The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform

Andrée Rathemacher - One of the best experts on this subject based on the ideXlab platform.

Simon Schwantzer - One of the best experts on this subject based on the ideXlab platform.

  • ECOWS - License4Grid: Adopting DRM for Licensed Content in Grid Environments
    2010 Eighth IEEE European Conference on Web Services, 2010
    Co-Authors: Joachim Götze, Tino Fleuren, Paul Müller, Simon Schwantzer
    Abstract:

    Processing of Licensed Content with automatic compliance checking of the license terms is currently not supported in Grid environments. However, applications processing large amounts of data is a topic recently gaining more and more attention. The use of high performance computing capabilities, e.g., provided by Grid environments, is an obvious choice to speed up the processing time. Currently, most of the input data required in such Grid applications is freely accessible by standard Grid technology. However, many upcoming applications for Grid Computing require data provided under a specific license, also leading to license violations on a regular basis, because license compliance can currently not be validated. Data under a specific license is often retrieved from outside the Grid over individual portals directly from a Content provider or distributor. Beside the additional efforts for user and security management, such external distribution approaches prevent an association between the license information and the Content. In this work, the internal distribution approach for Licensed Content in Grid environments (License4Grid) is designed, maintaining the association between the license information and the corresponding data. The design respects the requirements emerging from handling either unprotected and protected Content and makes use of the user and security mechanisms provided by common Grid technologies in order to fit into the environment homogeneously.

  • License4Grid: Adopting DRM for Licensed Content in Grid Environments
    2010 Eighth IEEE European Conference on Web Services, 2010
    Co-Authors: Joachim Götze, Tino Fleuren, Paul Müller, Simon Schwantzer
    Abstract:

    Processing of Licensed Content with automatic compliance checking of the license terms is currently not supported in Grid environments. However, applications processing large amounts of data is a topic recently gaining more and more attention. The use of high performance computing capabilities, e.g., provided by Grid environments, is an obvious choice to speed up the processing time. Currently, most of the input data required in such Grid applications is freely accessible by standard Grid technology. However, many upcoming applications for Grid Computing require data provided under a specific license, also leading to license violations on a regular basis, because license compliance can currently not be validated. Data under a specific license is often retrieved from outside the Grid over individual portals directly from a Content provider or distributor. Beside the additional efforts for user and security management, such external distribution approaches prevent an association between the license information and the Content. In this work, the internal distribution approach for Licensed Content in Grid environments (License4Grid) is designed, maintaining the association between the license information and the corresponding data. The design respects the requirements emerging from handling either unprotected and protected Content and makes use of the user and security mechanisms provided by common Grid technologies in order to fit into the environment homogeneously.

Lorely Ambriz - One of the best experts on this subject based on the ideXlab platform.

Rodrigo Fernandes De Mello - One of the best experts on this subject based on the ideXlab platform.

  • APSCC - A Novel Approach to Quantify Novelty Levels Applied on Ubiquitous Music Distribution
    2008 IEEE Asia-Pacific Services Computing Conference, 2008
    Co-Authors: Marcelo Keese Albertini, Kuan-ching Li, Rodrigo Fernandes De Mello
    Abstract:

    In order to take advantage and profit with the popularization of digital music, companies started marketing Licensed Content on high-storage portable media players. The introduction of wireless technology in such players motivates new business opportunities where music distribution is ubiquitous. However, in such high-supply scenario, consumers may have difficulties to find interesting Content. In such context, music recommender systems assist consumers in identifying their preferences and in supporting Content searches. An important feature in such market is the low attention given to new music styles, what increases the promotion costs. In order to assist consumers who, positive or negatively, pay attention to such novelty factor, this work proposes a novel method to estimate music preference profiles based on acoustic similarity measures. Such profiles are learnt by an artificial neural network, named self-organizing novelty detection neural network architecture (SONDE), which classifies and quantifies the novelty level of music titles regarding the user profile. Based on novelty levels, we suggest a discount rate model to support promotion strategies. The proposed method is evaluated by simulating some scenarios.

  • A Novel Approach to Quantify Novelty Levels Applied on Ubiquitous Music Distribution
    2008 IEEE Asia-Pacific Services Computing Conference, 2008
    Co-Authors: Marcelo Keese Albertini, Kuan-ching Li, Rodrigo Fernandes De Mello
    Abstract:

    In order to take advantage and profit with the popularization of digital music, companies started marketing Licensed Content on high-storage portable media players. The introduction of wireless technology in such players motivates new business opportunities where music distribution is ubiquitous. However, in such high-supply scenario, consumers may have difficulties to find interesting Content. In such context, music recommender systems assist consumers in identifying their preferences and in supporting Content searches. An important feature in such market is the low attention given to new music styles, what increases the promotion costs. In order to assist consumers who, positive or negatively, pay attention to such novelty factor, this work proposes a novel method to estimate music preference profiles based on acoustic similarity measures. Such profiles are learnt by an artificial neural network, named self-organizing novelty detection neural network architecture (SONDE), which classifies and quantifies the novelty level of music titles regarding the user profile. Based on novelty levels, we suggest a discount rate model to support promotion strategies. The proposed method is evaluated by simulating some scenarios.

Anne Aaron - One of the best experts on this subject based on the ideXlab platform.

  • PCS - Constant-slope rate allocation for distributed real-world encoding
    2016 Picture Coding Symposium (PCS), 2020
    Co-Authors: Jan De Cock, Anne Aaron
    Abstract:

    Parallel encoding systems face the problem of distributed coding decisions, an example of which is rate allocation between different encoding chunks. Distributed encoding is traditionally performed by assigning a fixed bitrate or quality setting to each chunk, after which the chunk uses rate control to achieve that particular target. In this paper, we discuss the use of constant-slope encoding, resulting in chunks which all operate at the same quality-rate trade-off. The (Lagrangian) optimization can be performed independent of the chosen codec or distortion/quality metric. In this paper, we report results for mobile resolutions for VP9 encodes, on both open-source and Netflix catalog original and Licensed Content titles. BD-rate gains are obtained of 6.8 % on average, and a more constant quality across titles is observed.

  • Constant-slope rate allocation for distributed real-world encoding
    2016 Picture Coding Symposium (PCS), 2016
    Co-Authors: Jan De Cock, Anne Aaron
    Abstract:

    Parallel encoding systems face the problem of distributed coding decisions, an example of which is rate allocation between different encoding chunks. Distributed encoding is traditionally performed by assigning a fixed bitrate or quality setting to each chunk, after which the chunk uses rate control to achieve that particular target. In this paper, we discuss the use of constant-slope encoding, resulting in chunks which all operate at the same quality-rate trade-off. The (Lagrangian) optimization can be performed independent of the chosen codec or distortion/quality metric. In this paper, we report results for mobile resolutions for VP9 encodes, on both open-source and Netflix catalog original and Licensed Content titles. BD-rate gains are obtained of 6.8 % on average, and a more constant quality across titles is observed.