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Recommending API sequences is crucial in software development, saving developers time and effort. While previous studies primarily focus on accuracy, often recommending popular APIs, they tend to overlook less frequent, or ‘tail,’ APIs. This oversight, ...

Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches learn the embedding of missing entities by a single triple only. ...

Retinex-based unsupervised low-light enhancement methods have demonstrated notable performance without paired data. However, existing Retinex-based unsupervised methods implicitly relax the constraints of Retinex theory and cannot predict the ...

Conventional learning-based approaches for enhancing low-light images typically rely on a large amount of paired training data, which is challenging to obtain in practice. To address this issue, some algorithms have been proposed using the ...

Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-...

Human activity recognition (HAR) is a crucial task in IoT systems with applications ranging from surveillance and intruder detection to home automation and more. Recently, non-invasive HAR utilizing WiFi signals has gained considerable attention due to ...

In recent years, user travel has generated massive mobile data. Recommending the next location of interest not only greatly facilitates user travel, but also provides a corresponding opportunity for merchants to mine potential customers. In view ...

Developers utilize issue tracking systems to track ideas, feedback, tasks, and bugs for projects in the open‐source software ecosystem of GitHub. In this context, extensive bug reports and feature requests are raised as issues that need to be ...

This study presents a multiclass classification method to predict issue resolution time in open‐source software projects by utilizing a comprehensive feature set derived from the aspects of issue, project, and developer. It explores the characteristics ...

We propose FlexibleBP, a novel cuffless blood pressure monitoring system using a wrist-worn flexible sensor to enhance comfort and accuracy. By capturing pulse wave signals from the radial artery, we develop a personalized estimation framework ...

Recent diffusion models have excelled in image generation, but adapting them incrementally to new, unseen classes remains difficult. This paper presents LeifDM, an incremental diffusion model that efficiently adapts pre-trained models to new classes in ...

Pansharpening is an important technique for remote sensing imaging systems to obtain high-resolution multispectral images. Existing deep learning-based methods mostly rely on using pseudo-groundtruth multi-spectral images for supervised learning. The ...

With the rise of "Metaverse" and "Web 3.0", Non-Fungible Token (NFT) has emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain ...

The ship information system has the characteristics of complex operation and large amount of information. Due to the limited cognitive ability of the operator, the overload of cognitive resources will reduce the efficiency of the task completion of the ...

The use of microservice architecture is gaining popularity in the development of web applications. However, identifying the root cause of a failure can be challenging due to the complexity of interconnected microservices, long service invocation ...

• Precisely localize root causes to the granularity of instances within microservices.
• Demonstrate robustness in the presence of new and previously unseen anomaly types.
• Adapt to dynamic topology changes, ensuring reliability in ...

Understanding the content of events occurring in the video and their inherent temporal logic is crucial for video-text retrieval. However, web-crawled pre-training datasets often lack sufficient event information, and the widely adopted video-...

Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy. Previous solutions for learning NeRFs ...$\mathrm{}$$\mathrm{}$

Privacy laws like GDPR necessitate effective approaches to safeguard data privacy. Existing works on data privacy protection of DNNs mainly concentrated on the model training phase. However, these approaches become impractical when dealing with ...

Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements after training. Thus, when facing new requirements, a typical approach ...

Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, the massive computational burden imposed by the retraining of ...${}^{\mathrm{}}$${}^{\mathrm{}}$

Fiber optic can not only be used as a data transmission device, but also as a signal acquisition device. Research on fiber optic data acquisition for temperature and sound signals (DTS&DAS) has been widely carried out both domestically and ...