Vol. 44 · No. 4

Multi-modal transportation, emerging as a sustainable travel option, has shown immense promise in reducing passengers' travel expenses and vehicles' energy consumption. To further promote green travel, this work studies a multi-modal route planning problem, focusing on the integration of shared taxis and buses. The objective is to devise an innovative route planning approach for shared taxis, enabling passengers a seamless transition between the two modes and arrive at their destinations within designated timeframes. It designs a new pricing rule and establishes a multi-objective optimization that takes into account both the interests of passengers and shared taxi operators. The objectives are to minimize the aggregate cost incurred by all passengers and the overall travel distance traversed by shared taxis, and maximizes the revenue earned per kilometer by shared taxi operators. A novel nondominated linear sorting genetic algorithm (NLSGA) is introduced to tackle the problem. This algorithm incorporates innovative evolution and selection strategies to preserve solution diversity and enhance convergence speed. NLSGA demonstrates superior performance compared to several widely used multi-objective optimization algorithms, including NSGA-II, MOPSO, and MOGWO. Experimental results reveal that the proposed algorithm effectively reduces passengers' cost and shared taxis' travel distance while simultaneously maximizing revenue per kilometer for shared taxi operators.

Given the widespread occurrence of global telecom fraud, the development of proactive measures for crime prevention and control has become increasingly crucial. This study introduces a data-driven Bayesian Network (BN) model, which incorporates D-S evidence theory to integrate prior knowledge for fraud risk analysis. Through the examination of real-world case data, the study identifies key risk-influencing factors (RIFs) and uncovers causal relationships by comparatively evaluating three structure learning algorithms: Peter-Clark (PC), Bayesian Search (BS), and Greedy Thick Thinning (GTT). A robust Directed Acyclic Graph (DAG) is then constructed, and the Expectation-Maximization (EM) algorithm is employed to estimate conditional probability distributions. The proposed model effectively captures the causal relationships and nonlinear complexities among RIFs. To validate the model's applicability, scenario reasoning and sensitivity analysis are conducted, confirming its effectiveness in prioritizing RIFs and supporting informed decision-making. This research presents a novel and practical framework for public security agencies to develop proactive strategies for telecom fraud prevention and control.

The business processes in the information system are complex and diverse, and a single machine learning method often relies excessively on the noise or specific patterns in the training data. When dealing with large datasets, the calculation amount of the model is heavy, resulting in poor performance on new data, and it is difficult to achieve accurate monitoring and prediction of business processes. For this reason, a two-layer machine learning framework is presented using stacking technology – Serial Stacking Framework. Based on the event log, the method carries out random grouping sampling with placement, trains the multi-objective regression model, and applies multiple machine learning models to predict in series. Generally speaking, it is to use the prediction results of the previous model to generate training data and use it for the prediction of the latter model, in order to achieve the sequential accumulation of the prediction efficiency of multiple models. Random Forest and XGBoost are used as specific stack ensemble models for prediction, and the proposed method is evaluated against the existing advanced method through experiments. The results show that the average absolute error of the model built by the serial stacking framework with random group sampling and multi-objective regression is at least 2.14

Service description suffers from short texts and contains few repeated words, which brings challenges to generate high-quality service function vector (SFV) in service clustering. Some works introduce service association to improve service clustering quality. However, they simply introduce single associations, such as tag associations or collaboration association. Single service association can only improve the clustering quality from one perspective of positive or negative categorical relevance. In this study, we propose semantic enhancement and heterogeneous correlation guided Web Service Clustering. A high-performance contrastive learning framework is employed to generate SFVs. Meanwhile, we propose a method for the semantic enhancement of SFVs by obtaining twin service descriptions through verb substitution. A heterogeneous association is established based on tag association and collaboration association. It quantitatively enhances the clustering quality from both positive and negative categorical relevance. Experiments show that the proposed method outperforms popular semantic enhancement ways in generating high-quality SFVs. The heterogeneous association can significantly improve service clustering quality compared to single tag association or collaborative association. The clustering quality obtained by our method is improved by 13.7

