Oscar Sapena Vercher

Prof. Oscar Sapena Vercher

Universitat Politècnica de València, Valencia, Spain
orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2014 in Applied Intelligence
Verified
Journal Article
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ATAlejandro Torreño
Alejandro Torreño
EOEva Onaindia
Eva Onaindia
OVOscar Sapena Vercher
Oscar Sapena Vercher

22 April 2008 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
AGAntonio Garrido
Antonio Garrido
EOEva Onaindia
Eva Onaindia
OVOscar Sapena Vercher
Oscar Sapena Vercher

14 August 2007 in Applied Intelligence
Verified
Journal Article
Closed access
PDF file
OVOscar Sapena Vercher
Oscar Sapena Vercher
EOEva Onaindia
Eva Onaindia

22 April 2008 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
OVOscar Sapena Vercher
Oscar Sapena Vercher
EOEva Onaindia
Eva Onaindia
AGAntonio Garrido
Antonio Garrido
MAMarlene Arangu
Marlene Arangu

1 November 2009
Verified
Conference Paper
Closed access
Miguel A. SalidoMiguel A. Salido
Miguel A. Salido
OVOscar Sapena Vercher
Oscar Sapena Vercher
MRMario Rodriguez
Mario Rodriguez
FBFederico Barber
Federico Barber
One of the more important problems in container terminal is related to the container stacking problem. A container stack is a type of temporary store where containers await further transport by truck, train or vessel. The main efficiency problem for an individual stack is to ensure easy access to containers at the expected time of transfer. Since stacks are 'last-in, first-out', and the cranes used to relocate containers within the stack are heavily used, the stacks must be maintained in a state that minimizes on-demand relocations. In this paper, we present a new domain-dependent planning heuristic for finding the best configuration of containers in a bay. Thus, given a set of outgoing containers, our planner minimizes the number of relocations of containers in order to allocate all selected containers in an appropriate order to avoid further reshuffles.

9 May 2022 in Applied Sciences
Verified
Journal Article
Open access
PDF file
OVOscar Sapena Vercher
Oscar Sapena Vercher
EOEva Onaindia
Eva Onaindia
The way of understanding online higher education has greatly changed due to the worldwide pandemic situation. Teaching is undertaken remotely, and the faculty incorporate lecture audio recordings as part of the teaching material. This new online teaching–learning setting has largely impacted university classes. While online teaching technology that enriches virtual classrooms has been abundant over the past two years, the same has not occurred in supporting students during online learning. To overcome this limitation, our aim is to work toward enabling students to easily access the piece of the lesson recording in which the teacher explains a theoretical concept, solves an exercise, or comments on organizational issues of the course. To that end, we present a multimodal classification algorithm that identifies the type of activity that is being carried out at any time of the lesson by using a transformer-based language model that exploits features from the audio file and from the automated lecture transcription. The experimental results will show that some academic activities are more easily identifiable with the audio signal while resorting to the text transcription is needed to identify others. All in all, our contribution aims to recognize the academic activities of a teacher during a lesson.