Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text

While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem. These models can potentially deceive by generating artificial text that appears to be human-generated. This issue is particularly significant in domains such as law, education, and science, where ensuring the integrity of text is of the utmost importance. This survey provides an overview of the current approaches employed to differentiate between texts generated by humans and ChatGPT. We present an account of the different datasets constructed for detecting ChatGPT-generated text, the various methods utilized, what qualitative analyses into the characteristics of human versus ChatGPT-generated text have been performed, and finally, summarize our findings into general insightsComment: Published in the Proceedings of the Student Research Workshop associated with RANLP-202

UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning

Training deep neural networks often forces users to work in a distributed or outsourced setting, accompanied with privacy concerns. Split learning aims to address this concern by distributing the model among a client and a server. The scheme supposedly provides privacy, since the server cannot see the clients' models and inputs. We show that this is not true via two novel attacks. (1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and obtain a functionally similar model to the client model, without being detected. (2) We show that if the client keeps hidden only the output layer of the model to "protect" the private labels, the honest-but-curious server can infer the labels with perfect accuracy. We test our attacks using various benchmark datasets and against proposed privacy-enhancing extensions to split learning. Our results show that plaintext split learning can pose serious risks, ranging from data (input) privacy to intellectual property (model parameters), and provide no more than a false sense of security.Comment: Proceedings of the 21st Workshop on Privacy in the Electronic Society (WPES '22), November 7, 2022, Los Angeles, CA, US

SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning

Distributed deep learning frameworks, such as split learning, have recently been proposed to enable a group of participants to collaboratively train a deep neural network without sharing their raw data. Split learning in particular achieves this goal by dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. However, this method introduces a unique attack vector for a malicious server attempting to steal the client\u27s private data: the server can direct the client model towards learning a task of its choice. With a concrete example already proposed, such training-hijacking attacks present a significant risk for the data privacy of split learning clients. In this paper, we propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not. We experimentally evaluate its effectiveness, and discuss in detail various points related to its use. We conclude that SplitGuard can effectively detect training-hijacking attacks while minimizing the amount of information recovered by the adversaries

An alternative distribution function estimation method using rational Bernstein polynomials

This paper gives a general method for nonparametric distribution function estimation using the rational Bernstein polynomials as an alternative to the current estimators. The proposed new method is compared with Bernstein polynomials and empirical distribution function methods by simulation studies. The new method guarantees monotone nondecreasing function by applying linear constraints on the coefficients of the rational Bernstein basis functions and smooth the empirical distribution function. Furthermore, as a special case, it reduces to Bernstein polynomial estimator method. Some theoretical properties of the new estimator are investigated. Simulation study shows that the proposed estimator is preferable to the Bernstein polynomials and empirical distribution function estimator methods. (C) 2018 Elsevier B.V. All rights reserved

Echocardiographic Epicardial Adipose Tissue Predicts Subclinical Atherosclerosis: Epicardial adipose tissue and Atherosclerosis

We examined the relationship between coronary flow rate and epicardial adipose tissue (EAT) in patients with slow coronary flow (SCF) and normal coronary arteries. The study included 40 consecutive patients with stable angina pectoris who had normal coronary arteries. All patients underwent echocardiography. To determine the SCF, thrombolysis in myocardial infarction (TIMI) frame count method was used. Half of the patients had SCF at least in 1 coronary artery. Thrombolysis in myocardial infarction frame counts, the mean TIMI frame count, and EAT thickness were significantly higher in patients with SCF. Slow coronary flow showed a significantly positive correlation with EAT thickness. Epicardial adipose tissue thickness was the only independent predictor of SCF. Our findings suggest that there is a significant correlation between the SCF and EAT. Therefore, echocardiographic EAT may become a predictor of subclinical atherosclerosis in patients with stable angina pectoris

Echocardiographic Epicardial Adipose Tissue Predicts Subclinical Atherosclerosis

We examined the relationship between coronary flow rate and epicardial adipose tissue (EAT) in patients with slow coronary flow (SCF) and normal coronary arteries. The study included 40 consecutive patients with stable angina pectoris who had normal coronary arteries. All patients underwent echocardiography. To determine the SCF, thrombolysis in myocardial infarction (TIMI) frame count method was used. Half of the patients had SCF at least in 1 coronary artery. Thrombolysis in myocardial infarction frame counts, the mean TIMI frame count, and EAT thickness were significantly higher in patients with SCF. Slow coronary flow showed a significantly positive correlation with EAT thickness. Epicardial adipose tissue thickness was the only independent predictor of SCF. Our findings suggest that there is a significant correlation between the SCF and EAT. Therefore, echocardiographic EAT may become a predictor of subclinical atherosclerosis in patients with stable angina pectoris

