Detecting plagiarism in the forensic linguistics turn

This study investigates plagiarism detection, with an application in forensic contexts. Two types of data were collected for the purposes of this study. Data in the form of written texts were obtained from two Portuguese Universities and from a Portuguese newspaper. These data are analysed linguistically to identify instances of verbatim, morpho-syntactical, lexical and discursive overlap. Data in the form of survey were obtained from two higher education institutions in Portugal, and another two in the United Kingdom. These data are analysed using a 2 by 2 between-groups Univariate Analysis of Variance (ANOVA), to reveal cross-cultural divergences in the perceptions of plagiarism. The study discusses the legal and social circumstances that may contribute to adopting a punitive approach to plagiarism, or, conversely, reject the punishment. The research adopts a critical approach to plagiarism detection. On the one hand, it describes the linguistic strategies adopted by plagiarists when borrowing from other sources, and, on the other hand, it discusses the relationship between these instances of plagiarism and the context in which they appear. A focus of this study is whether plagiarism involves an intention to deceive, and, in this case, whether forensic linguistic evidence can provide clues to this intentionality. It also evaluates current computational approaches to plagiarism detection, and identifies strategies that these systems fail to detect. Specifically, a method is proposed to translingual plagiarism. The findings indicate that, although cross-cultural aspects influence the different perceptions of plagiarism, a distinction needs to be made between intentional and unintentional plagiarism. The linguistic analysis demonstrates that linguistic elements can contribute to finding clues for the plagiarist’s intentionality. Furthermore, the findings show that translingual plagiarism can be detected by using the method proposed, and that plagiarism detection software can be improved using existing computer tools.

A comparison between static and kinetic visual attention as a means of detecting age-related deterioration of the visual system and driving performance

A critical review of previous research revealed that visual attention tests, such as the Useful Field of View (UFOV) test, provided the best means of detecting age-related changes to the visual system that could potentially increase crash risk. However, the question was raised as to whether the UFOV, which was regarded as a static visual attention test, could be improved by inclusion of kinetic targets that more closely represent the driving task. A computer program was written to provide more information about the derivation of UFOV test scores. Although this investigation succeeded in providing new information, some of the commercially protected UFOV test procedures still remain unknown. Two kinetic visual attention tests (DRTS1 and 2), developed at Aston University to investigate inclusion of kinetic targets in visual attention tests, were introduced. The UFOV was found to be more repeatable than either of the kinetic visual attention tests and learning effects or age did not influence these findings. Determinants of static and kinetic visual attention were explored. Increasing target eccentricity led to reduced performance on the UFOV and DRTS1 tests. The DRTS2 was not affected by eccentricity but this may have been due to the style of presentation of its targets. This might also have explained why only the DRTS2 showed laterality effects (i.e. better performance to targets presented on the left hand side of the road). Radial location, explored using the UFOV test, showed that subjects responded best to targets positioned to the horizontal meridian. Distraction had opposite effects on static and kinetic visual attention. While UFOV test performance declined with distraction, DRTS1 performance increased. Previous research had shown that this striking difference was to be expected. Whereas the detection of static targets is attenuated in the presence of distracting stimuli, distracting stimuli that move in a structured flow field enhances the detection of moving targets. Subjects reacted more slowly to kinetic compared to static targets, longitudinal motion compared to angular motion and to increased self-motion. However, the effects of longitudinal motion, angular motion, self-motion and even target eccentricity were caused by target edge speed variations arising because of optic flow field effects. The UFOV test was more able to detect age-related changes to the visual system than were either of the kinetic visual attention tests. The driving samples investigated were too limited to draw firm conclusions. Nevertheless, the results presented showed that neither the DRTS2 nor the UFOV tests were powerful tools for the identification of drivers prone to crashes or poor driving performance.

“I didn’t mean to steal someone else’s words!”:a forensic linguistic approach to detecting intentional plagiarism

