Detecting Machine-obfuscated Plagiarism

Related dataset is at https://doi.org/10.7302/bewj-qx93 and also listed in the dc.relation field of the full item record.Research on academic integrity has identified online paraphrasing tools as a severe threat to the effectiveness of plagiarism detection systems. To enable the automated identification of machine-paraphrased text, we make three contributions. First, we evaluate the effectiveness of six prominent word embedding models in combination with five classifiers for distinguishing human-written from machine-paraphrased text. The best performing classification approach achieves an accuracy of 99.0% for documents and 83.4% for paragraphs. Second, we show that the best approach outperforms human experts and established plagiarism detection systems for these classification tasks. Third, we provide a Web application that uses the best performing classification approach to indicate whether a text underwent machine-paraphrasing. The data and code of our study are openly available.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152346/1/Foltynek2020_Paraphrase_Detection.pdfDescription of Foltynek2020_Paraphrase_Detection.pdf : Foltynek2020_Paraphrase_Detectio

Maintaining research integrity in the age of GenAI: an analysis of ethical challenges and recommendations to researchers

Background This paper is a practice‑informed rapid review that maps the complex ethical challenges arising from the growing use of Generative Artificial Intelligence (GenAI) tools across the research life‑cycle. Earlier research primarily focused on academic integrity concerns related to students’ use of GenAI tools; however, limited information is available on the impact of GenAI on academic research. This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research and explores potential strategies to encourage its ethical use for research purposes. Methods We selected one or more GenAI platforms applicable to various research phases (e.g. developing research questions, conducting literature reviews, processing data, and academic writing) and analysed them to identify potential ethical concerns relevant for that stage. Results The analysis revealed several ethical concerns, including a lack of transparency, bias, censorship, fabrication (e.g. hallucinations and false data generation), copyright violations, and privacy issues. These findings underscore the need for cautious and mindful use of GenAI. Conclusions The advancement and use of GenAI are continuously evolving, necessitating an ongoing in-depth evaluation. We propose a set of practical recommendations to support researchers in effectively integrating these tools while adhering to the fundamental principles of ethical research practices

Jets and energy flow in photon-proton collisions at HERA

Properties of the hadronic final state in photoproduction events with large transverse energy are studied at the electron-proton collider HERA. Distributions of the transverse energy, jets and underlying event energy are compared to \overline{p}p data and QCD calculations. The comparisons show that the \gamma p events can be consistently described by QCD models including -- in addition to the primary hard scattering process -- interactions between the two beam remnants. The differential jet cross sections d\sigma/dE_T^{jet} and d\sigma/d\eta^{jet} are measured

Testing of detection tools for AI-generated text

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings

Разработка интерактивной моделирующей системы технологии низкотемпературной сепарации газа

We present a study of J ψ meson production in collisions of 26.7 GeV electrons with 820 GeV protons, performed with the H1-detector at the HERA collider at DESY. The J ψ mesons are detected via their leptonic decays both to electrons and muons. Requiring exactly two particles in the detector, a cross section of σ(ep → J ψ X) = (8.8±2.0±2.2) nb is determined for 30 GeV ≤ W γp ≤ 180 GeV and Q 2 ≲ 4 GeV 2 . Using the flux of quasi-real photons with Q 2 ≲ 4 GeV 2 , a total production cross section of σ ( γp → J / ψX ) = (56±13±14) nb is derived at an average W γp =90 GeV. The distribution of the squared momentum transfer t from the proton to the J ψ can be fitted using an exponential exp(− b ∥ t ∥) below a ∥ t ∥ of 0.75 GeV 2 yielding a slope parameter of b = (4.7±1.9) GeV −2

Structural insight into the membrane targeting domain of the Legionella deAMPylase SidD

AMPylation, the post-translational modification with adenosine monophosphate (AMP), is catalyzed by effector proteins from a variety of pathogens. Legionella pneumophila is thus far the only known pathogen that, in addition to encoding an AMPylase (SidM/DrrA), also encodes a deAMPylase, called SidD, that reverses SidM-mediated AMPylation of the vesicle transport GTPase Rab1. DeAMPylation is catalyzed by the N-terminal phosphatase-like domain of SidD. Here, we determined the crystal structure of full length SidD including the uncharacterized C-terminal domain (CTD). A flexible loop rich in aromatic residues within the CTD was required to target SidD to model membranes in vitro and to the Golgi apparatus within mammalian cells. Deletion of the loop (??loop) or substitution of its aromatic phenylalanine residues rendered SidD cytosolic, showing that the hydrophobic loop is the primary membrane-targeting determinant of SidD. Notably, deletion of the two terminal alpha helices resulted in a CTD variant incapable of discriminating between membranes of different composition. Moreover, a L. pneumophila strain producing SidD??loop phenocopied a L. pneumophila ??sidD strain during growth in mouse macrophages and displayed prolonged co-localization of AMPylated Rab1 with LCVs, thus revealing that membrane targeting of SidD via its CTD is a critical prerequisite for its ability to catalyze Rab1 deAMPylation during L. pneumophila infection