Conversational gesture generation holds pivotal importance in enhancing the natural flow of digital human interaction. However, the inherent weak correlation between gestures and language poses a significant challenge for automated gesture ...

Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item representations capturing both the individual items' semantics and cross-item ...

Fair clustering aims to prevent sensitive attributes (e.g., race or gender) from dominating the clustering process. However, real-world datasets, often characterized by low quality and high dimensionality, restrict existing fair clustering ...

Spiking neural networks (SNNs), which mimic biological neural systems to convey information via discrete spikes, are well-known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for ...

Penetration testing is an effective means of maintaining network security. To address the challenge of high labor costs in traditional penetration testing, researchers have been investigating the potential of automated solutions. In this paper, we ...

In the Internet of Vehicle (IoV) systems, intelligent vehicles generate huge amounts of data that supports diverse services and applications. In practice, database systems are deployed in the cloud to manage data uploaded from the vehicle side and ...

This paper presents a design method for a Filter-Integrated Single Pole Double Throw (FI-SPDT) switch based on transfer function analysis. Combining the equivalent model of pseudomorphic High Electron Mobility Transistor (pHEMT) and its parasitic ...

Heterogeneous graph refers to a type of graph data characterized by its diverse node types and relation types, containing rich structures, features and heterogeneous information. How to fully utilize and capture these key information to generate ...

Self-supervised monocular depth estimation has been a popular topic since it does not need labor-intensive depth ground truth collection. However, the accuracy of monocular network is limited as it can only utilize context provided in the single ...

• A new self-supervised multi-frame depth network incorporating IMU modality.
• A visual-inertial fusion Transformer to improve pose estimation involved in multi-frame depth.
• A monocular guided excitation module bridges monocular and ...

Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Addressing this challenge, we envision a recommendation simulator, ...

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-grained preferences. Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools that may help address these ...

Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has sparked interest in leveraging them for sequential recommendation, viewing it ...

With the rapid development of cloud services, it has become the core driving force for innovation and business development in the digital era. The emerging technology of cloud computing has transformed cloud services from simple resource providers ...

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by ...

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep ...

Graph augmentation is the key component to reveal instance-discriminative features of a graph as its rationale – an interpretation for it – in graph contrastive learning (GCL). And existing rationale-aware augmentation mechanisms in GCL frameworks roughly ...

This tutorial will delve into the fascinating realm of simulating human society using Large Language Model (LLM)-driven agents, exploring their applications in cities, social media, and economic systems. Through this tutorial, participants will gain ...

Graph anomaly detection (GAD) has various applications in finance, healthcare, and security. Graph Neural Networks (GNNs) are now the primary method for GAD, treating it as a task of semi-supervised node classification (normal vs. anomalous). However, ...

Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood information on a user-item interaction graph to enhance user/item ...

Graph representation learning on vast datasets, like web data, has made significant strides. However, the associated computational and storage overheads raise concerns. In sight of this, Graph condensation (GCond) has been introduced to distill these ...