The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating plausible yet ...

Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT’s recent success, fine-tuning small models remains the best approach for the ECE task. However, existing fine-tuning based ECE methods cannot ...

Icons are ubiquitous visual elements in graphic design, yet their creation is often complex and time-consuming. To resolve this problem, we draw inspiration from the booming text-to-image field and propose Text-Guided Icon Set Expansion, a novel task ...

In this paper, we present the One-shot In-context Part Segmentation (OIParts) framework, designed to tackle the challenges of part segmentation by leveraging visual foundation models (VFMs). Existing training-based one-shot part segmentation methods that ...

Recently, the Parameter Efficient Fine-Tuning (PEFT) method, which adjusts or introduces fewer trainable parameters to calibrate pre-trained models on downstream tasks, has been a hot research topic. However, existing PEFT methods within the traditional ...

The rising popularity of mini-programs deployed on super-app platforms has drawn significant attention due to their convenience. However, developers' improper handling of data permission application in mini-programs has raised concerns about non-...

Mobile apps often access personal information to meet business needs, raising concerns about privacy breaches. Compliance detection methods are proposed to check for inconsistencies between program code and privacy policies. However, existing methods ...

Smart contracts are susceptible to various vulnerabilities that can be exploited by hackers via developing adversarial contracts. Existing vulnerability detection techniques often concentrate solely on vulnerable contracts, neglecting adversarial ...

Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these ...

In this paper, we investigate the use of diffusion models which are pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic understanding. We propose a novel method, namely Diff2Scene, which leverages frozen representations ...$\mathrm{}$

Identifying the causal relations between events is an important task in natural language processing (NLP). However, existing methods mainly leverage human empirical information about causality from limited labeled corpus or pseudo-data, which ...

• A novel cognitively-inspired method for the event causality identification task.
• Application of dual process theory in natural language processing.
• Parallel competition between the intuitive and the logical reasoning processes.

Pre-trained language models have demonstrated remarkable performance in few-shot learning through the emergence of “prompt-based learning” methods, where the performance of these tasks highly rely on the quality of prompts. Existing prompt ...

• We present the weak consistency assumption that the prompts in a task are consistent in the whole task but diverse in individuals, which allows us to learn example-specific templates.
• We propose to train a prompt generator with the ...

As software continues to evolve and business functions become increasingly complex, architectural inconsistency arises when the implementation architecture deviates from the expected architecture design. This architectural problem makes maintenance ...

Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. ...

The recognition of underwater audio plays a significant role in identifying a vessel while it is in motion. Underwater target recognition tasks have a wide range of applications in areas such as marine environmental protection, detection of ship ...

Label noises, categorized into closed-set noise and open-set noise, are prevalent in real-world scenarios and can seriously hinder the generalization ability of models. Identifying noise is challenging because noisy samples closely resemble true ...

In the field of software development, the application of code quality check tools has become a key factor in improving product quality and development efficiency. While many existing tools are effective at detecting common problems in code, there are ...

Malicious Android applications often employ covert behaviors to exfiltrate sensitive data, thereby compromising user privacy. Traditional detection techniques predominantly utilize static analysis of the source code to detect such sensitive behaviors, ...

The Neural Radiance Field (NeRF) has marked a significant advance in 3D computer vision and graphics, offering the ability to generate high-quality views from multiple images. However, its performance is significantly limited in scenarios with ...

Neural Radiation Field (NeRF) is a scene model capable of achieving high-quality view synthesis, optimized for each specific scene. In this paper, we propose a conditional neural radiation field based on multi-resolution hash coding, enabling high-...