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Digital twins are increasingly used in wireless networks to provide geometry- and channel-aware replicas that support prediction and control, while large language models (LLMs) offer strong instruction-following capabili...

In classical vernacular Chinese fiction, inferring affective stance in the presence of rhetorical devices often depends on implicit semantics and contextual cues, which poses substantial challenges for joint pragmatic mo...

Despite the impressive strides of large language models (LLMs) in text summarization, their effectiveness still hinges on well-crafted prompts, a process that remains labor-intensive, subjective, and difficult to scale. ...

In recent years, Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) have gained significant momentum in the automation of software development. Although, previous research studies have also explo...

Dairy farms increasingly deploy sensor networks that generate high-frequency behavioral and spatial data, yet translating these multimodal streams into actionable welfare insights remains a key challenge. This study pres...

Graph-based text classification has become an effective paradigm for modeling corpus-level structure, particularly under limited supervision. However, most existing approaches rely on document–word representations...

Current watermarking approaches for LLM-generated text typically embed watermarks during the generation process, using a secret key controlled by the LLM. Although this enables verification of LLM-generated content, it m...

Column type annotation (CTA) is a critical task in semantic table interpretation (STI), where meaningful semantic labels are automatically assigned to table columns. This process enriches tabular data with essential sema...

ARM-based systems are widely deployed in edge and IoT settings, where penetration-testing tools must operate under strict memory, power, and thermal constraints, yet prior work offers limited deployment-oriented guidance...

Digital Twin Networks (DTNs) are emerging as a key enabler for the robust optimization and lifecycle management of next-generation networks. However, current DTN approaches still rely heavily on manual design, implementa...

The shift toward sustainable and intelligent buildings has accelerated the demand for advanced maintenance strategies that reduce energy waste, downtime, and improve reliability. Traditional maintenance approaches (react...

The remarkable success of large language models (LLMs) in code generation has transformed software development, with systems like Codex, CodeGen, and StarCoder demonstrating strong capabilities in synthesizing functional...

The growing demand for intensive artificial intelligence applications, such as deep neural networks and Large Language Models (LLMs), has driven the creation of architecture extensions and accelerators for matrix multipl...

Few-shot object detection (FSOD) recently garnered increasing attention in remote sensing images (RSIs). Although FSOD has achieved success in natural sense, the large size and complex backgrounds of RSIs make FSOD espec...