Articles
Publication Date: 2025
Information Sciences (0020-0255)692
Open relation extraction is a critical task in natural language processing aimed at automatically extracting relations between entities in open-domain corpora. Most existing systems focus on extracting binary relations (relations between two entities) while extracting more complex n-ary relations (involving more than two entities) remains a significant challenge. Additionally, many previous systems rely on hand-crafted patterns and natural language processing tools, which result in error accumulation and reduced accuracy. The current study proposes a novel approach to open n-ary relation extraction that leverages recent advancements in deep learning architectures. This approach addresses the limitations of existing open relation extraction systems, particularly their reliance on hand-crafted patterns and their focus on binary relations. It utilizes SpanBERT to capture relational patterns from text data directly and introduces entity embedding vectors to create distinct representations of entities within sentences. These vectors enhance the proposed system's understanding of the entities within the input sentence, leading to more accurate relation extraction. Notably, the proposed system in the present study achieves an F1-score of 89.79 and 92.67 on the LSOIE-wiki and OpenIE4 datasets, outperforming the best existing models by over 12% and 10%, respectively. These results highlight the effectiveness of the proposed approach in addressing the challenges of open n-ary relation extraction. © 2024 Elsevier Inc.
Publication Date: 2025
Cybernetics and Systems (01969722)56(1)pp. 1-20
The Controllability on temporal complex networks is one of the most important challenges among researchers in this field. The primary purpose of network controllability is to apply inputs by selecting minimum driver nodes set (MDS) to the network components to move the network from an initial state to a final state in a limited time. The most important challenges in the controllability of temporal networks can be mentioned the high complexity of the control algorithms used in these methods as well as the high data overhead of temporal network representation models such as the layered model. In this paper, centrality measures are used as the most important characteristics of networks for network controllability. For this purpose, centrality measures have been redefined based on temporal networks and a new controllability method has been proposed based on temporal centrality measures. Then, these properties are used for selecting the minimal driver nodes set, in such a way that the network can be fully controlled using these nodes. The experimental results demonstrate that by using temporal centrality measures the execution speed of control processes is improved (57% improvement) and the overhead is not increased and also the control process has led to the same length of MDS as other conventional controllability methods, it has even been better in some cases. © 2022 Taylor & Francis Group, LLC.
Publication Date: 2025
Dental Research Journal (20080255)22(10)
Background: Studying pathology is not a fascinating subject for many students. Today, novel educational methods have received attention worldwide. This study aimed to design an oral and maxillofacial pathology learning application and evaluate its effectiveness on dental students’ knowledge of the Isfahan dental faculty. Materials and Methods: In this experimental study, after designing a web application, including an oral pathology context (PathoGAME), its effectiveness on 112 junior dental students’ knowledge was investigated in 2022. After introducing the application, students were motivated to use the app for learning oral pathology. They were then examined in the midterm and final examinations. Subsequently, their scores on the questions related to the application’s contents were compared with the scores of other questions. Finally, students’ satisfaction with the application was evaluated. The data were analyzed using SPSS software and paired t-tests, Pearson’s tests, and analysis of variance. Results: In the midterm examination, there was no significant difference between the mean score of questions related or unrelated to the application’s content among those who had used the application. Furthermore, the mean scores of the related questions for the application users and those who did not, were not significantly different (P = 0.5). However, on the final examination, the mean score for questions related to the application was significantly greater for students who used the application than for those who did not (P = 0.03). Conclusion: Overall, novel educational methods, such as this application, were beneficial for improving students’ understanding of pathology. The users’ satisfaction was high in utilizing this application, indicating the application’s success and confirming its feasibility. © 2025 Dental Research Journal.
