Understanding Behavioral Sources of Process Variation Following Enterprise System Deployment

This paper extends the current understanding of the time-sensitivity of intent and usage following large-scale IT implementation. Our study focuses on perceived system misfit with organizational processes in tandem with the availability of system circumvention opportunities. Case study comparisons and controlled experiments are used to support the theoretical unpacking of organizational and technical contingencies and their relationship to shifts in user intentions and variation in work-processing tactics over time. Findings suggest that managers and users may retain strong intentions to circumvent systems in the presence of perceived task-technology misfit. The perceived ease with which this circumvention is attainable factors significantly into the timeframe within which it is attempted, and subsequently impacts the onset of deviation from prescribed practice and anticipated dynamics

Scientific Workflows and Provenance: Introduction and Research Opportunities

Scientific workflows are becoming increasingly popular for compute-intensive and data-intensive scientific applications. The vision and promise of scientific workflows includes rapid, easy workflow design, reuse, scalable execution, and other advantages, e.g., to facilitate "reproducible science" through provenance (e.g., data lineage) support. However, as described in the paper, important research challenges remain. While the database community has studied (business) workflow technologies extensively in the past, most current work in scientific workflows seems to be done outside of the database community, e.g., by practitioners and researchers in the computational sciences and eScience. We provide a brief introduction to scientific workflows and provenance, and identify areas and problems that suggest new opportunities for database research.Comment: 12 pages, 2 figure

Success and failure of programming environments - report on the design and use of a graphic abstract syntax tree editor

The STAPLE project investigated (at the end of the eighties), a persistent architecture for functional programming. Work has been done in two directions: the development of a programming environment for a functional language within a persistent system and an experiment on transferring the expertise of functional prototyping into industry. This paper is a report on the first activity. The first section gives a general description of Absynte - the abstract syntax tree editor developed within the Project. Following sections make an attempt at measuring the effectiveness of such an editor and discuss the problems raised by structured syntax editing - specially environments based on abstract syntax trees.Comment: This is an old paper (1990) of 29 page

Design of Web Questionnaires: The Effect of Layout in Rating Scales

This article shows that respondents gain meaning from visual cues in a web survey as well as from verbal cues (words).We manipulated the layout of a five point rating scale using verbal, graphical, numerical, and symbolic language. This paper extends the existing literature in four directions: (1) all languages (verbal, graphical, numeric, and symbolic) are individually manipulated on the same rating scale, (2) a heterogeneous sample is used, (3) in which way personal characteristics and a respondent's need to think and evaluate account for variance in survey responding is analyzed, and (4) a web survey is used.Our experiments show differences due to verbal and graphical language but no effects of numeric or symbolic language are found.Respondents with a high need for cognition and a high need to evaluate are affected more by layout than respondents with a low need to think or evaluate.Furthermore, men, the elderly, and the highly educated are the most sensible for layout effects.web survey;questionnaire lay out;context effects;need for cognition;need to evaluate

Models for discriminating image blur from loss of contrast

Observers can discriminate between blurry and low-contrast images (Morgan, 2017). Wang and Simoncelli (2004) demonstrated that a code for blur is inherent to the phase relationships between localized pattern detectors of different scale. To test whether human observers actually use local phase coherence when discriminating between image blur and loss of contrast, we compared phase-scrambled chessboards with unscrambled chessboards. Although both stimuli had identical amplitude spectra, local phase coherence was disrupted by phase-scrambling. Human observers were required to concurrently detect and identify (as contrast or blur) image manipulations in the 2x2 forced-choice paradigm (Nachmias & Weber, 1975; Watson & Robson, 1981) traditionally considered to be a litmus test for "labelled lines" (i.e. detection mechanisms that can be distinguished on the basis of their preferred stimuli). Phase scrambling reduced some observers’ ability to discriminate between blur and a reduction in contrast. However, none of our observers produced data consistent with Watson & Robson’s most stringent test for labelled lines, regardless whether phases were scrambled or not. Models of performance fit significantly better when either a) the blur detector also responded to contrast modulations, b) the contrast detector also responded to blur modulations, or c) noise in the two detectors was anticorrelate

A Logic-Independent IDE

The author's MMT system provides a framework for defining and implementing logical systems. By combining MMT with the jEdit text editor, we obtain a logic-independent IDE. The IDE functionality includes advanced features such as context-sensitive auto-completion, search, and change management.Comment: In Proceedings UITP 2014, arXiv:1410.785

OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning

A key aspect of human intelligence is the ability to imagine -- composing learned concepts in novel ways -- to make sense of new scenarios. Such capacity is not yet attained for machine learning systems. In this work, in the context of visual reasoning, we show how modularity can be leveraged to derive a compositional data augmentation framework inspired by imagination. Our method, denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes visual generative reasoning tasks into a series of primitives applied to objects without using a domain-specific language. We show that our modular architectural choices can be used to generate new training tasks that lead to better out-of-distribution generalization. We compare our model to existing and new baselines in proposed visual reasoning benchmark that consists of applying arithmetic operations to MNIST digits

DataVizard: Recommending Visual Presentations for Structured Data

Selecting the appropriate visual presentation of the data such that it preserves the semantics of the underlying data and at the same time provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our \emph{DataVizard} system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy. We also present the results of a user survey that we conducted in order to assess user views of the suitability of the presented charts vis-a-vis the plain text captions of the data

Towards the Ontology Web Search Engine

The project of the Ontology Web Search Engine is presented in this paper. The main purpose of this paper is to develop such a project that can be easily implemented. Ontology Web Search Engine is software to look for and index ontologies in the Web. OWL (Web Ontology Languages) ontologies are meant, and they are necessary for the functioning of the SWES (Semantic Web Expert System). SWES is an expert system that will use found ontologies from the Web, generating rules from them, and will supplement its knowledge base with these generated rules. It is expected that the SWES will serve as a universal expert system for the average user

Composition by Conversation

Most musical programming languages are developed purely for coding virtual instruments or algorithmic compositions. Although there has been some work in the domain of musical query languages for music information retrieval, there has been little attempt to unify the principles of musical programming and query languages with cognitive and natural language processing models that would facilitate the activity of composition by conversation. We present a prototype framework, called MusECI, that merges these domains, permitting score-level algorithmic composition in a text editor while also supporting connectivity to existing natural language processing frameworks.Comment: 6 pages, 8 figures, accepted to ICMC 201