In this paper, we propose a two fold generic parser. First, it simulates the behavior of multiple parsing automata. Second, it parses strings drawn from either a context free grammar, a regular tree grammar, or from both. The proposed parser is based on an approach that defines an extended version of an automaton, called positionparsing automaton (PPA) using concepts from LR and regular tree automata, combined with a newly introduced concept, called state instantiation and transition cloning. It is constructed as a direct mapping from a grammar, represented in an expanded list format. However, PPA is a non-deterministic automaton with a generic bottom-up parsing behavior. Hence, it is efficiently transformed into a reduced one (RBA). The proposed parser is then constructed to simulate the run of the RBA automaton on input strings derived from a respective grammar. Without loss of generality, the proposed parser is used within the framework of pattern matching and code generation. Comparisons with similar and well-known approaches, such as LR and RI, have shown that our parsing algorithm is conceptually simpler and requires less space and states.
Problem statement: Privacy and security over communication channels are of primary concerns. Due to their complexity and diversity, there is a need for continuous improvements of the adopted solutions. In this study, we consider two of the adopted ones, namely, steganogrphy and cryptography and propose a new information hiding system. Approach: The proposed system was based on a generic approach that incorporates text-based steganography and cryptography methods in a way that permits their combined or stand alone adoption. Thus, achieving message encryption incorporated with its concealing inside another unsuspicious one. Furthermore, two steganography methods (the inter-word spaces method and syntactic methods) had been combined with a hybrid textencoding in a form of binary representation of terns rewriting systems. Results: An information hiding system had been implemented. The system offered encrypting and hiding dynamic and static text within a cover-text. The conducted experiments using static texts had shown a non-noticeable increase (0.02%) in the size of their respective stego-texts. For the dynamic texts, cover-texts with a size proportional to the length of the secret messages were needed. Conclusion: A generic model for information hiding with a respective implementation framework had been used as an effective tool to develop a hybrid and scalable steganography system that combined good features from the existing ones.
Social media platforms allow users to share thoughts, experiences, and beliefs. These platforms represent a rich resource for natural language processing techniques to make inferences in the context of cognitive psychology. Some inaccurate and biased thinking patterns are defined as cognitive distortions. Detecting these distortions helps users restructure how to perceive thoughts in a healthier way. This paper proposed a machine learning-based approach to improve cognitive distortions' classification of the Arabic content over Twitter. One of the challenges that face this task is the text shortness, which results in a sparsity of co-occurrence patterns and a lack of context information (semantic features). The proposed approach enriches text representation by defining the latent topics within tweets. Although classification is a supervised learning concept, the enrichment step uses unsupervised learning. The proposed algorithm utilizes a transformer-based topic modeling (BERTopic). It employs two types of document representations and performs averaging and concatenation to produce contextual topic embeddings. A comparative analysis of F1-score, precision, recall, and accuracy is presented. The experimental results demonstrate that our enriched representation outperformed the baseline models by different rates. These encouraging results suggest that using latent topic distribution, obtained from the BERTopic technique, can improve the classifier's ability to distinguish between different CD categories.
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