Search

The Capsule Reviews are intended to provide a short succinct review of each paper in the issue in order to bring it to a wider readership. The Capsule Reviews were compiled by Fairouz Kamareddine. Professor Kamareddine is an Associate Editor of The Computer Journal and is based in the Department of Mathematical and Computer Sciences at Heriot-Watt University, Edinburgh, UK. [ABSTRACT FROM AUTHOR]

Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients. [ABSTRACT FROM AUTHOR]

Dynamic voltage scaling (DVS) is one of the most effective techniques for reducing energy consumption on battery-operated embedded systems. According to the granularity of units to which voltage scaling is applied, the DVS problem can be divided into two subproblems: (i) inter-task DVS problem and (ii) intra-task DVS problem. A lot of effective DVS techniques have addressed either one of the two subproblems, but none of them have attempted to solve both simultaneously. This paper examines the problem of combined inter- and intra-task DVS, called the combined DVS (CDVS) problem. We solve the CDVS problem in two embedded system domains: one is systems with a sleep state and the other without sleep state. For systems without sleep state, we propose a close-to-optimal algorithm for the CDVS problem. We show that the algorithm is optimal when the power is a quadratically increasing function of the system's clock speed or the applied voltage level. For systems with a sleep state, we propose a refinement algorithm that fine-tunes the solution to the CDVS problem without sleep state to further reduce energy consumption by exploiting sleep state. Experimental results show that our proposed CDVS algorithm without sleep state is able to reduce the energy consumption by 12.5% on average over the results by the method that sequentially performs two existing inter- and intra-task DVS techniques, which are both optimal under no sleep state. Furthermore, our CDVS algorithm with a sleep state can reduce the energy consumption by 7.1% on average over the results by the conventional representative method that utilizes sleep state, but does not consider intra- and inter-task DVS simultaneously. [ABSTRACT FROM PUBLISHER]

ALGOL was formulated in Zurich in 1958, and this paper explores some of the reasons why this happened in Zurich and not in the USA. One factor may have been that the first computer on the continent, the Z4, was installed in Zurich in 1950. This computer was built by Zuse during the Second World War. It used relays for its arithmetic and control units, but it had a mechanical memory. Though the machine had limited capabilities, it provided valuable programming experience to a number of researchers, in particular to Stiefel, Rutishauser and Speiser at the ETH Zurich. Already in 1952, Rutishauser formulated a higher programming construct, a counted loop, and he also indicated how to convert mathematical expressions into machine language. We also should mention that Bauer, who was influenced by Rutishauser, also developed a method to implement mathematical expressions, but he used computer hardware for the task. The two started to collaborate, and they formulated a preliminary language in 1957. This language was then formalized at a ACM/GAMM meeting in Zurich under the name International Algebraic Language, or IAL, a language later renamed ALGOL 58. [ABSTRACT FROM PUBLISHER]