The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
The characterization of a measure of information discrepancy
Overview
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Some other image encryption quality / randomness measurements [21, 40] also fail to include the property 4.”
From Local Shannon entropy measure with statistical tests for image randomness (2013) · cites this paper as [21]
In the citing paper
“Unlike other variants of Shannon entropy for image encryption [21], our measurement is directly designed for image data and is well mathematically grounded.”
From Local Shannon entropy measure with statistical tests for image randomness (2013) · cites this paper as [21]
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,287
Elements of Information Theory
Jacobs, K. (1992) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8645-1_7 · Cited by 3,075
Entropy optimization principles with applications
Kapur, J. N., & Kesavan, H. K. (1992) · Medical Entomology and Zoology · Cited by 858
On Measures of Information and their Characterizations
(1975) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(08)x6157-0 · Cited by 811
Entropy optimization principles with applications
Lind, N. (1993) · Structural Safety · doi:10.1016/0167-4730(93)90006-m · Cited by 493
On the Foundations of Information Theory
Rényi, A., & Rényi, A. (1965) · Revue de l Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1401301 · Cited by 203
On the entropy of DNA: algorithms and measurements based on memory and rapid convergence
Farach-Colton, M., Noordewier, M. O., Savari, S. A., Shepp, L. A., Wyner, A. J., & Ziv, J. (1995) · doi:10.5555/313651.313662 · Cited by 81
On the Axioms of Information Theory
Lee, P. M. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703765 · Cited by 80
Open combinatorial problems in computational molecular biology
Pevzner, P. A., & Waterman, M. S. (2002) · doi:10.1109/istcs.1995.377035 · Cited by 61
Measurable solutions of functional equations related to information theory
Kannappan, P., & Ng, C. T. (1973) · Proceedings of the American Mathematical Society · doi:10.1090/s0002-9939-1973-0312110-1 · Cited by 38
On the validity of Shannon-information calculations for molecular biological sequences
Hariri, A., Weber, B., & Olmsted, J. (1990) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(05)80054-2 · Cited by 32
Disagreement degree of multi-person judgements in an additive structure
Fang, W. (1994) · Mathematical Social Sciences · doi:10.1016/0165-4896(94)00751-9 · Cited by 20
Self-citationSpecial Functions and the Limit Properties of Probability Distributions. I
Kalinin, V. M. (1971) · Springer eBooks · doi:10.1007/978-1-4684-8211-9_1 · Cited by 12
An application of a functional equation to information theory
Kannappan, P., & Rathie, P. N. (1972) · Annales Polonici Mathematici · doi:10.4064/ap-26-1-95-101 · Cited by 10
On a Global Optimization Problem in the Study of Information Discrepancy
Fang, W. (1997) · Journal of Global Optimization · doi:10.1023/a:1008227113402 · Cited by 9
Self-citation
Research works printed but not linked: 2
- W. Fang, A measure of information discrepancy and an application in the DNA study, Chinese Science Abstract (research Reports), vol. 5, no. 8, pp. 1028–1029
- Nath, 1968, On the measures of errors in information, J. Math. Sci., 3, 1
Cited by
Local Shannon entropy measure with statistical tests for image randomness
Wu, Y., Zhou, Y., Saveriades, G., Agaian, S., Noonan, J. P., & Natarajan, P. (2013) · Information Sciences · doi:10.1016/j.ins.2012.07.049 · Cited by 722
“Shannon entropy [8,47] has been widely used in image encryption for years as a common measure for information and uncertainty [17,21,36,39].”
Method
Metadata
- Authors
- Fang, W.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Fang, Weiwu
- ORCID
- Fang, Weiwu
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2000-06
- Volume
- 125
- Issue
- 1-4
- Pages
- 207-232
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2076805164
- Cited by
- 23
- References
- 17
- Citations by year
- 2017: 1 · 2012: 1
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Information Sciences (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Statistical Mechanics and Entropy
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Keywords
- multi-source data, Shannon entropy, characterization of functions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
