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In recent years, the rapid expansion of ride-hailing platforms has reshaped the landscape of modern urban transportation systems. The growth of these services has underscored the importance of vehicle dispatching and resource allocation, making ...

The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of the generated images. Exploring diffusion models to enhance the generated ...

When using large language models (LLMs) for code translation of complex software, numerous compilation and runtime errors can occur due to insufficient context awareness. To address this issue, this paper presents a code translation method based on call ...

Urban undisciplined events (UUE) are of increasing concern to urban officials because they reduce the quality of life and cause societal disorder. How to accurately predict future occurrences is a key point in preventing these events. However, existing ...

Time series forecasting (TSF) consists of point prediction and probabilistic forecasting. Unlike point forecasting which predicts an expected value of a future target, probabilistic time series forecasting models the uncertainty in data by predicting the ...

Multimodal learning robust to missing modality has attracted increasing attention due to its practicality. Existing methods tend to address it by learning a common subspace representation for different modality combinations. However, we reveal ...

Dynamic time division duplex (D-TDD) is a promising technology for the future networks, enabling communication nodes to change uplink and downlink traffic opportunistically in the time domain to improve communication resource management. In this article, ...

Microservice architecture has become a driving force in enhancing the modularity and scalability of web applications, as evidenced by the Alipay platform's operational success. However, a prevalent issue within such infrastructures is the suboptimal ...

To predict the class label from a partially observable activity sequence can be quite challenging due to the high degree of similarity existing in early segments of different activities. In this paper, an innovative HARDness-Guided Discrimination Network (...

Although learning-based stereo matching methods have achieved remarkable performance, accurately recovering disparity maps for boundary areas (e.g., thin structures) remains an intractable issue. Existing stereo pipelines usually employ the ...

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This paper uses meta-analysis to quantitatively analyze 20 empirical or quasi-empirical literatures at home and abroad to study the effects of pre-assembled educational robots on students' learning outcomes, and the differences in the effects of ...

Few-shot learning, a promising technique for acquiring new concepts from limited data, assumes that testing samples belong to “unknown classes” and are regarded as new knowledge. However, real-world scenarios introduce uncertainty about the class ...

• Introduce few-shot incremental learning (FSIL) for unknown class detection.
• Propose the graph-based FSIL algorithm for unknown class detection.
• The proposed algorithm outperforms the baselines on two benchmark datasets.

Stereo matching is a prominent research area in autonomous driving and computer vision. Despite significant progress made by learning-based methods, accurately predicting disparities in hazardous regions, which is crucial for ensuring safe vehicle ...

Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with significant computational costs. In this paper, we propose an efficient LLM ...

The pneumoconiosis staging task suffers from the stage ambiguity, especially in the early stage of pneumoconiosis. The inter-stage reader variability caused by the stage ambiguity and the resulting noisy labels are even more detrimental to the ...

Minimally invasive surgery (MIS) is commonly used in some robotic-assisted surgery (RAS) systems. However, many RAS lack the strength and tactile sensation of surgical tools. Therefore, researchers have developed various force sensing techniques in robot-...

In large-scale systems, due to system complexity and demand volatility, diverse and dynamic workloads make accurate predictions difficult. In this work, we address an online interval prediction problem (OnPred-Int) and adopt ensemble learning to solve ...

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical ...

In this paper, the ordinal sum of a family of given triangular norms on subintervals in a bounded meet semi-lattice is constructed when the subintervals are pairwise non-overlapped and all the endpoints of the subintervals constitute a ...

Learning based on multimodal data has attracted increasing interest recently. While a variety of sensory modalities can be collected for training, not all of them are always available in practical scenarios, which raises the challenge to infer with ...