Abstract: The automated supervision system for online teaching is volatile in current teaching observation. Hence, it requires additional comprehensive, analytical, and realistic discussion on how the automatic supervision method can be applied to high school teaching. This paper integrated remote supervision with machine learning algorithms (IRS-MLA) proposed for the online English teaching audit process. Here, IRS-MLA simulates the implementation of supervision methodologies in the teaching process according to English online teaching’s real needs. Furthermore, searching the performance and stating the learning process for students from the teachers’ perspectives and their students measures the teacher’s teaching process. This paper presents the studies for evaluating the classic English language online supervision and explores this method’s functional impact. This analysis’s findings show that the model developed in this paper worked well and validated based on the case study report. This study validates the proposed IRS-MLA with the highest performance ratio of 97.8%, the accuracy of 96%, the efficiency of 99.3%, and a success ratio of 98%, compared to existing models.

Abstract: Abstract As business environments become more dynamic and complex, it becomes indispensable for organizations to objectively analyze business processes, monitor the existing and potential operational frictions, and take proactive actions to mitigate risks and improve performances. Process mining provides techniques to extract insightful knowledge of business processes from event data collected during the execution of the processes. Besides, various approaches have been suggested to support the real-time (predictive) monitoring of the process-related problems. However, the link between the insights from the continuous monitoring and the concrete management actions for the actual process improvement is missing. Action-oriented process mining aims at connecting the knowledge extracted from event data to actions. In this work, we propose a general framework for action-oriented process mining covering the continuous monitoring of operational processes and the automated execution of management actions. Based on the framework, we suggest a cube-based action engine where actions are generated by analyzing monitoring results in a multi-dimensional way. The framework is implemented as a ProM plug-in and evaluated by conducting experiments on both artificial and real-life information systems.

Abstract: Abstract Clustering is an unsupervised learning method that divides data into groups of similar features. Researchers use this technique to categorise and automatically classify unlabelled data to reveal data concentrations. Although there are other implementations of clustering algorithms in R, this paper introduces the Clustering library for R, aimed at facilitating the analysis and comparison between clustering algorithms. Specifically, the library uses relevant clustering algorithms from the literature with two objectives: firstly to group data homogeneously by establishing differences between clusters and secondly to generate a ranking between the algorithms and the attributes of a data set to obtain the optimal number of clusters. Finally, it is crucial to highlight the added value that the library provides through its interactive graphical user interface, where experiments can be easily configured and executed without requiring expert knowledge of the parameters of each algorithm.

Abstract: Abstract Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today’s companies, the task of discovering the set of features that should be considered in causal analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible causal effect on the problem. The causal analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for causal analysis. We have implemented the proposed method as a plugin in ProM, and we have evaluated it using real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.

Abstract: Abstract Due to the growth of using various devices and applications in modern life, the amount of data available is skyrocketing, but labeling all of this data is beyond the reach of data scientists. Thus, it is necessary to categorize data with a small amount of labeled data. In fact, it should be possible to prioritize data for labeling. To achieve this goal in this study, we have used few-shot learning with active learning and also used the power of graph convolutional networks in classifying data with a graphical structure. To implement the proposed model, we use two graph convolutional networks in parallel to calculate the embedding and the importance of each node. Using the output of both networks, we create prototypes of classes, and then, we classify them according to the distance of each node of these prototypes. We have also used active learning to select data more intelligently, which improves the overall model performance. As well as this, we have tested our proposed model in the field of electronic commerce for tagging goods in big online stores, which encounter a large number of diverse products, where high accuracy categorization in a short time without the interference of human factor and with the help of artificial intelligence is needed to reduce costs. The results of implementing the model on the Amazon dataset and its comparison with the state-of-the-art models in this field show the superiority of our method.

Abstract: New models of random forests jointly using the attention and self-attention mechanisms are proposed for solving the regression problem. The models can be regarded as extensions of the attention-based random forest whose idea stems from applying a combination of the Nadaraya-Watson kernel regression and the Huber's contamination model to random forests. The self-attention aims to capture dependencies of the tree predictions and to remove noise or anomalous predictions in the random forest. The self-attention module is trained jointly with the attention module for computing weights. It is shown that the training process of attention weights is reduced to solving a single quadratic or linear optimization problem. Three modifications of the general approach are proposed and compared. A specific multi-head self-attention for the random forest is also considered. Heads of the self-attention are obtained by changing its tuning parameters including the kernel parameters and the contamination parameter of models. Numerical experiments with various datasets illustrate the proposed models and show that the supplement of the self-attention improves the model performance for many datasets.