Substitution-based approach for linguistic steganography using antonym

Steganography has been a part of information technology security since a long time ago. The study of steganography is getting attention from researchers because it helps to strengthen the security in protecting content message during this era of Information Technology. In this study, the use of substitution-based approach for linguistic steganography using antonym is proposed where it is expected to be an alternative to the existing substitution approach that using synonym. This approach still hides the message as existing approach but its will change the semantic of the stego text from cover text. A tool has been developed to test the proposed approach and it has been verified and validated. This proposed approach has been verified based on its character length stego text towards the cover text, bit size types of the secret text towards the stego text and bit size types of the cover text towards the stego text. It has also been validated using four parameters, which are precision, recall, f-measure, and accuracy. All the results showed that the proposed approach was very effective and comparable to the existing synonym-based substitution approach

Model transformation by graph transformation: A comparative study

This is an electronic version of the paper presented at the Model Transformation in Practice, held in Montego Bay on 2005Graph transformation has been widely used for expressing model transformations. Especially transformations of visual models can be naturally formulated by graph transformations, since graphs are well suited to describe the underlying structures of models. Based on a common sample model transformation, four different model transformation approaches are presented which all perform graph transformations. At first, a basic solution is presented and crucial points of model transformations are indicated. Subsequent solutions focus mainly on the indicated problems. Finally, a first comparison of the chosen approaches to model transformation is presented where the main ingredients of each approach are summarized

Towards Translating Graph Transformation Approaches by Model Transformations

Recently, many researchers are working on semantics preserving model transformation. In the field of graph transformation one can think of translating graph grammars written in one approach to a behaviourally equivalent graph grammar in another approach. In this paper we translate graph grammars developed with the GROOVE tool to AGG graph grammars by first investigating the set of core graph transformation concepts supported by both tools. Then, we define what it means for two graph grammars to be behaviourally equivalent, and for the regarded approaches we actually show how to handle different definitions of both - application conditions and graph structures. The translation itself is explained by means of intuitive examples

REFRAMING DECISION PROBLEMS: A GRAPH-GRAMMAR APPROACH

One fundamental requirement in the expected utility model is that the preferences of rational persons should be independent of problem description. Yet an extensive body of research in descriptive decision theory indicates precisely the opposite: when the same problem is cast in two different, but normatively equivalent, "frames," people tend to change their preferences in a systematic and predictable way. In particular, alternative frames of the same decision tree are likely to invoke different sets of heuristics, biases, and risk-attitudes, in the user's mind. The paper presents a computational model in which decision-trees are cast as attributed graphs, and reframing operations on trees are implemented as graph-grammar productions. In addition to the basic functions of creating and analyzing decision-trees, the model offers a natural way to define a host of "debiasing mechanisms" using graphical programming techniques, Some of these mechanisms have appeared in the decision theory literature, whereas others were directly inspired by the novel use of graph grammars in modeling decision problems.Information Systems Working Papers Serie

Supporting user-oriented analysis for multi-view domain-specific visual languages

This is the post-print version of the final paper published in Information and Software Technology. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.The integration of usable and flexible analysis support in modelling environments is a key success factor in Model-Driven Development. In this paradigm, models are the core asset from which code is automatically generated, and thus ensuring model correctness is a fundamental quality control activity. For this purpose, a common approach is to transform the system models into formal semantic domains for verification. However, if the analysis results are not shown in a proper way to the end-user (e.g. in terms of the original language) they may become useless. In this paper we present a novel DSVL called BaVeL that facilitates the flexible annotation of verification results obtained in semantic domains to different formats, including the context of the original language. BaVeL is used in combination with a consistency framework, providing support for all steps in a verification process: acquisition of additional input data, transformation of the system models into semantic domains, verification, and flexible annotation of analysis results. The approach has been validated analytically by the cognitive dimensions framework, and empirically by its implementation and application to several DSVLs. Here we present a case study of a notation in the area of Digital Libraries, where the analysis is performed by transformations into Petri nets and a process algebra.Spanish Ministry of Education and Science and MODUWEB

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page