BUSCADOR RECOLECTA

The reemergence of China is changing the world, and that EU needs to develop a long-term approach based on research-based knowledge in order to engage strategically with an increasingly assertive China.

This dataset consists of a semi-structured interview with a person of Chinese nationality, carried out in Spanish.

Descripción del proyecto

Descripción del dataset

Diccionario datos

Privacidad

This dataset introduces a set of reproducibility resources with the aim of allowing the exact replication of the experiments introduced by our main paper, which is a reproducible experimental survey on biomedical sentence similarity with the following aims: (1) to elucidate the state of the art of the problem; (2) to solve some reproducibility problems preventing the evaluation of most of current methods; (3) to evaluate several unexplored sentence similarity methods; (4) to evaluate for the first time an unexplored benchmark, called Corpus-Transcriptional-Regulation (CTR); (5) to carry out a study on the impact of the pre-processing stages and Named Entity Recognition (NER) tools on the performance of the sentence similarity methods; and finally, (6) to bridge the lack of software and data reproducibility resources for methods and experiments in this line of research. This dataset sets a self-contained reproducibility platform which contains the Java source code and binaries of our main benchmark program, as well as a Docker image which allows the exact replication of our experiments in any software platform supported by Docker, such as all Linux-based operating systems, Windows or MacOS. Our benchmark program is distributed with the UMLS SNOMED-CT and MeSH ontologies by courtesy of the US National Library of Medicine (NLM), as well as all needed software components with the aim of making the setup process easier. Our Docker image provides an exact virtual replica of the machine in which we ran our experiments, thus removing the need to carry-out any tedious setup process, such as the setup of the Python virtual environments and other software components.

HESML library is freely distributed for any non-commercial purpose under a CC By-NC-SA-4.0 license, subject to the citing of the two mains HESML papers [17] as attribution requirement. However, HESML distribution also includes other datasets, databases or data files whose use require the attribution acknowledgement by any user of HEMSL. Thus, we urge to the HESML users to fulfill with licensing terms related to other resources distributed with the library as detailed in its companion release notes.

Descripción del proyecto

The main objective of the project in which the dataset has been elaborated is to design and implement a video encoder that produces videos that comply with the VVC standard specifications, requiring less computation time (and thus less power consumption), and to obtain a higher compression rate without affecting the subjective quality.

Specifically, this work focuses on improving the efficiency of the compression factor obtained by the VVC standard using perceptal video coding techniques. With this dataset we intend to perform a study of which parts of the frames viewers tend to pay more attention to.

4. Descripción del dataset

This dataset provides a basis for the study of the areas of interest to which viewers pay attention during the viewing of video sequences encoded with the VVC standard. Attention has been calculated on the Coding Tree Units (CTUs) of the VVC video standard, i.e., on blocks of 128x128 pixels.

The dataset consists of a single CSV file with a total of 10 variables, 9 of them as input variables for the models that are learnt using the dataset and 1 as a variable to be predicted:

1. pos_row: the relative position (between 0 and 1) of the CTU among all the rows of CTUs.

2. pos_col: the relative position (between 0 and 1) of the CTU among all the columns of CTUs.

3. Center_distance: the relative distance between the center of the CTU and the center of the frame (e.g., the center of the frame is represented as $(0.5, 0.5)$ in relative terms).

4. Mean: the mean value among the luma pixels of the CTU.

5. Median: the median value among the pixels of the CTU.

6. Std: the standard deviation of the pixels inside the CTU.

7. var: the variance value among the pixels of the CTU.

8. Asymmetry: the skweness (or asymmetry) coefficient of the pixels of the CTU.

9. Kurtosis: the kurtosis coefficient of the pixels of the CTU.

10. Interest: the number of viewers that paid attention to the CTU (out of 34 viewers).