Automated Web Applications Testing

Unit tests are a vital part of several software development practices and processes such as Test-First Programming, Extreme Programming and Test-Driven Development. This article shortly presents the software quality and testing concepts as well as an introduction to an automated unit testing framework for PHP web based applicationssoftware quality, continuous integration, unit testing

Operations Management Curricula: Literature Review and Analysis

A review and analysis of studies on the interface between Operations Management (OM) academicians and industry practitioners indicate the existence of a persistent gap between what is being taught and what is relevant to practitioners in their daily jobs. The majority of practitioner studies have been directed at upper management levels, yet academia typically educates students for entry level or management trainee (undergraduate) and mid-management (MBA) positions. A recurring finding was that academicians prefer to teach quantitative techniques while practitioners favor qualitative concepts. The OM curricula literature shows some disagreements between academicians concerning subject matter, and a wide variety of teaching opinions. This paper provides an extensive analytical review of OM curricula literature along with their respective authors’ conclusions. From this analysis we suggest a customer-focused business plan to close the gap between industry and academia. This plan can be modified to account for faculty teaching and research interests, local industry requirements and institution specific factors such as class sizes and resources

Mission Concept for the Single Aperture Far-Infrared (SAFIR) Observatory

The Single Aperture Far-InfraRed (SAFIR) Observatory's science goals are driven by the fact that the earliest stages of almost all phenomena in the universe are shrouded in absorption by and emission from cool dust and gas that emits strongly in the far-infrared and submillimeter. Over the past several years, there has been an increasing recognition of the critical importance of this spectral region to addressing fundamental astrophysical problems, ranging from cosmological questions to understanding how our own Solar System came into being. The development of large, far-infrared telescopes in space has become more feasible with the combination of developments for the James Webb Space Telescope and of enabling breakthroughs in detector technology. We have developed a preliminary but comprehensive mission concept for SAFIR, as a 10 m-class far-infrared and submillimeter observatory that would begin development later in this decade to meet the needs outlined above. Its operating temperature (<4K) and instrument complement would be optimized to reach the natural sky confusion limit in the far-infrared with diffraction-limited peformance down to at least 40 microns. This would provide a point source sensitivity improvement of several orders of magnitude over that of Spitzer or Herschel, with finer angular resolution, enabling imaging and spectroscopic studies of individual galaxies in the early universe. We have considered many aspects of the SAFIR mission, including the telescope technology, detector needs and technologies, cooling method and required technology developments, attitude and pointing, power systems, launch vehicle, and mission operations. The most challenging requirements for this mission are operating temperature and aperture size of the telescope, and the development of detector arrays.Comment: 36 page

Integrated Testlets and the Immediate Feedback Assessment Technique

The increased use of multiple-choice (MC) questions in introductory-level physics final exams is largely hindered by reservations about its ability to test the broad cognitive domain that is routinely accessed with typical constructed-response (CR) questions. Thus, there is a need to explore ways in which MC questions can be utilized pedagogically more like CR questions while maintaining their attendant procedural advantages. we describe how an answer-until-correct MC response format allows for the construction of multiple-choice examinations designed to operate much as a hybrid between standard MC and CR testing. With this tool - the immediate feedback assessment technique (IF-AT) - students gain complete knowledge of the correct answer for each question during the examination, and can use such information for solving subsequent test items. This feature allows for the creation of a new type of context-dependent item sets; the "integrated testlet". In an integrated testlet certain items are purposefully inter-dependent and are thus presented in a particular order. Such integrated testlets represent a proxy of typical CR questions, but with a straightforward and uniform marking scheme that also allows for granting partial credit for proximal knowledge. We present a case study of an IF-AT-scored midterm and final examination for an introductory physics course, and discuss specific testlets with varying degrees of integration. In total, the items are found to allow for excellent discrimination, with a mean item-total correlation measure for the combined 45 items of the two examinations of $\bar{r}'=0.41\pm 0.13$ (mean $\pm$ standard deviation) and a final examination test reliability of $\alpha=0.82$ ($n=25$ items). Furthermore, partial credit is shown to be allocated in a discriminating and valid manner in these examinations.Comment: 13 pages. 7 figures. Accepted to the American Journal of Physics (August 2013

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues