823 datasets

NCL Data2026 · dataset

<p dir="ltr">This item contains the code and derived data for the paper "Equivalent Modulo the Ledger: Document-Grounded Accounting Identities for Verifying Financial Programs" (submitted to Knowledge-Based Systems).</p><p dir="ltr">Contents:</p><p dir="ltr">(1) code/ – the identity miner, the three-outcome equivalence checker, labelling, verifier training and evaluation scripts;</p><p dir="ltr">(

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains. It takes many different forms and continues to shapeshift as technology advances. Though it arguably most widely spreads in textual form, e.g., th

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown

Online reviews are pivotal in influencing consumer purchasing decisions, making the detection of fake reviews and the prediction and ranking of review helpfulness critical areas of study. This thesis presents a comprehensive exploration of various Machine Learning solutions to address these challenges, employing an array of data representations and advanced neural network models. Initially, the in

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown

This thesis investigates the economic importance of oil narratives in affecting financial markets over the period 1984 to 2018. Over this period oil narratives are of particular interest due to geopolitical events that are communicated through media and influencing the oil market. Over 100,000 oil-related articles from Wall Street Journal and New York Times are collected and narratives are identif

ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown

A Hardware Trojan (HT) is a malicious circuit inserted into an Integrated Circuit (IC) or a malicious modification of a circuit to change its behaviour or leak secret information. As the production of ICs is now distributed globally through the use of third parties in the outsourcing of design services, the use of off-the-shelf intellectual property (IP) and different foundry services, there are m

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p dir="ltr">Its the zip file of Replay data</p>

ZivaHub + Deakin Research Online + DMU Figshare + UCL Research Data Repository2026 · dataset · unknown

The transition from requirements analysis to design has always been a difficult task in the software development process. Natural Language (NL) requirements descriptions of the problem domain, captured from the stakeholder, are commonly unclear and open to misinterpretation. To date, automated Natural Language Processing (NLP) tools for requirements elicitation and modelling have only limited expl

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown

Deep neural networks, and Transformer models in particular, have achieved unprecedented success in natural language processing tasks. Despite this success, they are infamous for their status as black boxes. Specific details on how they encode and process high-level linguistic task-relevant information remain difficult to characterise by humans. This hinders trust and reliability, particularly in c

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset

The field of journalism plays a powerful role in shaping public discourse, yet its traditional authority is fundamentally challenged by contemporary false information, epitomised by the form of ‘fake news’ that emerged from the 2016 US election. This thesis explores how false information disrupts journalistic practice and authority in a complex and contested media landscape. It argues that while f

ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset · unknown

The prevalence of false information in online health articles, particularly highlighted during the COVID-19 pandemic, poses significant risks as people increasingly seek health-related advice online. While advances in machine learning (ML) and natural language processing (NLP) offer potential tools to help identify false health information, existing research has largely focused on political news.

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>Comparison of Evaluation Metrics Before and After Fine-Tuning the LSTM Model.</p>

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p>Results Before and After Fine-Tuning XGBoost - 500 datasets.</p>

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>Comparison of Performance Metrics Before and After Fine-Tuning XGBoost- 1000 Dataset.</p>

Teesside University Research Data Repository2026 · dataset

This dataset is developed for Standard Bangla to regional Bangla dialect translation. It consists of 2,500 Standard Bangla source sentences collected from the Vashantor dataset, along with corresponding English and Banglish representations and translations into seven regional varieties: Pabna, Noakhali, Jashore, Rangpur, Mymensingh, Barishal, and Chittagong. Pabna, Noakhali, Jashore, and Rangpur c

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<div><p>Cross-lingual plagiarism detection (CLPD) remains a critical challenge in Natural Language Processing, particularly for low-resource language pairs where translation and paraphrasing obscure source materials. This study introduces a robust framework for Persian-English CLPD by integrating state-of-the-art sentence representations with optimized machine learning classifiers. We utilized the

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p>Results Before and After Fine-Tuning the LSTM Model – 500 datasets.</p>

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p>Results Before and After Fine-Tuning XGBoost - 1000 datasets.</p>

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p>Overview of the Dataset Classes and Generation Methods.</p>

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>XGBoost (a) performance metrics, (b) Confusion matrix – Scaled up dataset.</p>

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<div><p>Cross-lingual plagiarism detection (CLPD) remains a critical challenge in Natural Language Processing, particularly for low-resource language pairs where translation and paraphrasing obscure source materials. This study introduces a robust framework for Persian-English CLPD by integrating state-of-the-art sentence representations with optimized machine learning classifiers. We utilized the

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<div><p>Cross-lingual plagiarism detection (CLPD) remains a critical challenge in Natural Language Processing, particularly for low-resource language pairs where translation and paraphrasing obscure source materials. This study introduces a robust framework for Persian-English CLPD by integrating state-of-the-art sentence representations with optimized machine learning classifiers. We utilized the

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>Comparison of Performance Metrics Before and After Fine-Tuning LR- 1000 Dataset.</p>

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>Confusion Matrices Before and After Fine-Tuning the LSTM Model.</p>

ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue

<p>Comparative Table of Related Works and This Research.</p>

ZivaHub + Deakin Research Online + DMU Figshare2026 · Astronomical catalogue

<p>LSTM (a) performance metrics, (b) Confusion matrix.</p>