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Mobile Edge Computing (MEC) brings computation close to end-users to support latency-sensitive applications, such as tele-manipulation and autonomous driving. While Network Function Virtualization (NFV) facilitates the d...

The validity of research findings critically depends on the selection of software samples. However, Mining Software Repositories studies often rely on outdated datasets and lack systematic, transparent procedures for rep...

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...

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. ...

This paper presents an innovative methodology for modeling and predicting high-frequency harmonic losses in the rotating field of an electrical machine powered by a frequency converter. Conventional approaches are often ...

In a decentralized organization such as a university, recruitment is critical for the development of departments. With the limited resources for recruitment, how to allocate the recruitment quotas to different department...

Network Function Virtualization (NFV) offers flexibility but poses a critical challenge in multi-tenant environments: ensuring fairness (resource usage limits) and system security (memory/packet isolation) for each tenan...

To achieve high accuracies in deep learning-based inspection of IC markings, a large number of manually labeled images of good and defective markings are needed. In many IC production lines, only a handful of labeled ima...

Stroke recovery involves reorganization of large-scale brain networks, yet most neurophysiological biomarkers assess either motor or cognitive function in isolation. To address this limitation, we introduce phase-modulat...

An accurate intraoperative assessment of tissue perfusion in digestive surgery is critical to prevent complications such as anastomotic leakage, an event in which a surgical connection between organs fails to heal proper...

Due to the light-scattering effect of inhomogeneous media, achieving non-invasive focusing within scattering media remains a challenge. Existing techniques suffer from poor noise resistance, slow optimization processes, ...

Heterogeneous SoCs enhance system performance across various applications. However, managing and integrating components, interfacing hardware and software, and overseeing shared resources pose significant challenges for ...

Hollow-core photonic bandgap fibers (HC-PBGFs) exhibit exceptional environmental stability, making them as prime candidates for next-generation, high-precision fiber optic gyroscopes (FOGs). High fiber birefringence is n...

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...

Infrared thermal radiation signals are highly susceptible to environmental interference, leading to dynamic fluctuations. Consequently, weak targets are frequently submerged in heavy clutter backgrounds under extreme sce...

This paper presents an inductorless design methodology for compact single-pole double-throw (SPDT) switches in Sub-6 GHz applications. To achieve compactness and meet key performance metrics below 5.0 GHz, the methodolog...

Public service organisations, particularly universities, lack structured methods to evaluate their operational readiness for digital transformation, hindering strategic planning in the Industry 4.0 era. This paper addres...

Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike...

Ising machines, including quantum annealing machines, are promising next-generation computers for combinatorial optimization problems. However, due to hardware limitations, most Ising-type hardware can only solve objecti...

Terrestrial evaporation ($E$) plays a crucial role in the water, energy, and carbon cycles and modulates climate change through multiple feedback mechanisms. While process-based models estimate $E$ using satellite-derive...

Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (...

Accurate velocity estimation plays a key role in ensuring precise motion control and safe navigation of ground robots. Ground-view event cameras provide a reliable sensing modality for this task. They exploit the static ...

Test-based automated program repair (TB-APR) techniques automatically fix buggy programs by relying on a failing test suite. This test suite serves a dual purpose: pinpointing bugs and evaluating the validity of potentia...