization of beam management, interference mitigation, resource allocation, and traffic prediction. However, existing evaluation methods rely predominantl...
Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and s...
Underwater images often suffer from color distortion, low contrast, and structural blurring caused by wavelength-dependent absorption and scattering, which degrade both visual observation and downstream perception. Existing underwater image enhanceme...
Extremely low overlap leads to severely scarce local geometric correspondences across frame pairs. Pure geometric descriptors—encoding merely low-level shape signatures—inherently fail to impose sufficient constraints for reliable transfo...
Biomimetic perception systems integrate heterogeneous cues selectively rather than treating all available information as equally reliable. Inspired by this principle, this study proposes QGFace-LLaVA, a multimodal large language model (MLLM)-centered...
Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale vari...
The transportation sector faces growing supply chain disruptions driven by workforce shortages, digital skills gaps, and weak cultural readiness for risk. Human Resource Management (HRM) offers a credible path toward stronger supply chain resilience...
Incremental object detection (IOD) is critical for autonomous underwater perception, where detectors deployed on long-duration underwater platforms must continuously adapt to evolving marine environments while retaining previously learned recognition...
Fine-grained sentiment analysis requires a deep understanding of emotional intensity in the text to distinguish subtle shifts in polarity, such as moving from positive to more positive or from negative to more negative, and to clearly separate emotio...
With rapid advancements in large language models for natural language processing, their role in semantic-aware process mining is growing. We study semantics-aware process mining, where decisions must reflect both event logs and textual rules. We prop...
Accurate polyp segmentation in colonoscopic images remains challenging due to low contrast, irregular morphology, and significant distribution shifts across datasets, which often lead to unreliable boundary delineation and poor generalization. Existi...
Maritime logistics relies on documents that combine dense multilingual text, irregular layouts, and task-specific output schemas, requiring operational systems to recover text, structure, location, and semantics. We trained Hi-Dolphin-OCRusing a task...
Multi-UAV cooperative persistent tracking in complex obstacle environments requires agents to approach dynamic targets while ensuring obstacle avoidance and flight safety; however, standard multi-agent reinforcement learning (MARL) methods typically...
While large language models (LLMs) have shown impressive performance on free-text questions, their reliability on tabular question answering (Tabular QA) is constrained, especially for multi-step reasoning and numerical aggregation. These challenges...
Three-dimensional (3D) object detection constitutes a fundamental task in the field of environmental perception. While LiDAR provides high-precision 3D geometric data, its performance significantly degrades under adverse weather conditions like dense...
As airport operations expand and ground handling becomes more complex, airport vehicle scheduling has evolved into a system-level decision problem constrained by operational rules, task dependencies, and resource availability. However, existing appro...
Roman Urdu hate and offensive language detection is difficult because informal social-media posts combine non-standard spelling, code-mixing, local abusive expressions, and severe label imbalance. Existing transformer-based detectors improve contextu...
Situation awareness (SA) involves analyzing sensory data, such as audio signals, to identify anomalies. While acoustic features are widely used in audio analysis, existing methods face critical limitations; they often overlook the relevance of SA aud...
Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potentia...
Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fi...
High-resolution remote sensing understanding requires models to preserve small spatial evidence, account for acquisition-dependent appearance, and separate genuine geographic change from nuisance variation. We introduce GeoGATE, a geo-sensor-guided f...
Quantum kernels hold significant promise for achieving computational advantages in quantum machine learning (QML), yet their effectiveness critically depends on the design of expressive and hardware-compatible feature maps, a challenge that is partic...
The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge comp...
Learning transferable region embeddings is fundamental to urban computing, supporting applications from economic forecasting to public safety. Existing multi-view fusion methods predominantly rely on coarse-grained fusion, applying uniform weights ac...
This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as grap...
The spectrogram-based wideband signal detection framework has garnered increasing attention in various wireless communication applications. However, the front-end spectrograms in existing methods suffer from visual and informational deficiencies. Thi...
Inverse Synthetic Aperture Radar (ISAR) imagery plays a crucial role in space situational awareness, yet its interpretation remains largely confined to tasks such as classification and segmentation, lacking the ability to generate detailed natural la...
Transfer learning enables pretrained deep networks to adapt to new tasks but typically preserves their original computational structure, leaving much of the inference cost unchanged. Structured pruning can reduce this complexity; however, introducing...
Marine oil spill monitoring plays a critical role in environmental protection and emergency response. Synthetic aperture radar (SAR) provides all-day, all-weather, and wide-area imaging capabilities and has become a primary sensor for operational oil...
Segmenting key components of space targets using Inverse Synthetic Aperture Radar (ISAR) images is an important interpretation task in space situational awareness. However, the scarcity of pixel-level annotated data, inter-class confusion caused by m...
Accurate upper-limb motor assessment is critical for post-stroke rehabilitation but relies on subjective clinical scales. This study proposes the Action-Aware Multimodal Wavelet Fusion Network (AMWFNet), integrating surface electromyography (sEMG) an...
Cooperative unmanned aerial vehicle (UAV) swarms require secure transaction-level admission decisions when tasks, relay roles, control privileges, or mission assets are transferred between aircraft. Existing authentication and intrusion-detection mec...
This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach intr...
Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum infor...
Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurrin...
Unmanned aerial vehicle (UAV) object detection plays an essential role in modern visual perception, but it remains challenging because UAV imagery typically contains extremely small, densely distributed objects embedded in complex backgrounds. Conven...
With the advancement of information technology, artificial intelligence (AI) has demonstrated significant potential in clinical prediction, helping to improve the level of intelligent medical care. Current clinical practice primarily relies on patien...
Multi-modal object tracking (MMOT) has received widespread attention for the ability to overcome single-sensor perception limitations. However, existing methods encounter several critical challenges. Representation learning and generalization capabil...
Modulation and signal classification are two highly correlated core tasks in wireless communications and are the core foundation of intelligent spectrum management in Future Internet and 6G networks. Although their objectives differ, the two tasks of...
Automated warehouses must coordinate fleets of mobile robots online while meeting order deadlines. In online Multi-Agent Pickup and Delivery with Deadlines (MAPD-D), committing to a feasible task immediately may restrict flexibility and increase down...
Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, t...
Part-of-speech (POS) tagging in low-resource, morphologically rich languages (LRLs/MRLs) remains challenging due to extensive affixation, high out-of-vocabulary (OOV) rates, and pervasive polysemy. We propose MRL-POS, a unified Transformer-CRF framew...
Inverse Synthetic Aperture Radar (ISAR) imaging plays a crucial role in reconnaissance and target monitoring. However, the presence of uncertain factors often leads to indistinct component visualization and significant noise contamination in imaging...
Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover,...
In Industrial Internet of Things (IIoT) deployments, mobile edge computing (MEC) offloads computation-intensive tasks, a constrained biobjective problem trading time delay against energy consumption (MTOP). We show that this benchmark is exactly sepa...
Knowledge graph completion (KGC) necessitates comprehensive modeling of heterogeneous relations by effectively integrating both graph structural information and textual semantics. Current approaches often exhibit fragmented feature utilization or sub...
Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural net...
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