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Unsupervised and Distributional Detection of Machine-Generated Text [article]

Matthias Gallé, Jos Rozen, Germán Kruszewski, Hady Elsahar
2021 arXiv   pre-print
In this paper, we frame the problem in an unsupervised and distributional way: we assume that we have access to a large collection of unannotated documents, a big fraction of which is machine-generated  ...  The power of natural language generation models has provoked a flurry of interest in automatic methods to detect if a piece of text is human or machine-authored.  ...  We therefore frame the problem of detecting machine-generated texts as follows: given a set of documents, and the suspicion that a large fraction of them is generated by machine; is it possible to detect  ... 
arXiv:2111.02878v1 fatcat:swqvuzl5gjexlbjjanuqf6qjbq

Exploring the Recent Trends of Paraphrase Detection

Mohamed I., Wael H.
2019 International Journal of Computer Applications  
Also will give an idea about text similarity, machine learning and deep learning approaches.  ...  This study will focus on the discussion of recent studies of the PD methods and will categorize them in two categories, supervised learning and unsupervised learning.  ...  PARAPHRASE DETECTION BASED ON UNSUPERVISED APPROACH As mentioned above, the unsupervised learning on PD relies on text similarity features.  ... 
doi:10.5120/ijca2019918317 fatcat:7sxb27g42bdclgmj3pyd7uefn4

Authors Index

2021 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)  
El-Wakad An Influence of The Microfluidic channel Height and Distribution of Dielectrophoretic Force on The Impedance Extraction in Microfluidic Systems Mohammad El-Ramly CairoDep: Detecting Depression  ...  : A Machine Learning Approach In-Silico Screening of Potential Anti-Glycoprotein of Nipah Virus A Multi-agent Reinforcement Learning Risk Management Model for Distributed Agile Software Projects Determination  ...  Radwa Reda Attention Detection using Electro-oculography  ... 
doi:10.1109/icicis52592.2021.9694196 fatcat:bx3n453xyrafjbbbelaxqhmbfm

Artificial Intelligence and Big Data in Fraud Detection

Mehmet Emre ÖZENGEN, Ali OKATAN, Can BALKAYA
2021 EURAS Journal of Engineering and Applied Sciences  
Supervised machine learning, unsupervised machine learning or semi-supervised machine learning as well as adaptive machine learning techniques against adaptive attacks with the advantage of big data and  ...  On the other hand, artificial intelligence methods are used in fraud detection for increasing the efficiency of corporations.  ...  On the other hand, unsupervised machine learning will be the future of machine learning for detection of unknown attacks.  ... 
doi:10.17932/ejeas.2021.024/ejeas_v01i2001 fatcat:l7zdy6bfujfllkdb4pcrmpzbou

Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study [article]

Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, Cliff Brunk, Andrew Tomkins
2020 arXiv   pre-print
Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of "page quality", able  ...  Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning.  ...  RELATED WORK In this section, we briefly review work on text generation, human vs. machine detection, socially good and bad uses of neural generative models, and linguistic text quality.  ... 
arXiv:2008.13533v1 fatcat:skpjf4gs5ffhpfgrlhbbvncsqu

Approaches for Fake Content Detection: Strengths and Weaknesses to Adversarial Attacks

Matthew Carter, Michail Tsikerdekis, Sherali Zeadally
2020 IEEE Internet Computing  
However, these techniques have not been extensively explored beyond the analysis of fake text. Fake Text: Unsupervised machine learning has been used to detect fake news on social media.  ...  Unsupervised Machine Learning for Fake Content Detection Unsupervised machine learning allows for clustering of data without requiring an extensively annotated data set.  ... 
doi:10.1109/mic.2020.3032323 fatcat:xl5z7eos6jax7cbftjpl53jweq

Learning Representations Using RNN Encoder-Decoder for Edge Security Control

Wei Guo, Hexiong Chen, Feilu Hang, Yingjun He, Jun Zhang, Shakeel Ahmad
2022 Computational Intelligence and Neuroscience  
In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum.  ...  The most common form of machine learning is supervised learning.  ...  Machine Learning Methods. Machine learning methods can be divided into supervised learning methods and unsupervised learning methods [6] .  ... 
doi:10.1155/2022/4199044 pmid:35655517 pmcid:PMC9152390 fatcat:ale5d5uiofgd3cccanqvxycxa4

