Performance analysis for object-oriented software: a systematic mapping

Performance is a crucial attribute for most software, making performance analysis an important software engineering task. The difficulty is that modern applications are challenging to analyse for performance. Many profiling techniques used in real-world software development struggle to provide useful results when applied to large-scale object-oriented applications. There is a substantial body of research into software performance generally but currently there exists no survey of this research that would help identify approaches useful for object-oriented software. To provide such a review we performed a systematic mapping study of empirical performance analysis approaches that are applicable to object-oriented software. Using keyword searches against leading software engineering research databases and manual searches of relevant venues we identified over 5,000 related articles published since January 2000. From these we systematically selected 253 applicable articles and categorised them according to ten facets that capture the intent, implementation and evaluation of the approaches. Our mapping study results allow us to highlight the main contributions of the existing literature and identify areas where there are interesting opportunities. We also find that, despite the research including approaches specifically aimed at object-oriented software, there are significant challenges in providing actionable feedback on the performance of large-scale object-oriented applications.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Application of extended multivariate modeling for information flow analysis of event related responses

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Differentially private multi-task learning

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.

The effect of dual-task difficulty on the inhibition of the motor cortex

Dual-tasking is intrinsic to many daily activities, including walking and driving. However, the activity of the primary motor cortex (M1) in response to dual-tasks (DT) is still not well characterised. A recent meta-analysis (Corp in Neurosci Biobehav Rev 43:74-87, 2014) demonstrated a reduction in M1 inhibition during dual-tasking, yet responses were not consistent between studies. It was suggested that DT difficulty might account for some of this between-study variability. The aim of this study was to investigate whether corticospinal excitability and M1 inhibition differed between an easier and more difficult dual-task. Transcranial magnetic stimulation (TMS) was applied to participants' abductor pollicis brevis muscle representation during a concurrent pincer grip task and stationary bike-riding. The margin of error in which to maintain pincer grip force was reduced to increase task difficulty. Compared to ST conditions, significantly increased M1 inhibition was demonstrated for the easier, but not more difficult, DT. However, there was no significant difference in M1 inhibition between easy and difficult DTs. The difference in difficulty between the two tasks may not have been wide enough to result in significant differences in M1 inhibition. Increased M1 inhibition for the easy DT condition was in opposition to the reduction in M1 inhibition found in our meta-analysis (Corp in Neurosci Biobehav Rev 43:74-87, 2014). We propose that this may be partially explained by differences in the timing of the TMS pulse between DT studies.

Multiplexed detection: fast comprehensive sample analysis of tobacco leaf extracts using HPLC with AFT columns

Detector-based comprehensive screening analysis of complex samples of natural origin using High Performance Liquid Chromatography (HPLC) can be a complicated and time-consuming task. There are a number of ways multidetection characterization can be achieved; however, there are limitations associated with each technique. Active Flow Technology (AFT) in Parallel Segmented Flow (PSF) mode allows for multiplexed detection HPLC analysis within a single injection, whereas maintaining chromatographic performance and allowing the use of multiple destructive detectors to achieve a comprehensive yet efficient screening of a complex sample. In this study, a comprehensive characterization analysis of tobacco leaf extract was carried out through multiplexed detection using a PSF column for the detection of biomolecules by UV-Vis detection, DPPH• for reactive-oxygen species (ROS) detection, and mass spectrometry, the latter two detection methods being sample destructive.

Do individual perceptions of group cohesion reflect shared beliefs? An empirical analysis

This study's primary purpose was to examine the degree to which individual perceptions of cohesiveness reflect shared beliefs in sport teams. The secondary purposes were to examine how the type of cohesion, the task interactive nature of the group, and the absolute level of cohesion relate to the index of agreement. Teams (n = 192 containing 2,107 athletes) were tested on the Group Environment Questionnaire. Index of agreement values were greater for the group integration (GI) manifestations of cohesiveness (GI-task, rwg(j) = .721; GI-social,rwg(j) = .694) than for the individual attractions to the group (ATG) manifestations (ATG-task, rwg(j) = .621; ATG-social, rwg(j) = .563). No differences were found for interactive versus coactive/independent sport teams. A positive relationship was observed between the absolute level of cohesiveness and the index of agreement. Results were discussed in terms of their implication for the aggregation of individual perceptions of cohesion to represent the group construct.

