Dense SLAM based on monocular cameras does indeed have immense applicati...
The introduction and advancements in Local Differential Privacy (LDP)
va...
This paper delves into the intricate landscape of privacy notions,
speci...
There has a major problem in the current theory of hypothesis testing in...
Prompt engineering is an essential technique for enhancing the abilities...
Anomaly detection aims to detect data that do not conform to regular
pat...
This paper proposes a stable sparse rapidly-exploring random trees (SST)...
Deep learning methods have advanced quickly in brain imaging analysis ov...
APT detection is difficult to detect due to the long-term latency, cover...
Federated Learning (FL) is a novel distributed machine learning approach...
In this paper, we propose a practically efficient model for securely
com...
This paper proposes a rapidly-exploring random trees (RRT) algorithm to ...
Personalized text generation has broad industrial applications, such as
...
In this paper, we propose a tightly-coupled SLAM system fused with RGB,
...
U-Nets have achieved tremendous success in medical image segmentation.
N...
Effective control of time-sensitive industrial applications depends on t...
This paper proposed a novel anomaly detection (AD) approach of High-spee...
In this work, we focus on the challenging task, neuro-disease classifica...
Most real-world optimization problems have multiple objectives. A system...
This paper proposes a joint detection and estimation (JDE) scheme based ...
Graph Convolutional Neural Networks (GCNs) are widely used for graph
ana...
Multiclass classification (MCC) is a fundamental machine learning proble...
Energy storage provides an effective way of shifting temporal energy dem...
As recommendation is essentially a comparative (or ranking) process, a g...
Graph embedding techniques have been increasingly employed in real-world...
In the field of radar target detection, the false alarm and detection
pr...
Semantic Role Labeling (SRL) aims at recognizing the predicate-argument
...
In this paper, we consider a second-order scalar auxiliary variable (SAV...
Deep learning techniques have made an increasing impact on the field of
...
We propose a bandit algorithm that explores purely by randomizing its pa...
Textual explanations have proved to help improve user satisfaction on
ma...
For high-dimensional data, there are huge communication costs for distri...
Cloud computing has been a dominant paradigm for a variety of informatio...
With the development of medical imaging technology, medical images have
...
The persistence over space and time of flash flood disasters – flash flo...
User-provided multi-aspect evaluations manifest users' detailed feedback...
Implicit feedback, such as user clicks, is a major source of supervision...
Edge computing is the next Internet frontier that will leverage computin...
Recent years have witnessed the fast development of the emerging topic o...
The network information system is a military information network system ...
Fog computing is an emerging paradigm that aims to meet the increasing
c...
Scheduling is important in Edge computing. In contrast to the Cloud, Edg...
In real-world underwater environment, exploration of seabed resources,
u...
Multi-aspect user preferences are attracting wider attention in recommen...
Fog computing envisions that deploying services of an application across...
Latent factor models have achieved great success in personalized
recomme...
Multi-tenancy in resource-constrained environments is a key challenge in...
Explaining automatically generated recommendations allows users to make ...
Generative Adversarial Networks are proved to be efficient on various ki...
We present an Edge-as-a-Service (EaaS) platform for realising distribute...