Comparative Analysis of Existing and a Novel Approach to Topic Detection on Conversational Dialogue Data

Haider Khalid, Vincent Wade
2022 Zenodo  
In this paper, we proposed unsupervised and semi-supervised techniques for topic detection in the conversational dialogue dataset and compared them with existing topic detection techniques.  ...  Topic detection in dialogue datasets has become a significant challenge for unsupervised and unlabeled data to develop a cohesive and engaging dialogue system.  ...  Another exemplar-based approach detects topics from short text and represents a topic as an exemplar.  ... 
doi:10.5281/zenodo.7063759 fatcat:zvyhj2elhnczngmkouoza72t4u

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Mingzhu Tang, Qi Zhao, Huawei Wu, Ziming Wang, Caihua Meng, Yifan Wang
2021 Frontiers in Energy Research  
In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses.  ...  These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed.  ...  Cost-sensitive Large Margin Distribution Machine for Fault Detection of Wind Turbines.  ... 
doi:10.3389/fenrg.2021.751066 fatcat:bzniyxsgofh3pltcnrxd6avhbq

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Ayman Mohamed Mostafa
2023 Intelligent Automation and Soft Computing  
Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects.  ...  Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms.  ...  The supervised sentiment analysis uses machine learning algorithms are applied on training and testing datasets while the unsupervised sentiment analysis is based on generating a lexicon for obtaining  ... 
doi:10.32604/iasc.2023.028041 fatcat:tw3obf4yvjbzpaxzppnqfjlocm

Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media

Bedoor Y. AlHarbi, Mashael S. AlHarbi, Nouf J. AlZahrani, Meshaiel M. Alsheail, Dina M. Ibrahim
2020 Journal of Information Technology Management  
Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms.  ...  In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques  ...  Machine learning, unsupervised learning.  ... 
doi:10.22059/jitm.2020.75796 doaj:28ca55dee9a14054b7c358c1fbf38e18 fatcat:qsr5fn7rzbb6bmncusokxblv4e

Can Generative Adversarial Networks Teach Themselves Text Segmentation?

Mohammed Al-Rawi, Dena Bazazian, Ernest Valveny
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
The results are promising, and to the best of our knowledge, constitute the first step towards reliable unsupervised text segmentation.  ...  Our work opens a new research path in unsupervised text segmentation and poses many research questions with a lot of trends available for further improvement.  ...  Acknowledgement This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 665919 and from the project TIN2017  ... 
doi:10.1109/iccvw.2019.00416 dblp:conf/iccvw/0002BV19 fatcat:orqvue7e45c25n6b535xbkre6e

Polarity Detection of Foursquare Tips [chapter]

Felipe Moraes, Marisa Vasconcelos, Patrick Prado, Daniel Dalip, Jussara M. Almeida, Marcos Gonçalves
2013 Lecture Notes in Computer Science  
This paper presents an empirical study of supervised and unsupervised techniques to detect the polarity of Foursquare tips.  ...  However, the automatic detection of polarity of tips faces challenges due to their short sizes and informal content.  ...  Acknowledgments This research is partially funded by the Brazilian National Institute of Science and Technology for the Web (MCT/CNPq/INCT grant number 573871/2008-6), CNPq, CAPES and FAPEMIG.  ... 
doi:10.1007/978-3-319-03260-3_14 fatcat:io6vddaqhzexpehboas7muqlwa

Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics [article]

Shilin He, Jieming Zhu, Pinjia He, Michael R. Lyu
2020 arXiv   pre-print
In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research  ...  In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications,  ...  There are mainly two categories of anomaly detection approaches: supervised and unsupervised.  ... 
arXiv:2008.06448v1 fatcat:peiwdzvb6fdjbbrucwxy7j5b7i

Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Yi Ji Bae, Midan Shim, Won Hee Lee
2021 Sensors  
This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts.  ...  texts.  ...  We demonstrated that our approach using machine learning methods and social media texts can be effectively used to detect signs of schizophrenia.  ... 
doi:10.3390/s21175924 pmid:34502815 fatcat:vwwoqgk24vcibp4i6ik65b6kwu
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