The concept of plagiarism is not uncommonly associated with the concept of intellectual property, both for historical and legal reasons: the approach to the ownership of ‘moral’, nonmaterial goods has evolved to the right to individual property, and consequently a need was raised to establish a legal framework to cope with the infringement of those rights. The solution to plagiarism therefore falls most often under two categories: ethical and legal. On the ethical side, education and intercultural studies have addressed plagiarism critically, not only as a means to improve academic ethics policies (PlagiarismAdvice.org, 2008), but mainly to demonstrate that if anything the concept of plagiarism is far from being universal (Howard & Robillard, 2008). Even if differently, Howard (1995) and Scollon (1994, 1995) argued, and Angèlil-Carter (2000) and Pecorari (2008) later emphasised that the concept of plagiarism cannot be studied on the grounds that one definition is clearly understandable by everyone. Scollon (1994, 1995), for example, claimed that authorship attribution is particularly a problem in non-native writing in English, and so did Pecorari (2008) in her comprehensive analysis of academic plagiarism. If among higher education students plagiarism is often a problem of literacy, with prior, conflicting social discourses that may interfere with academic discourse, as Angèlil-Carter (2000) demonstrates, we then have to aver that a distinction should be made between intentional and inadvertent plagiarism: plagiarism should be prosecuted when intentional, but if it is part of the learning process and results from the plagiarist’s unfamiliarity with the text or topic it should be considered ‘positive plagiarism’ (Howard, 1995: 796) and hence not an offense. Determining the intention behind the instances of plagiarism therefore determines the nature of the disciplinary action adopted. Unfortunately, in order to demonstrate the intention to deceive and charge students with accusations of plagiarism, teachers necessarily have to position themselves as ‘plagiarism police’, although it has been argued otherwise (Robillard, 2008). Practice demonstrates that in their daily activities teachers will find themselves being required a command of investigative skills and tools that they most often lack. We thus claim that the ‘intention to deceive’ cannot inevitably be dissociated from plagiarism as a legal issue, even if Garner (2009) asserts that generally plagiarism is immoral but not illegal, and Goldstein (2003) makes the same severance. However, these claims, and the claim that only cases of copyright infringement tend to go to court, have recently been challenged, mainly by forensic linguists, who have been actively involved in cases of plagiarism. Turell (2008), for instance, demonstrated that plagiarism is often connoted with an illegal appropriation of ideas. Previously, she (Turell, 2004) had demonstrated by comparison of four translations of Shakespeare’s Julius Caesar to Spanish that the use of linguistic evidence is able to demonstrate instances of plagiarism. This challenge is also reinforced by practice in international organisations, such as the IEEE, to whom plagiarism potentially has ‘severe ethical and legal consequences’ (IEEE, 2006: 57). What plagiarism definitions used by publishers and organisations have in common – and which the academia usually lacks – is their focus on the legal nature. We speculate that this is due to the relation they intentionally establish with copyright laws, whereas in education the focus tends to shift from the legal to the ethical aspects. However, the number of plagiarism cases taken to court is very small, and jurisprudence is still being developed on the topic. In countries within the Civil Law tradition, Turell (2008) claims, (forensic) linguists are seldom called upon as expert witnesses in cases of plagiarism, either because plagiarists are rarely taken to court or because there is little tradition of accepting linguistic evidence. In spite of the investigative and evidential potential of forensic linguistics to demonstrate the plagiarist’s intention or otherwise, this potential is restricted by the ability to identify a text as being suspect of plagiarism. In an era with such a massive textual production, ‘policing’ plagiarism thus becomes an extraordinarily difficult task without the assistance of plagiarism detection systems. Although plagiarism detection has attracted the attention of computer engineers and software developers for years, a lot of research is still needed. Given the investigative nature of academic plagiarism, plagiarism detection has of necessity to consider not only concepts of education and computational linguistics, but also forensic linguistics. Especially, if intended to counter claims of being a ‘simplistic response’ (Robillard & Howard, 2008). In this paper, we use a corpus of essays written by university students who were accused of plagiarism, to demonstrate that a forensic linguistic analysis of improper paraphrasing in suspect texts has the potential to identify and provide evidence of intention. A linguistic analysis of the corpus texts shows that the plagiarist acts on the paradigmatic axis to replace relevant lexical items with a related word from the same semantic field, i.e. a synonym, a subordinate, a superordinate, etc. In other words, relevant lexical items were replaced with related, but not identical, ones. Additionally, the analysis demonstrates that the word order is often changed intentionally to disguise the borrowing. On the other hand, the linguistic analysis of linking and explanatory verbs (i.e. referencing verbs) and prepositions shows that these have the potential to discriminate instances of ‘patchwriting’ and instances of plagiarism. This research demonstrates that the referencing verbs are borrowed from the original in an attempt to construct the new text cohesively when the plagiarism is inadvertent, and that the plagiarist has made an effort to prevent the reader from identifying the text as plagiarism, when it is intentional. In some of these cases, the referencing elements prove being able to identify direct quotations and thus ‘betray’ and denounce plagiarism. Finally, we demonstrate that a forensic linguistic analysis of these verbs is critical to allow detection software to identify them as proper paraphrasing and not – mistakenly and simplistically – as plagiarism.

