Software end-users need to sign licenses to seal an agreement with the product
providers. Habitually, users agree with the license (i.e. terms and conditions) without fully
understanding the agreement. To address this issue, an ontological model is developed to
formulate the user requirements and license agreements formally. This paper, introduces
ontological model that includes the abstract license ontology of common features found in
di?erent license agreements. The abstract license ontology is then extended to a few real
world license agreements. The resulting model can be used for di?erent purposes such as
querying the appropriate licenses for a speciˉc requirement or checking the license terms and
conditions with user requirements. 相似文献
Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.
Multimedia Tools and Applications - The High Efficiency Video Coding (HEVC) efficiently reduces the size of the multimedia contents, but at the cost of high computation complexity. In order to make... 相似文献
Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.
International Journal of Control, Automation and Systems - The surface temperature of workpieces in a multi-temperature zone sintering furnace is an important parameter to characterize the... 相似文献