Hybrid followee recommendation in microblogging systems

Bug reports are widely employed to facilitate software tasks in software maintenance. Since bug reports are contributed by people, the authorship characteristics of contributors may heavily impact the perfor-mance of resolving software tasks. Poorly written bug reports may delay developers when fixing bugs. However, no in-depth investigation has been conducted over the authorship characteristics. In this study, we first leverage byte-level N-grams to model the authorship characteristics and employ Normalized Simplified Profile Intersection (NSPI) to identify the similarity of the authorship characteristics. Then, we investigate a series of properties related to contributors’ authorship characteristics, including the evolvement over time and the variation among distinct products in open source projects. Moreover, we show how to leverage the authorship characteristics to facilitate a well-known task in software maintenance, namely Bug Report Summarization (BRS). Experiments on open source projects validate that incorporating the authorship characteristics can effectively improve a state-of-the-art method in BRS. Our findings suggest that contributors should retain stable authorship characteristics and the authorship characteristics can assist in resolving software tasks.

     

作者识别研究综述

作者识别是根据已知文本推断未知文本作者的交叉学科. 其传统研究通常基于文学或语言学的经验知识, 而现代研究则主要依靠数学方法量化作者的写作风格. 近些年, 随着认知科学、系统科学和信息技术的发展, 作者识别受到越来越多研究者的关注. 本文主要站在计算语言学的角度综述作者识别领域现代研究中的方法和思路. 首先, 简要介绍了作者识别的发展历程. 然后, 详述了文体风格特征、作者识别方法以及该领域中多层面的研究. 接着介绍了与作者识别相关的一些评测、数据集及评价指标. 最后, 指出该领域存在的一些问题, 结合这些问题分析并展望了作者识别的发展趋势.     

A note on an identity-based ring signature scheme with signer verifiability

Recently, Herranz presented an identity-based ring signature scheme featuring signer verifiability where a signer can prove that he or she is the real signer by releasing an authorship proof. In this paper we show that this scheme is vulnerable to a key recovery attack in which a user’s secret signing key can be efficiently recovered through the use of two known ring signatures and their corresponding authorship proofs. In addition, we present a simple method to fix this security vulnerability by slightly modifying the authorship proof. Our modified scheme simplifies the original scheme and improves performance. To show that the modified scheme is unforgeable, we define two types of unforgeability notions for both signatures and authorship proofs. In these notions an adversary has opening capability to confirm the real signers of ring signatures and thus can manipulate authorship proofs in an adaptive way. We then prove that our modified scheme is secure in terms of these unforgeability notions.     

基于语句节奏特征的作者身份识别研究

提出一种新的写作风格相似度评估方法,利用不同作者写作时在文章语句节奏控制方面的特点,鉴别作者的写作风格,从而达到作者身份识别的目的。该方法构建节奏特征矩阵模型来描述文本的语句节奏,利用点积相似度算法以及改进的KL距离算法来度量节奏特征矩阵之间的差异。实验表明,该方法在文学作品的作者识别方面具有较高的准确率。     

SAR: Stroke Authorship Recognition

Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR's ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools.     

Delta for Middle Dutch Author and Copyist Distinction in Walewein

The Middle Dutch Arthurian romance Roman van Walewein (‘Romanceof Gawain’) is attributed in the text itself to two authors,Penninc and Vostaert. Very little quantitative research intothis dual authorship has been done. This article describes ourprogress in applying different non-traditional authorship attributionmethods to the text of Walewein. After providing an introductionto the romance and an overview of earlier research, we evaluateprevious statements on authorship and stylistics by applyingboth Yule's measure of lexical richness and Burrows's Delta.To find out whether these new methods would confirm or evenenhance our present knowledge about the differences betweenthe two authors, we applied an adapted version of John Burrows'sDelta procedure. The adapted version seems to be able to distinguishthe double authorship of the romance. It also helps us to confirmsome and to reject other earlier statements about the positionin the text where the second author started his work.     

软件取证和一个身份分析模型MBSFAM*

计算机取证目前在国外正逐步成为研究与开发的热点,但在国内仅有少量研究文章。给出了软件取证的定义,分析了软件取证的任务和所使用的方法,对可执行代码和源代码的取证作了详细分析,并给出了一个取证分析模型MBSFAM。     

Using Literal and Grammatical Statistics for Authorship Attribution

Markov chains are used as a formal mathematical model for sequences of elements of a text. This model is applied for authorship attribution of texts. As elements of a text, we consider sequences of letters or sequences of grammatical classes of words. It turns out that the frequencies of occurrences of letter pairs and pairs of grammatical classes in a Russian text are rather stable characteristics of an author and, apparently, they could be used in disputed authorship attribution. A comparison of results for various modifications of the method using both letters and grammatical classes is given. Experimental research involves 385 texts of 82 writers. In the Appendix, the research of D.V. Khmelev is described, where data compression algorithms are applied to authorship attribution.     