Method of detecting a significant change of scene

A significant change of scene in a gradually changing scene is detected with the aid of a least one camera means for capturing digital images of the scene. A current image of the scene is formed together with a present weighted reference image which is formed from a plurality of previous images of the scene. Cell data is established based on the current image and the present weighted reference image. The cell data is statistically analysed so as to be able to identify at least one difference corresponding to a significant change of scene. When identified, an indication of such significant change of scene is provided.

Theoretical investigation into methods of detecting pulmonary arteriovenous blood shunts

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

The importance of detecting and managing comorbidities in people with dementia?

Dementia is a debilitating condition characterised by global loss of cognitive and intellectual functioning, which gradually interferes with social and occupational performance. It is a common worldwide condition with a significant impact on society. There are currently 36 million people worldwide with Alzheimer's disease (AD) and other dementias [1]. This is expected to more than double by 2030 (65 million) and reach ∼115 million in 2050, unless a major breakthrough is made. The worldwide societal costs were estimated at USD 604 billion in 2010 and rising [2]. To date research on the specific physical healthcare needs of people with dementia has been neglected. Yet, physical comorbidities are reported as common in people with dementia [3] and have been shown to lead to increased disability and reduced quality of life for the affected person and their carer [4]. Dementia is most frequently associated with older people who often present with other medical conditions, known as co-morbidities. Such co-morbidities include diabetes, chronic obstructive pulmonary disorder, musculoskeletal disorders and chronic cardiac failure and are common, 61% of people with …

Summarization of scientific documents by detecting common facts in citations

Reading scientific articles is more time-consuming than reading news because readers need to search and read many citations. This paper proposes a citation guided method for summarizing multiple scientific papers. A phenomenon we can observe is that citation sentences in one paragraph or section usually talk about a common fact, which is usually represented as a set of noun phrases co-occurring in citation texts and it is usually discussed from different aspects. We design a multi-document summarization system based on common fact detection. One challenge is that citations may not use the same terms to refer to a common fact. We thus use term association discovering algorithm to expand terms based on a large set of scientific article abstracts. Then, citations can be clustered based on common facts. The common fact is used as a salient term set to get relevant sentences from the corresponding cited articles to form a summary. Experiments show that our method outperforms three baseline methods by ROUGE metric.©2013 Elsevier B.V. All rights reserved.

Detecting and monitoring the symptoms of Parkinson's disease using smartphones:a pilot study

Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.

Detecting EPCIS exceptions in linked traceability streams across supply chain business processes

The EPCIS specification provides an event oriented mechanism to record product movement information across stakeholders in supply chain business processes. Besides enabling the sharing of event-based traceability datasets, track and trace implementations must also be equipped with the capabilities to validate integrity constraints and detect runtime exceptions without compromising the time-to-deliver schedule of the shipping and receiving parties. In this paper we present a methodology for detecting exceptions arising during the processing of EPCIS event datasets. We propose an extension to the EEM ontology for modelling EPCIS exceptions and show how runtime exceptions can be detected and reported. We exemplify and evaluate our approach on an abstraction of pharmaceutical supply chains.

Targeted mass spectrometry methods for detecting oxidative post-translational modifications

Oxidative post-translational modifications (oxPTMs) can alter the function of proteins, and are important in the redox regulation of cell behaviour. The most informative technique to detect and locate oxPTMs within proteins is mass spectrometry (MS). However, proteomic MS data are usually searched against theoretical databases using statistical search engines, and the occurrence of unspecified or multiple modifications, or other unexpected features, can lead to failure to detect the modifications and erroneous identifications of oxPTMs. We have developed a new approach for mining data from accurate mass instruments that allows multiple modifications to be examined. Accurate mass extracted ion chromatograms (XIC) for specific reporter ions from peptides containing oxPTMs were generated from standard LC-MSMS data acquired on a rapid-scanning high-resolution mass spectrometer (ABSciex 5600 Triple TOF). The method was tested using proteins from human plasma or isolated LDL. A variety of modifications including chlorotyrosine, nitrotyrosine, kynurenine, oxidation of lysine, and oxidized phospholipid adducts were detected. For example, the use of a reporter ion at 184.074 Da/e, corresponding to phosphocholine, was used to identify for the first time intact oxidized phosphatidylcholine adducts on LDL. In all cases the modifications were confirmed by manual sequencing. ApoB-100 containing oxidized lipid adducts was detected even in healthy human samples, as well as LDL from patients with chronic kidney disease. The accurate mass XIC method gave a lower false positive rate than normal database searching using statistical search engines, and identified more oxidatively modified peptides. A major advantage was that additional modifications could be searched after data collection, and multiple modifications on a single peptide identified. The oxPTMs present on albumin and ApoB-100 have potential as indicators of oxidative damage in ageing or inflammatory diseases.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.