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

The Best Nix for a Combined Honeypot Sensor Server

The paper will examine (through case-study) the usability of open source operating systems software for a combined Honeypot sensor server. The study will scrutinize the use of two Unix variants, Linux Red Hat and the Sun Solaris operating systems as candidates for deployment of a combined Honeypot sensor server. Appropriate unbiased metrics, such as extensibility, reliability, ease of install and use, will be employed as a likely criterion to evaluate the operating systems for the role of hosting Honeypot sensor server software

Adaptive building envelope simulation in current design practice: findings from interviews with practitioners about their understanding of methods, tools and workarounds and implications for future tool developments

Adaptive building envelopes can dynamically adapt to environmental changes to improve thermal building performance. To predict the performance of design proposals with adaptive building envelopes, Building Performance Simulation (BPS) tools can be employed. However, one shortcoming of existing tools is their limited extensibility, which implies that accurately predicting adaptive building envelope performance remains a challenge and requires ad hoc approaches. This challenge has made practitioners reticent in considering adaptive building envelopes, which in turn has led to a slow uptake of them in the built environment. This study seeks to advance the understanding of the limitations of adaptive building envelope simulation in current design practice and to suggest implications for future tool developments. To this aim, the study adopts a user-centred perspective through interviews with experts in the field. Findings suggest that current BPS tools hinder the reliable prediction of adaptive building envelope performance, as accurately representing the level of detail of the building envelope is challenging. The subsequent workarounds applied are either time- and cost-intensive or do not consider the dynamic building envelope components. More flexible modelling approaches that allow for rapid prototyping and easy integration are required to enable designers to take full advantage of adaptive building envelopes

Comparison of composition engines and identification of shortcomings with respect to cloud computing

Most workflow engines are currently not Cloud-aware. This is due to multiple reasons like no support for transparent scalability, no multi-tenancy support, no ability to store process related data in a Cloud storage, or no support for quality of service enforcements. Recently Cloud based workflow services appeared in the workflow landscape and promise to run workflows in the Cloud. This student reports evaluates current state of the art BPEL and BPMN workflow engines and Cloud based workflow services according to their Cloud- awareness and general workflow functionalities. Identified shortcomings are described and prioritized. As a result of this evaluation the workflow engine WSO2 Stratos is best suited for running workflows in the Cloud, but it lacks native clustering support and quality of service enforcement

The Development Of Project Grade-Up

The university classroom has greatly evolved from a simple syllabus and in class discussion to the modern online documentation and virtual classrooms. These developments have changed the way students review their grades and balance their workloads. With the plethora of new technologies, students are often burdened with a full school schedule, work, and social events, with few tools to help them effectively understand their grades or manage their time. Current solutions addressing this issue do not present data in an organized way that allows the student to easily comprehend their past performance or up coming work load. Our solution builds upon the Moodle system by adding visual, progress-specific information that is comprehensible at a glance. This in turn allows the student to answer the following questions: 1. What have I completed, and what do I have left to complete? 2. What is my current grade and projected grade at my current pace? 3. Given what I’ve done so far what is the best possible grade I could get if I ace the remaining work? 4. What if I stopped now, what would my grade be? 5. How am I doing compared to the average in this class? 6. If I got a particular grade(s) on a specific assignment(s) how would that change the answers to the preceding questions? 7. Where is the work left concentrated in the temporal domain? I.e. when should I start working on the items left to complete in my course(s)

A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study