Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT

With ChatGPT under the spotlight, utilizing large language models (LLMs) for academic writing has drawn a significant amount of discussions and concerns in the community. While substantial research efforts have been stimulated for detecting LLM-Generated Content (LLM-content), most of the attempts are still in the early stage of exploration. In this paper, we present a holistic investigation of detecting LLM-generate academic writing, by providing a dataset, evidence, and algorithms, in order to inspire more community effort to address the concern of LLM academic misuse. We first present GPABenchmark, a benchmarking dataset of 600,000 samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of research papers in CS, physics, and humanities and social sciences (HSS). We show that existing open-source and commercial GPT detectors provide unsatisfactory performance on GPABenchmark, especially for GPT-polished text. Moreover, through a user study of 150+ participants, we show that it is highly challenging for human users, including experienced faculty members and researchers, to identify GPT-generated abstracts. We then present CheckGPT, a novel LLM-content detector consisting of a general representation module and an attentive-BiLSTM classification module, which is accurate, transferable, and interpretable. Experimental results show that CheckGPT achieves an average classification accuracy of 98% to 99% for the task-specific discipline-specific detectors and the unified detectors. CheckGPT is also highly transferable that, without tuning, it achieves ~90% accuracy in new domains, such as news articles, while a model tuned with approximately 2,000 samples in the target domain achieves ~98% accuracy. Finally, we demonstrate the explainability insights obtained from CheckGPT to reveal the key behaviors of how LLM generates texts

Choosing between Order-of-Entry Assumptions in Empirical Entry Models: Evidence from Competition between Burger King and McDonald’s Restaurant Outlets

We demonstrate how a non-nested statistical test developed by Vuong (1989) can be used to assess the suitability of alternate order-of-entry assumptions used for identification purposes in empirical entry models. As an example, we estimate an entry model of McDonald’s and Burger King restaurant outlets in United States. The data set focuses on relatively small “isolated” markets. For these markets, the non-nested tests suggest that order-of-entry assumptions that give Burger King outlets a first-mover advantage are statistically preferred. Last, a Monte Carlo experiment provides encouraging results suggesting that the Vuong-type test yields reliable results within the entry model framework

Choosing between Order-of-Entry Assumptions in Empirical Entry Models: Evidence from Competition between Burger King and McDonald’s Restaurant Outlets

We demonstrate how a non-nested statistical test developed by Vuong (1989) can be used to assess the suitability of alternate order-of-entry assumptions used for identification purposes in empirical entry models. As an example, we estimate an entry model of McDonald’s and Burger King restaurant outlets in United States. The data set focuses on relatively small “isolated” markets. For these markets, the non-nested tests suggest that order-of-entry assumptions that give Burger King outlets a first-mover advantage are statistically preferred. Last, a Monte Carlo experiment provides encouraging results suggesting that the Vuong-type test yields reliable results within the entry model framework

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

High fear of cancer recurrence in Chinese newly diagnosed cancer patients

Authors thank the President Foundation of Nanfang Hospital, Southern Medical University (2007L001), and the Guangzhou Science and Technology Project (201804010132) for funding the study.Background: Fear of cancer recurrence (FCR) is common among cancer patients and of high clinical relevance. This study explores the prevalence and correlates of FCR in Chinese newly diagnosed cancer population. Methods: This is a multicentre, cross-sectional study that includes 996 patients with mixed cancer diagnosis. All recently diagnosed patients completed a questionnaire consisting of the following: Fear of Progression Questionnaire-Short Form (FoP-Q-SF), General Anxiety Disorder Questionnaire (GAD-7), and Patient Health Questionnaire (PHQ-9). Univariate analyses, multivariate logistic regression analyses, and structural equation modeling (SEM) was performed to examine the association between tested variables and FCR. Results: Of the 996 patients, 643 (64.6%) reported high FCR (scored ≥ 34 in the FoP-Q-SF). Chemotherapy (OR = 1.941), Childhood severe illness experience (OR = 2.802), depressive (OR = 1.153), and anxiety (OR = 1.249) symptoms were positively associated with high FCR, while higher monthly income (OR = 0.592) was negatively associated with high FCR. SEM indicated that emotional disturbances (anxiety and depression) directly influenced FCR, while emotional disturbances partly mediated the association between personal monthly income and FCR. Conclusion: High FCR is a frequently reported problem among newly diagnosed cancer patients. Various factors increased the likelihood of the development of FCR. Flexible psychological interventions are needed for patients with high FCR.Publisher PDFPeer reviewe

Research on the Evaluation System of Scientific Research Ethics Based on AHP-Fuzzy Comprehensive Evaluation

Scientific research ethics is the value concept and code of conduct to be followed in scientific research, technological development and other scientific and technological activities. However, in recent years, some researchers have ignored ethical constraints in scientific research activities, resulting in more prominent ethical issues in scientific research. This study takes China Southern Power Grid Corporation as an example, based on ISO9000 standards, uses AHP-fuzzy comprehensive evaluation theory to establish a scientific research ethics evaluation system, aiming at improving the timeliness and targeting of scientific research ethics management