某任务管理计算机航电系统测试与研究

为了解决某机载任务管理计算机航电系统测试设备对故障的快速定位及故障诊断、隔离问题,对机载计算机航电测试设备的系统结构、硬件、软件进行了分析,阐述了系统测试设备的设计思想及实现方法,最后通过对机载任务管理计算机各模块故障模拟、数据分析,对测试系统设备进行了评价,验证了某机载任务管理计算机测试设备设计的合理性和可靠性,为机载计算机测试系统设计、研发、故障隔离诊断提供借鉴和参考。     

俄语学习策略、动机和效果的相关性探究—基于济宁学院的实证研究

语言学习策略和学习动机是促进第二外语学习的必要手段.俄语是一门复杂多变的屈折语,想要取得理想的学习效果,各种学习策略的作用不可小觑.济宁学院《二外俄语学习者学习策略调查表》中的数据显示:俄语学习者的学习策略、动机和效果之间有一定相关性.探究这种相关性,对提高学生的的学习效果大有裨益.     

用图式理论改革大学英语听力教学的探讨

学生在语言功底与背景知识方面的差异性是我国各层次本科院校普遍面临的问题。根据认知语言学领域的图式理论,教师可以通过分级教学、竞赛教学以及组织第二课堂活动的方式,有针对性地填补学生在语言图式和内容图式方面存在的空白。     

认知心理视角下的壮母语学生英语学习双迁移研究

壮母语学生英语学习有其自身特点,英语的学习过程是其母语、汉语和英语交互影响相互作用的过程,这三语间相互影响、作用或干扰导致其语言迁移的动态性、复杂和多样性。从心理认知的角度来研究壮母语学生英语学习中的语言迁移现象,可帮助我们进一步认清壮族学生语言习得中的障碍,把握好教学内容的难重点,帮助壮族学生克服语言学习中的困难。     

Reviewer assignment algorithms for peer review automation: A survey

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Transfer language selection for zero-shot cross-lingual abusive language detection

We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.     

AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks.The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.     

改革开放以来外资检验检测认证机构在华发展历程研究

为系统深入研究改革开放以来外资检验检测认证机构(TIC)在华发展状况,以14家典型外资TIC机构为研究对象,将外资TIC机构在华发展历程分为探索、培育、快速发展、高质量发展4个阶段。研究发现,不同阶段外商投资政策与行业监管政策的调整直接影响到外资TIC机构在华发展战略、经营策略、业务方向、成长速度等;外资TIC机构在华发展的主要经验包括全国布局分支机构、多元业务统筹经营、开展并购抢占市场、紧贴中国市场需求、积极进军新兴领域、参与社会公益事业。基于研究发现,提出继续深化改革、强化科学监管和扶持民营机构等启示对策,以期为推进国有与民营TIC机构做大做强做优以及中国外资TIC机构监管体制机制改革提供参考借鉴。