Applying ergonomics to Applied Ergonomics: using structured abstracts

Background. Previous research with structured abstracts (i.e. those that contain sub-headings such as this one) has indicated that structured abstracts are of a higher quality, contain more information, and are easier to search than are abstracts produced in the traditional manner.

Aim. The aim of this article is to indicate how such structured abstracts might be appropriate for Applied Ergonomics.

Method. Three abstracts taken from a recent issue of Applied Ergonomics were re-written in a structured form. This involved re-sequencing the information presented in the originals, and including additional information – particularly that of a quantitative kind – to meet the requirements of the sub-headings. Measures of word length, information content, readability, and reader preferences were then made.

Results. The results showed that there were differences between the three pairs of abstracts on these various measures but that, overall, in line with previous research, the structured abstracts were longer, more informative and judged to be clearer by their readers.

Comment. The findings support the author’s view that structured abstracts are more effective than traditional ones.     

RAST: finding related documents based on triplet similarity

With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only searches abstracts by the given keyword(s) but also displays abstracts related to a single or multiple selection. It also displays highly relevant abstracts together with possible keywords to help users refine their search. Analysis of the conditional similarity algorithm proposed here has shown that it does provide better output compared to ordinary cosine similarity, as well as the list of possible keywords reflects results of latent topic analysis. An interface for storing and sorting selected abstracts for future review and/or printing is also provided.     

Successful Aging

This issue's Works in Progress department presents six abstracts for projects that are developing interesting solutions to the elderly's quality of life challenges. The first two abstracts discuss projects that will help provide the elderly with freedom and independence by instrumenting their environments with supportive technology. The next two abstracts discuss projects building specialized user interfaces for addressing some of the challenges associated with aging, such as vision impairment. The final two abstracts present projects that will aid independence for the elderly by providing remote monitoring and assistance.     

科技论文英文摘要撰写规范化问题

针对国内科技论文英文摘要这一应用文件在撰写中产生的错误现象。本文仅对《数据采集与处理》英文摘要作出例析,并试图探讨撰写规范。基于常规英文写作知识,从美国工程索引（Ei)英文摘要撰写规范与检索要求出发,提出英文摘要撰写中可把握的若干实用规则,同时较完整地介绍了Ei英文摘要的撰写规范,以求解决科技论文英文摘要撰写规范化问题。     

Language supports for journal abstract writing across disciplines

Various writing assistance tools have been developed through efforts in the areas of natural language processing with different degrees of success of curriculum integration depending on their functional rigor and pedagogical designs. In this paper, we developed a system, WriteAhead, that provides six types of suggestions when non‐native graduate students of English from different disciplines are composing journal abstracts, and assessed its effectiveness. The method involved automatically building domain‐specific corpora of abstracts from the Web via domain names and related keywords as query expansions, and automatically extracting vocabulary and n‐grams from the corpora in order to offer writing suggestions. At runtime, learners' input in the writing area of the system actively triggered a set of corresponding writing suggestions. This abstract writing assistant system facilitates interactions between learners and the system for writing abstracts in an effective and contextualized way, by providing suggestions such as collocations or transitional words. For assessment of WriteAhead, we compared the writing performance of two groups of students with or without using the system, and adopted student perception data. Findings show that the experiment group wrote better, and most students were satisfied with the system concerning most suggestion types, as they can effectively compose quality abstracts through provided language supports from WriteAhead.     

一种新的基于分块颜色和形状的图像检索方法

基于图像单一特征的检索方法只能反映图像的部分属性，应用范围受到限制。本文在分析图像颜色和形状特征的基础上，根据各自的特点，提出了一种新的基于分块颜色和形状的图像检索方法。首先，在图像分块的基础上提取颜色和特征点；其次，根据特征点提取图像的形状信息；最后，以颜色和形状作为综合特征，采用相应的匹配算法进行图像检索。实验证明该方法不仅准确度高，而且还可应用于基于对象或区域的图像查询中。     

