Catálogo de publicaciones - libros

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

This chapter looks at the position occupied by the home as the central focus of the information society. A working definition of the home is used and various interactions and activities that take place in the home are discussed with relation to the home, and its changing place in the move to an information society. The chapter also discusses various information activities that impinge upon the function of everyday life in the home including the use of information and information systems. Finally some of the consequences of the move to an information-based model of the home are outlined.

This chapter looks at the position occupied by the home as the central focus of the information society. A working definition of the home is used and various interactions and activities that take place in the home are discussed with relation to the home, and its changing place in the move to an information society. The chapter also discusses various information activities that impinge upon the function of everyday life in the home including the use of information and information systems. Finally some of the consequences of the move to an information-based model of the home are outlined.

Technology has influenced human lives since the beginning of mankind. Sociologists have described the effects that technology has on society as structuring or symbolic. We will use the same concepts to investigate the capability of the Internet to influence privacy. We analyse the structuring and symbolic effects of technology on privacy and thereby demonstrate some potentials of Internet technology and their effect on our life in society

This paper provides instructors of computing a method to integrate computing history in the computing curriculum and to elevate the awareness of the social context of the subject. It provides suggestions by which instructors can enrich the curriculum by including history in the subjects they teach, even though they may not have had formal education in computing history or the history of science. Using history in computing very often stimulates discussion and dialogue among students and makes them aware of the social consequences of the computer systems they will use, help design, or create. Instructors can enrich the courses they now teach by integrating social and historical interludes within them.

This critical appraisal of the role and impact of the information society model in the United States educational system begins with a discussion of three dominant paradigms: automation, “information society,” and “mind tools;” and then traces the history of information and communication technology (ICT) in education in the United States, describing how ideas, intertwined with the conceptions of the information society, led to distractions that have impeded vigorous development of ICT to improve education. These distractions included the claim that schools should take up the responsibility of training all students in ICT for the so-called information workforce. Promoters of the information society model together with the marketing arms of the IT industry perpetuated the adoption of ICT as an end in itself rather than as a means toward improving the main business of education, learning. These pressures over the past three decades have left the ICT in education in a state of confusion, lacking adequate support and, in many instances, the infrastructure to adapt to the demands of the twenty-first century.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

This chapter looks at the position occupied by the home as the central focus of the information society. A working definition of the home is used and various interactions and activities that take place in the home are discussed with relation to the home, and its changing place in the move to an information society. The chapter also discusses various information activities that impinge upon the function of everyday life in the home including the use of information and information systems. Finally some of the consequences of the move to an information-based model of the home are outlined.