Authoring and Sharing of Programming Exercises

In recent years, a number of exercises have been developed and published for educating students in the field of Computer Science. But these exercises exist in their own silos. There is no apparent mechanism to share these exercises among researchers and instructors in an effective and efficient manner. Moreover, the developers of these programming exercises are generally using a proprietary system for automatic submission and grading of these exercises. Each of these systems dictates the persistent format of an exercise that may not be inter-operable with other automatic submission and grading systems. This project provides a solution to this problem by modeling a programming exercise into a Learning Object metadata definition. This metadata definition describes the learning resource in terms of its contents, classifications, lifecycle and several other relevant properties. A learning Object (LO) is persisted in a repository along with its metadata. This repository supports simple and advanced queries to retrieve LO s and export them to various commercially available or home-grown e-learning systems. In a simple query, keywords given by the user are matched against a number of metadata elements whereas an advanced query allows a user to specify values for specific metadata elements

Adaptive content mapping for internet navigation

The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database

Formal Analysis of Vulnerabilities of Web Applications Based on SQL Injection (Extended Version)

We present a formal approach that exploits attacks related to SQL Injection (SQLi) searching for security flaws in a web application. We give a formal representation of web applications and databases, and show that our formalization effectively exploits SQLi attacks. We implemented our approach in a prototype tool called SQLfast and we show its efficiency on real-world case studies, including the discovery of an attack on Joomla! that no other tool can find

A Knowledge-Based E-mail System Using Semantic Categorization and Rating Mechanisms.

Knowledge-based e-mail systems focus on incorporating knowledge management approach in order to enhance the traditional e-mail systems. In this paper, we present a Knowledge-based e-mail systems called KS-Mail where people do not only send and receive e-mail conventionally but are also able to create a sense of knowledge flow. We introduce semantic processing on the e-mail contents by automatically assigning categories and providing links to semantically related e-mails

Acknowledgement Lag and Impact: Domain Differences in Published Research Supported by the National Science Foundation

This research combined archives of grant awards with a five-year period of bibliographic data from Web of Science in order to conduct an input-output study of research supported by the National Science Foundation. Acknowledgement lag is proposed as a new bibliometric term, defined as the time elapsed between when a grant is awarded and when a document is published which acknowledges that award. Acknowledgement lag was computed for the dataset, and domain differences in lag times were analyzed. Some areas, such as Plant & Animal Science or Social Science, were found to be more likely than other categories to acknowledge a grant seven or more years later, while other categories, such as Physics, were most likely to publish a grant acknowledgement in two years or less. In addition, rank-normalized impact factors were computed for journals in which these articles were published, as a measure of journal impact that is comparable across categories of research. The overall distribution of ranknormalized journal impact factors for research articles acknowledging support by the National Science Foundation was analyzed. Category-level analysis was also performed, and it was found that there were differences in the journal impact factor trends for publications from different domains in the dataset. Research in Materials Science was substantially more likely than other categories to publish in the most elite journals of its respective domain. Social Sciences research was also found to be one of the strongest research areas in terms of impact factor, despite being one of the smaller categories in terms of publication counts. However, other categories were found to be disproportionately more likely to have been published in lower impact factor journals for their respective fields, such as Mathematics and Computer Science. The methodology developed in this project demonstrates a workflow that could be implemented by the NSF or other agencies. The findings demonstrate that systematically linking grants to publications can yield information of strategic value, allowing agencies to better understand field differences in outcomes and providing a means for tracking changes in publication-related metrics over time