Notice of Violation of IEEE Publication Principles: Hand gesture recognition framework for recognizing sign gestures and handling movement epenthesis using Level Building nested dynamic programming approach
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Notice of Violation of IEEE Publication Principles“Hand Gesture Recognition Framework for Recognizing Sign Gestures and Handling Movement Epenthesis Using Level Building Nested Dynamic Programming Approach”by Elakkiya A., Selvamani K., Kanimozhi S.in the Proceedings of the IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), May 2014After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.This paper duplicated extensive amounts of text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. A. Elakkiya was solely responsible for the copied material.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:“Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language Recognition”by Ruiduo Yang, Sudeep Sarkar, Barbara Loedingin the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2007In this research paper, two crucial problems in continuous sign language recognition from unaided video sequences are considered. At the feature level, the problem of hand segmentation and grouping is considered and at the sentence level, the movement epenthesis problem is considered. A framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach is constructed. To handle movement epenthesis problem, a nested version of a dynamic programming framework, called Level Building is used to simultaneously segment and to match signs from continuous sign language sentences. This approach is then coupled with a trigram grammar model to optimally segment and label sign language sentences. This approach will show improvement over past approaches in terms of the frame labeling rate and also our approach shows the flexibility when handling a changing context. The proposed approach is novel since it does not need explicit any models for movement epenthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".