浅谈《化工基础》课堂教学导入设计

教学导入是课堂教学中的一个重要环节,是课堂教学内容的前奏,如同一出戏的“序幕”。课堂教学导入的合理设计可为一堂课的教学效果创造一个良好开端。根据《化工基础》课程的特点,本文笔者主要介绍了几种该课程教学中常用的导入方法。     

Research is Elementary: How Blue's Clues Can Help Teach Communication Research Methods

Objective: First, this activity is to make research fun and reduce communication students’ anxiety concerning the research methods class. Second, this activity is to create a vivid illustration of the basic characteristics of the research process, and to illustrate that the research process is non-threatening and should be thought of as a process that can be done by students. Third, this activity is to help students realize research methods are not “math,” nor are they limited to statistical analyses.

Courses: Beginning and intermediate research methods courses, as well as advanced communication courses that require students to read research articles, particularly for those classes whose students have not had a research methods course     

基于关键词和摘要相关度的文献聚类研究

现有的文献聚类方法都是通过文献关键词来进行的.本文在研究大量文献聚类方法的基础上,提出了一种通过文献关键词和摘要进行加权的新的文献聚类算法.首先,改进了传统相似度计算的方法,设计出基于关键词和摘要词加权的相似度公式,使文献相似度计算更加精确.其次,基于"文献距离越大,聚为一类的概率越小"的思想,提出了一种"最大距离聚类法",并给出了算法的详细步骤.最后,实现算法并进行了大量的实验仿真.通过改进相似度计算公式,调整关键词和摘要词的权重,提高了聚类的质量.结果表明,本文提出的文献聚类算法是一种行之有效的方法.     

Relational Framing Theory and Coming-Out Narratives: A Data Analysis Activity

Courses: This single class activity would be most useful in interpersonal, family, gender/sexuality communication, or research methods courses. This activity facilitates better discussion in a small or seminar-style class with students who have had pervious exposure to or who are in the process of learning about interpersonal communication theory or research methods.

Objectives: The goals for this activity are to (a) practice data analysis utilizing a specific theory, (b) identify the presence of relational frames and judgments to become more familiar with the interpersonal dimensions of coming-out interactions, (c) appreciate a multiplicity of data interpretations through the practice of group coding, and (d) practice evaluating the quality of data interpretations.     

A general approximation framework for direct optimization of information retrieval measures

Recently direct optimization of information retrieval (IR) measures has become a new trend in learning to rank. In this paper, we propose a general framework for direct optimization of IR measures, which enjoys several theoretical advantages. The general framework, which can be used to optimize most IR measures, addresses the task by approximating the IR measures and optimizing the approximated surrogate functions. Theoretical analysis shows that a high approximation accuracy can be achieved by the framework. We take average precision (AP) and normalized discounted cumulated gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that the algorithms deduced from our framework are very effective when compared to existing methods. The empirical results also agree well with the theoretical results obtained in the paper.     

信息商品的盈余定价理论

信息商品的盈余定价理论林平忠，张建松Ａｂｓｔｒａｃｔ：Ｔｈｅｐａｐｅｒｐｒｅｓｅｎｔｓｂｏｔｈｔｈｅａｐｐｒｏｘｉｍａｔｉｏｎｍｅｔｈｏｄｓｆｏｂｏｏｋｖｅｎｄｏｒａｎｄｐｕｒｃｈａｓｅｒｆｏｒｔｈｅｉｎｆｏｒｍａｔｉｏｎｃｏｍｍｏｄｉｔｙｐｒｉｃｅ...     

Bibliometric approximation of a scientific specialty by combining key sources,title words,authors and references

Bibliometric methods for the analysis of highly specialized subjects are increasingly investigated and debated. Information and assessments well-focused at the specialty level can help make important decisions in research and innovation policy. This paper presents a novel method to approximate the specialty to which a given publication record belongs. The method partially combines sets of key values for four publication data fields: source, title, authors and references. The approach is founded in concepts defining research disciplines and scholarly communication, and in empirically observed regularities in publication data. The resulting specialty approximation consists of publications associated to the investigated publication record via key values for at least three of the four data fields. This paper describes the method and illustrates it with an application to publication records of individual scientists. The illustration also successfully tests the focus of the specialty approximation in terms of its ability to connect and help identify peers. Potential tracks for further investigation include analyses involving other kinds of specialized publication records, studies for a broader range of specialties, and exploration of the potential for diverse applications in research and research policy context.     

