Ubiquitous Computing: Any Ethical Implications?

In this article, the authors investigate, from an interdisciplinary perspective, possible ethical implications of the presence of ubiquitous computing systems in human perception/action. The term ubiquitous computing is used to characterize information-processing capacity from computers that are available everywhere and all the time, integrated into everyday objects and activities. The contrast in approach to aspects of ubiquitous computing between traditional considerations of ethical issues and the Ecological Philosophy view concerning its possible consequences in the context of perception/action are the underlying themes of this paper. The focus is on an analysis of how the generalized dissemination of microprocessors in embedded systems, commanded by a ubiquitous computing system, can affect the behaviour of people considered as embodied embedded agents.

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

Entropy effect in quantum computing and information: an open-source environment simulation

The technologies are rapidly developing, but some of them present in the computers, as for instance their processing capacity, are reaching their physical limits. It is up to quantum computation offer solutions to these limitations and issues that may arise. In the field of information security, encryption is of paramount importance, being then the development of quantum methods instead of the classics, given the computational power offered by quantum computing. In the quantum world, the physical states are interrelated, thus occurring phenomenon called entanglement. This study presents both a theoretical essay on the merits of quantum mechanics, computing, information, cryptography and quantum entropy, and some simulations, implementing in C language the effects of entropy of entanglement of photons in a data transmission, using Von Neumann entropy and Tsallis entropy.

An Informatics Technical Note on Interaction of DNA - Graphene Chemical Sensor System as Reaction-Diffusion Wave-Based Computing System in Ionised Gaseous environments and their Applications Using Theoretical Studies and Scientific Computation Overview

The physics of plasmas encompasses basic problems from the universe and has assured us of promises in diverse applications to be implemented in a wider range of scientific and engineering domains, linked to most of the evolved and evolving fundamental problems. Substantial part of this domain could be described by R–D mechanisms involving two or more species (reaction–diffusion mechanisms). These could further account for the simultaneous non-linear effects of heating, diffusion and other related losses. We mention here that in laboratory scale experiments, a suitable combination of these processes is of vital importance and very much decisive to investigate and compute the net behaviour of plasmas under consideration. Plasmas are being used in the revolution of information processing, so we considered in this technical note a simple framework to discuss and pave the way for better formalisms and Informatics, dealing with diverse domains of science and technologies. The challenging and fascinating aspects of plasma physics is that it requires a great deal of insight in formulating the relevant design problems, which in turn require ingenuity and flexibility in choosing a particular set of mathematical (and/or experimental) tools to implement them.

Implementação do algoritmo de treinamento do classificador floresta de caminhos ótimos em GPU

Técnicas de reconhecimento de padrões tem como principal objetivo classificar um conjunto de amostras, sendo o processo de aprendizado a fase de maior consumo de tempo. O problema pode piorar em ferramentas de classificação interativas, o que pode ser inaceitável para grandes bases de dados. Um exemplo de classificador é o baseado em Floresta de Caminhos Ótimos [8] - OPF. Dado que muitos trabalhos tem sido orientados à implementação de algoritmos de reconhecimento de padrões em ambiente General Purpose Graphics Processing Unit - GPGPU, o presente estudo objetivou a implementação da etapa de treinamento do classificador Floresta de Caminhos Ótimos em CUDA, visando aumentar a sua eficiência. A otimização do classificador em CUDA demonstrou uma fase de treinamento mais rápida que a versão original.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

A quantitative analysis of current security concerns and solutions for cloud computing

The development of cloud computing services is speeding up the rate in which the organizations outsource their computational services or sell their idle computational resources. Even though migrating to the cloud remains a tempting trend from a financial perspective, there are several other aspects that must be taken into account by companies before they decide to do so. One of the most important aspect refers to security: while some cloud computing security issues are inherited from the solutions adopted to create such services, many new security questions that are particular to these solutions also arise, including those related to how the services are organized and which kind of service/data can be placed in the cloud. Aiming to give a better understanding of this complex scenario, in this article we identify and classify the main security concerns and solutions in cloud computing, and propose a taxonomy of security in cloud computing, giving an overview of the current status of security in this emerging technology.

Computing inverse optical flow

[EN] We propose four algorithms for computing the inverse optical flow between two images. We assume that the forward optical flow has already been obtained and we need to estimate the flow in the backward direction. The forward and backward flows can be related through a warping formula, which allows us to propose very efficient algorithms. These are presented in increasing order of complexity. The proposed methods provide high accuracy with low memory requirements and low running times.In general, the processing reduces to one or two image passes. Typically, when objects move in a sequence, some regions may appear or disappear. Finding the inverse flows in these situations is difficult and, in some cases, it is not possible to obtain a correct solution. Our algorithms deal with occlusions very easy and reliably. On the other hand, disocclusions have to be overcome as a post-processing step. We propose three approaches for filling disocclusions. In the experimental results, we use standard synthetic sequences to study the performance of the proposed methods, and show that they yield very accurate solutions. We also analyze the performance of the filling strategies.