Originstamp – Trusted Timestamping using Bitcoin
OriginStamp is a web-based, trusted timestamping service that uses the decentralized Bitcoin block chain to store anonymous, tamper-proof time stamps for any digital content. OriginStamp allows users to hash files, emails, or plain text, and subsequently store the created hashes in the Bitcoin block chain as well as retrieve and verify time stamps that have been committed to the block chain. OriginStamp is free of charge and easy to use and thus allows anyone, e.g., students, researchers, authors, journalists, or artists, to prove that they were the originator of certain information at a given point in time. The procedures maintain complete privacy of your data. Common use cases of OriginStamp include proving that:
- a contract has been signed or a tasks was completed prior to a certain date.
- a photo or video has been recorded prior to a certain date.
- an idea for a patent already existed prior to a certain date, e.g., prior to signing a NDA.
The idea of timestamping is not new. Even before computers existed, information could be encoded and the code could be published, for example, in a newspaper. However, we use the block chain of the crypto currency Bitcoin as a decentralized, tamper proof, and cost-efficient timestamping authority.
To see the OriginStamp project for yourself, please visit: www.originstamp.org.
CitePlag – Citation-based Plagiarism Detection
CitePlag is the first plagiarism detection system to implement Citation-based Plagiarism Detection (CbPD) – a novel approach capable of detecting also heavily disguised plagiarism in academic texts.
Existing software only examines literal text similarity to detect plagiarism, and thus typically fails to detect disguised plagiarism forms, including paraphrases, translations, or idea plagiarism. CbPD addresses this shortcoming by additionally analyzing the citation placement in the full-text of documents to form a language-independent semantic “fingerprint” of document similarity.
CitePlag implements several citation-based algorithms to analyze the citation patterns of publications. The screenshot shows two publications visualized in the CitePlag prototype. Matching citations are highlighted and connected in a central column for quick document examination. The documents share no literal text similarity: the left publication is in English and the right in Chinese. However, one can see that the overlap of citations is high, and the order in which sources are cited is nearly identical in several paragraphs.
Docear (previously “SciPlore Mindmapping”)
More information on our various projects and ongoing research on citation-based similarity measures can be found here:
- Citation Proximity Analysis: Recommendation and Clustering Algorithms for Academic Literature
- Machine-readable Digital Library (Mr. DLib)
- Bibliographic Metadata Extraction
- Probability Abstract Service (PAS)
If you are interested in working with us, e.g. during a student project or for compiling a thesis, please see our dedicated page at the University of Konstanz for project proposals.