Resultados totales (Incluyendo duplicados): 34260
Encontrada(s) 3426 página(s)
CORA.Repositori de Dades de Recerca
doi:10.34810/data478
Dataset. 2018

TIMBRE CLASSIFICATION EXPERIMENTS

  • Ó Nuanáin, Cárthach
This repository contains datasets and scripts for timbre classification experiments conducted as part the Ph.D. thesis. Two datasets were used. The first one concentrates on drum/percussion sounds while the other generalises to orchestral sounds. See the relevant iPython notebooks to re-run experiments. The orchestral sample is quite large, there is a script that pulls N number samples randomly in the folder, for performing smaller analyses. Each episode directory contains word-level and segment-level information of the whole episode and also parallel samples extracted under segments_eng and segments_spa subdirectories. Each sample is stored as an WAV audio file, text file and a CSV file containing word timing information and word-level paralinguistic and prosodic features.

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DOI: https://doi.org/10.34810/data478
CORA.Repositori de Dades de Recerca
doi:10.34810/data478
HANDLE: https://doi.org/10.34810/data478
CORA.Repositori de Dades de Recerca
doi:10.34810/data478
PMID: https://doi.org/10.34810/data478
CORA.Repositori de Dades de Recerca
doi:10.34810/data478
Ver en: https://doi.org/10.34810/data478
CORA.Repositori de Dades de Recerca
doi:10.34810/data478

CORA.Repositori de Dades de Recerca
doi:10.34810/data479
Dataset. 2023

RAW DATA INFANTS' REPRESENTATION OF SOCIAL HIERARCHIES IN ABSENCE OF PHYSICAL DOMINANCE

  • Bas Villalba, Jesús Antonio
  • Sebastián Gallés, Núria
Infants' raw eye-tracker data used in the study presented at the paper entitled "Infants' representation of social hierarchies in absence of physical dominance"

Proyecto: //
DOI: https://doi.org/10.34810/data479
CORA.Repositori de Dades de Recerca
doi:10.34810/data479
HANDLE: https://doi.org/10.34810/data479
CORA.Repositori de Dades de Recerca
doi:10.34810/data479
PMID: https://doi.org/10.34810/data479
CORA.Repositori de Dades de Recerca
doi:10.34810/data479
Ver en: https://doi.org/10.34810/data479
CORA.Repositori de Dades de Recerca
doi:10.34810/data479

CORA.Repositori de Dades de Recerca
doi:10.34810/data47
Dataset. 2021

COMPARATIVE ANALYSES OF ALTERNATIVE BISCUITS MADE WITH PURPLE BARLEY FLOURS AND FRACTIONS

  • Martínez Subirà, Mariona
  • Romero Fabregat, Mª Paz
  • Puig, Eva
  • Macià i Puig, Ma Alba
  • Romagosa Clariana, Ignacio
  • Moralejo Vidal, Mª Angeles
The data reported in the dataset includes the β-glucans, arabinoxylans and phenolic compounds contents, the antioxidant capacity, the effect of baking and the physical parameters of biscuits containing different proportions of whole barley flour and pearling fractions as well as biscuits prepared with 100% refined and 100% whole wheat flour.

Proyecto: //
DOI: https://doi.org/10.34810/data47
CORA.Repositori de Dades de Recerca
doi:10.34810/data47
HANDLE: https://doi.org/10.34810/data47
CORA.Repositori de Dades de Recerca
doi:10.34810/data47
PMID: https://doi.org/10.34810/data47
CORA.Repositori de Dades de Recerca
doi:10.34810/data47
Ver en: https://doi.org/10.34810/data47
CORA.Repositori de Dades de Recerca
doi:10.34810/data47

CORA.Repositori de Dades de Recerca
doi:10.34810/data480
Dataset. 2015

CONVEX INFERENCE FOR COMMUNITY DISCOVERY IN SIGNED NETWORKS (EUROPEAN PARLIAMENT VOTING DATASET)