Different epidemiology of bloodstream infections in COVID-19 compared to non-COVID-19 critically ill patients: a descriptive analysis of the Eurobact II study

Funder: European society of Intensive Care MedicineFunder: European Society of Clinical Microbiology and Infectious Diseases (ESCMID)Funder: Norva Dahlia foundation and the Redcliffe Hospital Private Practice Trust FundAbstract Background The study aimed to describe the epidemiology and outcomes of hospital-acquired bloodstream infections (HABSIs) between COVID-19 and non-COVID-19 critically ill patients. Methods We used data from the Eurobact II study, a prospective observational multicontinental cohort study on HABSI treated in ICU. For the current analysis, we selected centers that included both COVID-19 and non-COVID-19 critically ill patients. We performed descriptive statistics between COVID-19 and non-COVID-19 in terms of patients’ characteristics, source of infection and microorganism distribution. We studied the association between COVID-19 status and mortality using multivariable fragility Cox models. Results A total of 53 centers from 19 countries over the 5 continents were eligible. Overall, 829 patients (median age 65 years [IQR 55; 74]; male, n = 538 [64.9%]) were treated for a HABSI. Included patients comprised 252 (30.4%) COVID-19 and 577 (69.6%) non-COVID-19 patients. The time interval between hospital admission and HABSI was similar between both groups. Respiratory sources (40.1 vs. 26.0%, p &lt; 0.0001) and primary HABSI (25.4% vs. 17.2%, p = 0.006) were more frequent in COVID-19 patients. COVID-19 patients had more often enterococcal (20.5% vs. 9%) and Acinetobacter spp. (18.8% vs. 13.6%) HABSIs. Bacteremic COVID-19 patients had an increased mortality hazard ratio (HR) versus non-COVID-19 patients (HR 1.91, 95% CI 1.49–2.45). Conclusions We showed that the epidemiology of HABSI differed between COVID-19 and non-COVID-19 patients. Enterococcal HABSI predominated in COVID-19 patients. COVID-19 patients with HABSI had elevated risk of mortality. Trial registration ClinicalTrials.org number NCT03937245. Registered 3 May 2019. </jats:sec

Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study

Background Current management practices and outcomes in weaning from invasive mechanical ventilation are poorly understood. We aimed to describe the epidemiology, management, timings, risk for failure, and outcomes of weaning in patients requiring at least 2 days of invasive mechanical ventilation. Methods WEAN SAFE was an international, multicentre, prospective, observational cohort study done in 481 intensive care units in 50 countries. Eligible participants were older than 16 years, admitted to a participating intensive care unit, and receiving mechanical ventilation for 2 calendar days or longer. We defined weaning initiation as the first attempt to separate a patient from the ventilator, successful weaning as no reintubation or death within 7 days of extubation, and weaning eligibility criteria based on positive end-expiratory pressure, fractional concentration of oxygen in inspired air, and vasopressors. The primary outcome was the proportion of patients successfully weaned at 90 days. Key secondary outcomes included weaning duration, timing of weaning events, factors associated with weaning delay and weaning failure, and hospital outcomes. This study is registered with ClinicalTrials.gov, NCT03255109. Findings Between Oct 4, 2017, and June 25, 2018, 10 232 patients were screened for eligibility, of whom 5869 were enrolled. 4523 (77·1%) patients underwent at least one separation attempt and 3817 (65·0%) patients were successfully weaned from ventilation at day 90. 237 (4·0%) patients were transferred before any separation attempt, 153 (2·6%) were transferred after at least one separation attempt and not successfully weaned, and 1662 (28·3%) died while invasively ventilated. The median time from fulfilling weaning eligibility criteria to first separation attempt was 1 day (IQR 0–4), and 1013 (22·4%) patients had a delay in initiating first separation of 5 or more days. Of the 4523 (77·1%) patients with separation attempts, 2927 (64·7%) had a short wean (≤1 day), 457 (10·1%) had intermediate weaning (2–6 days), 433 (9·6%) required prolonged weaning (≥7 days), and 706 (15·6%) had weaning failure. Higher sedation scores were independently associated with delayed initiation of weaning. Delayed initiation of weaning and higher sedation scores were independently associated with weaning failure. 1742 (31·8%) of 5479 patients died in the intensive care unit and 2095 (38·3%) of 5465 patients died in hospital. Interpretation In critically ill patients receiving at least 2 days of invasive mechanical ventilation, only 65% were weaned at 90 days. A better understanding of factors that delay the weaning process, such as delays in weaning initiation or excessive sedation levels, might improve weaning success rates