In the current research on Federated Learning, the issue of heterogeneous data has become a key topic. Some methods attempt to decouple neural network into feature extraction component and classifier component and app...
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Although Multi-Access Edge Computing (MEC) has evolved into a key technology for provisioning mobility-oriented and delay-sensitive applications, the storage and computing power limitations of user-end MEC devices sti...
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Witness encryption (WE) is a new encryption paradigm that allows encrypting a message using the instance of a particular NP problem, and someone who knows a solution to this problem (i.e., a witness) can effectiv...
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Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extens...
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It is widely acknowledged that artificial intelligence (AI) technology has been extensively applied and has achieved remarkable advancements in various fields. The field of computational materials science has also emb...
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The online social platforms witnessed enormous growth in its networked structure as users continue to connect and interact through e-social dialogues. This eventually causes the transformational emergence of online so...
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Group signatures allow users to sign messages on behalf of the group without prevealing their identities. However, the opening authority can trace signatures back to their source, raising concerns about privacy. To ad...
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Unification has been introduced in Description Logic (DL) as a means to detect redundancies in ontologies. In particular, it was shown that testing unifiability in the DL EL is an NP-complete problem, and this result ...
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Students in open-ended educational games have a number of different pathways that they can select to work productively through a learning activity. Educators and system designers may want to know which of these pathwa...
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ISBN:
(纸本)9783031741371;9783031741388
Students in open-ended educational games have a number of different pathways that they can select to work productively through a learning activity. Educators and system designers may want to know which of these pathways are most effective for engagement, learning, or other desirable outcomes. In this paper, we investigate which prior jobs and factors are associated with higher rates of student quitting behavior in an educational science exploration game. We use a series of Chi squared analyses to identify the jobs with the highest rates of quitting overall, and we calculate logistic regressions within specific jobs to determine the potential factors that lead to students quitting those jobs. Our analysis revealed that for 23 of the 40 jobs examined, having experience in at least one previous job significantly decreased the chances of students quitting the subsequent job, and that completing specific prior jobs reduces quit rates on specific later jobs. In our discussion, we describe the challenges associated with modeling quitting behavior, and how these analyses could be used to better optimize students' pathways through the game environment. Specially, guiding students through specific sequences of preliminary jobs before tackling more challenging jobs can improve their engagement and reduce dropout rates, thus optimizing their learning pathways.
The history of medicine is a path of progress and discoveries, characterized by a constant search for knowledge for human care and well-being. Nowadays medical practice is based on three principles: knowledge (model),...
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ISBN:
(纸本)9783031782374;9783031782381
The history of medicine is a path of progress and discoveries, characterized by a constant search for knowledge for human care and well-being. Nowadays medical practice is based on three principles: knowledge (model), experience and statistics (evidence-based medicine, EBM). Mathematics is increasingly entering medicine with two distinct approaches: namely, data-driven and model-driven. However, this must not limit professional freedom or distort the traditional pathophysiological modelling approach that is the only one accepted by doctors, who must remain the protagonists of the therapeutic decisions. This paper wants to show how model-driven approaches offer more guarantees, compared to data-driven ones, to maintain a pathophysiological model (accepted by clinicians) and therefore bring an important contribution to the knowledge for human care. On the other side, only data-driven approaches can discover variables involved in a complex system. So, the integration of these two approaches is the future. In addition, only the collaboration between different skills and the construction of a common language can bring innovative solutions for the management of health care and patient treatment.
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