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Generating disease-aware cardiac cine magnetic resonance imaging (cine MRI) images has immense potential in medical research, with recent advancements in text-driven image generation technology offering a viable solution. However, establishing ...

The concurrent execution of deep neural networks (DNNs) inference tasks on the intermittently-powered batteryless devices (IPDs) has recently garnered much attention due to its potential in a broad range of smart sensing applications. While the ...

Recently, Handwritten Mathematical Expression Recognition (HMER) has gained considerable attention in pattern recognition for its diverse applications in document understanding. Current methods typically approach HMER as an image-to-sequence ...$$$$

Natural language processing (NLP) has gained widespread adoption in the development of real-world applications. However, the black-box nature of neural networks in NLP applications poses a challenge when evaluating their performance, let alone ensuring ...

ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored to accelerate various Deep Neural Network (DNN) applications because they can achieve extremely high performance and energy-efficiency for in-situ analog Matrix-Vector ...

Proactive traffic management is increasingly critical in maritime intelligent transportation systems. Central to this is maritime traffic forecasting, which leverages specific structures and properties of the problem. This study focuses on the ...

The recognition of chemical molecular structures is crucial in fields such as education and biochemistry. Due to the significant challenges in data acquisition and annotation, current methods mainly focus on recognizing printed structures with ...

Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for ...

Generally, a collaborative business process is a distributed process, in which a set of parallel business processes are involved. These business processes have complementary competencies and knowledge, and cooperate with each other to achieve their common ...

Call centers are critical for gathering customer feedback, making them essential for business communication. Predicting the ongoing business process status accurately has become a focus in both academia and industry. However, current methods ...

• Innovates business process prediction in call centers using multi-modal fusion.
• Proposes a multitask model for effective multi-source data integration.
• Provides empirical validation of improved prediction performance in call center ...

Text recognition in scene images is still considered as a challenging task for the computer vision and pattern recognition community. For text images affected by multiple adverse factors, such as occlusion (due to obstacles) and poor quality (due ...

• The NDOrder can recognize text images with occluded and low-quality characters.
• The ROG module ensures the method work with a novel decoding order.
• The VCP module constructs a robust connection among image, content and position.

Deep learning-based code generation (DL-CG) applications have shown great potential for assisting developers in programming with human-competitive accuracy. However, lacking transparency in such applications due to the uninterpretable nature of deep ...

In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Within the surge of LCGM proposals, a critical aspect of code generation ...

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This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven ...

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and ...

Feature fusion for multimodal sentiment analysis is a challenging but worthwhile research topic. With the extension of the time dimension, there are interactions between multimodal signals and the lack of control over the target modal ...

Table structure recognition is an indispensable element for enabling machines to comprehend tables. Its primary purpose is to identify the internal structure of a table. Nevertheless, due to the complexity and diversity of their structure and ...

• Introducing SEMv2: Enhances ’split’ stage in table line detection for robustness.
• Proposed parallel decoder in “merge” stage boosts efficiency and maintains accuracy.
• iFLYTAB: Challenging new dataset for diverse table structure ...

Deep learning-based image interpolation methods are confronted with various challenges in its application to anisotropic medical volumetric data (i.e., CT and MR images) out of the complex nonlinear deformation and the scarcity of high-quality ...

Scene text recognition is inherently a vision-language task. However, previous works have predominantly focused either on extracting more robust visual features or designing better language modeling. How to effectively and jointly model vision and ...

A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and costly process, particularly for low-resource languages. To address ...