Knowledge and information systems

By E. Šilerová, L. Kučírková. 2018

191 citations recorded by the source corpus.

Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership. The journal focuses on knowledge systems and advanced information systems, including their theoretical foundations, infrastructure and enabling technologies. We solicit submissions of original research, and experience and vision papers that address this theme. We publish critical review papers to discuss the state of the art in particular areas, as well as state-of-the-art research reports. Accepted papers are grouped for publication so that individual issues focus on a small number of theme areas. In addition to archival papers, the journal also publishes significant on-going research in the form of Short Papers (limited to 3000 words), and very short papers on "visions and directions" (no more than 1000 words, excluding bibliography). We conduct reviews in a timely fashion and inform authors of decisions with a target turnaround time of 3 months. Selected papers from relevant conferences are welcome. Good papers with high quality reviews can be accepted after the expansion and revision is verified by an Associate Editor of the Editorial Board. Conference organizers are invited to contact the Editor-in-Chief kais@cs.uvm.edu for further information.

Subfields: Computational Theory and Mathematics, Information Systems, Management Information Systems

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In Computational Theory and Mathematics

In Information Systems

In Management Information Systems

Citations in the corpus

Evaluating and Aggregating Feature-based Model Explanations (2020, methodology): “Other explanation functions perturb input values to a reference output and measure the change in the model’s output [Štrumbelj and Kononenko, 2014; Lundberg and Lee, 2017].”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “Following [Wang et al. , 2016a], We set λ 1 = 2 in Eq.”
A Survey on Computational Propaganda Detection (2020, background): “Coordinated behavior appears as near-fully connected communities in graphs, dense blocks in adjacency matrices, or peculiar patterns in spectral subspaces [Jiang et al., 2016].”