BadPrompt: Backdoor Attacks on Continuous Prompts

The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available at https://github.com/papersPapers/BadPrompt.Comment: Accepted at NeurIPS 202

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

Study on Physicochemical Properties and Rock-Cracking Mechanism of High-Energy Expansion Agent

Aiming at the shortcomings of the current rock-breaking technology, a new type of high-energy expansion agent for energetic materials based on combustion-to-detonation was developed. By characterizing the basic physical and chemical properties of the high-energy expansion agent (HEEA) such as morphology, particle size distribution, and pyrolysis characteristics, the work performance of different types of high-energy expansion agents was analyzed in combination with the energy characteristics. The results showed that the relationship between the expansion work done by the gas to the outside world was WHEEA-I > WHEEA-II > WHEEA-III under the same quality of HEEA combustion. The damage effect of high-temperature and high-pressure gas cracking specimens generated by deflagration of HEEA was obvious, having the rule that the disturbance damage of rock caused by low heat and high gas specific volume was smaller, and the damage degree of rock caused by high heat and low gas specific volume was larger. The mechanism of HEEA combustion and detonation in confined space is revealed, which provides a theoretical basis for the application of HEEA-cracked rock

Study on Physicochemical Properties and Rock-Cracking Mechanism of High-Energy Expansion Agent

Aiming at the shortcomings of the current rock-breaking technology, a new type of high-energy expansion agent for energetic materials based on combustion-to-detonation was developed. By characterizing the basic physical and chemical properties of the high-energy expansion agent (HEEA) such as morphology, particle size distribution, and pyrolysis characteristics, the work performance of different types of high-energy expansion agents was analyzed in combination with the energy characteristics. The results showed that the relationship between the expansion work done by the gas to the outside world was WHEEA-I > WHEEA-II > WHEEA-III under the same quality of HEEA combustion. The damage effect of high-temperature and high-pressure gas cracking specimens generated by deflagration of HEEA was obvious, having the rule that the disturbance damage of rock caused by low heat and high gas specific volume was smaller, and the damage degree of rock caused by high heat and low gas specific volume was larger. The mechanism of HEEA combustion and detonation in confined space is revealed, which provides a theoretical basis for the application of HEEA-cracked rock

MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization

Automated medical named entity recognition and normalization are fundamental for constructing knowledge graphs and building QA systems. When it comes to medical text, the annotation demands a foundation of expertise and professionalism. Existing methods utilize active learning to reduce costs in corpus annotation, as well as the multi-task learning strategy to model the correlations between different tasks. However, existing models do not take task-specific features for different tasks and diversity of query samples into account. To address these limitations, this paper proposes a multi-task adversarial active learning model for medical named entity recognition and normalization. In our model, the adversarial learning keeps the effectiveness of multi-task learning module and active learning module. The task discriminator eliminates the influence of irregular task-specific features. And the diversity discriminator exploits the heterogeneity between samples to meet the diversity constraint. The empirical results on two medical benchmarks demonstrate the effectiveness of our model against the existing methods

Re-Attention for Visual Question Answering

Visual Question Answering~(VQA) requires a simultaneous understanding of images and questions. Existing methods achieve well performance by focusing on both key objects in images and key words in questions. However, the answer also contains rich information which can help to better describe the image and generate more accurate attention maps. In this paper, to utilize the information in answer, we propose a re-attention framework for the VQA task. We first associate image and question by calculating the similarity of each object-word pairs in the feature space. Then, based on the answer, the learned model re-attends the corresponding visual objects in images and reconstructs the initial attention map to produce consistent results. Benefiting from the re-attention procedure, the question can be better understood, and the satisfactory answer is generated. Extensive experiments on the benchmark dataset demonstrate the proposed method performs favorably against the state-of-the-art approaches

Social support and mental health among health care workers during Coronavirus Disease 2019 outbreak: A moderated mediation model

PURPOSES: During the outbreak of Coronavirus Disease 2019 (COVID-19) all over the world, the mental health conditions of health care workers are of great importance to ensure the efficiency of rescue operations. The current study examined the effect of social support on mental health of health care workers and its underlying mechanisms regarding the mediating role of resilience and moderating role of age during the epidemic. METHODS: Social Support Rating Scale (SSRS), Connor-Davidson Resilience scale (CD-RISC) and Symptom Checklist 90 (SCL-90) were administrated among 1472 health care workers from Jiangsu Province, China during the peak period of COVID-19 outbreak. Structural equation modeling (SEM) was used to examine the mediation effect of resilience on the relation between social support and mental health, whereas moderated mediation analysis was performed by Hayes PROCESS macro. RESULTS: The findings showed that resilience could partially mediate the effect of social support on mental health among health care workers. Age group moderated the indirect relationship between social support and mental health via resilience. Specifically, compared with younger health care workers, the association between resilience and mental health would be attenuated in the middle-aged workers. CONCLUSIONS: The results add knowledge to previous literature by uncovering the underlying mechanisms between social support and mental health. The present study has profound implications for mental health services for health care workers during the peak period of COVID-19

Symmetry-Engineering-Induced In-Plane Polarization Enhancement in Ta₂NiS₅/CrOCl van der Waals Heterostructure

Van der Waals (vdW) interfaces can be formed via layer stacking regardless of the lattice constant or symmetry of the individual building blocks. Herein, we constructed a vdW interface of layered Ta2NiS5 and CrOCl, which exhibited remarkably enhanced in-plane anisotropy via polarized Raman spectroscopy and electrical transport measurements. Compared with pristine Ta2NiS5, the anisotropy ratio of the Raman intensities for the B2g, 2Ag, and 3Ag modes increased in the heterostructure. More importantly, the anisotropy ratios of conductivity and mobility in the heterostructure increased by one order of magnitude. Specifically speaking, the conductivity ratio changed from ~2.1 (Ta2NiS5) to ~15 (Ta2NiS5/CrOCl), while the mobility ratio changed from ~2.7 (Ta2NiS5) to ~32 (Ta2NiS5/CrOCl). Such prominent enhancement may be attributed to the symmetry reduction caused by lattice mismatch at the heterostructure interface and the introduction of strain into the Ta2NiS5. Our research provides a new perspective for enhancing artificial anisotropy physics and offers feasible guidance for future functionalized electronic devices