The prevalent class imbalance in real-world graphs significantly affects the performance of Graph Neural Networks (GNNs). Existing methods for analyzing graph imbalance ignore the influence of minority nodes during the dynamic model training process, resulting in performance limitations. In this paper, we focus on minority class information during model training, identifying and defining the minority class forgetting phenomenon that exists in graph imbalanced method training processes. To address this issue, we propose Graph Imbalance Experience Replay(GIER) framework. On one hand, the method enhances the model's ability to mine minority node information in historical data, thereby achieving feature completion for minority class nodes. On the other hand, the proposed short-term confidence mechanism allows the model to adaptively calibrate the topological relationships in high-confidence nodes, thereby mitigating the model's tendency to propagate erroneous information about minority classes during training. GIER is a unified framework consisting of two synergistic components: Long-term Subgraph Memory (LSM) constructs multi-period feature-representative subgraphs to address distribution imbalance, and Short-term Confidence Calibration (SCC) dynamically reconstructs graph topology through degree-aware node selection and confidence-based filtering to address topological imbalance. The extensive experimental results demonstrate that GIER effectively improves the classification performance of GNNs on imbalanced graphs, achieving up to a 3.44% improvement in BAcc over the state-of-the-art, and is particularly effective in extreme scenarios with very small minority classes.

Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of shape need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of shape need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: disruptions of popularity, spatial distance, and category diversity. Furthermore, hybrid metrics are proposed to ensure perturbation effectiveness. We conduct comprehensive benchmarking on iTIMO to analyze the capabilities and limitations of state-of-the-art LLMs. Overall, iTIMO provides a comprehensive testbed for the modification task, and empowers the evolution of traditional travel recommender systems into adaptive frameworks capable of handling dynamic travel needs. Dataset, code and supplementary materials are available at https://github.com/zelo2/iTIMO.

It is challenging to achieve an optimal worker allocation for production lines of large-scale industrial enterprises due to the complex requirements for workers’ capabilities. Some optimization approaches have been proposed to solve this problem from different perspectives. However, they have not fully explored the multidimensional features of both workers and production lines, making it hard to obtain an optimal allocation. This article proposes a novel approach to collaborative allocation optimization of production line workers by incorporating multidimensional feature measurement and the environment-classes, agents, roles, and objects (E-CARGO) model. First, we develop a comprehensive evaluation system to quantify the diverse features of workers and production lines. Based on it, an adaptability assessment mechanism is designed to measure the matching degree of workers for different production lines. Afterward, the role-based collaboration theory and the E-CARGO model are innovatively utilized to formalize the worker allocation problem. Meanwhile, the key constraints are identified to guarantee the reasonability of allocation, and an efficient solution via CPLEX package is proposed. Finally, the case analysis and simulation experiments verify the effectiveness of the proposed approach.

Recently, the city road trips have become one of the mainstream travel styles for Chinese tourists. Existing research does not comprehensively consider tourists' subjective preferences for sightseeing, dining, and accommodation, and analyze the objective constraints related to city road trips. It is difficult for tourists to obtain route planning solutions with high satisfaction of city road trips. A route planning approach for city road trips is proposed to meet these preferences and constraints. This approach defines two types of POIs (points of interest), i.e., attractions and rest spots, and identifies key constraints possibly affecting POI selections. Based on them, the tourist’s route for one day is divided into three sub-routes. Each sub-route consists of two different POIs including an attraction and a rest spot. First, an appropriate attraction is selected as the center of a sub-route. Second, a suitable rest spot is assigned to each sub-route according to its center. The E-CARGO model is utilized to formalize the route planning problem of city road trips and an effective solution is provided. Case study and simulation experiments show that the approach is efficient and feasible to achieve the maximum tourist satisfaction in city road trips.

It is important to select appropriate national image resources to build a nation’s images for targeted international communication of national images. Existing research only focusing on the methodologies, lacks the systematic modeling and solving of national image resources recommendation. A collaborative recommendation approach to national image resources is proposed. In the approach, an evaluation model of national image resources and an evaluation model of communication audiences are put forward, and an evaluation mechanism is proposed to measure the comprehensive compatibility between national image resources and communication audiences. By innovatively introducing the role-based collaboration (RBC) theory and the environment-classes, agents, roles, groups, and objects (E-CARGO) model, the national image resources recommendation is formalized as a collaborative optimization problem. The mathematical model is built and solved via an optimization package. Finally, the case study and experiments show that the approach is efficient, feasible, and conducive to enhancing the efficiency of national image resources recommendation. It offers a novel research paradigm for targeted international communication of national images.

