There are always multi-stage and multi-type precision requirements for robotic arms in industrial manufacturing. Due to uncertainties and disturbances, the tracking error is forced to exceed the time-varying precision boundary and the system is even broken. Therefore, an intelligent optimization framework of fuzzy adaptive prescribed time control for robotic arms with multi-stage precision is developed in this paper. Firstly, the multi-stage precision requirements are explained as time-varying piecewise inequality constraints of the tracking error. Based on homeomorphic mapping, a model reconstruction method is proposed by designing the transformation function to form a reconstructed system. Then, based on the constraint force analysis of the reconstructed system, a leaky adaptive prescribed time control method is proposed, which simultaneously handles uncertainties, disturbances and inequality constraints. The error convergence is achieved within a prescribed time by setting piecewise constraints. The overcompensation is avoided via the leaky-type adaptive law. The proposed method is proven to be uniformly bounded and uniformly ultimately bounded. Furthermore, an optimization strategy is designed based on fuzzy set theory to optimize system performance and control cost, forming a set of general trade-off rules. Finally, the effectiveness of the proposed method is verified. The results show that the time-varying piecewise inequality constraints are fully satisfied at low control cost.

The proliferation of multimodal data provides a valuable repository of information for financial distress prediction. However, the use of multimodal data faces critical challenges, such as heterogeneity within and among modalities and difficulties in discriminating complementary and redundant information among modalities. To this end, we propose an attentive and regularized deep learning method for predicting financial distress using multimodal data, including financial indicators, current reports, and interfirm networks. Specifically, considering heterogeneity within and among modalities, we design three modality-specific attentions, i.e., ratio-aware, report-aware, and neighbor-aware attentions, for adaptively extracting key information from financial indicators, current reports, and interfirm networks, respectively. Considering difficulties in discriminating complementary and redundant information among modalities, we design a conditional entropy-based regularization to guide the method focusing on complementary information while discarding redundant information during modality fusion. We also propose the use of focal loss to address the class imbalance problem. Empirical evaluation shows that the proposed method significantly outperformed all benchmarked methods in terms of predictive and representation performance. We also provide key findings and implications for stakeholders.

Credit Default Prediction (CDP) has received increasing attention with the prevalence of financial loaning services. Many research efforts have been dedicated to developing novel soft features (i.e. non-financial features), such that they can complement hard features (i.e. financial features) and assist to learn a better default predicting model. But most works combine those features from various sources by just concating them together, and ignore that inappropriate feature fusion methods would compromise model performances. Therefore, in this paper, we propose an Attentive Feature Fusion (AFF) framework for credit default prediction using deep neural networks (DNNs). According to distinct characteristics of the data features, we divide features into multiple groups, and learn their latent representations with separate DNNs, respectively. Then the attention mechanism is applied to integrate those representations together, which allows the important features to be always emphasized and contribute more to the final decision. Experiments on the Lending Club dataset demonstrate that the proposed method can effectively improve the default predicting performances.

Mixing patterns (MPs) in social trust networks (STNs) are increasingly attracting attention because they can assist analysts in designing information dissemination tactics and planning electronic word-of-mouth (eWOM) campaigns. However, the existing studies on MPs do not explain the assortative or disassortative tendencies of STNs due to their omission of the support of the sociological theory, as well as that of network theory. To address this issue, this study investigates the MPs in STNs from the standpoint of social identity theory (SIT). The user trust networks (UTNs) are modeled by a directed multigraph (DMG). Then, the structural properties of homogeneous trust networks and heterogeneous trust networks are explored via measures that include degree centrality, the correlation coefficient (CC), the cumulative distribution of the ratio of trust degree to distrust degree (CDRTD), and the assortativity coefficient. The MPs of homogeneous trust networks and heterogeneous trust networks are explained from the perspective of SIT. An experiential evaluation is conducted in the constructed homogeneous trust networks and heterogeneous trust networks using a real-world data set crawled from Epinions. The research findings indicate that the MPs in homogeneous trust networks tend toward assortative mixing (AM), and those in heterogeneous trust networks tend toward disassortative mixing (DM). The experimental results show that the performance of the proposed approach is superior to that of the state-of-the-art approach to influential user identification.