An artist life cycle model for digital media content: Strategies for the Light Web and the Dark Web

This paper surveys and categorizes emerging digital media business models. We apply the customer activity cycle of Vandermerwe (2000) to the consumption of digital media, taking three phases into account: pre-consumption, consumption and post-consumption. Our analysis of the business models focuses on their social costs and benefits. We derive the parameters as follows: convenience of use, exposure, ease of compliance and administration. We distinguish two polar environments for digital media: the Dark Web with content created by the masses, and the Light Web with content created by big media. We develop an artist life cycle model in which different business models appear to be optimal at different stages of an artist’s career. Voluntary payment-based models seem to be ideal for newcomers in the Dark Web, while digital rights management-based and complementary product and service-based models are the likely choice of established artists in the Light Web. Established artists might change their approach again, using voluntary payment-based or complementary product and service-based models when they retire.     

Inductively coupled probe for the measurement of partial discharges

Partial discharges are a transient phenomena whose measurement is remarkably important for electrical equipment diagnosis and maintenance. These discharges appear in the measurement circuit as very narrow current pulses of some nanoseconds. Therefore, discharge pulse detection is a particularly difficult problem, especially because they are superposed on the high voltage waveforms that cause them. In this paper, we present an inductively coupled probe able to measure this physical phenomenon by means of a very simple and inexpensive device that can be installed in the equipment under test. After modeling the probe, its response will be compared to that of commercial devices using both calibrated discharges and partial discharges occurring at real power.     

A logical approach to efficient Max-SAT solving

Weighted Max-SAT is the optimization version of SAT and many important problems can be naturally encoded as such. Solving weighted Max-SAT is an important problem from both a theoretical and a practical point of view. In recent years, there has been considerable interest in finding efficient solving techniques. Most of this work focuses on the computation of good quality lower bounds to be used within a branch and bound DPLL-like algorithm. Most often, these lower bounds are described in a procedural way. Because of that, it is difficult to realize the logic that is behind.In this paper we introduce an original framework for Max-SAT that stresses the parallelism with classical SAT. Then, we extend the two basic SAT solving techniques: search and inference. We show that many algorithmic tricks used in state-of-the-art Max-SAT solvers are easily expressible in logical terms in a unified manner, using our framework.We also introduce an original search algorithm that performs a restricted amount of weighted resolution at each visited node. We empirically compare our algorithm with a variety of solving alternatives on several benchmarks. Our experiments, which constitute to the best of our knowledge the most comprehensive Max-SAT evaluation ever reported, demonstrate the practical usability of our approach.     

New approach for optimizing the interpretation and representation of the degree of historical-archaeological evidence in the virtual reconstructions

Virtual Reality - Virtual reconstruction is defined as the visual recovery of a building or object through the creation of a three-dimensional model of the asset to be reconstructed, in a...     

Comparing the Slack-Variable Mixture Model With Other Alternatives

There have been many linear regression models proposed to analyze mixture experiments including the Scheffé model, the slack-variable model, and the Kronecker model. The use of the slack-variable model is somewhat controversial within the mixture experiment research community. However, in situations that the slack-variable ingredient is used to fill in the formulation and the remaining ingredients have constraints such that they can be chosen independently of one another, the slack-variable model is extremely popular by practitioners mainly due to the ease of interpretation. In this article, we advocate that for some mixture experiments the slack-variable model has appealing properties including numerical stability and better prediction accuracy when model-term selection is performed. We also explain how the effects of the slack-variable model components should be interpreted and how easy it is for practitioners to understand the components effects. We also investigate how to choose the slack-variable component, what transformation should be used to reduce collinearity, and under what circumstances the slack-variable model should be preferred. Both simulation and practical examples are provided to support the conclusions.     

A new Fortran 90 program to compute regular and irregular associated Legendre functions

We present a modern Fortran 90 code to compute the regular and irregular associated Legendre functions for all x∈(−1,+1) (on the cut) and |x|>1 and integer degree (l) and order (m). The code applies either forward or backward recursion in (l) and (m) in the stable direction, starting with analytically known values for forward recursion and considering both a Wronskian based and a modified Miller's method for backward recursion. While some Fortran 77 codes existed for computing the functions off the cut, no Fortran 90 code was available for accurately computing the functions for all real values of x different from x=±1 where the irregular functions are not defined.