Sentiment detection with auxiliary data

As an important application in text mining and social media, sentiment detection has aroused more and more research interests, due to the expanding volume of available online information such as microblogging messages and review comments. Many machine learning methods have been proposed for sentiment detection. As a branch of machine learning, transfer learning is an important technique that tries to transfer knowledge from one domain to another one. When applied to sentiment detection, existing transfer learning methods employ articles with human labeled sentiments from other domains to help the sentiment detection on a target domain. Although most existing transfer learning methods are devoted to handle the data distribution difference between different domains, they only resort to some approximation methods, which may introduce some unnecessary biases. Furthermore, the popular assumption of existing transfer learning techniques on conditional probability is often too strong for practical applications. In this paper, we propose a novel method to model the distribution difference between different domains in sentiment detection by directly modeling the underlying joint distributions for different domains. Some of the important properties of the proposed method, such as the convergence rate and time complexity, are analyzed. The experimental results on the product review dataset and the twitter dataset demonstrate the advantages of the proposed method over the state-of-the-art methods.     

Gradient descent optimization of smoothed information retrieval metrics

Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criteria that are adopted to measure the quality of the web page ranking results. To overcome this problem, we propose an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision. The basic idea is to minimize a smooth approximation of these measures with gradient descent. Crucial to this kind of approach is the choice of the smoothing factor. We provide various theoretical analysis on that choice and propose an annealing algorithm to iteratively minimize a less and less smoothed approximation of the measure of interest. Results on the Letor benchmark datasets show that the proposed algorithm achieves state-of-the-art performances.     

Who's in the picture: Comparing census data with newspaper coverage

Courses: Communications Research, Mass Communications in Modern Society, and Race, Gender, and Media.

Objectives: As a single or multiple class activity (depending on the length of the class period), the aims of this assignment are to increase the student's ability to apply content analysis methods in an actual media context, to develop an understanding of the agenda setting/framing function of the press, and to analyze the portrayal of gender and diversity in media. The activity can be completed in one class period if the class is at least 1:15 minutes long, but at least two class periods are recommended. Alternatively, the instructor could introduce each of the three sections with a brief lecture and then complete the activity over a three-class period span.     

Topic set size design

Traditional pooling-based information retrieval (IR) test collections typically have \(n= 50\)–100 topics, but it is difficult for an IR researcher to say why the topic set size should really be n. The present study provides details on principled ways to determine the number of topics for a test collection to be built, based on a specific set of statistical requirements. We employ Nagata’s three sample size design techniques, which are based on the paired t test, one-way ANOVA, and confidence intervals, respectively. These topic set size design methods require topic-by-run score matrices from past test collections for the purpose of estimating the within-system population variance for a particular evaluation measure. While the previous work of Sakai incorrectly used estimates of the total variances, here we use the correct estimates of the within-system variances, which yield slightly smaller topic set sizes than those reported previously by Sakai. Moreover, this study provides a comparison across the three methods. Our conclusions nevertheless echo those of Sakai: as different evaluation measures can have vastly different within-system variances, they require substantially different topic set sizes under the same set of statistical requirements; by analysing the tradeoff between the topic set size and the pool depth for a particular evaluation measure in advance, researchers can build statistically reliable yet highly economical test collections.     

Text Categorization Based on Regularized Linear Classification Methods

A number of linear classification methods such as the linear least squares fit (LLSF), logistic regression, and support vector machines (SVM's) have been applied to text categorization problems. These methods share the similarity by finding hyperplanes that approximately separate a class of document vectors from its complement. However, support vector machines are so far considered special in that they have been demonstrated to achieve the state of the art performance. It is therefore worthwhile to understand whether such good performance is unique to the SVM design, or if it can also be achieved by other linear classification methods. In this paper, we compare a number of known linear classification methods as well as some variants in the framework of regularized linear systems. We will discuss the statistical and numerical properties of these algorithms, with a focus on text categorization. We will also provide some numerical experiments to illustrate these algorithms on a number of datasets.     

