Demo Data from EDBT 2026 / reviewers in the wild / expert
Christos P. Kitsos
dblp:33/8860
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1Theory of computation · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 77% User interface design and tools · 23% | |
| Theoretical computer science
1 paper |
Information theory · 70% Algorithms and data structures · 30% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing › computer-mediated communication › messaging
mobile messaging |
0.2 | 1 | 2016 | Forget-me-not: History-less Mobile Messaging · CHI 2016 |
Information theory › information measures › entropy › entropy inequalities
entropy power inequality |
0.1 | 1 | 2009 | Logarithmic Sobolev inequalities for information measures · IEEE Trans. Inf. Theory 2009 |
Information theory
information measures |
0.1 | 1 | 2009 | Logarithmic Sobolev inequalities for information measures · IEEE Trans. Inf. Theory 2009 |
Algorithms and data structures › markov chains › mixing time
log-sobolev inequality |
0.1 | 1 | 2009 | Logarithmic Sobolev inequalities for information measures · IEEE Trans. Inf. Theory 2009 |
Information theory › information measures
fisher information |
0.0 | 1 | 2009 | Logarithmic Sobolev inequalities for information measures · IEEE Trans. Inf. Theory 2009 |
Methods — techniques the papers use, named apart from their topics
field trial · 0.2design probe · 0.2logarithmic sobolev inequality · 0.1entropy power · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Forget-me-not: History-less Mobile MessagingabstractText messaging has long been a popular activity, and today smartphone apps enable users to choose from a plethora of mobile messaging applications. While we know a lot about SMS practices, we know less about practices of messaging applications. In this paper, we take a first step to explore one ubiquitous aspect of mobile messaging -- messaging history. We designed, built, and trialled a mobile messaging application without history named forget-me-not. The two-week trial showed that history-less messaging no longer supports chit-chat as seen in e.g. WhatsApp, but is still considered conversational and more 'engaging'. Participants expressed being lenient and relaxed about what they wrote. Removing the history allowed us to gain insights into what uses history has in other mobile messaging applications, such as planning events, allowing for distractions, and maintaining multiple conversation threads. Mattias Rost, Christos P. Kitsos, Alexander Morgan, Martin Podlubny, Pietro Romeo, Edoardo Russo, Matthew Chalmers |
CHI | 2 |
| 2009 | Logarithmic Sobolev inequalities for information measuresabstractForalphages1, the new Vajda-type information measureJalpha(X) is a quantity generalizing Fisher's information (FI), to which it is reduced foralpha=2. In this paper, a corresponding generalized entropy powerNalpha(X) is introduced, and the inequalityNalpha(X)Jalpha(X) gesnis proved, which is reduced to the well-known inequality of Stam foralpha=2. The cases of equality are also determined. Furthermore, the Blachman-Stam inequality for the FI of convolutions is generalized for the Vajda informationJalpha(X) and both families of results in the context of measure of information are discussed. That is, logarithmic Sobolev inequalities (LSIs) are written in terms of new more general entropy-type information measure, and therefore, new information inequalities are arisen. This generalization for special cases yields to the well known information measures and relative bounds. Christos P. Kitsos, Nikolaos K. Tavoularis |
IEEE Trans. Inf. Theory | 1 |