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Underwater Salient Object Detection (USOD) has gradually garnered attention because of its wide range of applications in underwater environments. However, the complexity of underwater conditions often results in poor performance in preserving contour ...

In recent years, drones illegally spy on military bases and steal military secrets, especially in dark scenes. In the face of illegal detection by drones, physical infrared adversarial patches can play a defensive role. Existing methods are either ...

Temporal Knowledge Graph (TKG) reasoning involves predicting future facts based on historical information by learning correlations between entities and relations. Recently, many models have been proposed for the TKG reasoning task. However, most existing ...

We study power approximation formulas for peak detection using Gaussian random field theory. The approximation, based on the expected number of local maxima above the threshold u, E [ M u ], is proved to work well under three asymptotic scenarios:...

Recent research has achieved advancements in animated fashion video synthesis. However, existing methods generate videos only with the guidance of poses, thus resulting in the generated characters appearing stiff and the expressions becoming monotonous. ...

The Visual Spatial Description Challenge (VSD) is the first competition event focused on visual spatial understanding, organized under the auspices of the ACM Multimedia Conference 2024. The goal of the VSD challenge is to assess the the ability of ...

Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from text has received ample research and attention for years, yet there can be ...

Vulnerability detection is garnering increasing attention in software engineering, since code vulnerabilities possibly pose significant security. Recently, reusing various code pre-trained models (e.g., CodeBERT, CodeT5, and CodeGen) has become common ...

Traffic allocation is a process of redistributing natural traffic to products by adjusting their positions in the post-search phase, aimed at effectively fostering merchant growth, precisely meeting customer demands, and ensuring the maximization of ...

Trusted Execution Environments (TEEs) have long served as a prominent security measure for ensuring isolation and data privacy in cloud environments. However, their security foundations face challenges from numerous side-channel threats, ...

The Malmquist index constructed from distance functions is a useful tool for measuring productivity growth. In this paper, we assume the presence of random noise in empirical data and enhance the literature by introducing a panel-data model in ...

• We propose a stochastic nonparametric estimation of the Malmquist-type indices.
• We establish a consistent estimator for measuring intertemporal efficiencies.
• The proposed approach measures productivity changes under noise.
• The ...

In software development, bug report reproduction is a challenging task. This paper introduces ReBL, a novel feedback-driven approach that leverages GPT-4, a large-scale language model (LLM), to automatically reproduce Android bug reports. Unlike ...

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Autism spectrum disorder (ASD) is a common neurological condition. Early diagnosis and treatment are essential for enhancing the life quality of individuals with ASD. However, most existing studies either focus solely on the brain networks of ...

Temporal graph representation learning has drawn considerable attention in recent years. Most existing works mainly focus on modeling local structural dependencies of temporal graphs. However, underestimating the inherent global structural role ...

In the medical image segmentation task, many studies choose to increase the network depth to achieve higher detection accuracy, but the network parameters of the deep convolutional neural network are too large, and the number of medical images is very ...

Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. The architecture based on autoencoding (BERT-...

Advancements in pretrained and large language models have significantly propelled the development of text summarization in recent years, making the generation of fluent and readable text possible. However, issues related to factuality, faithfulness, and ...

The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling. Despite its exceptional performance across various tasks, we have identified two limitations: First, ...

Directed evolution, a cornerstone of protein optimization, is to harness natural mutational processes to enhance protein functionality. Existing Machine Learning-assisted Directed Evolution (MLDE) methodologies typically rely on data-driven strategies ...

A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the inductive MKGC (IMKGC) involving emerging entities unseen during training...