In recent years, electric vehicles (EVs) have emerged as a sustainable alternative to conventional automobiles. Distinguished by their environmental friendliness, superior performance, reduced noise, and low maintenance requirements, EVs offer numerous advantages over traditional vehicles. The integration of electric vehicles with cloud computing has heralded a transformative shift in the automotive industry. However, as EVs become increasingly interconnected with the internet, various devices, and infrastructure, they become susceptible to cyberattacks. These attacks pose a significant risk to the safety, privacy, and functionality of both the vehicles and the broader transportation infrastructure. In this paper, we delve into the topic of electric vehicles and their connectivity to the cloud. We scrutinize the potential attack vectors that EVs are vulnerable to and the consequential impact on vehicle operations. Moreover, we outline both general and specific strategies aimed at thwarting these cyberattacks. Additionally, we anticipate future developments aimed at enhancing EV performance and reducing security risks.

Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools' curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning.

An imbalanced dataset is characterized by a substantial disparity in the distribution of examples among its classes, with one class containing significantly more instances than others. Most of the credit fraud datasets are imbalanced. Addressing the challenges posed by imbalanced datasets in classification problems is a complex task, as many classification algorithms struggle to provide satisfactory performance under such conditions. In this article, we have conducted a comparative analysis of various classifiers to assess their performance in handling imbalanced data related to credit card fraud. Then, we employed the Synthetic Minority Oversampling Technique (SMOTE) to synthesize imbalanced data into a relatively balanced dataset. Subsequently, we reevaluated the classification results using different classifiers. Ultimately, our findings revealed that the Naive Bayes classifier was less sensitive to the dataset imbalance, which the AUC score increase rate is 40.19%, the KNN classifier is the most sensitive one, the AUC score increase rate is 61.27%. In all, AdaBoost and Random Forest perform much higher AUC score, which are both higher than 95% after the SMOTE.