Underwater sound classification has been an area of interest in the research community because of its applications in military, commercial, and environmental domains. Underwater sound classification is a challenging task because of the high ...

• A comprehensive review of research and the latest developments in the field.
• Highlight the contributions and challenges from over 250 recent research papers.
• Discuss methods for vessel sound classification and fish sound ...

This paper proposes new joint flow control and link scheduling (JFCLS) algorithms for the classical network utility maximization (NUM) problem with unknown utility functions. Our algorithm leverages the idea of optimism, i.e., being optimistic in using ...

Graph classification task plays a crucial role in many practical applications, for which the model is required to be capable of learning an accurate representation with discriminative characteristics, but existing methods typically fail to ...

In competitive distributed learning, organizations face the challenge of collaboratively training machine learning models without sharing sensitive raw data, while competing for the same customer base using model-based services. Federated learning ...

Career path prediction is an important task in computational jobs marketplace. Recent advances in data science and artificial intelligence have imposed a huge recruitment demand on talents in the IT field. Previous studies predict a talent’s next ...

Code summarization aims to automatically generate natural language descriptions for code snippets, which help people maintain and understand code snippets. Existing code summarization methods are mostly based on the encoder-decoder structure, ...

In recent years, existing studies have sought to enhance the effectiveness of portfolio optimization by modeling asset relations. However, employing conventional graph neural network methodologies for effective aggregation and final representation ...

Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing, yielding impressive zero-shot video editing performance. Nonetheless, the generated videos usually show spatial irregularities and temporal inconsistencies ...

The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there ...

Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to address the challenges of ever-growing real-world graphs. However, ...

Using appropriately selected indexes can dramatically improve the performance of query workloads in database systems. Typically, the access patterns of the workloads in real-world applications change frequently. This poses the challenge of ...

It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank mapping optimized based on ...

Accurately computing the flow in the nasal cavity with computational fluid dynamics (CFD) simulations requires highly resolved computational meshes based on anatomically realistic geometries. Such geometries can only be obtained from computer ...

• The resolution of CT data was increased for reliable respiratory flow simulations.
• Hyperparameters of a super-resolution network were determined by an AutoML framework.
• A data efficient method significantly improved the convergence ...

In this paper, we first proposed mathematical model equations for wave propagation in a multi-functional transformation optical device, namely, an electromagnetic rotation concentrator. This device can concentrate electromagnetic energy while ...

Ubiquitous computing, the seamless integration of sensing, analytics, and feedback into daily life envisioned by Weiser [12], has come closer to reality with the broad adoption of smartphones and wearable devices. These devices, integral to users' daily ...

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as ...

Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on ...

In this paper, we investigate the physical-layer security performance for a reconfigurable intelligent surface (RIS) assisted free-space optical (FSO) communication-radio frequency (RF) system with non-orthogonal multiple access (NOMA). In ...

This paper investigates a class of output-mask-based adaptive neural network (NN) tracking control for nonlinear stochastic time-delayed multi-agent systems (STMASs) based on a unified event-triggered approach. The output signal relies on an ...

• This paper proposes a privacy-protection-like scheme for continuous systems to make those signals transmitted in ciphertext.
• A unified approach for event-triggered control, namely dynamic saturation threshold event trigger, was ...

Unsupervised cross-domain image retrieval is designed to facilitate the retrieval between images in different domains in an unsupervised way. Without the guidance of labels, both intra-domain semantic learning and inter-domain semantic alignment pose ...