Program summary

Program title: Associated Legendre FunctionsCatalogue identifier: AEHE_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHE_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 6722No. of bytes in distributed program, including test data, etc.: 310 210Distribution format: tar.gzProgramming language: Fortran 90Computer: Linux systemsOperating system: LinuxRAM: bytesClassification: 4.7Nature of problem: Compute the regular and irregular associated Legendre functions for integer values of the degree and order and for all real arguments. The computation of the interaction of two electrons, 1/|r₁−r₂|, in prolate spheroidal coordinates is used as one example where these functions are required for all values of the argument and we are able to easily compare the series expansion in associated Legendre functions and the exact value.Solution method: The code evaluates the regular and irregular associated Legendre functions using forward recursion when |x|<1 starting the recursion with the analytically known values of the first two members of the sequence. For values of the argument |x|<1, the upward recursion over the degree for the regular functions is numerically stable. For the irregular functions, backward recursion must be applied and a suitable method of starting the recursion is required. The program has two options; a modified version of Miller's algorithm and the use of the Wronskian relation between the regular and irregular functions, which was the method considered in [1]. Both approaches require the computation of a continued fraction to begin the recursion. The Wronskian method (which can also be described as a modified Miller's method) is a convenient method of computations when both the regular and irregular functions are needed.Running time: The example tests provided take a few seconds to run.References:

• [1] 

  A. Gil, J. Segura, A code to evaluate prolate and oblate spheroidal harmonics, Comput. Phys. Commun. 108 (1998) 267–278.

     

Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers

Recently, a multi-objective Sensitivity–Accuracy based methodology has been proposed for building classifiers for multi-class problems. This technique is especially suitable for imbalanced and multi-class datasets. Moreover, the high computational cost of multi-objective approaches is well known so more efficient alternatives must be explored. This paper presents an efficient alternative to the Pareto based solution when considering both Minimum Sensitivity and Accuracy in multi-class classifiers. Alternatives are implemented by extending the Evolutionary Extreme Learning Machine algorithm for training artificial neural networks. Experiments were performed to select the best option after considering alternative proposals and related methods. Based on the experiments, this methodology is competitive in Accuracy, Minimum Sensitivity and efficiency.     

Angle-of-arrival localization based on antenna arrays for wireless sensor networks

Among the large number of contributions concerning the localization techniques for wireless sensor networks (WSNs), there is still no simple, energy and cost efficient solution suitable in outdoor scenarios. In this paper, a technique based on antenna arrays and angle-of-arrival (AoA) measurements is carefully discussed. While the AoA algorithms are rarely considered for WSNs due to the large dimensions of directional antennas, some system configurations are investigated that can be easily incorporated in pocket-size wireless devices.A heuristic weighting function that enables decreasing the location errors is introduced. Also, the detailed performance analysis of the presented system is provided. The localization accuracy is validated through realistic Monte-Carlo simulations that take into account the specificity of propagation conditions in WSNs as well as the radio noise effects. Finally, trade-offs between the accuracy, localization time and the number of anchors in a network are addressed.     

Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus

We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model.     

An experiment for improving students performance in secondary and tertiary education by means of m-learning auto-assessment

Mobile learning is considered an evolution of e-learning that embraces the ubiquitous nature of current computational systems in order to improve teaching and learning. Within this context it is possible to develop mobile applications oriented to learning, but it is also important to assess to what extent such applications actually work. In this paper we present a new tool designed to reinforce students’ knowledge by means of self-assessment. Improvement in student achievement was evaluated and an attitudinal survey was also carried out to measure student attitudes towards this new tool. Three different experimental groups were selected for this research, with students aged from 14 to 21 years old, including high-school and university students. Results show that this kind of tool improves student achievement, especially amongst younger learners, with a relatively low impact on current teaching activities and methodology.