Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

Generative video art

Generative art is historically and widely used for the production of abstract images and animations, each frame corresponding to a generation or iteration of the generative system, which runs within the aesthetic boundaries defined by its author. But rather than being limited to image or sound synthesis, generative systems can also manipulate video samples and still images from external sources, and include vectors that can be mapped to the concepts of shot, sequence, rhythm and montage. Furthermore, generative systems need not be limited to the visual plane and can also render audio, either through sound synthesis or by manipulating sound samples. And in this case, since the output is a constant and uninterrupted audio-visual stream, is it not possible to speak of generative video art, as it becomes indistinguishable from its modern-day video art digital counterparts? Within this perspective, this article traces back the historical roots of generative video art, and proposes a theoretical model for generative video art systems, as a creative intersection of two artistic genres, often seen as disjoint.info:eu-repo/semantics/publishedVersio

Mitigating the limited view problem in photoacoustic tomography for a planar detection geometry by regularised iterative reconstruction

The use of a planar detection geometry in photoacoustic tomography results in the so-called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularisation were evaluated using in-silico , experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularisation parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude (�100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure

Gene expression patterns in anterior pituitary associated with quantitative measure of oestrous behaviour in dairy cows

Intensive selection for high milk yield in dairy cows has raised production levels substantially but at the cost of reduced fertility, which manifests in different ways including reduced expression of oestrous behaviour. The genomic regulation of oestrous behaviour in bovines remains largely unknown. Here, we aimed to identify and study those genes that were associated with oestrous behaviour among genes expressed in the bovine anterior pituitary either at the start of oestrous cycle or at the mid-cycle (around day 12 of cycle), or regardless of the phase of cycle. Oestrous behaviour was recorded in each of 28 primiparous cows from 30 days in milk onwards till the day of their sacrifice (between 77 and 139 days in milk) and quantified as heat scores. An average heat score value was calculated for each cow from heat scores observed during consecutive oestrous cycles excluding the cycle on the day of sacrifice. A microarray experiment was designed to measure gene expression in the anterior pituitary of these cows, 14 of which were sacrificed at the start of oestrous cycle (day 0) and 14 around day 12 of cycle (day 12). Gene expression was modelled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model on data from day 0 cows alone (analysis 1), day 12 cows alone (analysis 2) and the combined data from day 0 and day 12 cows (analysis 3). Genes whose expression patterns showed significant linear or non-linear relationships with average heat scores were identified in all three analyses (177, 142 and 118 genes, respectively). Gene ontology terms enriched among genes identified in analysis 1 revealed processes associated with expression of oestrous behaviour whereas the terms enriched among genes identified in analysis 2 and 3 were general processes which may facilitate proper expression of oestrous behaviour at the subsequent oestrus. Studying these genes will help to improve our understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines

AI Thinking for Cloud Education Platform with Personalized Learning

Artificial Intelligence (AI) thinking is a framework beyond procedural thinking and based on cognitive and adaptation to automatically learn deep and wide rules and semantics from experiments. This paper presents Cloud-eLab, an open and interactive cloud-based learning platform for AI Thinking, aiming to inspire i) Deep and Wide learning, ii) Cognitive and Adaptation learning concepts for education. It has been successfully used in various machine learning courses in practice, and has the expandability to support more AI modules. In this paper, we describe the block diagram of the proposed AI Thinking education platform, and provide two education application scenarios for unfolding Deep and Wide learning as well as Cognitive and Adaptation learning concepts. Cloud-eLab education platform will deliver personalized content for each student with flexibility to repeat the experiments at their own pace which allow the learner to be in control of the whole learning process

The Oracle Problem in Software Testing: A Survey

Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test oracle problem”. Test oracle automation is important to remove a current bottleneck that inhibits greater overall test automation. Without test oracle automation, the human has to determine whether observed behaviour is correct. The literature on test oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of test oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of test oracles, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice