16 research outputs found
Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model
The large language based-model chatbot ChatGPT gained a lot of popularity
since its launch and has been used in a wide range of situations. This research
centers around a particular situation, when the ChatGPT is used to produce news
that will be consumed by the population, causing the facilitation in the
production of fake news, spread of misinformation and lack of trust in news
sources. Aware of these problems, this research aims to build an artificial
intelligence model capable of performing authorship attribution on news
articles, identifying the ones written by the ChatGPT. To achieve this goal, a
dataset containing equal amounts of human and ChatGPT written news was
assembled and different natural processing language techniques were used to
extract features from it that were used to train, validate and test three
models built with different techniques. The best performance was produced by
the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving
91.57\% accuracy when tested against the data from the testing set
UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning
Consider a scenario where an author-e.g., activist, whistle-blower, with many
public writings wishes to write "anonymously" when attackers may have already
built an authorship attribution (AA) model based off of public writings
including those of the author. To enable her wish, we ask a question "Can one
make the publicly released writings, T, unattributable so that AA models
trained on T cannot attribute its authorship well?" Toward this question, we
present a novel solution, UPTON, that exploits black-box data poisoning methods
to weaken the authorship features in training samples and make released texts
unlearnable. It is different from previous obfuscation works-e.g., adversarial
attacks that modify test samples or backdoor works that only change the model
outputs when triggering words occur. Using four authorship datasets (IMDb10,
IMDb64, Enron, and WJO), we present empirical validation where UPTON
successfully downgrades the accuracy of AA models to the impractical level
(~35%) while keeping texts still readable (semantic similarity>0.9). UPTON
remains effective to AA models that are already trained on available clean
writings of authors
Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications
International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201
Crossing linguistic barriers: authorship attribution in Sinhala texts
Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. The author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application in languages such as Sinhala due to linguistic disparities and a lack of language processing tools. We present the first comprehensive study on cross-topic authorship attribution for Sinhala texts and propose a solution that can effectively perform the authorship attribution task even if the topics within the test and training samples differ. Our solution consists of three main parts: (i) extraction of topic-independent stylometric features, (ii) generation of a small candidate author set with the help of similarity search, and (iii) identification of the true author. Several experimental studies were carried out to demonstrate that the proposed solution can effectively handle real-world scenarios involving a large number of candidate authors and a limited number of text samples for each candidate author
Building information modeling (BIM) adoption among Libyan construction organizations : the moderating effect of organizational culture
Building Information Modelling (BIM) is well-known in the construction sector as an important tool for improving organizational performance. In this sense, worldwide BIM adoption is rapidly expanding, however this new phenomenon is not growing at the same rate as in Libya. Despite the fact that BIM has been existed for over 20 years, construction organizations in Libya are still struggling to adopt integrated BIM technology. Although previous studies have looked at the factors that influence technology adoption, there are still crucial concerns that have not been completely investigated and must be addressed. They include: (1) Previous research on the factors that influence BIM adoption has yielded inconclusive results. As a result, further study is needed to investigate potential moderators in the processes of a firm experiencing, interpreting, and controlling internal and external important factors. Investigating the moderating influence of organizational culture may assist in resolving inconsistencies in prior studies. (2) Despite BIM processes requiring organization-wide adoption, However only few study sought to integration of variables of the most important theories at organisations level such as TOE , DOI and INT, the constructs of these of theories have not clearly identified the factors that influence BIM adoption in construction organisations , especially in Libya construction organisations. As a result, the aim of this study is to close these gaps by identifying the variables impacting BIM technology adoption in Libya. Organizational culture has been applied to understand the moderating effect between influential factors and BIM adoption. An integrated research model was constructed based on the Technology Organization Environment (TOE) theory to explain the relative effect of seven known factors. The information was gathered through a survey of 411 Libyan construction organization. PLS-SEM (Partial Least Squares-Structural Equation Modelling) was used to analyse the data, evaluate the measurement and structural model, and test the hypotheses. According to the data, Libyan construction enterprises are not technological sophisticated, and they continue to use common technologies such as 2D CAD. The path analysis results demonstrated that the technological factors (Perceived Relative Advantage and Compatibility) related positively to BIM adoption, while Complexity related negatively to BIM adoption. Organizational factors (top management support) also related positively with environmental factors (Coercive Pressure) on the adoption of BIM. Organizational culture was also found to have a moderating effect on the relationship between environmental factors (Normative pressure) and the adoption of BIM in Libyan construction organizations. The study's findings provide significant insight into important factors that might increase the level of BIM adoption. In summary, the integration of the research model gave a comprehensive explanation for BIM adoption in organizations. The adoption of BIM could serve as a base for future research in other emerging technology adoptions in organizations
DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC
This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data
Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic
This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data