The attention mechanism has the advantage of handling long-term correlations, and has been widely adopted in multivariate time series (MTS) prediction. As an important application of MTS, traffic flow prediction has the most popular solution using ...

Prior research on neural architecture search (NAS) for adversarial robustness has revealed that a lightweight and adversarially robust sub-network could exist in a non-robust large teacher network. Such a sub-network is generally discovered based on ...

The medical dialogue system aims to create a smart consultation platform for diagnosing diseases. Prior research uses doctor-patient dialogue history for responses, neglecting medical clues guidance. This oversight can lead to inconsistencies ...

To alleviate the reliance on expensive annotations, contrastive learning techniques have been applied to diagnose diseases from various types of medical images. However, popular contrastive learning methods, which generate positive pairs by ...

Text-to-image generation is a challenging task that aims to generate visually realistic images semantically consistent for a given text. Existing methods mainly exploit the global semantic information of a single sentence while ignoring fine-...

As technology advances, the steganalysis methods based on Convolutional Neural Network (CNN)can more precisely detect steganography behavior. However, the existing CNN-based steganalysis methods have the problem of insufficient robustness in ...

Scene text image super-resolution aims to upgrade the visual quality of low-resolution images and contributes to the accuracy of the subsequent scene text recognition task. However, advanced super-resolution methods with more attention to text-...

Creating accessible software is imperative for making software inclusive for all users.Unfortunately, the topic of accessibility is frequently excluded from computing education, leading to scenarios where students are unaware of either how to develop ...

Linguistic steganography based on text generation is a hot topic in the field of text information hiding. Previous studies have managed to improve the syntactic quality of steganography texts using natural language processing techniques based on deep ...

The recently developed sparse network training methods, such as Lottery Ticket Hypothesis (LTH) and its variants, have shown impressive learning capacity by finding sparse sub-networks from a dense one. While these methods could largely sparsify deep ...

In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model evaluation process of using empirical risk to estimate the intractable true generalization ...

With the development of smart internet-connected vehicles, in-vehicle networks are facing increasing security threats. Controller Area Network (CAN), as the most commonly used communication method in vehicles, has become the main target of malicious ...

Existing image generation tasks produce blurry, unrealistic results and images that lack layers and structure. Depth information can be used to accurately control the relative positions and hierarchies between different objects in an image. Our ...

In the current era of rapid development in 5G networks, the flexibility and customization options inherent in these networks have led to the emergence of more complex network architectures and topologies. The need to achieve network intelligence and ...

This paper presents a high-speed, low-jitter, and high-precision clock receiver circuit (CLKRX) for time-interleaved ADCs with a sampling rate of 4 GS/s operating in the 3–5 GHz frequency range. The CLKRX circuit is composed of a ...

This paper presents a power and area efficient pseudo single ended switching ADC for portable biomedical signal monitoring applications. The switching method features an 8-bit binary-weighted DAC array with a novel layout method called ...

Neuromorphic computing realizes low-latency and low-power computing by emulating the neural structure and operation of the human brain, and is considered a key research area for third-generation artificial intelligence. However, ...

Text-to-image Synthesis is to obtain images with both authenticity and semantic consistency from natural language descriptions. However, in previous methods, the fusion of semantic information during image generation is insufficient. ...

Medical image captioning alleviates the burden of physicians and possibly reduces medical errors by automatically generating text descriptions to describe image contents and convey findings. It is more challenging than conventional image captioning due to ...

We propose to approach active learning (AL) from a novel perspective of discovering and then ranking potential support vectors by leveraging the key properties of the dual space of a sparse kernel max-margin predictor. We theoretically analyze the change ...