Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis.

To effectively evaluate the gross motor ability of autistic children, we proposed a method of computerised evaluation of gross motor skills (CEGM). The CEGM integrates Dynamic Time Warping (DTW) method and OpenPose technology to automatically detect key joints and return a score. Ten items were selected for evaluation based on the gross motor subtest of the Psychoeducational Profile – Third Edition (PEP-3) scale, including upper limb movement, lower limb movement, and body coordination performance. 30 autistic participants (males: 23, female: 7) with an average age of 5.00 years were recruited in this study. Then we compared the results of evaluation using CEGM and the original PEP-3 gross motor subtest in autistic children. The results showed that in the evaluations using CEGM and PEP-3, Cronbach’s α coefficients and Spearman-rank correlation coefficients were all greater than 0.80, intraclass correlation coefficient (ICC) were all greater than 0.90, indicating good agreement in evaluating the gross motor ability of autistic children. Moreover, compared to the PEP-3, the evaluation using CEGM provided precise quantitative indicators (trajectory, velocity, and angle of joint). Therefore, our findings demonstrate that CEGM can be used in the initial evaluation of the gross motor ability of autistic children.

Abstract In the era of the Internet and big data, online social media platforms have been developing rapidly, which accelerate rumors circulation. Rumor detection on social media is a worldwide challenging task due to rumor’s feature of high speed, fragmental information and extensive range. Most existing approaches identify rumors based on single-layered hybrid features like word features, sentiment features and user characteristics, or multimodal features like the combination of text features and image features. Some researchers adopted the hierarchical structure, but they neither used rumor propagation nor made full use of its retweet posts. In this paper, we propose a novel model for rumor detection based on Graph Neural Networks (GNN), named Hierarchically Aggregated Graph Neural Networks (HAGNN). This task focuses on capturing different granularities of high-level representations of text content and fusing the rumor propagation structure. It applies a Graph Convolutional Network (GCN) with a graph of rumor propagation to learn the text-granularity representations with the spreading of events. A GNN model with a document graph is employed to update aggregated features of both word and text granularity, it helps to form final representations of events to detect rumors. Experiments on two real-world datasets demonstrate the superiority of the proposed method over the baseline methods. Our model achieves the accuracy of 95.7% and 88.2% on the Weibo dataset Ma et al. 2017 and the CED dataset Song et al. IEEE Trans Knowl Data Eng 33(8):3035–3047, 2019respectively.

The distance between the intuitionistic fuzzy sets (IFSs) is a notable and has been widely used information measure to enhance decision-making performance. However, different numerical results are derived when using different distance measures. Therefore, it is worthwhile to explore in depth how to select an appropriate formula for distance computation. This paper uses the line integral to define the distance between IFSs. The presented study divided into three folds. First, the existing distances between IFSs are examined, and their flaws are listed. Second, the distances between IFSs are redefined based on the analysis of the geometric importance of the line integral. More importantly, some existing distances happen to be special cases of the distance we define. Finally, we introduce the accuracy function into the defined distance for evaluating the accuracy of distance by applying the physical meaning of line integral. In other words, the distance accuracy is emphasized as a crucial standard by which to assess the effectiveness of distances between IFSs. To demonstrate the stated measures, some numerical examples are provided to show the superiority of our approach.

As a useful tool, probabilistic hesitant fuzzy set is an enhanced version for hesitant fuzzy set. It could be used to model the uncertainty very effectively. However, in probabilistic hesitant fuzzy risky multiple attribute decision making problems, the occurrence probabilities of elements in a probabilistic hesitant fuzzy element and the probability of risk status are often difficult to obtain by subjective evaluation of a decision maker. This paper aims to propose two nonlinear programming models for calculating the probabilities of elements in a probabilistic hesitant fuzzy element and the probability of risk status respectively. First, a nonlinear programming model using maximum entropy principle is established for determining the probabilities of elements in a probabilistic hesitant fuzzy element. Second, by introducing the water-filling theory, we put forward its extension and design a novel mathematical programming model to determine the probability of risk status. Moreover, we have proved that both the two mathematical programming models are convex programming models and their global optimal solutions can be found. Thirdly, the collective overall expected values of alternatives are calculated and the ranking order can be derived. Then, the selection of investment project is investigated, and comparison analysis shows the superiority of the presented approach.

The multi-robot formation is a key technology for underwater searching and other tasks. This paper aims to use underactuated underwater robots to establish a leader-follower formation model. Firstly, the kinematics and dynamics characteristics of the single AUV are analyzed and the coordinate transformation is introduced to decouple the dynamics of the AUV. Then the leader-follower model is obtained and the linear feedback controller is proposed, and the Lyapunov direct method is applied in the stability analysis. The simulation experimental results show that the formation of two AUVs was controlled effectively by the leader-follower formation model. We also design hardware platform to verify the reliability of the simulation. Moreover, the experimental results of expanding from two AUVs formation to three AUVs formation also prove the applicability of the method for the multi-AUVs system.

Fire safety education is essential to every student on campus.Fire safety knowledge learning and operational practice are both important.There is evidence that the virtual reality (VR) based educational method can be a novel and effective approach to learning and practice.However, the existing VR-based system for fire safety education has some shortcomings such as lack of interactivity and high equipment complexity, resulting in low practicability.In order to improve the effect of fire safety education on campus, this paper establishes the model and architecture of fire safety education system based on VR technology.The framework and various elements of fire safety education system are designed and implemented according to the combination of relevant fire safety education theory and VR technology.Finally the prototype version of fire safety education system based on VR technology is built on the HTC VIVE helmet equipment.Through the usability test and comparative analysis of the application experiment, the experiment results prove the feasibility and effectiveness of the proposed approach.