This study examines the regional data in transportation carbon emissions across China and investigates the shifting trends of the carbon emission centroid over time. Using the IPCC (2006) carbon emission calculation formula, emissions data from 30 provinces for the years 2005, 2010, 2015, and 2020 were analyzed using an Exploratory Spatial Data Analysis (ESDA) model. The Economic Centroid Model and standard deviation ellipse were applied to assess the movement of the carbon emission centroid, which was consistently located in Henan Province. Further analysis using the Kaya model identifies the key factors influencing transportation carbon emissions in Henan from 2005 to 2020. The findings offer insights into regional carbon reduction strategies and the challenges in achieving China's dual carbon goals.
The performance of Large Language Models (LLMs) is highly dependent on the input prompt, which has led to several recent studies focused on prompt optimization. Optimizing prompts for generation tasks that align with user intent is particularly challenging due to the subjective nature of these tasks and the prohibitive costs associated with the extensive human annotations required for repeated evaluations. This paper addresses the problem of optimizing prompts for LLMs in generation tasks by utilizing user pairwise comparison feedback instead of numerical scores. We propose a framework that integrates user pairwise comparison feedback into the prompt optimization process by maintaining a dynamically ranked set of prompts. In each iteration, a new prompt is generated based on previous user pairwise annotations. We evaluated our approach and an existing score-based method on 26 generation tasks based on the Big-Gen benchmark, demonstrating that our method requires only $\mathrm{O}(n)$ human annotations for n iterations, whereas the score-based method requires $\mathrm{O}(n^{2})$ annotations for the same number of iterations. Furthermore, we conducted at-test analysis, which indicated no statistically significant difference in effectiveness between our approach and the score-based method. These results suggest that our approach achieves comparable effectiveness while substantially reducing the annotation workload.
The General Data Protection Regulation (GDPR) provides data subjects with several rights ranging from data access to the erasure of personal data. Data subjects who want to use their rights must pose subject access requests (SARs) to the controllers. The practice has shown that controllers often do not properly authenticate subjects during requests, leading to potential disclosures of personal data to the wrong people. We propose an approach that provides strong security for SAR processes to prevent unwanted data leaks. The approach is also user-friendly, minimizing the hassles for users when posing SARs. We are the first to propose to use the Self-Sovereign Identity (SSI) paradigm for SARs.
Social media platforms have become essential in modern society, with usage rapidly increasing. These platforms serve various roles, from entertainment and personal expression to education and e-commerce. Users frequently share opinions, generating valuable but largely unstructured data. Businesses and governments utilize sentiment analysis on this data to understand public opinions and predict behaviors, aiding in the improvement of products, services, and policies. However, the unstructured na-ture of the data makes its analysis complex. This article provide a comprehensive evaluation of several machine learning (ML) mod-els, including Support Vector Machine (SVM), Bernoulli Naive Bayes (BernoulliNB), Random Forest (RF), Gradient Boosting (GB), K-nearest Neighbors (KNN), and Logistic Regression (LR) on Twitter data for Arabic sentiment classification. Additionally, it provides valuable insights into the preprocessing steps required to prepare the data for analysis. It also discusses the selection of effective feature extraction techniques for Saudi dialect and modern Arabic sentiment analysis to gauge their efficacy in analyzing social media data. Among various feature extraction methods, TF-IDF consistently demonstrated superior accuracy, with SVM emerging as the top-performing model at 92.4%. Finally, a public dataset focusing on customer sentiment towards Saudi banks is employed to further validate our findings and their generalizability across different domains such as government policies and commercial sectors.