This paper focuses on the analysis of rolling bearing vibration signal, presenting a comprehensive investigation into vibration signal analysis and fault signal feature extraction methods. The research primarily investigates a Variational Modal Decomposition (VMD) method, with enhancements made through the Tucked Swarm Algorithm (TSA) optimization and the use of Maximum Correlated Kurtosis Deconvolution (MCKD). It proposes a method for identifying the optimal parameter configurations for VMD. The proposed method is applied to analyze the rolling bearing vibration signal, and its efficacy in feature extraction has been validated through comparative analysis. This study employs a feature extraction methodology using kurtosis, envelope spectral kurtosis, and other indicators as basic features of vibration signals. It constructs a multi-feature feature vector dataset and utilizes the Least Squares Support Vector Machine (LSSVM) as a fault type classifier to validate the effectiveness of the proposed feature extraction method. The results demonstrate that the fault identification accuracy achieved by the proposed method consistently exceeds 96

Information security is an important issue in the design and development of information systems. As a well-known information security policy, Chinese Wall policy concerns the conflict of interest among sensitive information items. Since it is widely applied in many fields, it is important to explore the verification methods. Petri nets are a widely used formal method in the modeling and verification of information systems, and they are suitable for verifying Chinese Wall policy due to the capability of characterizing the concurrency. Particularly, some studies utilize colored Petri nets for modeling and verification of Chinese Wall policy. However, they do not characterize data operations including read, write and delete, which may affect the verification results. In this paper, we utilize Petri nets with data (PD-nets) to model and verify this policy. Specifically, we propose PD-nets for Chinese Wall policy to depict the control-flows, data-flows and data operations of information systems and introduce configurations and reachability graphs to describe the running states. We give theorems to prove the correctness of our method. Based on these theorems, we develop an algorithm to detect the violations of Chinese Wall policy. Furthermore, a case study is presented to show the effectiveness of our method, especially in modeling data operations and verifying their relevant CW policy.

Maintaining stable and orderly intelligent autonomous driving behavior in a closing scene is an important challenge. Compared with traditional chaos caused by an individual autonomous vehicle based on central control, when it breaks down, an intelligent cooperative autonomous driving group may effectively mitigate or alleviate the issue. There is no method to formulate an autonomous vehicle group and analyze its cooperative behavior by taking the aggregation, leading node change rate, and algorithm complexity of a vehicle group into account. This work formulates an aggregation degree-based Cooperative Model for Autonomous Vehicle Groups in a closing scene (CMAVG). First, we construct multi-roles and hierarchical autonomous vehicle groups. Then, we analyze their evolution behavior and present a dynamic evolution method based on it. Finally, we formulate CMAVG and give its solving method. We conduct extensive simulations in a simulated closing scene and a real one. Experimental results show that our autonomous vehicle group formation method outperforms a VANET clustering method and an autonomous vehicle group formation method in terms of aggregation degree, running time, and leading node change rate. CMAVG outperforms two cooperation methods for Internet of vehicles and an autonomous vehicle group cooperation method in terms of aggregation degree, leading node change rate, and vehicle group survival time.

Depression has become a common mental illness, and the number of patients has shown a noticeable rising trend. However, the exploration of the connection between hormone levels and physical state changes in depression patients is still open. Hormone levels are complex and play a key role in regulating multiple body systems and functions, directly or indirectly influencing overall health and physical state. This work utilizes Petri nets to establish a corresponding model for the transition of hormone levels and states in depression, focusing on the association between different hormone levels and states in depressive patients. At the same time, machine learning methods offer a new approach to predicting the reachability of depression patients' states. This work enables healthcare professionals to quickly assess patients' emotional changes and their impact on outcomes, improving resource allocation.

E-learning is increasingly popular because it allows learners to freely choose their class times and locations. However, traditional E-learning platforms face issues of information overload and fragmented resources. The proposition of the concept of personalized learning has effectively alleviated these problems. However, current personalized learning recommendation methods fail to comprehensively and systematically address learners' needs. To solve this issue, this paper proposes a learning path recommendation method based on learner profiles. First, by collecting learners' personal information, learning history, and behavior data, a learner profile is established considering multiple aspects. Then, generating a path evaluation function for learners from the profile. Using the Ant Colony Optimization algorithm, the most suitable personalized learning path for the learner's needs is searched within the knowledge graph. Experimental results demonstrate that the personalized learning path recommendations generated by our algorithm meet expectations and achieve the best overall performance in comparative experiments.