Quantitative Authorship Attribution: An Evaluation of Techniques

The basic assumption of quantitative authorship attributionis that the author of a text can be selected from a set of possibleauthors by comparing the values of textual measurements in thattext to their corresponding values in each possible author'swriting sample. Over the past three centuries, many types oftextual measurements have been proposed, but never before havethe majority of these measurements been tested on the same dataset.A large-scale comparison of textual measurements is crucialif current techniques are to be used effectively and if newand more powerful techniques are to be developed. This articlepresents the results of a comparison of thirty-nine differenttypes of textual measurements commonly used in attribution studies,in order to determine which are the best indicators of authorship.Based on the results of these tests, a more accurate approachto quantitative authorship attribution is proposed, which involvesthe analysis of many different textual measurements.     

Examining the significance of high-level programming features in source code author classification

The use of Source Code Author Profiles (SCAP) represents a new, highly accurate approach to source code authorship identification that is, unlike previous methods, language independent. While accuracy is clearly a crucial requirement of any author identification method, in cases of litigation regarding authorship, plagiarism, and so on, there is also a need to know why it is claimed that a piece of code is written by a particular author. What is it about that piece of code that suggests a particular author? What features in the code make one author more likely than another? In this study, we describe a means of identifying the high-level features that contribute to source code authorship identification using as a tool the SCAP method. A variety of features are considered for Java and Common Lisp and the importance of each feature in determining authorship is measured through a sequence of experiments in which we remove one feature at a time. The results show that, for these programs, comments, layout features and package-related naming influence classification accuracy whereas user-defined naming, an obvious programmer related feature, does not appear to influence accuracy. A comparison is also made between the relative feature contributions in programs written in the two languages.     

A situated cognition view about the effects of planning and authorship on computer program debugging

Two experiments were conducted to investigate the relationship between planning and debugging and the effect of program authorship on debugging strategies. Three groups of participants with different programming experiences were recruited. In the first experiment, the participants were asked to develop and debug their self-generated program whereas in the second experiment, they were asked to debug an otherwritten program where some logical errors were planted. Situated cognition approach, being an emergent cognitive paradigm, furnishes an alternative framework to understand the problems of interest. Deweyan notion of inquiry and Gibsonian theory of affordance are of particular relevance. The results show that planning is ineffective for debugging, irrespective of the programming expertise level and program authorship. Besides, situated debugging is demonstrated to be the preferred strategy which is not significantly related to the program authorship. A model of planning for program debugging and a theory of two-faceted transparency are postulated for explicating the observations.     

基于注意力机制的文本作者识别

基于神经网络的作者识别在面临较多候选作者时识别准确率会大幅降低。为了提高作者识别精度,提出一种由快速文本分类（fastText）和注意力层构成的神经网络,并将该网络结合连续的词性标签n元组合（POS n-gram）特征进行中文小说的作者识别。与文本卷积神经网络（TextCNN）、文本循环神经网络（TextRNN）、长短期记忆（LSTM）网络和fastText进行对比,实验结果表明,所提出的模型获得了最高的分类准确率,与fastText模型相比,注意力机制的引入使得不同POS n-gram特征对应的准确率平均提高了2.14个百分点;同时,该模型保留了fastText的快速高效,且其所使用的文本特征可以推广到其他语言上。     

A situated cognition view about the effects of planning and authorship on computer program debugging

Two experiments were conducted to investigate the relationship between planning and debugging and the effect of program authorship on debugging strategies. Three groups of participants with different programming experiences were recruited. In the first experiment, the participants were asked to develop and debug their self-generated program whereas in the second experiment, they were asked to debug an otherwritten program where some logical errors were planted. Situated cognition approach, being an emergent cognitive paradigm, furnishes an alternative framework to understand the problems of interest. Deweyan notion of inquiry and Gibsonian theory of affordance are of particular relevance. The results show that planning is ineffective for debugging, irrespective of the programming expertise level and program authorship. Besides, situated debugging is demonstrated to be the preferred strategy which is not significantly related to the program authorship. A model of planning for program debugging and a theory of two-faceted transparency are postulated for explicating the observations.     

诗人密码:唐诗作者身份识别

作者身份识别是对作者个人写作风格的分析。虽然这一任务在多种语言中都得到了广泛的研究,但对中文而言,研究还没有涉及古典诗歌领域。唐诗同时具有跳跃性和整体性,为了兼顾这两种特点,该文提出了一种双通道的Cap-Transformer集成模型。上通道Capsule模型可以在提取特征的同时降低信息损失,能够更好地捕获唐诗各个意象的语义特征;下通道Transformer模型通过多头自注意力机制充分学习唐诗所有意象共同反映的深层语义信息。实验表明,该文提出的模型适用于唐诗作者身份识别任务,并通过错误分析,针对唐诗文本的特殊性,讨论了唐诗作者身份识别任务目前存在的问题及未来的研究方向和面临的挑战。     

Neural network applications in stylometry: The Federalist Papers

Neural Networks have recently been a matter of extensive research and popularity. Their application has increased considerably in areas in which we are presented with a large amount of data and we have to identify an underlying pattern. This paper will look at their application to stylometry. We believe that statistical methods of attributing authorship can be coupled effectively with neural networks to produce a very powerful classification tool. We illustrate this with an example of a famous case of disputed authorship, The Federalist Papers. Our method assigns the disputed papers to Madison, a result which is consistent with previous work on the subject.     

A Pedagogy of Resistance Toward Plagiarism Detection Technologies

This article interrogates the use of plagiarism detection devices from a critical and rhetorical standpoint, using both plagiarism detection technologies as well as essay mills as sites for analysis and subversion. My goal is to argue for a pedagogy of resistance to plagiarism detection technologies. Both plagiarism detection sites and online paper mills play into the very issue we as rhetoricians and compositionists should be resisting; that is, by upholding the singular notion of authorship as something individualistic, commercialized, and commodified, these sites reinforce individual authorship to the detriment of more communal forms of writing that are prized in online environments such as social networking sites, blogs, wikis, and so on. If we are forced into the circular logic of avoiding plagiarism/catching plagiarists/punishing plagiarism and prizing singular authorship above all other forms, then we risk failing to find the ability to break free and move beyond to more challenging modes of writing that rely on community. The potential time-saving benefits of plagiarism detection services—that is, the ease of discovering potential plagiarism—may unfortunately lull us into compliance and cause us to forget that there are larger issues regarding copyright law and ownership of ideas still up for debate.     

Another Perspective on Vocabulary Richness

This article examines the usefulness ofvocabulary richness for authorship attributionand tests the assumption that appropriatemeasures of vocabulary richness can capture anauthor's distinctive style or identity. Afterbriefly discussing perceived and actualvocabulary richness, I show that doubling andcombining texts affects some measures incomputationally predictable but conceptuallysurprising ways. I discuss some theoretical andempirical problems with some measures anddevelop simple methods to test how wellvocabulary richness distinguishes texts bydifferent authors. These methods show thatvocabulary richness is ineffective for largegroups of texts because of the extremevariability within and among them. I concludethat vocabulary richness is of marginal valuein stylistic and authorship studies because thebasic assumption that it constitutes awordprint for authors is false.     

AUTHORSHIP ATTRIBUTION OF RESPONSA USING CLUSTERING

Authorship attribution of text documents is a “hot” domain in research; however, almost all of its applications use supervised machine learning (ML) methods. In this research, we explore authorship attribution as a clustering problem, that is, we attempt to complete the task of authorship attribution using unsupervised machine learning methods. The application domain is responsa, which are answers written by well-known Jewish rabbis in response to various Jewish religious questions. We have built a corpus of 6,079 responsa, composed by five authors who lived mainly in the 20th century and containing almost 10 M words. The clustering tasks that have been performed were according to two or three or four or five authors. Clustering has been performed using three kinds of word lists: most frequent words (FW) including function words (stopwords), most frequent filtered words (FFW) excluding function words, and words with the highest variance values (HVW); and two unsupervised machine learning methods: K-means and Expectation Maximization (EM). The best clustering tasks according to two or three or four authors achieved results above 98%, and the improvement rates were above 40% in comparison to the “majority” (baseline) results. The EM method has been found to be superior to K-means for the discussed tasks. FW has been found as the best word list, far superior to FFW. FW, in contrast to FFW, includes function words, which are usually regarded as words that have little lexical meaning. This might imply that normalized frequencies of function words can serve as good indicators for authorship attribution using unsupervised ML methods. This finding supports previous findings about the usefulness of function words for other tasks, such as authorship attribution, using supervised ML methods, and genre and sentiment classification.     

Authorship attribution in the wild

Most previous work on authorship attribution has focused on the case in which we need to attribute an anonymous document to one of a small set of candidate authors. In this paper, we consider authorship attribution as found in the wild: the set of known candidates is extremely large (possibly many thousands) and might not even include the actual author. Moreover, the known texts and the anonymous texts might be of limited length. We show that even in these difficult cases, we can use similarity-based methods along with multiple randomized feature sets to achieve high precision. Moreover, we show the precise relationship between attribution precision and four parameters: the size of the candidate set, the quantity of known-text by the candidates, the length of the anonymous text and a certain robustness score associated with a attribution.     

Two-layered Blogger identification model integrating profile and instance-based methods

This paper introduces a two-layered framework that improves the result of authorship identification within larger sample numbers of bloggers as compared with earlier work. Previous studies are mainly divided into two categories: profile-based and instance-based methods. Each of these approaches has its advantages and limitations. The two-layered framework presented here integrates the two previous approaches and presents a new solution to a key problem in authorship identification, namely the drop in accuracy experienced as the number of authors increases. The paper begins by illustrating the regular instance-based core model and the investigated features. It then introduces a new psycholinguistic profile representation of authors, presents similarity grouping extraction over profiles, and applies blogger identification utilizing the two-layered approach. The results confirm the improvement introduced by the proposed two-layered approach against our regular classifier, as well as a selected baseline, for an extended number of users.