Seamless steel pipes (SSPs) are vital material for industries. However, internal surface defects (ISDs) in SSPs are challenging to detect, and will significantly affect SSPs performance and lifespan. Existing detection methods are labor-intensive and have low visualization of detection results. Therefore, this article present a novel detection system comprising thePipelineAll-aspect internalSurface defectSpiral detecting robot and an interactive visualization software. After testing in the SSPs factory, the system achieves comprehensive, wireless and efficient detection and visualization for ISDs. In addition, we construct a dataset for ISDs in SSPs, named as SSP2000. The dataset contains 2000 images across nine defect categories, with many challenges in it. Furthermore, to accurately detect defects, we design the SRPCNet which can effectively address the challenges. Specifically, we first use the synergize perception augmentation module to enrich the feature space and to enhance the perception. Then, the hierarchical attention integrate module merges deep and shallow features using adaptive attention weights. Finally, the bilateral self-fusion module fully exploits intralayer features and produce prediction results. The proposed SRPCNet outperforms existing methods on eight evaluation metrics.

Recommender systems continuously strive to recommend items that the users potentially like accurately. Most recommender systems assume that latent user preferences and item features are linearly combined. However, the existing linear interaction patterns do not realistically reflect users’ decision-making processes. The formation of users’ opinions on items and the evolutionary preference interaction process among users needs to be explored. In our work, we bridge social psychology and recommender systems to develop a social recommendation model, nonlinearly utilizing latent user preferences and item features to simulate the intrinsic formation of users’ decision-making. We extend the cognitive opinion formation mechanism by improving the two-stage process and seamlessly combine it and matrix factorization, simulating the nonlinear interactions between users and items. We incorporate the implicit user influence and explicit social dynamics with bounded confidence effect into the nonlinear cognitive recommendation framework to characterize the evolutionary preference interactions among users. We conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that our method makes notable improvements in rating prediction for all users and cold-start users. In addition, the nonlinear cognitive opinion formation has a significant effect on improving performance, conferring higher interpretability to the recommendation.

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.

Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition, rich attributes are often unavailable, which prevents graph neural networks from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and makes a difference on future ratings. The implicit influence is analyzed on the mechanism of information propagation, and fused with the user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships, and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.

Transfer learning uses auxiliary domains to help complete learning tasks of the target domain. However, the combination of recommendation and transfer learning often has two problems. One is that it's difficult to find an auxiliary domain which is highly related to the target domain. The other is that useful information in auxiliary domains cannot be fully utilized. To make use of the knowledge in auxiliary domains as much as possible, this paper proposes a cyclic transfer learning method which can transfer the shared knowledge in the auxiliary domain and target domain multiple times. Combining this method with recommendation, this paper presents a recommendation framework based on heterogeneous feedbacks and cyclic transfer learning (HCTL-Rec). By studying the relationship between different behaviors of users, this paper proposes two specific recommendation algorithms which combine the novel framework with two auxiliary domains. One is to use users' binary attitude information as an auxiliary domain to better represent users' ratings. The other is to use users' trust relationship as an auxiliary domain and make social recommendation. Experiments are carried out on two real-world datasets with trust relationship. The results show that recommendation quality of the two specific algorithms can achieve significant improvement compared with other state-of-the-art algorithms and can effectively relieve the cold-start problem.

Social recommender systems have attracted a lot of attention from academia and industry. On social media, users' ratings and reviews can be observed by all users, and have implicit influence on their future ratings. When these users make subsequent decisions about an item, they may be affected by existing ratings on the item. Thus, implicit influence propagates among the users who rated the same items, and it has significant impact on users' ratings. However, implicit influence propagation and its effect on recommendation rarely have been studied. In this article, we propose an information propagation-based social recommendation method (SoInp) and model the implicit user influence from the perspective of information propagation. The implicit influence is inferred from ratings on the same items. We investigate the concrete effect of implicit user influence in the propagation process and introduce it into recommender systems. Furthermore, we incorporate the implicit user influence and explicit trust information in the matrix factorization framework. To demonstrate the performance, we conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that SoInp makes notable improvements in rating prediction.

Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text mining result in existing research, and at the same time deal with the vagueness of linguistic terms to assist thematic evaluation, this research proposes a fuzzy set-based topic development measurement (FTDM) approach to estimate and evaluate the topics hidden in a large volume of patent claims using Latent Dirichlet Allocation. In this study, latent semantic topics are first discovered from patent corpus and measured by a temporal-weight matrix to reveal the importance of all topics in different years. For each topic, we then calculate a temporal-weight coefficient based on the matrix, which is associated with a set of linguistic terms to describe its development state over time. After choosing a suitable linguistic term set, fuzzy membership functions are created for each term. The temporal-weight coefficients are then transformed to membership vectors related to the linguistic terms, which can be used to measure the development states of all topics directly and effectively. A case study using solar cell related patents is given to show the effectiveness of the proposed FTDM approach and its applicability for estimating hidden topics and measuring their corresponding development states efficiently.

Technology Intelligence (TI) indicates the concept and applications that transform data hidden in patents or scientific literature into technical insight for technology development planning and strategies formulation. Although much effort has been put into technology trend analysis in existing research, the majority of the results are still obtained from expert opinions on the basis of historical trends presented by content-based Technology Intelligence tools. To improve this situation, this paper proposes a time-series-based framework for TI that enables the system to be more effective when dealing with trend prediction requirements. Time-series analysis module is first applied in TI framework to process patent time series for technology trend predictions in a real sense, at the same time overcome the problem that prediction of future data points' values is insufficient to support TI construction. Based on explicit patent attributes and unknown patterns learned from the historical data, the framework combines the "trend" and "content" knowledge by analyzing both time-related property and semantic attributes of patent data, to support technology development planning more efficiently and satisfactorily. A case study is presented to demonstrate the validity of trend prediction functionality, which is the emphasis of the whole framework.