The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions

Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.     

关于巴基球文献标引衍变过程的分析

本文通过对美国《化学文摘》（ＣＡ）中关于巴基球（富勒烯）文献标引的衍变过程所进行的个案分析,揭示当代科学技术中的新概念、新发现在一次期刊和二次期刊中的传播规律。     

A learning-based method to detect and segment text from scene images

This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorithm. Then all the CCs including text CCs and non-text CCs are verified on their text features by a 2-stage classification module, where most non-text CCs are discarded by an attentional cascade classifier and remaining CCs are further verified by an SVM. All the accepted CCs are output to result in text only binary image. Experiments with many images in different scenes showed satisfactory performance of our proposed method.     

衔接理论与大学英语阅读教学

在英语听说读写四大技能中,阅读占有极其重要的地位。提高学生的阅读理解能力,在很大程度上是提高了他们运用所学外语的全部技能。将衔接理论运用于大学英语教学具体实践,可知阅读教学中有意识地运用衔接理论对学生进行阅读策略训练,能够有效地提高阅读理解水平。     

对《论语》心性思想现代价值的思考

加强社会主义思想道德建设,需要借鉴西方现代解释学的基本方法,回到经典文本,弘发孔子以“仁学”为核心的心性思想的现代价值。从对《论语》的解读看,其心性思想的现代内涵主要包括:倡导道德自律,注重推己及人;“仁为己任”,强调责任意识的人格追求;以“好学”、“善道”为重要内容的实践理性;仁是情理,具有浓厚的情感色彩。“仁”还是一种人生修养的方法和人生修行的境界。     

论文本课程与教师对教学过程的控制

在中学课程的微观层次上 ,文本课程与教师都对教学过程有一定的控制作用。这种控制作用 ,从空间结构的角度分析 ,表现为课程线性结构与非线性结构 ;从教学信息流的角度分析 ,表现为课程信息流的集中控制结构与非集中控制结构。不同的控制形式对课程的影响各有利弊 ,并有各自的形成原因     

Automatic indexing of online health resources for a French quality controlled gateway

The profusion of online resources calls for tools and methods to help Internet users find precisely what they are looking for. Quality controlled gateway CISMeF provides such services for health resources. However, the human cost of maintaining and updating the catalogue are increasingly high. This paper presents the automatic indexing system currently developed in the CISMeF team to be used as such for preliminary indexing, or after human reviewing for the final indexing. The system architecture, using the INTEX platform for MeSH term extraction is detailed. The results of a first evaluation tend to indicate that the automatic indexing strategy is relevant, as it achieves a precision comparable to that of other existing operational systems. Moreover, the system presented in this paper retrieves keyword/qualifier pairs as opposed to single terms, therefore providing a significantly more precise indexing. Further development and tests will be carried out in order to improve the coverage of the dictionaries, and validate the efficiency of the system in the indexers’ everyday work.     

文本知识的自动分类方法初探

本文结合运用信息管理和人工智能的原理与技术，探讨了文本知识的自动分类方法，包括：自动归类与聚类方法、基于实例的学习分类方法和基于特征值的元学习方法。     

Application of machine learning techniques to assess the trends and alignment of the funded research output

Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada.     

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.