Toward a Declarative Language to Generate Explorable Sets of Models

Model transformation has proven to be an effective technique to produce target models from source models. Most transformation approaches focus on generating a single target model from a given source model. However there exists situations where a collection of target models is preferred over a single one. Such situations arise when some choices cannot be encoded in the transformation. In this paper, we introduce an approach that combines model transformation and constraints programming to generate explorable sets of target models from source models. Our approach is built around the notion of the bridge variable that binds target model properties to decision variables. To help users apply the approach, we also introduce a declarative language to write such transformations. We evaluate our approach and language on a case study for diagram visualization

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

An ontological approach to the study of European popular culture

Like any other field of contemporary scholarly research, the Humanities in general, and Cultural Studies in particular are today confronted with the challenges of complexity at an unprecedented scale. What has been described as the \u201castonishing growth\u201d of academic publications worldwide is paralleled by a similar proliferation of browsable online databases, like digital archives, collections and catalogues, which offer access to an immense and continuously increasing volume of virtually interesting research material, stored in the form of information bytes. As we discussed in Deliverable 2.1, \u201cSorting out the archive for the study of European popular culture\u201d, the problem of how to cope with such an unseizable of virtually relevant sources of evidence is all the more sensible in the case of a project like DETECt, which deals with one of the most prolific narrative genres of contemporary media production, that is, the European crime narrative genre. Not only an exhaustive catalogue of this production could easily count\u2014especially when considered in all of its transnational scope\u2014in thousands of thousands, and even\u2014in historical perspective\u2014millions of items, but the transdisciplinary scope of the studies it has inspired has produced a wealth of research in many domains of knowledge. These difficult challenges make DETECt an ideal laboratory for experimenting new methods to manage complexity in a transnational/transcultural research environment. This methodological experimentation aims to respond to the problem of how to generate effective syntheses of portions and/or aspects of a given knowledge domain in a context of information overload. To this purpose, the ontological approach chosen by DETECt focuses on the application of knowledge mapping techniques to encourage the formulation of partial knowledge syntheses within a \u201crealist\u201d, and even \u201cpragmatic\u201d theoretical framework

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

Multimedia presentations : the support of passive and active viewing

In contrast to conventional printed text which allows for passive viewing only, computer-based presentations can support various forms of user interaction and let the user play an active part at presentation time. As each presentation style has its own strengths and weaknesses, we aim at a multimedia presentation system that supports both passive and active viewing of the generated material. We start from our previous work on the plan-based synthesis of multimedia presentations where all presentation acts have been planned and realized by the system. However, in the approach presented in this paper, we allow certain presentation acts to be planned and/or realized by the user as well. We augment structuring principles for non-interactive multimedia presentations and integrate them into a uniform framework. Finally, we sketch how modules of WIP, our existing presentation system, can be reused and extended

The Larch Environment - Python programs as visual, interactive literature

The Larch Environment' is designed for the creation of programs that take the form of interactive technical literature. We introduce a novel approach to combined textual and visual programming by allowing visual, interactive objects to be embedded within textual source code, and segments of source code to be further embedded within those objects. We retain the strengths of text-based source code, while enabling visual programming where it is bene�cial. Additionally, embedded objects and code provide a simple object-oriented approach to extending the syntax of a language, in a similar fashion to LISP macros. We provide a rapid prototyping and experimentation environment in the form of an active document system which mixes rich text with executable source code. Larch is supported by a simple type coercion based presentation protocol that displays normal Java and Python objects in a visual, interactive form. The ability to freely combine objects and source code within one another allows for the construction of rich interactive documents and experimentation with novel programming language extensions

Eliciting and understanding commonsense reasoning about motion.

The focus of the present research is on children's commonsense reasoning in mechanics. The important effect of pre-instructional ideas on children's learning is now widely recognised and much effort has gone into investigating what these ideas are like in various domain areas in science in the past few years. Early researches in this area have provided us with a comprehensive catalog of phenomenological descriptions of various aspects of children's reasoning about forces and motion. A related line of research has grown over recent years, which attempts to probe into whether there are deeper explanations underlying these misconceptions. If we take scientific theories and commonsense reasoning as two ends of a dichotomy, then early researches in this field have predominantly started from the scientific end, looking towards the intuitive end, trying to find out where the intuitive ideas go astray. To look for deeper levels of analysis, some have since turned to looking from the opposite end, trying to take children's ideas seriously, in their own right and not as a distortion of the scientific view. This latter perspective is the one taken by the present research and is believed to be appropriate if an understanding of the phenomenological descriptions of children's intuitive ideas is to be attained. The present research sets out to investigate the possible cognitive models used in the spontaneous interpretation of and reasoning about motion by students with varying amounts of Physics instruction. It is hoped that the resulting models will not only provide a context for interpreting children's misconceptions, but also provide insight into the evolution of naive cognitive models to more scientific ones. The research consists of two tasks. The first is a classification task asking students to categorize comic strip pictures about motion and to explain their underlying reasoning. The second is a programming task, asking students to write expert systems about motion in the language PROLOG. The second task is in fact one of self elicitation of knowledge by the students themselves under the assistance of the researcher. The advantage of such an exercise is that the representation is not only open for inspection by the students but is also explorable. The results from both tasks will be analysed and synthesized in the thesis