LPN-Based Model Repair Method for Changed Business Processes

Information systems play an important role in realizing business processes. Process mining uses event logs produced by information systems to construct the models of business processes. Then, the business process model can be used to verify and improve business processes. To represent a changing business process, some structures of the process model need to be replaced with other structures. However, existing model repair methods do not consider replacing the structures of the process model. This work proposes a logical Petri net-based approach to repair models based on the order relations among activities. First, we construct two relation sets of an event log and a process model, respectively. Comparing the two relation sets, the differences between a process model and an event log are collected in a deviation set. According to the deviation set, the model is repaired by constructing concurrent structures. Finally, we perform some experiments to illustrate the effectiveness and correctness of the proposed approach. The results show that the proposed method produces a more precise and simpler repaired model compared to state-of-the-art methods

Constructing and Repairing Process Models Containing Loop Return Structures via Logic Petri Nets

Business processes realized by enterprise information systems are usually designed and verified in advance by process models. Event logs generated from such systems can be used to ensure the correctness of the business process. With the upgrade of systems or changing of customers’ requests, it often appears that the actual behaviors observed in event logs are not consistent with those in process models. Process mining techniques are thus used to construct and repair process models by mining event logs. In this work, we propose a new model repair approach based on logic Petri nets (LPNs). It can repair a Petri-net-based process model containing a loop return structure. According to alignments, relations between the activities in event logs and the process models are analyzed such that the deviations can be found. Then, logic expressions of logic transitions are constructed and an LPN-based process model is thus constructed. The repaired model can accurately describe the same processes as the event logs. Finally, some cases related to a thoracic surgery process in a hospital are given. The correctness and effectiveness of the proposed approach are illustrated by experiments

Human Emotion Recognition by Facial Expression Using Modified EfficientNet-B3 Model

Human emotion plays a critical role in the purpose of communication, with the correct detection of emotion, one can analyze the human feeling without even asking, as it is non-verbal communication method. Human emotion through facial expression is one of the highlighted topics of study because of its wide range of application from robotics, security, artificial intelligence, marketing and health monitoring where human-computer interaction (HCI) is a key ingredient. To tackle the challenges that arise in accurate human facial emotion recognition, a modified version of the EfficientNet-B3 model is proposed, which shows promising results for emotion detection using the FER-2013 dataset. By leveraging the universal FER-2013 dataset, which contains more than 35 000 grey-scale human facial images, the proposed model aims to improve recognition performance. The modified architecture incorporates EfficientNet-B3 as the base model, with an additional batch normalization layer followed by a dense layer and an output layer. The model has been trained on the facial dataset while considering these crucial factors. Performance evaluation of the model is conducted using confusion matrix, precision, recall, accuracy, and F1 score as performance metrics. Remarkably, the proposed model achieved an impressive accuracy of 92 % for training and 83.0 % for validation of the dataset. This implies that the proposed model yields highly accurate emotion recognition with a given dataset and can provide improvement in the overall efficiency of emotional recognition applications, particularly in human-to-machine interactions

Depth-Wise and Depth-Wise Separable YOLO Models for Concealed Object Detection Using Terahertz Images

Terahertz imaging is highly effective for detecting concealed objects due to its non-harming nature and its ability to penetrate materials like clothes, paper and plastic, etc. In environment where detection technologies are limited, terahertz imaging emerges as one of the effective and safest methods available. Unlike techniques such as X-rays, it does not emit harmful radiation, making it suitable for surveillance applications. However, many existing object detection models are computationally intensive which can hinder their deployment in real time or resource constrained environments. To address this issue, traditional convolutional operations in the deep learning models have been replaced with depth-wise convolutions and depth-wise separable convolutions in proposed approach. These modifications significantly reduce the number of trainable parameters and computational load during model training. The optimized architecture has been integrated into widely used object detection models – namely YOLOv5m and YOLOv8m, using terahertz images of concealed objects as input. This integration enhances training efficiency with minimal loss in accuracy, making the models more suitable for deployment on devices with limited computational power and memory

A Travel Point-of-Interest Recommendation Algorithm Incorporating Social Features and Logistic Matrix Factorisation

With the growing demand for personalised travel experiences, the development and application of travel point-of-interest (POI) recommendation systems have become increasingly important. However, many existing systems often underperform owing to insufficient integration of social features and contextual information. To address this issue, the S-LMF algorithm is proposed, combining social features with logistic matrix factorisation to improve recommendation accuracy. This approach simulates social influence by incorporating joint check-in similarity and friendship factors, while logistic matrix factorisation leverages check-in frequency data to refine POI recommendations. The effectiveness of social features and logistic matrix factorisation (S-LMF) was tested against five baseline algorithms using publicly available data sets from Yelp and Gowalla. Results demonstrated that S-LMF outperformed the best baseline model by improving Precision@20 by 22.95% on Yelp and 28.60% on Gowalla. Moreover, it increased Recall@10 by 17.95% on Yelp and 8.19% on Gowalla

CDGAN: Collaborative Diffusion Generative Adversarial Networks for Recommendation Systems