  • Santamaría, Guillermo
  • Gómez, Vicenç
This repository contains the necessary tools to reproduce the experiments of the paper: G. Santatmaría, V. Gómez (2015) Convex inference for community discovery in signed networks. NIPS 2015 Workshop: Networks in the Social and Information Sciences ------The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here) --------The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/ * data/ : json files with the european votes * network.net : signed network built from the votes * political_parties.txt : "ground truth" party * community_results/ : results for different number of communities and initial vertices * dataComputations.py : used to build the signed network * dataProcessing.py : used to build the signed network We would appreciate if you cite the paper after using the data or the code. --------DEPENDENCIES-------- The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04 - For PSL library, you need to have java 1.8 you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER' maven 3.x - For libDAI you will need: make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev --------CODE TO RUN THE FOLLOWING EXPERIMENTS:-------- Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities --------INSTALL-------- To install the experiments you have to follow the next steps: 1 Build the libdai library by doing: make -B on the folder (libdai) 2 Generate the class path of the groovy project: mvn clean install mvn dependency:build-classpath-Dmdep.outputFile=classpath.out on the psl root folder (You need to have java 1.8 and maven 3.x installed) 3 Grant exec permissions to the run.sh script --------Options-------- The main python file to run the experiments is evaluatebalanceon_sn.py. It accepts the following parameters: 1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less 2 (Int) The number of underlying communities 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default. An example of execution would be: python evaluate_balance_on_sn.py 120 3 0.45 True True The results of the experiments are save in the folder results/ Scripts The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy --------For further questions, please contact vicen.gomez@upf.edu

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DOI: https://doi.org/10.34810/data480
CORA.Repositori de Dades de Recerca
doi:10.34810/data480
HANDLE: https://doi.org/10.34810/data480
CORA.Repositori de Dades de Recerca
doi:10.34810/data480
PMID: https://doi.org/10.34810/data480
CORA.Repositori de Dades de Recerca
doi:10.34810/data480
Ver en: https://doi.org/10.34810/data480
CORA.Repositori de Dades de Recerca
doi:10.34810/data480

CORA.Repositori de Dades de Recerca
doi:10.34810/data481
Dataset. 2017

DATA FOR NFVSDN EXPERIMENTS

  • Rankothge, Windhya
  • Le, Franck
  • Russo, Alessandra
  • Lobo, Jorge
##Project Structure: 1. GeneratePolicies. 2. DistributeTrafficOverPolicies. 3. PoliciesToChange. 4. TopologyCreator. 5. ExampleDataSet. ##Guidelines to use the data and programs in the repository. There are two ways that this repository can be useful for anyone that needs data about VNFs and their traffic on the cloud. 1.Directly use the already generated data set. 2.Generate your own data set using the given programs. ##How to use the already generated data set: ExampleDataSet. We have generated data for: 1.Possible policy requests with initial traffic passing through them defined. 2.Scaling requirements for each 15 minutes for 2 days. 3.Topology data (nodes, links, paths) for K-Fat Tree, BCube and VL2 architectures with 64 servers. You can use these data directly as inputs for your experiments. ##How to use the programs and generate the required data sets. If you want to generate your own data sets according to your requirements, you can use the given programs. 1) First step is to generate the policy requests data set using the policy requests generation program: GeneratePolicies. - Inputs to the program: number of large scaled enterprise networks. - Output of the program: a set of policies for each enterprise with 100 NFs. 2) After we have created the policy requets data set, the seconds step is to create the traffic data set for the policies using the initial traffic distribution program: DistributeTrafficOverPolicies. - Inputs to the program: the set of policies, initial traffic load. - Output of the program: distribution of the traffic load over policies. 3) The third step is to create the scaling requirements data set to reflect the traffic changes over the time using the scaling requirements over the time program: PoliciesToChange. 4) The last step is to generate the required topology data for different network architectures (K-Fat tree, BCube, VL2) using the topology generation program: TopologyCreator. - Inputs to the program: network architecture and number of servers. - Output of the program: the topology: nodes, links and paths.

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DOI: https://doi.org/10.34810/data481
CORA.Repositori de Dades de Recerca
doi:10.34810/data481
HANDLE: https://doi.org/10.34810/data481
CORA.Repositori de Dades de Recerca
doi:10.34810/data481
PMID: https://doi.org/10.34810/data481
CORA.Repositori de Dades de Recerca
doi:10.34810/data481
Ver en: https://doi.org/10.34810/data481
CORA.Repositori de Dades de Recerca
doi:10.34810/data481

CORA.Repositori de Dades de Recerca
doi:10.34810/data483
Dataset. 2023

EMOTWI50 [RESEARCH DATA]

  • Barbieri, Francesco
  • Ronzano, Francesco
  • Saggion, Horacio
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Proyecto: //
DOI: https://doi.org/10.34810/data483
CORA.Repositori de Dades de Recerca
doi:10.34810/data483
HANDLE: https://doi.org/10.34810/data483
CORA.Repositori de Dades de Recerca
doi:10.34810/data483
PMID: https://doi.org/10.34810/data483
CORA.Repositori de Dades de Recerca
doi:10.34810/data483
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CORA.Repositori de Dades de Recerca
doi:10.34810/data483

CORA.Repositori de Dades de Recerca
doi:10.34810/data484
Dataset. 2023

PUNKPROSE [SOFTWARE]

