Cleft lip and/or palate(CLP)are the most common craniofacial malformations in *** problems often persist even after cleft repair,such that follow-up articulation training is usually ***,the neural mechanism behind eff...
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Cleft lip and/or palate(CLP)are the most common craniofacial malformations in *** problems often persist even after cleft repair,such that follow-up articulation training is usually ***,the neural mechanism behind effective articulation training remains largely *** used fMRI to investigate the differences in brain activation,functional connectivity,and effective connectivity across CLP patients with and without articulation training and matched normal *** found that training promoted task-related brain activation among the articulation-related brain networks,as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the *** results reveal the neural correlates of effective articulation training in CLP patients,and this could contribute to the future improvement of the post-repair articulation training program.
Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for ...
Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called $TradeFL$ , to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract 1 1 Illustration of the prototype: https://***/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.
In recent years, traditional energy sources have begun to give way to alternative and renewable energy sources such as solar energy. Therefore, the interest in the use of solar cells in energy production is increasing...
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Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information ...
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Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to their accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often ...
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Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough ...
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This paper considers online convex optimization (OCO) with generated i.i.d. stochastic constraints, where the distribution of environment is changing and the performance is measured by \textit{adaptive regret}. The st...
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Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training with data centralization suffers from th...
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In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. Wit...
In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.
With the growth of knowledge graphs (KGs), question answering systems make the KGs easily accessible for end-users. Question answering over KGs aims to provide crisp answers to natural language questions across facts ...
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With the growth of knowledge graphs (KGs), question answering systems make the KGs easily accessible for end-users. Question answering over KGs aims to provide crisp answers to natural language questions across facts stored in the KGs. This paper proposes a graph-driven approach to answer questions over a KG through four steps, including (1) knowledge subgraph construction, (2) question graph construction, (3) graph matching, and (4) query execution. Given an input question, a knowledge subgraph, which is likely to include the answer is extracted to reduce the KG’s search space. A graph, named question graph, is built to represent the question’s intention. Then, the question graph is matched over the knowledge subgraph to find a query graph corresponding to a SPARQL query. Finally, the corresponding SPARQL is executed to return the answers to the question. The performance of the proposed approach is empirically evaluated using the 6th Question Answering over Linked data Challenge (QALD-6). Experimental results show that the proposed approach improves the performance compared to the-state-of-art in terms of recall, precision, and F1-score.
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