In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.

A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact. Utilizing a dataset comprising 18,398 instances derived from a quality assurance protocol, we address the scarcity of break events (124 cases) that pose a challenge for machine learning predictive models. With the help of Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), we implement a novel data augmentation framework. This method ensures that the synthetic data mirrors the distribution of the real operational data but also seeks to enhance the performance metrics of predictive modeling. Before and after the data augmentation, we evaluate three different machine learning algorithms-Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). Utilizing the CTGAN-enhanced dataset, our study achieved significant improvements in predictive maintenance performance metrics. The efficacy of CTGAN in addressing data scarcity was evident, with the models' detection of machine breaks (Class 1) improving by over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. With this methodological advancement, this study contributes to industrial quality control and maintenance scheduling by addressing rare event prediction in manufacturing processes.

It's an unusual time to be in the paper and pulp industry. The markets for paper products change on an almost monthly basis. It can be difficult to know how you should respond to keep your operations productive, profitable, and efficient. Should you invest in automation for long-term efficiency gains? Or should you batten down the hatches and reduce spending until the markets level out? Several market forces have impacted the paper and pulp industry in recent years.

Today, the recycling market is changing rapidly due to global changes where the quality requirements of the incoming and outgoing material are increased. As a result, systems that are separating waste material from the target material need to be improved continuously to cope with this change. Stora Enso's Langerbrugge Mill in northwest Belgium, which is one of the largest paper mills in Europe, required a more effective paper-cardboard sorting solution and technology that can easily be retrained for anomaly detection. This technology was developed by Robovision, a company specializing in deep learning-based machine vision and robot programming, and Imec, which is the world-leading R&D and innovation hub in nanoelectronics and digital technologies within the framework of the AUTOWARE project. A big challenge in paper recycling is the separation of cardboard and waste materials from paper.

A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The primary purpose of the data is thought to be building a classification model for early prediction of the rare event. However, it can also be used for multivariate time series data exploration and building other supervised and unsupervised models.

Unstructured data is the most prevalent form of information on the planet. It exists in our e-mails, surveys, social media accounts, call center logs, etc. With a strong text analytics strategy in place, companies can get critical information from this data to drive better business decisions.