Recently, the road trip in regional central cities has become one of the mainstream travel styles for Chinese tourists. However, existing research fails to plan the road trip route in Chinese regional central cities due to its flexibility, complexity, and long-term characteristic. A collaborative route planning approach for road trips, namely CoRPoRT, is proposed. This approach decomposes the route planning of a road trip into two role-based collaboration subproblems to alleviate the complexity issue. Then, the environments–classes, agents, roles, groups, and objects (E-CARGO) model is innovatively employed to formalize the problems and guide the optimization process for dealing with the long-term characteristic of road trips. Moreover, we identify the complex constraints affecting point of interest selections and propose an efficient solution via the IBM CPLEX solver to handle the flexibility of road trips. Finally, a case study and simulation experiments verify the effectiveness of CoRPoRT. CoRPoRT presents a general problem modeling method and a novel research paradigm for the route planning of road trips.

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.

Abstract With the excessive consumption and pollution of water resources, the sustainable development of water resources poses a serious threat currently. How to perceive and prevent the degradation of water resource in advance is an urgent problem. The water resource carrying capacity (WRCC) is a significant indicator to reflect the condition of water resources in a region. Resounding to these circumstances, our research establishes a decision support framework to solve WRCC risk evaluation issues. First, hesitant probabilistic fuzzy sets (HPFSs) are selected as a representation for the evaluation information in the expert group. Aimed at existing studies of HPFSs, some limitations are overcome involving the distance and comparison rule. Secondly, a TODIM‐PROMETHEE II based multi‐criteria group decision making (MCGDM) method is developed to overcome the inherent restrictions of PROMETHEE II method and make it suitable for a practical decision‐making condition with bounded rationality. Subsequently, a case study is utilized to demonstrate the feasibility of our newly proposed decision support framework, followed by a sensitivity analysis and a comparison analysis. The outcome indicates that the framework has an excellent performance to solve this kind of MCGDM issues.

Recently, growing cloud services (CSs) have been leased by organizations for high-performance computation and massive data storage of data-intensive applications (DiAs). To improve the resource utilization of leased CSs, it has become a challenging task to optimize infrastructure as a service CS composition for DiAs (ICSCDs) from the user side. This paper proposes a resource utilization-aware collaborative optimization approach. Targeting the collaboration features of tasks in a DiA, the environments-classes, agents, roles, groups, and objects model is used to formalize the ICSCD problem from the perspective of role-based collaboration. Aiming at the dynamic characteristics of the cloud environment, an integrated method is presented to evaluate the qualification of CSs via the interval numbers with multiple parameters. Based on the exact qualification values, the ICSCD can be optimized for improving the resource utilization of the CSs. A solution using the IBM ILOG CPLEX optimization package is put forward to solve the problem. The experimental results demonstrate that the approach can provide high precision, performance, stability, resource utilization, and low usage cost for the resource utilization-aware ICSCD from the user side.

Abstract In the multi‐criteria group decision‐making (MCGDM) problems with great uncertainty, making full use of participants' evaluation information could help improve the accuracy and reliability of decision results. Probabilistic linguistic term set (PLTS) is an effective tool to represent qualitative data and can fully express the hesitation and preference of decision makers. Therefore, this paper aims to propose an MCGDM method based on PLTSs. In the proposed method, the projection of PLTSs is explored to measure the distance and angle differences between two objects, and Bayesian best–worst method (Bayesian BWM) is used to determine the aggregated final weights of criteria. Besides, the elimination and choice translating reality III (ELECTRE III) method combined with distillation algorithm deals with the projection of PLTSs to obtain the alternatives' ranking of each decision maker. Then, the weighted convex median voting rule is developed to integrate the rankings results regarding all decision makers, which can solve the conflict of ranking results among experts and ensure that the comprehensive ranking results are reasonable and practical. Finally, a case study of health‐care waste management is designed and comparative analyses are implemented to show the effectiveness and advantages of the proposed method.

Abstract The objective of this study is to develop an integrated approach for solving multicriteria group decision‐making problems with multigranular unbalanced hesitant fuzzy linguistic term sets (HFLTSs). Firstly, a signed distance‐based transformation function is proposed to unify multigranular unbalanced hesitant fuzzy linguistic (HFL) assessments. Secondly, a mathematical programming model based on the maximum consensus is constructed to allocate decision‐makers (DMs)' weights objectively. Thirdly, a new signed distance‐based preference score function is defined to aggregate HFL assessments and determine the weak ranking of alternatives, and a novel preference, indifference, and incomparability test framework is constructed to identify the subtle relations among alternatives. On these bases, a signed distance‐based ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) method, in which knowledge regarding criterion values and weights are expressed as multigranular unbalanced HFLTSs, is developed to obtain the ranking of alternatives. Finally, an illustrative example, followed by sensitivity and comparative analyses, is presented to verify the feasibility and effectiveness of the proposed approach.