SORTAL ANAPHORA RESOLUTION IN MEDLINE ABSTRACTS

This paper reports our investigation of machine learning methods applied to anaphora resolution for biology texts, particularly paper abstracts. Our primary concern is the investigation of features and their combinations for effective anaphora resolution. In this paper, we focus on the resolution of demonstrative phrases and definite determiner phrases, the two most prevalent forms of anaphoric expressions that we find in biology research articles. Different resolution models are developed for demonstrative and definite determiner phrases. Our work shows that models may be optimized differently for each of the phrase types. Also, because a significant number of definite determiner phrases are not anaphoric, we induce a model to detect anaphoricity, i.e., a model that classifies phrases as either anaphoric or nonanaphoric. We propose several novel features that we call highlighting features , and consider their utility particularly for processing paper abstracts. The system using the highlighting features achieved accuracies of 78% and 71% for demonstrative phrases and definite determiner phrases, respectively. The use of the highlighting features reduced the error rate by about 10%.     

利用Medline文摘数据库研究文本分类

文本分类领域的困难,在于如何获得大量人工标记好的分类样本数据集,Medline数据库在专家的长期维护下,具有完善的基于MeSH(Medical Subject Headings)的分类体系,以及大量的文摘,可用来制作分类样本数据集。本文介绍和研究Medline数据库,提出如何利用它构建良好的分类模型,实验表明,利用Medline文摘数据库,通过Major标记,特征项数目采用5000,训练样本采用600,利用SVM分类器,可得较好的分类模型,从而为文本分类研究提供一种实用、高效的数据集制作方式。     

Reporting computing projects through structured abstracts: a quasi-experiment

Previous work has demonstrated that the use of structured abstracts can lead to greater completeness and clarity of information, making it easier for researchers to extract information about a study. In academic year 2007/08, Durham University’s Computer Science Department revised the format of the project report that final year students were required to write, from a ‘traditional dissertation’ format, using a conventional abstract, to that of a 20-page technical paper, together with a structured abstract. This study set out to determine whether inexperienced authors (students writing their final project reports for computing topics) find it easier to produce good abstracts, in terms of completeness and clarity, when using a structured form rather than a conventional form. We performed a controlled quasi-experiment in which a set of ‘judges’ each assessed one conventional and one structured abstract for its completeness and clarity. These abstracts were drawn from those produced by four cohorts of final year students: two preceding the change, and the two following. The assessments were performed using a form of checklist that is similar to those used for previous experimental studies. We used 40 abstracts (10 per cohort) and 20 student ‘judges’ to perform the evaluation. Scored on a scale of 0.1–1.0, the mean for completeness increased from 0.37 to 0.61 when using a structured form. For clarity, using a scale of 1–10, the mean score increased from 5.1 to 7.2. For a minimum goal of scoring 50% for both completeness and clarity, only 3 from 19 conventional abstracts achieved this level, while only 3 from 20 structured abstracts failed to reach it. We conclude that the use of a structured form for organising the material of an abstract can assist inexperienced authors with writing technical abstracts that are clearer and more complete than those produced without the framework provided by such a mechanism.     

Using fuzzy set theory and scale-free network properties to relate MEDLINE terms

Automated construction and annotation of biological networks is becoming increasingly important in bioinformatics as the amount of biological data increases. At the center of this are metrics required for relating biological entities such as genes, diseases, signaling molecules and chemical compounds. Co-occurrence of terms within abstracts is widely used to establish tentative relationships because it is easy to use, implement, understand, and is reasonably accurate. However, it is also very imprecise – the cutoffs for how many co-occurrences of terms are necessary to establish a relationship is arbitrary and the nature of the relationship is generic. Since the frequency of co-occurrence for terms usually follows a scale-free distribution, this property can be exploited to define degree of membership in fuzzy sets. Beginning with a set of co-occurrences for any biomedical term (or its synonyms), relations are defined by the overlap of sets, normalizing by the area under the curve that the two sets share. The ability of this method to rank the relative specificity of biological relationships is tested by comparing cumulative term co-occurrences within 7.5 million MEDLINE abstracts with focused summaries of gene function and disease association within LocusLink. On average, the fuzzy set ranking (FSR) was in the top 0.6% of all potential associations, showing a good correlation between domain overlap and the biological association between two terms.     

