Smart contracts have revolutionized the credit landscape. However, their security remains intensely scrutinized due to numerous hacking incidents and inherent logical challenges. One well-known issue is reentrancy vulnerability, exemplified by DAO attacks that lead to substantial economic losses. Previous approaches have employed rule-based and deep learning-based (DL) algorithms to detect and repair reentrancy vulnerability. Large language models (LLM) have been distinguished in recent years for their excellent understanding of text and code. However, less attention has been paid to LLM-based reentrancy vulnerability detection and repair, and direct prompt-based approaches often suffer from inefficiencies and high false positives. To overcome the above shortcomings, this paper proposes a hybrid model framework combining LLM with DL to enhance the detection and repair of reentrancy vulnerabilities. This unified framework comprises three crucial phases: the data processing phase, the vulnerability detection phase, and the vulnerability repair phase. Extensive experimental results validate the superiority of our approach over state-of-the-art baselines, and ablation studies demonstrate the effectiveness of each component. Our approach demonstrates significant improvements in vulnerability detection, with increases of 3.51% in accuracy, 2.31% in recall, 0.42% in precision, and 0.85% in F1-score. Furthermore, our approach can achieve a notable 9.62% enhancement in the repair rate. Finally, we also conducted a user study to emphasize its potential to fortify the security of smart contracts.

Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time series domain, the proposed VisionTS could achieve better zero-shot forecast performance than existing TSF foundation models. With fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting that visual models may offer a "free lunch" for TSF and highlight the potential for future cross-modality research. Our code is available in the https://github.com/Keytoyze/VisionTS.

Prompt tuning for pre-trained language models (PLMs) has been an effective approach for few-shot text classification. To make a prediction, a typical prompt tuning method employs a template wrapping the input text into a cloze question, and a verbalizer mapping the output embedding to labels. However, current methods typically depend on handcrafted templates and verbalizers, which require much domain-specific prior knowledge by human efforts. In this work, we investigate how to build a good human-free prompt tuning using soft prompt templates and soft verbalizers, which can be learned directly from data. To address the challenge of data scarcity, we integrate a set of trainable bases for sentence representation to transfer the contextual information into a low-dimensional space. By jointly pre-training the soft prompts and the bases using contrastive learning, the projection space can catch critical semantics at the sentence level, which could be transferred to various downstream tasks. To better bridge the gap between downstream tasks and the pre-training procedure, we formulate the few-shot classification tasks as another contrastive learning problem. We name this Jointly Pretrained Template and Verbalizer (JPTV). Extensive experiments show that this human-free prompt tuning can achieve comparable or even better performance than manual prompt tuning.

Unbiased Learning to Rank (ULTR) aims to train unbiased ranking models from biased click logs, by explicitly modeling a generation process for user behavior and fitting click data based on examination hypothesis. Previous research found empirically that the true latent relevance is mostly recoverable through click fitting. However, we demonstrate that this is not always achievable, resulting in a significant reduction in ranking performance. This research investigates the conditions under which relevance can be recovered from click data in the first principle. We initially characterize a ranking model as identifiable if it can recover the true relevance up to a scaling transformation, a criterion sufficient for the pairwise ranking objective. Subsequently, we investigate an equivalent condition for identifiability, articulated as a graph connectivity test problem: the recovery of relevance is feasible if and only if the identifiability graph (IG), derived from the underlying structure of the dataset, is connected. The presence of a disconnected IG may lead to degenerate cases and suboptimal ranking performance. To tackle this challenge, we introduce two methods, namely node intervention and node merging, designed to modify the dataset and restore the connectivity of the IG. Empirical results derived from a simulated dataset and two real-world LTR benchmark datasets not only validate our proposed theory, but also demonstrate the effectiveness of our methods in alleviating data bias when the relevance model is unidentifiable.

Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether observed or unobserved. Such context-driven distribution shift (CDS) introduces biases in predictions within specific contexts and poses challenges for conventional training paradigms. In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained model. To this end, we propose a novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", which quantifies the model's vulnerability to CDS by evaluating the mutual information between prediction residuals and their corresponding contexts. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation. In this circumstance, we put forth a straightforward yet potent adapter framework for model calibration, termed the "sample-level contextualized adapter" or "SOLID". This framework involves the curation of a contextually similar dataset to the provided test sample and the subsequent fine-tuning of the model's prediction layer with a limited number of steps. Our theoretical analysis demonstrates that this adaptation strategy can achieve an optimal bias-variance trade-off. Notably, our proposed Reconditionor and SOLID are model-agnostic and readily adaptable to a wide range of models. Extensive experiments show that SOLID consistently enhances the performance of current forecasting models on real-world datasets, especially on cases with substantial CDS detected by the proposed Reconditionor, thus validating the effectiveness of the calibration approach.

Threshold Elliptic Curve Digital Signature Algorithm (ECDSA) has attracted a lot of attention due to the wide applications of ECDSA in crypto asset. Although several variants of threshold signature protocols can provide functions, such as key generation and signing, they suffer from two shortfalls. First, these schemes only discuss a single signature computation task in a synchronous algorithm context, which is difficult to adapt to real crypto-asset applications, such as custody. Second, these schemes are computing intensive and not scalable, hence can hardly support large-scale processing operations in real life even after traditional optimization, such as multithreading, is applied. In this article, we propose an innovative computation method called asynchronous threshold ECDSA with batch processing, based on the interactive threshold signature protocols. The method provides a reliable solution for critical operational scenarios, such as threshold signing and distributed key generation (DKG) in crypto-asset custody, and can be a future reference in secure data distribution mechanisms. The performance and scalability of our methods are validated through a benchmark testing.

Smart contracts are essential for executing computing logic on blockchain networks. However, they are also susceptible to various vulnerabilities. In recent years, the detection of smart contract vulnerabilities has become a significant concern due to the substantial losses caused by hacker attacks. Traditional vulnerability detection approaches rely on expert rules, which often suffer from limitations in accuracy and completeness. Deep learning-based methods offer better coverage of vulnerabilities but may overlook certain vulnerability characteristics and suffer from overfitting during training. In this paper, we propose a novel approach called ConvMHSA-SCVD, which combines knowledge-driven and data-driven algorithms together to detect smart contract vulnerabilities. By incorporating feature selection, data balancing, and a combination of multi-channel convolution and multi-head self-attention neural networks, our ConvMHSA-SCVD achieves effective vulnerability detection in smart contracts. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in accuracy and F1 score, with improvements ranging from 0.4% to 3.84% and 1.28% to 1.90%, respectively.