THE SCIENTIFIC BUSINESS OF THOMSON REUTERS TO OFFER PLAGIARISM DETECTION SERVICE VIA MANUSCRIPT CENTRAL IN CONJUNCTION WITH CROSSCHECK
Manuscript Central v4.2 to Offer its Customers Access to Services Verifying the Originality of All Scholarly Content Submitted on the System
Philadelphia , PA USA - May 1, 2008 - The Scientific business of Thomson Reuters today announced that Manuscript Central’s online workflow system will incorporate the iThenticate checking tool into its submission and peer review process, and will develop suitable policies and guidelines. CrossRef recently announced an agreement with iParadigms, LLC to launch the CrossCheck service to aid in verifying the originality of scholarly content. Following on the success of CrossRef’s recent pilot of CrossCheck, the service is scheduled to go live in June and will be offered via Manuscript Central.
Manuscript Central’s integration with the iThenticate tool will allow CrossCheck member journals and publishers to send submissions for comparison to the iThenticate service at any point in the peer review or acceptance workflow. With status indicators and a quick view of comparison results, journals will be enabled to investigate suspected documents much further back in the peer review process, potentially saving valuable time and resources in the peer review workflow.
We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.
Krithika, Lakshitha, Monica, Priya, und Veena. International Journal of Innovative Research in Information Security, 09 (2):
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Bhavya, Nalini, Deepika, Nagaraj, und Jagadamba. International Journal of Innovative Research in Information Security, 9 (2):
10-14(Mai 2023)1. Adler, A.; Schuckers, S. Biometric vulnerabilities, overview. In Encyclopedia of Biometrics; Li, S.Z., Jain, A., Eds.; Springer: Boston, MA, USA, 2009. https://doi.org/10.1007/978-0-387-73003-5_65 2. Nguyen, H.T. Fingerprints Classification through Image Analysis and Machine Learning Method. Algorithms 2019, 12, 241. https://doi.org/10.3390/a12110241 3. Biometric Systems Lab—FVC2000: Fingerprint Verification Competition. Available online: FVC2000 (unibo.it)(accessed on 22 January 2021). 4. Tang, Y.; Gao, F.; Feng, J. Latent fingerprint minutia extraction using fully convolutional network. In Proceedings of the 2017 IEEE International Joint Conference on Biometrics, Denver, CO, USA, 1–4 October 2017; pp. 117–123. https://doi.org/10.1109/btas.2017.8272689 5. Huang, X.; Qian, P.; Liu, M. Latent fingerprint image enhancement based on progressive generative adversarial network. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14–19 June 2020; pp. 3481–3489. https://doi.org/10.1109/cvprw50498.2020.00408 6. Neurotechnology Company—Sample Fingerprint Databases. Available online: Download biometric algorithm demo software, SDK trials, product brochures. (neurotechnology.com)(accessed on 22 January 2021). 7. Fingerprint Image identification for crime detection (2019) Fingerprint Image Identification for Crime Detection | IEEE Conference Publication | IEEE Xplore https://doi.org/10.1109/iccsp.2019.8698014.