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Data clones are defined as multiple copies of the same data among datasets. The presence of data clones between datasets can cause issues such as difficulties in managing data assets and data license violations when using datasets with clones to build AI ...

The rapid growth of IoT devices generates valuable data. However, current centralized IoT device data trading solutions pose privacy and security risks. Emerging blockchain technology aims to address these issues by decentralizing data trading, reducing ...

Autonomous driving is a crucial area of research and a key focus for industrial advancement, with the advancement of high-level autonomous driving technology depending significantly on creating precise 3D point cloud maps (PCM$\backslash$PCMs) of the driving ...

This paper introduces a DR-YOLOv8-based equipment recognition method for regional rail transit network by incorporating DC_C2f, RC_FPN, and PLI modules into the original YOLOv8 network architecture. The deformable convolution (DC_C2f) module enables ...

In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing user data across domains, ...

Many multimodal multi-objective evolutionary algorithms (MMEAs) are effective in solving multimodal multi-objective problems (MMOPs), which have multiple equivalent Pareto optimal sets (PSs) mapping to the same Pareto optimal front (PF). Due to ...

• A new dual-population weak interaction coevolutionary mechanism (DPCE) is proposed.
• An SLCQ evaluation method is proposed.
• We propose a niche-based truncation strategy (NBT).
• We propose a new distance-based subset selection (...

The rate–distortion (RD) performance of learning-based image compression (LIC) has already surpassed that of traditional Versatile Video Coding (VVC) intra-coding. However, the gain of the compression efficiency is at the cost of high ...

• Learning-based image compression method for real-time applications is proposed.
• Feature extraction enhanced with stacked lightweight attention units.
• Channel-gained adaptive module re-distributes channel importance.
• The ...

Pre-trained vision transformers have revolutionized few-shot image classification, and it has been recently demonstrated that the previous common practice of meta-learning in synergy with these pre-trained transformers still holds significance. In this ...

This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the "Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D, the first ...

Deep learning has advanced through the combination of large datasets and computational power, leading to the development of extensive pretrained models like Vision Transformers (ViTs). However, these models often assume a one-size-fits-all utility, ...

Predicting cryptocurrency volatility is crucial for investors, traders, and decision-makers but is complicated by the market’s high non-linearity, volatility, and noise. This paper presents a novel approach, the CEEMDAN-RF-LSTM hybrid model, ...

• We introduce the CEEMDAN-RF-LSTM hybrid model to accurately predict cryptocurrency volatility.
• Our proposed three-dimensional framework is superior to other models in predicting cryptocurrency volatility.
• We apply CEEMDAN, RF and ...

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the situation where the model generates ...

• A feature selection method mines the intrinsic information of samples.
• A novel learnable adaptive method for feature selection.
• A counterfactual learning method uses causal inference to mitigate language bias.
• An adaptive ...

Graphs have more expressive power and are widely researched in various search demand scenarios, compared with traditional relational and XML models. Today, many graph search services have been deployed on a third-party server, which can alleviate ...

Object detection plays a important role within the field of remote sensing, boasting significant applications including intelligent monitoring and urban planning. However, traditional models are constrained by predefined classes and encounter a ...

• We propose an instance-aware knowledge distillation approach for class-incremental object detection, utilizing a previous model as a teacher to guide learning on new data for incremental detection of new classes without forgetting old ...

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the ...

This paper is devoted to the derivation and analysis of a simple pseudospectral computational method for a two-dimensional time-dependent Schrödinger equations with periodic coefficients, modeling electromagnetic waves propagating in photonic ...

Transformers, having the superior ability to capture both adjacent and long-range dependencies, have been applied to the graph representation learning field. Existing methods are permanently established in the supervised setting with several high-quality ...

Although spiking neural networks (SNNs) have made great progress on both performance and efficiency over the last few years, their unique working pattern makes it hard to train high-performance low-latency SNNs and their development still lags ...

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• We design the CT module and gain essential improvement of accuracy on DVS-Gesture.
• We integrate spatial-temporal attention into SNN and demonstrate its advantages.
• We propose a directly trained Transformer-based SNN, termed ”...

The constraint action of the unbound aggregate layer underneath plays an important role in affecting the temperature strains in the top asphalt layer. The focus of the present paper is to investigate the interactive thermal contraction ...