In this paper, we use empirical measurements to show that container network startup is a key factor that contributes to the slow startup of secure containers in multi-tenant clouds, especially in the scenario of serverless computing, where the issue is ...

Large language models (LLMs) have revolutionized code completion tasks. IDE plugins such as MarsCode can generate code recommendations, saving developers significant time and effort. However, current evaluation methods for code completion are limited by ...

With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy. In ...

RGB-Event based tracking is an emerging research topic, focusing on how to effectively integrate heterogeneous multi-modal data (synchronized exposure video frames and asynchronous pulse Event stream). Existing works typically employ Transformer ...$\mathrm{}$$\mathrm{}$$\mathrm{}$

RGB-Thermal Salient Object Detection(SOD) aims to identify common salient regions or objects from both the visible and thermal infrared modalities. Existing methods usually based on the hierarchical interactions within the same modality or between ...

In the task of disease prediction, medical data with different modalities can provide much complementary information for disease diagnosis. However, existing multi-modal learning methods often tend to focus on learning shared representation across ...

With the increasing complexity of cyber threats and the expanding scope of cyberspace, there exist progressively more challenges in cyber threat detection. It is proven that most previous threat detection models may become inadequate due to the ...

• Comprehensive review of LLMs for cyber threat detection stage.
• Explore four suitable cyber threat detection scenarios for LLMs.
• Explore different roles of LLMs in common cyber threat detection tasks.
• Discussion of extra ...

Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks ...

Autonomous driving relies on real-time perception of environmental semantic maps to make decisions, accurate and real-time perception of environmental semantics is crucial for safe navigation, as well as for efficient construction of high-...

Recent years have seen the development of LLM-based code generation. Compared to generating code in a software project, incremental code edits are empirically observed to be more frequent. The emerging code editing approaches usually formulate the ...

Cognitive diagnosis is a vital upstream task in intelligent education systems. It models the student-exercise interaction, aiming to infer the students' proficiency levels on each knowledge concept. This paper observes that most existing methods can ...

Knowledge tracing (KT) is a crucial task in intelligent education, focusing on predicting students' performance on given questions to trace their evolving knowledge. The advancement of deep learning in this field has led to deep-learning knowledge ...

Unikernels are simple, customizable, efficient, and small in code size, which makes them highly applicable to embedded scenarios. However, most existing unikernels are developed and optimized for cloud computing, and they do not fully meet the ...

• We have built Unishyper, a Rust-based embedded unikernel to achieve both high performance and reliability.
• Unishyper achieves thread-level memory isolation between different applications and between user/kernel code.
• Unishyper ...

Patch robustness certification is an emerging kind of defense technique against adversarial patch attacks with provable guarantees. There are two research lines: certified recovery and certified detection. They aim to correctly label malicious samples ...

• The emotion-cause pair extraction (ECPE) task is first transformed as a natural language inference issue using the textual entailment paradigm.
• Multi-view symmetric hypothesis templates are designed to encode emotion-cause relation ...

Emotion-cause pair extraction (ECPE) is a challenging task that aims to automatically identify pairs of emotions and their causes from documents. The difficulty of ECPE lies in distinguishing valid emotion-cause pairs from many irrelevant ones. ...

Pedestrian re-identification is a technique to locate the same individual in data from different cameras. Currently, the method of multi-source domain adaptation for pedestrian re-identification is becoming popular. In this approach, the core challenge ...

This paper presents an integration visual navigation algorithm called PnP-ORBSLAM for UAV position estimation in Urban Air Mobility (UAM). ORBSLAM is a popular and benchmark algorithm for vision based navigation applications. The proposed method ...

A self supervised convolutional neural network-parallel extreme learning machine classification model based on big data is proposed to address the subjectivity and inaccuracy of traditional methods for identifying vegetation pests and diseases ...

Malicious traffic detection has been a focal point in the field of network security, and deep learning-based approaches are emerging as a new paradigm. However, most of them are supervised methods, which highly depend on well-labeled data, and fail to ...

Field-Programmable Gate Arrays (FPGAs), renowned for their high performance per watt, are extensively utilized to accelerate Convolutional Neural Networks (CNNs) in edge computing environments, primarily employing dataflow-based and instruction ...

• We propose a customized graph reconstruction method and integrate it into Paddle-Lite to reduce the overhead across various platforms.
• Overlay shields the specific details in hardware, implement the integration of framework and ...