Deep generative models are widely used in recommendation systems because of their ability to deal with uncertainty by learning inherent data distribution. Among deep generative models, Generative Adversarial Networks (GAN) perform well in recommendation tasks. However, existing Collaborative Filtering (CF) recommendation algorithms based on GAN generally have problems such as mode collapse and training instability, which are further aggravated by sparse and noisy recommendation data. To solve these problems, a Collaborative Diffusion Generative Adversarial Networks (CDGAN) framework for recommendation systems is proposed in this paper. Specifically, CDGAN framework is mainly composed of three parts: feature encoder, diffusion generator, and self-attention discriminator. The feature encoder extracts the rating information and side information to obtain potential feature vectors to alleviate the data sparsity problem. The diffusion generator simulates the complex nonlinear mode of the user-item interaction matrix through forward diffusion and reverse denoising to reconstruct the user-item interaction matrix to alleviate the problem of data noise. The self-attention discriminator obtains the user\u27s specific behaviors and preferences through the self-attention mechanism to improve the discriminator\u27s discriminating ability. Furthermore, a corresponding CDGAN recommendation algorithm is designed based on our proposed CDGAN framework. Comprehensive experiments are conducted on three real-world recommendation datasets. The experimental results indicate that, when compared with multiple representative recommendation models, the proposed CDGAN model achieves superior performance in the evaluation metrics Precision and Recall on all datasets, thereby proving its effectiveness

Multi-Scale Multi-Load Federated Forecasting Method with Mode Decomposition

Accurate load forecasting is the premise of efficient and stable operation of integrated energy systems. For multiple integrated energy systems that have insufficient energy consumption data but similar energy consumption behavior, federated learning can establish a higher accuracy multi-load forecasting model for each system without disclosing data privacy. However, the existing federated learning methods cannot fully utilize common and individual characteristics in the energy consumption data of different nodes (that is, integrated energy systems), which obviously affects their prediction accuracy. In view of this, we propose a multi-time scale multi-load federated forecasting method based on mode decomposition (MD-MMFF). Firstly, a multivariate empirical mode decomposition method is introduced to decompose the energy consumption data of each node into two types, i.e., regular components and irregular components. Each node uses the local LSTM model to learn the regular components and predict their outputs. For the irregular components, a multi-load federated forecasting training mechanism based on knowledge distillation is proposed, and the corresponding multi-load forecasting model is jointly established for each node. Then, the predicted values of regular components and irregular components are integrated to obtain the final multivariate load forecasting results. Experimental results show that compared with the existing multi-load forecasting algorithms, the proposed MD-MMFF method can obtain higher accuracy multi-source load forecasting results

CF-YOLO: Towards Highly Effective Small Face Detection in Crowded Scenes

To address the issue of low recall rates in detecting small faces within crowded scenes, this paper conducts an analysis of the primary reasons behind this challenge and introduces a real-time face detection system named CF-YOLO (Crowded-Face-YOLO). The study identifies a crucial factor contributing to this problem, which is the insufficient provision of positive samples for small faces during the training phase by conventional face detectors. To tackle this limitation, a Sa-SimOTA strategy is proposed to enhance the availability of positive samples for small targets. Additionally, in the post-processing stage, the utilization of the non-maximum suppression (NMS) algorithm for assigning optimal bounding boxes to detected faces is discussed. The traditional fixed threshold employed in the NMS algorithm for decision-making often results in the loss of small face detection boxes in crowded scenarios. To alleviate this issue, a Soft-Face-NMS algorithm is introduced, which incorporates facial feature variables into the Soft-NMS algorithm for weighted processing, facilitating the selection of face boxes with higher confidence in overlapping regions. Furthermore, to augment the feature extraction capabilities of the YOLO backbone, an EMA+ attention module is proposed, and modifications are made to the network structure of YOLOv7 to enhance the extraction of more effective features conducive to small face detection. The proposed model demonstrates impressive accuracy rates of 97.3 %, 96.4 %, and 92.8 % on the easy, medium, and hard subsets of the Wider-Face dataset, respectively. Notably, the accuracy achieved on the hard subset approaches the state-of-the-art level, which further demonstrates the effectiveness of our proposed approach for face detection in crowded scenes

Performance Analysis and Optimization Method Based on Petri Net for Manufacturing Execution System

The Manufacturing Execution System (MES) is a critical component of intelligent manufacturing systems, enabling the automation and intelligent control of production processes. However, conventional MESs generally lack sufficient flexibility in coping with process reconfiguration and dynamic resource variations, thereby resulting in production bottlenecks and prolonged manufacturing time. To alleviate production bottlenecks while reducing overall manufacturing time, we propose a modeling, analysis, and resource allocation optimization framework for MESs. This framework employs rewritable timed Petri nets (RTPNs) to model and analyze MES behavior. Furthermore, a resource allocation optimization algorithm is developed to minimize production time. A clothes customization manufacturing system is adopted as a case study to demonstrate the effectiveness of the proposed method. The production process is reconstructed and optimized based on the RTPN model, and system performance is validated through simulation. Experimental results indicate that the proposed method significantly reduces production blocking and waiting rates, thereby improving overall operational efficiency

An Improved Genetic Algorithm for Solving the Clustered Steiner Tree Problem

In a complex network comprising many devices, a set of nodes may be partitioned into multiple local clusters with distinct functions, properties, or communication protocols. Thus, there has been an increase in network design problems with additional constraints regarding the clustering of vertices, one of which is the Clustered Steiner Tree Problem – a variant of the Steiner Tree Problem. There have been a few studies working on this problem in the literature, but they either solve it only in the metric case, or their exploration capability remains limited. Therefore, their results are not good in many cases. To overcome the drawbacks, we propose a Priority-Based Genetic Algorithm to solve the Clustered Steiner Tree Problem. The proposed algorithm maintains a balance between exploration and exploitation to prevent the search from getting stuck in local optima. Experiments and comparisons to existing works in non-metric and metric cases are carefully conducted to prove the remarkable performance of the proposed algorithm