Document–document similarity approaches and science mapping: Experimental comparison of five approaches

This paper treats document–document similarity approaches in the context of science mapping. Five approaches, involving nine methods, are compared experimentally. We compare text-based approaches, the citation-based bibliographic coupling approach, and approaches that combine text-based approaches and bibliographic coupling. Forty-three articles, published in the journal Information Retrieval, are used as test documents. We investigate how well the approaches agree with a ground truth subject classification of the test documents, when the complete linkage method is used, and under two types of similarities, first-order and second-order. The results show that it is possible to achieve a very good approximation of the classification by means of automatic grouping of articles. One text-only method and one combination method, under second-order similarities in both cases, give rise to cluster solutions that to a large extent agree with the classification.     

用于界面设计的文献识别模型

文献分析的自动化方法在因特网环境中尤其
必要。采用若干术语进行标引确定了一个类目中的组成成分，但抑制了对这些成分的深入理
解和辨析。文献集合中一个术语的平均频率是一个单纯的功能，并且是一个有趣的、可视化
的和友好特性的功能。一个为用户开发的界面，使得用户容易的操纵和发现内容的上下文，
内容定位了术语在全文中的位置。本文描述了一个实验，它确定了这个方法的一些有用的可
能性。所列出的可视化的含义可以帮助用户分析文献集合和在上下文信息结构中分析文献。     

文本分类中一种基于选择的二次特征降维方法

特征选择和特征抽取是文本分类中特征降维的主要方法.目前各种特征选择方法主要致力于度量特征与文本类别的相关性,却很少考虑特征之间的冗余性问题,从而影响特征降维的效果.本文提出一种基于选择的两步特征选择方法,既考虑一些类别信息较强的特征的选取,又减少一些类别判定方面的冗余特征,在尽量减少信息损失的前提下达到有效缩减特征维数的目的.对中文文本的分类实验结果表明,本文提出的特征降维方法在文本分类的准确率方面效果较好.     

“Once a Foster Child … ”: Identity Construction in Former Foster Children's Narratives

The present study is a thematic narrative analysis (Riessman, 2008 Riessman, C. K. (2008). Narrative methods for the human sciences. Thousand Oaks, CA: Sage Publications. [Google Scholar]) of 17 former foster children's online stories. The author explores the question, “What identities are constructed in narrative tellings about one's foster child experience?” Three pervasive, non-exclusive identities emerged from the former foster children's narratives: victim, survivor, and victor. These identity constructions are discussed in relation to redemptive tellings, wellbeing, and the foster care system.     

On the logical tools of intelligent sociological data analysis

The logical tools for formalizing of qualitative sociological data analysis are considered. The capabilities of approaches employing JSM reasoning, a class of cognitive plausible reasoning, and the methods of Boolean algebra are considered and compared.     

Learning to rank with (a lot of) word features

In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.     

Connecting with What Is Out There!: Using Twitter in the Large Lecture

Objectives: Students and teachers will develop both practical skills and theoretical/cultural understanding with regards to how Twitter can be used to present ideas and create dialogue between individuals and communities within and outside the classroom.

Course(s): Primarily, this student engagement method can be used in any large lecture class (e.g., Introduction to Mass Communication, Introduction to Communication Studies). However, such methods can be used for any course where frequent and short communications are required and/or encouraged between members of the class.     

Detecting shilling attacks in private environments

Privacy-preserving collaborative filtering algorithms are successful approaches. However, they are susceptible to shilling attacks. Recent research has increasingly focused on collaborative filtering to protect against both privacy and shilling attacks. Malicious users may add fake profiles to manipulate the output of privacy-preserving collaborative filtering systems, which reduces the accuracy of these systems. Thus, it is imperative to detect fake profiles for overall success. Many methods have been developed for detecting attack profiles to keep them outside of the system. However, these techniques have all been established for non-private collaborative filtering schemes. The detection of shilling attacks in privacy-preserving recommendation systems has not been deeply examined. In this study, we examine the detection of shilling attacks in privacy-preserving collaborative filtering systems. We utilize four attack-detection methods to filter out fake profiles produced by six well-known shilling attacks on perturbed data. We evaluate these detection methods with respect to their ability to identify bogus profiles. Real data-based experiments are performed. Empirical outcomes demonstrate that some of the detection methods are very successful at filtering out fake profiles in privacy-preserving collaborating filtering schemes.