The role of Transaction Cost Economics in information technology outsourcing research : a meta-analysis of the choice of contract type

Two recent reviews report that the empirical findings in information technology outsourcing (ITO) research are frequently inconsistent with the prevailing dominant analytical framework of transaction cost economics (TCE). While employing similar methodologies, the two reviews propose different strategies to resolve the inconsistencies. One is to improve the methodological rigor, specifically, the operationalization of TCE constructs. The other is to abandon TCE in favor of a new analytical framework. This paper presents a meta-analysis of the empirical findings on the choice of contract type as a function of task uncertainty. The results support both strategies. Refining the operationalization of TCE constructs, specifically of task uncertainty, would have improved the reliability of findings on TCE-based relationships between task uncertainty and the choice of contract type. However, independent of such methodological improvements, TCE is of limited relevance in recent ITO research for predicting the choice of contract type. Generalizing these findings, we conclude that ITO research requires a new analytical framework to further develop the theory of ITO and to provide sound guidance to the ITO industry.

Cluster analysis and optimization in color-based clustering for image abstract

Cluster analysis has been identified as a core task in data mining. What constitutes a cluster, or a good clustering, may depend on the background of researchers and applications. This paper proposes two optimization criteria of abstract degree and fidelity in the field of image abstract. To satisfy the fidelity criteria, a novel clustering algorithm named Global Optimized Color-based DBSCAN Clustering (GOC-DBSCAN) is provided. Also, non-optimized local color information based version of GOC-DBSCAN, called HSV-DBSCAN, is given. Both of them are based on HSV color space. Clusters of GOC-DBSCAN are analyzed to find the factors that impact on the performance of both abstract degree and fidelity. Examples show generally the greater the abstract degree is, the less is the fidelity. It also shows GOC-DBSCAN outperforms HSV-DBSCAN when they are evaluated by the two optimization criteria.

Automating performance and energy consumption analysis for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

Developing physical and physiological employment standards: translation of job analysis findings to assessments and performance standards - a systematic review

Physical employment standards (PES) are developed with the aim of ensuring that an employee's physical and physiological capacities are commensurate with the demands of their occupation. While previous commentaries and narrative reviews have provided frameworks for the development of PES, this is the first systematic review of the methods used to translate job analysis findings to PES tests and performance standards for physically demanding occupations. A search of PubMed and Google Scholar was conducted for research articles published in English up to and including March 2015. Two authors independently reviewed and extracted data.

The search yielded 87 potentially eligible papers, including 60 peer reviewed journal articles and 17 technical reports. 57 papers were excluded leading to a final data set of 31 papers, representing 22 studies. Job analysis was most commonly conducted through subjective determination of job tasks followed by objective quantification and validation. Determination of criterion tasks was evenly distributedthrough subjective and objective methods with criterion tasks being defined most commonly as most demanding, critical and/or frequent. Generic predictive and task-related predictive tests were more commonly observed in isolation or in combination when compared to task simulation tests. Performance standards were more commonly criterion-referenced than norm-referenced with a variety of statistical methods utilised. This review provides recommendations for researchers when developing physical employment standards for a variety of occupations.

Typography and the branding of culture: a systemic functional analysis of typography’s performance in branding cultural festivals in Australia

This paper offers a social semiotic analysis of logotypes used to brand cultural festivals in 21st century Australia. A contemporary method is explored that suits the significant role typography performs within this context and offers a contribution to design research and the festival scape that not only engages with the artefacts of design but with the conceptualization of designed meaning in visual culture. Branding is a vital part of the festival space and relies on typography to establish the symbolic values and representations of urban freedoms; rich histories, cultured places, playfulness and stimulation that seek to subvert our daily existence while performing the task of engaging local, national, and international visitors and participants. However, professional practices demonstrated in the design, media and arts industries have far outpaced the extent to which this phenomenon has been written about in the academic or public realm. This paper addresses this shortfall and offers the foundation for a systemic functional method in the decoding of typography in visual culture