Presenting software engineering results using structured abstracts: a randomised experiment

When conducting a systematic literature review, researchers usually determine the relevance of primary studies on the basis of the title and abstract. However, experience indicates that the abstracts for many software engineering papers are of too poor a quality to be used for this purpose. A solution adopted in other domains is to employ structured abstracts to improve the quality of information provided. This study consists of a formal experiment to investigate whether structured abstracts are more complete and easier to understand than non-structured abstracts for papers that describe software engineering experiments. We constructed structured versions of the abstracts for a random selection of 25 papers describing software engineering experiments. The 64 participants were each presented with one abstract in its original unstructured form and one in a structured form, and for each one were asked to assess its clarity (measured on a scale of 1 to 10) and completeness (measured with a questionnaire that used 18 items). Based on a regression analysis that adjusted for participant, abstract, type of abstract seen first, knowledge of structured abstracts, software engineering role, and preference for conventional or structured abstracts, the use of structured abstracts increased the completeness score by 6.65 (SE 0.37, p < 0.001) and the clarity score by 2.98 (SE 0.23, p < 0.001). 57 participants reported their preferences regarding structured abstracts: 13 (23%) had no preference; 40 (70%) preferred structured abstracts; four preferred conventional abstracts. Many conventional software engineering abstracts omit important information. Our study is consistent with studies from other disciplines and confirms that structured abstracts can improve both information content and readability. Although care must be taken to develop appropriate structures for different types of article, we recommend that Software Engineering journals and conferences adopt structured abstracts.

采用蚁群算法及移动Agent的网格服务发现设计

在对网格进行分簇的基础上,设计了一种基于蚁群算法及移动Agent的网格服务发现机制模型。通过使用移动Agent技术收集网格服务信息,并将收集到的网格服务信息摘要进行跨簇缓存,以提高蚁群算法中信息素信息的准确性,并且在进行服务查找时采用之前缓存的服务摘要信息作为蚁群算法的启发因子,从而提高网格服务发现的准确性以及发现速度,进而提高网格使用效率。通过仿真实验验证了所述服务发现机制的有效性。     

Semantic decomposition of computer programs: An aid to program testing

Summary Semantic decomposition results in a hierarchy of partitions which are small enough to be easily understood, and large enough to be meaningful to a program tester. Each partition results in the elimination of at least one local variable from consideration during error analysis, which further simplifies the testing process. Semantic decomposition reduces the complexity of the testing process, enhances the understandability of the program for the tester, and thus abstracts a program to a higher level. The decomposition yields a plan for hierarchical testing which, if followed conscientiously, should result in faster and more thorough testing for large or poorly structured programs. By reorganizing poorly structured programs into the modules indicated by semantic decomposition, an improved source level representation of the programs may be obtained.     

Superconductivity information extraction from the literature: A new corpus and its evaluations

This paper describes a superconductivity corpus for materials informatics (SC-CoMIcs) tailored for the extraction of superconductive material information from the literature. Currently, few corpora exist for materials informatics in contrast to the situation for biomedical informatics. In particular, there is no sizable corpus that can be used to assist with the search for superconducting materials. The SC-CoMIcs consists of 1,000 abstracts with manually annotated named entities, main materials, and relations/events associated with superconductivity. In particular, the main material is a selling point of our corpus, which can be regarded as a hub that binds implicitly related physical entities and properties in abstracts. We conduct named entity recognition, main material identification, and relation/event extraction experiments to determine the effectiveness of the corpus. The experimental results show that we can obtain F1 scores of approximately 74%–95%, 84% and 73%–97% for named entity recognition, main material identification and relation extraction tasks, respectively. We also demonstrate that the extracted doping information is consistent with that of the well-known Hume–Rothery rules, which implies that the constructed corpus can provide an opportunity to revisit or find physical chemical rules from the literature.     

Automatic personalized video abstraction for sports videos using metadata

Video abstraction is defined as creating a video abstract which includes only important information in the original video streams. There are two general types of video abstracts, namely the dynamic and static ones. The dynamic video abstract is a 3-dimensional representation created by temporally arranging important scenes while the static video abstract is a 2-dimensional representation created by spatially arranging only keyframes of important scenes. In this paper, we propose a unified method of automatically creating these two types of video abstracts considering the semantic content targeting especially on broadcasted sports videos. For both types of video abstracts, the proposed method firstly determines the significance of scenes. A play scene, which corresponds to a play, is considered as a scene unit of sports videos, and the significance of every play scene is determined based on the play ranks, the time the play occurred, and the number of replays. This information is extracted from the metadata, which describes the semantic content of videos and enables us to consider not only the types of plays but also their influence on the game. In addition, user’s preferences are considered to personalize the video abstracts. For dynamic video abstracts, we propose three approaches for selecting the play scenes of the highest significance: the basic criterion, the greedy criterion, and the play-cut criterion. For static video abstracts, we also propose an effective display style where a user can easily access target scenes from a list of keyframes by tracing the tree structures of sports games. We experimentally verified the effectiveness of our method by comparing our results with man-made video abstracts as well as by conducting questionnaires.

Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics

We focus on the problem of predicting functional properties of the proteins corresponding to genes in the yeast genome. Our goal is to study the effectiveness of approaches that utilize all data sources that are available in this problem setting, including relational data, abstracts of research papers, and unlabeled data. We investigate a propositionalization approach which uses relational gene interaction data. We study the benefit of text classification and information extraction for utilizing a collection of scientific abstracts. We study transduction and co-training for using unlabeled data. We report on both, positive and negative results on the investigated approaches. The studied tasks are KDD Cup tasks of 2001 and 2002. The solutions which we describe achieved the highest score for task 2 in 2001, the fourth rank for task 3 in 2001, the highest score for one of the two subtasks and the third place for the overall task 2 in 2002.     

基于篇章修辞结构的自动文摘连贯性研究

尽管抽取式自动文摘方法是目前自动文摘领域的主流方法,并且取得了长足的进步,但抽取式自动文摘形成的摘要由于缺乏句子之间的合理指代或篇章结构,使得文摘缺乏连贯性而影响可读性。为提高自动摘要的可读性,该文尝试将篇章修辞结构信息应用于中文自动文摘。首先,基于汉语篇章修辞结构抽取摘要,然后使用基于LSTM的方法对文本连贯性进行建模,并使用该模型对文摘的连贯性做出评价。实验结果表明: 在摘要抽取方面,基于篇章修辞结构的自动文摘相比于传统的抽取方法具有更好的ROUGE评价值;在使用基于LSTM连贯性模型评价摘要连贯性方面,篇章结构信息在自动抽取文摘时可以很好地提炼出文章的主旨,同时使摘要具有更好的结果。     

基于高斯混合模型的生物医学领域双语句子对

双语术语词典在生物医学跨语言检索系统中有着非常重要的地位,而双语句子对齐是构建双语词典的第一步工作。为了构想面向生物医学领域的双语词典,该文将分类思想和迁移学习方法引入汉英句子对齐任务中,将句子对齐任务看成一个多类分类任务,考虑生物医学领域双语摘要的锚信息,利用高斯混合模型完成分类目标。同时,在模型训练过程中,该文引入了迁移学习的思想,结合无噪音的《新概念英语》双语语料对模型的句子长度特征进行训练,使得模型在测试语料上句子对齐的正确率得到较大提高。     

基于改进潜在语义分析的跨语言检索

该文采用基于SVD和NMF矩阵分解相结合的改进潜在语义分析的方法为生物医学文献双语摘要进行建模,该模型将英汉双语摘要映射到同一语义空间,不需要外部词典和知识库,建立不同语言之间的对应关系,便于在双语空间中进行检索。该文充分利用医学文献双语摘要语料中的锚信息,通过不同的k值构建多个检索模型,计算每个模型的信任度,使得多个模型都对查询和文本的相似度做出贡献。在语义空间上进行项与项、文本与文本、项与文本之间的相似度计算,实现了双语摘要的跨语言检索,取得了较好的实验效果。     

An image construction method for visualizing managerial data

High volume data with complicated relationships can render human decision-making a frustrating task. Computer-generated visualization is an approach that can assist decision-makers in gaining insight into the data so that eventually superior solutions can be developed. Current research in visualization has addressed how to deal with high volume data that have some inherent structures (such as hierarchy, network, or geographical relationships). Many management domains, however, have data that lack obvious structures to provide a base for computer-generated visualization. This paper reports a specially designed technique for visualizing such management data. Data objects involved in the decision-making tasks are assigned with geometry (called visual abstract) in Euclidean space. Then a set of image construction rules are applied to connect multiple visual abstracts into images that can be displayed on a computer screen. We use two business domains, manufacturing production planning and resource constrained project scheduling, to illustrate this visualization technique.