  • Öktem, Alp
Punctuation marks support understandability and readability in written language. In spoken language, punctuation of the transcribed speech is influenced by two phenomena: (1) syntax and (2) prosody. We present a software architecture that makes it possible to train punctuation restoration models from any combination of lexical, morphosyntactic, prosodic and acoustic features. Architecture is language independent and feeds on word-segmented data. A dataset compiled from English TED talks is given in http://hdl.handle.net/10230/33981

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DOI: https://doi.org/10.34810/data484
CORA.Repositori de Dades de Recerca
doi:10.34810/data484
HANDLE: https://doi.org/10.34810/data484
CORA.Repositori de Dades de Recerca
doi:10.34810/data484
PMID: https://doi.org/10.34810/data484
CORA.Repositori de Dades de Recerca
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Ver en: https://doi.org/10.34810/data484
CORA.Repositori de Dades de Recerca
doi:10.34810/data484

CORA.Repositori de Dades de Recerca
doi:10.34810/data485
Dataset. 2018

FLABASE: A FLAMENCO KNOWLEDGE BASE

  • Oramas, Sergio
FlaBase (Flamenco Knowledge Base) is the acronym of a new knowledge base of flamenco music. Its ultimate aim is to gather all available online editorial, biographical and musicological information related to flamenco music. A first version is just being released. Its content is the result of the curation and extraction processes. FlaBase is stored in JSON format, and it is freely available for download. This first release of FlaBase contains information about 1,102 artists, 74 palos (flamenco genres), 2,860 albums, 13,311 tracks, and 771 Andalusian locations. Data was compiled and curated from different sources: Wikipedia, DBpedia, Andalucia.org, elartedevivirelflamenco.com, MusicBrainz, flun.cica.es/index.php/grabaciones/base-datos-grabaciones and juntadeandalucia.es/institutodeestadisticaycartografia/sima

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DOI: https://doi.org/10.34810/data485
CORA.Repositori de Dades de Recerca
doi:10.34810/data485
HANDLE: https://doi.org/10.34810/data485
CORA.Repositori de Dades de Recerca
doi:10.34810/data485
PMID: https://doi.org/10.34810/data485
CORA.Repositori de Dades de Recerca
doi:10.34810/data485
Ver en: https://doi.org/10.34810/data485
CORA.Repositori de Dades de Recerca
doi:10.34810/data485

CORA.Repositori de Dades de Recerca
doi:10.34810/data486
Dataset. 2023

RAW DATA INFLUENCE OF AGENTS' SOCIAL STATUS ON 18-TO-21-MONTH-OLD INFANTS' LEARNING

  • Bas Villalba, Jesús Antonio
  • Sebastián Gallés, Núria
Infants' raw eye-tracker data used in the study presented at the paper entitled "Influence of agents' social status on 18-to-21-month-old infants' learning".

Proyecto: //
DOI: https://doi.org/10.34810/data486
CORA.Repositori de Dades de Recerca
doi:10.34810/data486
HANDLE: https://doi.org/10.34810/data486
CORA.Repositori de Dades de Recerca
doi:10.34810/data486
PMID: https://doi.org/10.34810/data486
CORA.Repositori de Dades de Recerca
doi:10.34810/data486
Ver en: https://doi.org/10.34810/data486
CORA.Repositori de Dades de Recerca
doi:10.34810/data486

CORA.Repositori de Dades de Recerca
doi:10.34810/data487
Dataset. 2023

INFERRING DIRECTED NETWORKS USING A RANK-BASED CONNECTIVITY MEASURE [SOFTWARE]

  • Grau Leguia, Marc
  • Martínez, Cristina G. B.
  • Malvestio, Irene
  • Tauste Campo, Adrià
  • Rocamora, Rodrigo
  • Levnajić, Zoran
  • Andrzejak, Ralph Gregor
This page provides the source code underlying the manuscript: Leguia MG, Martínez CGB, Malvestio I, Campo A T, Rocamora R, Levnajic Z, Andrzejak RG. 2019. Inferring directed networks using a rank-based connectivity measure. Phys. Rev. E. 99, 012319 If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

Proyecto: //
DOI: https://doi.org/10.34810/data487
CORA.Repositori de Dades de Recerca
doi:10.34810/data487
HANDLE: https://doi.org/10.34810/data487
CORA.Repositori de Dades de Recerca
doi:10.34810/data487
PMID: https://doi.org/10.34810/data487
CORA.Repositori de Dades de Recerca
doi:10.34810/data487
Ver en: https://doi.org/10.34810/data487
CORA.Repositori de Dades de Recerca
doi:10.34810/data487

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