Road extraction from high-resolution remote sensing images has long been a focal and challenging research topic in the field of computer vision. Accurate extraction of road networks holds extensive practical value in various fields, such as urban planning, traffic monitoring, disaster response and environmental monitoring. With rapid development in the field of computational intelligence, particularly breakthroughs in deep learning technology, road extraction technology has made significant progress and innovation. This paper provides a systematic review of deep learning-based methods for road extraction from remote sensing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. According to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learning approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations. Additionally, this review summarizes the metrics used to evaluate the performance of road extraction models and the high-resolution remote sensing image datasets applied for road extraction. Finally, we discuss the main challenges and prospects for leveraging computational intelligence techniques to enhance the precision, automation, and intelligence of road network extraction.

Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in many cyber physical systems and the Internet of Things for guaranteeing information security. In order to improve the performance and efficiency of CP-ABE, this paper makes a change to the access structure of describing access polices in CP-ABE, and presents a new CP-ABE system based on the ordered binary decision diagram (OBDD). The new system makes full use of both the powerful description ability and the high calculating efficiency of OBDD. First, in the access structure, the new system allows multiple occurrences of the same attribute in a strategy, supports both positive attribute and negative attribute in the description of access polices, and can describe free-form access polices by using Boolean operations. Second, in the key generation stage, the size of secret keys generated by the new system is constant and not affected by the number of attributes; furthermore, time complexity of the key generation algorithm is O(1). Third, in the encryption stage, both the time complexity of the encryption algorithm and the size of generated ciphertext are determined by the number of valid paths contained in the OBDD instead of the number of attributes occurring in access polices. Finally, in the decryption stage, the new system supports fast decryption and the time complexity of the decryption algorithm is only O(1). As a result, compared with existing CP-ABE schemes, the new system has better performance and efficiency. It is proved that the new CP-ABE system can also resist collision attack and chosen-plaintext attack under the decisional bilinear Diffie Hellman assumption.

With the emergence of more new teaching systems, such as Massive Open Online Courses (MOOCs), massive amounts of data are constantly being collected. There is a huge value in these massive teaching data. However, the data, including both student behavior data and student comment data about the course, is not processed to discover models and paradigms which can be useful for school management. There is no multimodal dataset with tabular and textual data for educational data mining yet. We first collect a dataset that included student behavior data and course comments textual data. Then we fuse the student behavior data with course comments textual data to predict student performance, using a Transformer-based framework with a uniform vector representation. The empirical results of the collected dataset show the effectiveness of our proposed method. In terms of F1 and AUC the performance of our method improves by up to 3.33% and 4.37% respectively. We find that the uniform feature vector representation learned by our proposed method can indeed improve the classifier’s performance, compared with existing works. Further, we validate our approach on an open dataset. The results of the empirical study show that our proposed method has a strong generalization capability. Moreover, we perform interpretability analysis using the SHapley Additive exPlanation (SHAP) method and find that text features have a more important influence on the classification model. This further illustrates that fusing text features can improve the performance of classification models.

As a privacy-preserving machine learning paradigm, federated learning (FL) has attracted widespread attention from both academia and industry. Decentralized FL (DFL) overcomes the problems of untrusted aggregation server, single point of failure and poor scalability in traditional FL, making it suitable for industrial Internet of Things (IIoT). However, DFL provides more convenient conditions for malicious participants to launch attacks. This article focuses on the model poisoning attack in DFL for the first time, and proposes a novel attack method called collusive model poisoning attack (CMPA). To implement CMPA, we propose the dynamic adaptive construction mechanism, in which malicious participants can dynamically and adaptively construct malicious local models that meet distance constraints, reducing the convergence speed and accuracy of consensus models. Furthermore, we design the collusion-based attack enhancement strategies, where multiple participants can collude in the process of constructing malicious local models to improve the strength of attack. Empirical experiments conducted on MNIST and CIFAR-10 datasets reveal that CMPA significantly impacts the training process and results of DFL. Attack tests against representative defense methods show that CMPA not only invalidates statistical-based defenses but also skillfully overcomes performance-based methods, further proving its effectiveness and stealthiness. In addition, experiments based on practical IIoT scenario have also shown that CMPA can effectively disrupt system functionality.

Co-location pattern mining plays an important role in spatial data mining. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the huge amount of discovered patterns. Although several methods have been proposed to reduce the number of discovered patterns, these statistical algorithms are unable to guarantee that the extracted co-location patterns are user preferred. Therefore, it is crucial to help the decision maker discover his/her preferred co-location patterns via efficient interactive procedures. This article proposes a new interactive approach that enables the user to discover his/her preferred co-location patterns. First, we present a novel and flexible interactive framework to assist the user in discovering his/her preferred co-location patterns. Second, we propose using ontologies to measure the similarity of two co-location patterns. Furthermore, we design a pruning scheme by introducing a pattern filtering model for expressing the user’s preference, to reduce the number of the final output. By applying our proposed approach over voluminous sets of co-location patterns, we show that the number of filtered co-location patterns is reduced to several dozen or less and, on average, 80% of the selected co-location patterns are user preferred.