Current approaches for emulating cellular base-bands inherently fall short in comparison to over-the-air testing due to their limited support for the complex peripherals involved in a modern baseband, such as DSPs, SIM cards and RF frontends. Improving such support is a daunting task, requiring deep reverse-engineering which is extremely time consuming - resulting in slow progress. Consequently, techniques such as fuzzing are only able to find relatively shallow bugs, since they are unable to reach the states required for the majority of the baseband to function. To fill this gap, we propose Basebridge, which enables far more comprehensive simulation of baseband behavior by restoring relevant state from memory dumps of real devices. Our prototype implementation supports baseband firmware from two major vendors (MediaTek and Samsung), and - in contrast to current state-of-the-art emulators - correctly responds to 97% of tested RRC and NAS messages while improving coverage by an average factor of 2.41 (Samsung) and 5.54 (MediaTek). Basebridge also passes several LTE conformance tests. Our empirical evaluation demonstrates that this enhanced fidelity enables faster discovery of a wider range of bugs thanks to the scalability of emulation; our fuzzing campaign shows that coverage improves by a factor of 2.3-5x overall, and by a factor of 9.0-22.5x for functionality targeted by our approach. Basebridge unveiled 5 new vulnerabilities, which we have disclosed to affected vendors.

The ever increasing amount of publications in the biomedical domain leads to the challenge of finding the right answer to a specific question within a vast sea of information. For instance, the biomedical search engine PubMed has an index of over 27 million publications. PubMed implements a keyword-based search. While easy to operate, the results are shown as a paginated textual list, which is time-consuming to navigate. To give the users a more convenient way of searching for information and displaying it, a variety of interfaces which operate on top of PubMed have emerged. One of those platforms is ViLiP, developed as visual exploratory interface to PubMed and primarily used in the neuroscience domain. ViLiP presents the result of a user's query in form of an in-situ heat map. In this work, ViLiP is extended by an NLP-based semantic search engine, for the use-case of detecting drug information within a query. Based on linguistic annotations, potential candidates for drug names are selected from an RDF data source, and matching publications are searched for.

A thorough literature research is usually the first step when dealing with a novel topic in science. Regarding the medical domain, a popular starting point for this task is the web based search engine PubMed, which has an index of over 27 million medical publications from the MEDLINE database and other sources. Given certain search terms of interest, e.g. neurodegeneration or the drug FTY 720, results are presented by PubMed in form of textual lists which can span over several hundred pages. Thus, getting a reasonable overview over published literature for a given topic can be a very time consuming and challenging task. To facilitate a more intuitive approach to medical literature research, interactive visual interfaces can be helpful. The novel web platform ViLiP (Visual Literature Platform) is built around this idea. It provides an easy to operate interactive interface to PubMed and presents the result of a user's query in form of in-situ heat map or word cloud visualizations. The ViLiP tool is currently actively used in the neuroanatomy domain, namely in multiple-sclerosis research.