Abstract Probabilistic linguistic term sets (PLTSs) are an important expression for hesitant linguistic preference information under group decision‐making circumstances. This study investigates problems of multicriteria group decision making (MCGDM) with PLTSs. A novel and rational comparison method is first proposed, and two distance measures for PLTSs are defined. The weight of each criterion is then obtained via maximum deviation method. Subsequently, extended Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ‐ VIseKriterijumska Optimizacija I Kompromisno Resenje a Serbian name (VIKOR) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) methods are developed as decision support models to handle MCGDM problems. An illustrative example is also analysed to demonstrate the rationality and feasibility of the proposed methods.

Decision support is a vital function in electronic commerce (e-commerce). The purpose of this paper is to construct a review-based decision support model for items comparison in e-commerce. The proposed model uses probability multivalued neutrosophic linguistic numbers (PMVNLNs) to characterize online reviews. It overcomes the limitation of existing models by considering neutral information and hesitancy in text reviews. The fuzzy characterization of reviews (i.e., PMVNLN) can reflect similarities and differences in positive (negative) information. In addition, the model considers consumers' bounded rational behaviors by combining the regret theory with an outranking method. We empirically compare the proposed model with models in PConline.com and four existing models with data from PConline.com. The performance of these models in terms of accuracy is measured by the total relative difference metric. Results indicate the good performance of the proposed model. Our model is a promising option for e-commerce to provide consumers with good decision support service.

With the growing cloud services (CSs) rented by an organization, it has become a challenging problem to optimize the CS composition for data-intensive applications (DiAs) from the user side, with consideration of improving the resource utilization of rented CSs. This paper proposes a resource utilization-aware approach to optimizing the CS composition for DiAs (CSCD). From the perspective of role-based collaboration, this approach utilizes the environments - classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD problem. The qualification of a CS for one task is assessed and the compatibility between new tasks and the running task is identified. A solution using IBM ILOG CPLEX package is put forward to optimize the CSCD problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing the resource utilization-aware CSCD problem from the user side.

In a team of collectivism, the team performance or the team interest is emphasized. Many individuals may find that the team interest is not always consistent with their own. Assigning positions to team members is one such scenario. An individual may be assigned to a position that is not preferred. Is there any policy for an individual to apply in order to get the preferred position? Role-Based Collaboration (RBC) is a methodology that advocates collectivism. The Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) is a good tool to investigate this issue. The contributions of this work include: 1) a data analysis method for a person to pursue the preferred position (role) in a team based on the E-CARGO model and Group Role Assignment (GRA) algorithm; 2) a set of experiments for the proposed methods; and 3) the confirmation of a list of common sense principles with the support of experiments.

Due to the limitation of knowledge and the vagueness of human being thinking, decision makers prefer to use hesitant fuzzy linguistic sets (HFLSs) to estimate alternatives. Some methods of HFLSs have been researched based on the more familiar means such as the arithmetic mean and the geometric mean; however, Maclaurin symmetric mean (MSM) that can be used to reflect the interrelationships among input arguments have not been applied to solve hesitant fuzzy linguistic multi-criteria decision-making problems. In this paper, two hesitant fuzzy linguistic harmonic averaging operators are proposed: the hesitant fuzzy linguistic MSM (HFLMSM) operator and the hesitant fuzzy linguistic weighted MSM (HFLWMSM) operator. Furthermore, an approach based on the HFLWMSM operator is proposed. Finally, to verify the validity and feasibility of the proposed approach, an illustrative example and corresponding comparison analysis are presented in the end.

Induced Choquet integral is a powerful tool to deal with imprecise or uncertain nature. This study proposes a combination process of the induced Choquet integral and neutrosophic information. We first give the operational properties of simplified neutrosophic numbers (SNNs). Then, we develop some new information aggregation operators, including an induced simplified neutrosophic correlated averaging (I-SNCA) operator and an induced simplified neutrosophic correlated geometric (I-SNCG) operator. These operators not only consider the importance of elements or their ordered positions, but also take into account the interactions phenomena among decision criteria or their ordered positions under multiple decision-makers. Moreover, we present a detailed analysis of I-SNCA and I-SNCG operators, including the properties of idempotency, commutativity and monotonicity, and study the relationships among the proposed operators and existing simplified neutrosophic aggregation operators. In order to handle the multi-criteria group decision-making (MCGDM) situations where the weights of criteria and decision-makers usually correlative and the criterion values are considered as SNNs, an approach is established based on I-SNCA operator. Finally, a numerical example is presented to demonstrate the proposed approach and to verify its effectiveness and practicality.

