From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computerscience evolves rapidly and XA tackles the gap betw...
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Mental health is a significant issue worldwide,and the utilization of technology to assist mental health hasseen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have ...
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Mental health is a significant issue worldwide,and the utilization of technology to assist mental health hasseen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data issensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conductssurveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the con...
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ISBN:
(纸本)9783031717062;9783031717079
The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the context in which the activities occur, the content they have access to, with peers and their teachers. The wider adoption of generative artificial intelligence introduces new interactions that researchers have to look at when learning analytics techniques are applied to monitor learning patterns. The task interaction framework proposed in this paper explores how AI-based tools affect student-content and student-context interactions during mobile learning activities, thus focusing on the interplay of learning Analytics and Artificial Intelligence advances in the educational domain. A use case scenario that explores the framework's application in a real educational context is also presented. Finally, we describe the architectural design of an environment that leverages the task interaction framework to analyze enhanced mobile learning experiences in which structured content extracted from a Knowledge Graph is elaborated by a large language model to provide students with personalized content.
solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning...
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This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification...
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This paper focuses on document-level event factuality identification (DEFI), which predicts the factual nature of an event from the view of a document. As the document-level sub-task of event factuality identification (EFI), DEFI is a challenging and fundamental task in natural language processing (NLP). Currently, most existing studies focus on sentence-level event factuality identification (sEFI). However, DEFI isstill in the early stage and related studies are quite limited. Previous work is heavily dependent on various NLP tools and annotated information, e.g., dependency trees, event triggers, speculative and negative cues, and does not consider filtering irrelevant and noisy texts that can lead to wrong results. To address these issues, this paper proposes a reinforced multi-granularity hierarchical network model: Reinforced semantic learning Network (RsLN), which means it can learn semantics from sentences and tokens at various levels of granularity and hierarchy. since integrated with hierarchical reinforcement learning (HRL), the RsLN model is able to select relevant and meaningful sentences and tokens. Then, RsLN encodes the event and document according to these selected texts. To evaluate our model, based on the DLEF (Document-Level Event Factuality) corpus, we annotate the ExDLEF corpus as the benchmark dataset. Experimental resultsshow that the RsLN model outperformsseveral state-of-the-arts.
Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***, experimen...
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Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***, experimental investigations of metal alloys can be time-consuming and expensive, often requiring extensive experimental design and effort. In thisstudy, we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN) to screen alloy anode materials for seven battery systems, including lithium(Li), sodium(Na), potassium(K), zinc(Zn), magnesium(Mg), calcium(Ca), and aluminum(Al). We utilized data with tens of thousands of alloy materials from the Materialsproject(MP) and Automatic FLOW for Materials Discovery(AFLOW) databases. Without any experimental voltage input, we identified over 30 alloy systems that have been experimentally validated with good precision. Additionally, we predicted over 100 alloy anodes with low potential and high specific capacity. We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.
A graph structure is a powerful mathematical abstraction, which can not only represent information about individuals but also capture the interactions between individuals for reasoning. Geometric modeling and relation...
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As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data *** keeps data locally and trains local models through local data to protect the privacy of local *** feder...
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As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data *** keeps data locally and trains local models through local data to protect the privacy of local *** federated learning method effectively solves the problem of artificial smart data islands and privacy protection ***,existing researchshows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server *** solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated ***,adding Gaussian noise perturbations to the data degrades the model learning *** address these issues,this paper proposes a differential privacy federated learningscheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise isspecifically added in the training process to hide the real parameters uploaded by the *** addition,this paper proposes an adaptive noise reduction *** the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced *** paper conducts a series of simulation experiments on realMNIsT and CIFAR-10 datasets,and the resultsshow that the DPFL-AGN algorithmperforms better compared to the other algorithms.
The advancement of autonomous driving heavily relies on the ability to accurate lane lines *** deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection hav...
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The advancement of autonomous driving heavily relies on the ability to accurate lane lines *** deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the ***,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane *** precision of lane line detection is limited by the unpredictable quantity and diversity of lane *** address the aforementioned challenges,we propose a novel deep learning approach for lane line *** method leverages the swin Transformer in conjunction with LaneNet(called sT-LaneNet).The experience resultsshowed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such asscenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane *** 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 *** programming scripts and associated models for thisproject can be accessed openly at the following GitHub repository:https://***/Duane 711/Lane-line-detec tion-sT-LaneNet.
With the continuous promotion and deepened application of Machine learning-as-a-service (MLaas) across varioussocietal domains, its privacy problems occur frequently and receive more and more attention from researche...
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