可视化LaTeX编辑软件LyX在科技期刊论文排版中的应用     

黄岚 《中国科技期刊研究》2018,29(1):37-42

【目的】 探究具有可视化用户界面的LaTeX编辑器LyX在科技期刊论文排版中的应用。【方法】 以《东南大学学报(自然科学版)》为例,基于编辑器LyX设计论文模板。【结果】 LyX模板简单直观,不仅具有LaTeX排版质量高、内容与格式分离的优点,还克服了其代码繁杂、可读性较差的缺点。作者无需掌握LaTeX命令便可在LyX模板上撰写投稿论文,编辑则可以直接对投稿论文进行修改、校订以及排版。【结论】 作者和编辑使用LyX进行编排,可提高编校质量和排版效率,实现期刊编校排一体化的工作模式。  相似文献   

Gene ontology annotation as text categorization: An empirical study     

Kazuhiro Seki  Javed Mostafa 《Information processing & management》2008

Gene ontology (GO) consists of three structured controlled vocabularies, i.e., GO domains, developed for describing attributes of gene products, and its annotation is crucial to provide a common gateway to access different model organism databases. This paper explores an effective application of text categorization methods to this highly practical problem in biology. As a first step, we attempt to tackle the automatic GO annotation task posed in the Text Retrieval Conference (TREC) 2004 Genomics Track. Given a pair of genes and an article reference where the genes appear, the task simulates assigning GO domain codes. We approach the problem with careful consideration of the specialized terminology and pay special attention to various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extract the words around the spotted gene occurrences and used them to represent the gene for GO domain code annotation. We regard the task as a text categorization problem and adopt a variant of kNN with supervised term weighting schemes, making our method among the top-performing systems in the TREC official evaluation. Furthermore, we investigate different feature selection policies in conjunction with the treatment of terms associated with negative instances. Our experiments reveal that round-robin feature space allocation with eliminating negative terms substantially improves performance as GO terms become specific.  相似文献   

The potential of immersive virtual reality to enhance learning: A meta-analysis     

《Educational Research Review》2022

Research on the impact of immersive virtual reality (I-VR) technology on learning has become necessary with the decreasing cost of virtual reality technologies and the development of high-quality head-mounted displays. This meta-analysis investigates the overall effect size by combining the results of primary experimental studies that reveal the effect of I-VR on learning outcomes. Besides, effect sizes were calculated based on measuring moment, types of measurement, education level, the field of education, control group educational resources, and immersion type subgroups. One hundred five independent results were calculated from 48 primary studies published between 2016 and September 2020, including 39 randomized controlled trials and nine quasi-experimental studies. The sample size of primary studies includes 3179 students, 847 from K12, and 2332 from higher education. Random effects model was used in the calculation of effect size. As a result of the meta-analysis, it was determined that the overall effect size on the learning outcomes of I-VR was small (g = 0.38). Additionally, according to the subgroup analysis results, it was revealed that I-VR significantly differentiated effect size based on educational level, the field of education, and computer-based/traditional sources. There was no significant difference in terms of the other subgroups.  相似文献   

Cyberbullying detection for low-resource languages and dialects: Review of the state of the art     

《Information processing & management》2023,60(5):103454

The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying — a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available.We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted. We conclude with additional discussions on ethical issues regarding such studies, highlight how our survey improves on similar surveys done in the past, and discuss the usefulness of recently popular AI-enhanced tools for streamlining such scientific surveys.  相似文献   

From statistical methods to deep learning,automatic keyphrase prediction: A survey     

《Information processing & management》2023,60(4):103382

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to 167 previous works, achieving greater coverage of this task than previous surveys. Particularly, we focus highly on deep learning-based keyphrase prediction, which attracts increasing attention of this task in recent years. Afterwards, we conduct several groups of experiments to carefully compare representative models. To the best of our knowledge, our work is the first attempt to compare these models using the identical commonly-used datasets and evaluation metric, facilitating in-depth analyses of their disadvantages and advantages. Finally, we discuss the possible research directions of this task in the future.  相似文献   

Adapting multilingual speech representation model for a new,underresourced language through multilingual fine-tuning and continued pretraining     

《Information processing & management》2023,60(2):103148

  相似文献