In this paper, a new approach is presented for solving multi-criteria group decision-making (MCGDM) problems, which is based on new arithmetic operations and the ranking rules of trapezoidal interval type-2 fuzzy numbers (IT2FNs). Firstly, the shortcomings of some existing arithmetic operations of trapezoidal IT2FNs are discussed along with their ranking methods, before some new arithmetic operations and ranking rules are proposed. Secondly, some new aggregation operators including the arithmetic averaging aggregation operator, the ordered weighted averaging aggregation operator and the hybrid weighted averaging aggregation operator for trapezoidal IT2FNs are also developed. Thirdly, a new approach for MCGDM problems is developed based on the proposed operators and ranking rules. Finally, an example is provided to illustrate the feasibility and validity of this new approach, and a comparison analysis referring to the same example is also presented.

The cloud model, which can implement uncertain transformation between a qualitative concept and its quantitative instantiations, has attracted great attention in multicriteria decision-making problems with linguistic information. This paper proposes some operations and a possibility degree of trapezium clouds, as well as several new aggregation operators: the trapezium cloud weighted arithmetic averaging operator, the trapezium cloud ordered weighted arithmetic averaging operator, and the trapezium cloud hybrid arithmetic operator. Moreover, a method with Atanassov's interval-valued intuitionistic linguistic numbers (AIVILNs) based on trapezium clouds is presented, which can provide solutions to multicriteria group decision-making problems with Atanassov's interval-valued intuitionistic linguistic information. In this method, AIVILNs are first converted into trapezium clouds and aggregated by trapezium cloud aggregation operators. Then, the ranking of alternatives is determined by the possibility degree matrix of trapezium clouds. Finally, an illustrative example confirming the validity and feasibility of the proposed method is given.

To handle multicriteria fuzzy decision-making problems, a new multicriteria decision-making method is proposed in which the information about criteria's weights is not completely certain, and the criteria values of alternatives are Atanassov's intuitionistic fuzzy sets (A-IFSs). Using evidential reasoning algorithms, the criteria values are aggregated; receiving the overall A-IFS for alternatives and the distances between each alternative and the ideal, as well as anti-ideal alternative, are computed. Combining the incomplete certain information of weights, a nonlinear programming model is developed and resolved by particle swarm optimization algorithms to obtain the optimal criteria's weights. The corresponding decision-making procedure is given in detail. Finally, two examples are given to show the feasibility and availability of the proposed method.

In this paper, we first defined intuitionistic normal fuzzy numbers as well as their operational laws and score function. Next, we proposed some aggregation operators including ordered intuitionistic normal ordered fuzzy weighted averaging operator, intuitionistic normal fuzzy ordered weighted geometric averaging operator, intuitionistic normal fuzzy related ordered weighted averaging operator, intuitionistic normal fuzzy related ordered weighted geometric averaging operator, induced intuitionistic normal fuzzy related ordered weighted averaging operator and induced intuitionistic normal fuzzy related ordered weighted geometric averaging operator. After that, similarity measure between two intuitionistic normal fuzzy numbers is defined. For multi-criteria decision making problems, in which the criteria are interactive and the criteria values are intuitionistic normal fuzzy numbers, an approach based on induced intuitionistic normal fuzzy related aggregation operators is proposed. And the comprehensive evaluation values of all alternatives can be derived by applying induced intuitionistic normal fuzzy related aggregation operators. Finally, the ranking of the whole alternatives set can be obtained by comparing the relative closeness of alternatives to the ideal solution. In the end, an example is given to show the validity and the feasibility of the method.

Web service is a key distributed computing technology to achieve "software as a service" (SaaS) in cloud computing. How to integrate various web services according to business processes correctly and efficiently, realize the seamless integration of services, and form enterprise level service processes with abundant functions has become an important problem. In this paper, we presented a new web services composition model and its verification algorithm based on interface automata. By extending interface automata, this model supports semantic descriptions of web services. We proposed the transformation rules and transformation algorithm between BPEL and the semantic service interface automata model. Furthermore, an interface automata composition algorithm was designed to achieve the concurrent composition of interface automata. In order to judge whether the service process generated satisfies the business function requirements, a verification algorithm was designed to validate the execution sequence of the concurrent composite interface automaton, and the simulation experiment showed that verification algorithm could be used to validate the consistency of the service process and the business process correctly and effectively.