@article{CaruccioDeufemiaNaumannetal.2021,
  author    = {Caruccio, Loredana and Deufemia, Vincenzo and Naumann, Felix and Polese, Giuseppe},
  title     = {Discovering relaxed functional dependencies based on multi-attribute dominance},
  series = {IEEE transactions on knowledge and data engineering},
  volume    = {33},
  journal   = {IEEE transactions on knowledge and data engineering},
  number    = {9},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {New York, NY},
  issn      = {1041-4347},
  doi       = {10.1109/TKDE.2020.2967722},
  pages     = {3212 -- 3228},
  year      = {2021},
  abstract  = {With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.},
  language  = {en}
}
@article{SiddiqiDoerrStrydis2020,
  author    = {Siddiqi, Muhammad Ali and D{\"o}rr, Christian and Strydis, Christos},
  title     = {IMDfence},
  series = {IEEE access},
  volume    = {8},
  journal   = {IEEE access},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {Piscataway},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.3015686},
  pages     = {147948 -- 147964},
  year      = {2020},
  abstract  = {Over the past decade, focus on the security and privacy aspects of implantable medical devices (IMDs) has intensified, driven by the multitude of cybersecurity vulnerabilities found in various existing devices. However, due to their strict computational, energy and physical constraints, conventional security protocols are not directly applicable to IMDs. Custom-tailored schemes have been proposed instead which, however, fail to cover the full spectrum of security features that modern IMDs and their ecosystems so critically require. In this paper we propose IMDfence, a security protocol for IMD ecosystems that provides a comprehensive yet practical security portfolio, which includes availability, non-repudiation, access control, entity authentication, remote monitoring and system scalability. The protocol also allows emergency access that results in the graceful degradation of offered services without compromising security and patient safety. The performance of the security protocol as well as its feasibility and impact on modern IMDs are extensively analyzed and evaluated. We find that IMDfence achieves the above security requirements at a mere less than 7\% increase in total IMD energy consumption, and less than 14 ms and 9 kB increase in system delay and memory footprint, respectively.},
  language  = {en}
}
@article{SinnGieseStuiveretal.2022,
  author    = {Sinn, Ludwig R. and Giese, Sven Hans-Joachim and Stuiver, Marchel and Rappsilber, Juri},
  title     = {Leveraging parameter dependencies in high-field asymmetric waveform ion-mobility spectrometry and size exclusion chromatography for proteome-wide cross-linking mass spectrometry},
  series = {Analytical chemistry : the authoritative voice of the analytical community},
  volume    = {94},
  journal   = {Analytical chemistry : the authoritative voice of the analytical community},
  number    = {11},
  publisher = {American Chemical Society},
  address   = {Columbus, Ohio},
  issn      = {0003-2700},
  doi       = {10.1021/acs.analchem.1c04373},
  pages     = {4627 -- 4634},
  year      = {2022},
  abstract  = {Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.},
  language  = {en}
}