Sarcasm is a perplexing form of human expression that presents distinct challenges in understanding. The problem of sarcasm detection has centered around analyzing individual utterances in isolation which may not provide a comprehensive understanding of the speaker’s sarcastic intent. Our work addresses this problem by exploring and understanding the specific contextual cues that contribute to sarcasm. In this paper, we propose an enhanced approach for sarcasm detection using contextual features. Our methodology involves employing pre-trained transformer models, RoBERTa and DistilBERT, and fine-tuning them on two datasets: the News Headlines and the Mustard datasets. Incorporating contextual information, the proposed approach yielded the best performance, achieving an impressive F1 score of 99% on News Headlines dataset and 90% on Mustard dataset. Moreover, we experimented summarizing the context into a concise short sentence. This enhancement reduced training time by 35.5% of the original time. We further validated the model trained on the News headlines dataset against the Reddit dataset, which resulted in 49% F1 score without context data. However, with the inclusion of context data, the F1 score surged to 75%. Proposed approach enhances the understanding of sarcasm in different contextual settings, enabling more accurate sentiment analysis and better decision-making in various applications.

To protect the images and provide a more secure cipher image, DNA encoding is crucial in image encryption. Applying a single, easily detectable coding rule to the image during DNA encoding has no impact on the encryption model's security level. Therefore, using various coding rules while applying encryption to the image, dynamic DNA-coding techniques have emerged to strengthen and improve the encryption of the image and its security. This study integrates a dynamic DNA-coding method with an encryption model. The model is applied to gray-scale images, where using a predetermined coding rule, every two bits are DNA-encoded in the image. The proposed model generates the key by sending the image and its metadata to hash functions. Following that, the hyperchaotic system constructs three chaotic sequences using the key, and the Lorenz–Liu chaotic system generates a sequence of coding rules. Then, the image is passed to Arnold Transform, where the resulted image is diffused by applying five chaotic maps. Last, using the coding rules, it is DNA-encoded, provided with the chaotic sequences to DNA, and DNA-decoded. Twelve metrics were used to assess the proposed model on ten widely used images. Results show a promising improvement in performance, since it enhanced the security of the model.

Unsecured networks have recently become widely used for the transmission of confidential images. Consequently, cryptography is crucial for ensuring data confidentiality. Developing a key that is resistant to statistical and differential attacks has always been a challenging objective. In this paper, a novel model is proposed to boost image encryption while maintaining key strength. The proposed model adapts MD5 and SHA-256 hash functions to produce a key. It generates four matrices, X, Y, Z, and W, by using a memristor hyperchaotic system. Arnold’s transform was applied to the original image once the key was created. The images were then scrambled using five chaotic maps. The image is then DNA-encoded, diffused using three matrices, and finally DNA-decoded. The proposed model was assessed using twelve performance measures on nine popular images. Compared to previous studies, the results of the proposed model indicate a promising improvement in performance. It achieves a better performance by expanding the key space and increasing its sensitivity.

The driving force behind content dissemination in Social network (SN) is the users’ interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic user clusters “interest groups”. The objective of this work is to rank interest-based communities using influence propagation. The contribution of this work is three-fold: First, to highlight the significance of the indirect influence among interest-based user groups. Second, to study its impact on content dissemination capability. Third, to propose an ultimate ranking model (UltRank) that uniquely considers direct and indirect influences which are reflected in a new reachability metric that considers: 1. Distance among interest groups. 2. Percentage of reachable interest groups. 3. Percentage of reachable nodes. UltRank model has been evaluated in comprehensive experiments. First, clustering quality perspective, the Silhouette coefficient for the identified interest groups is on average 0.996 and the Jaccard coefficient of 97% of different interest groups members equals 0. Second, ranking capability perspective, UltRank model can rank up to 91% of interest groups in SN. Finally, ranking effectiveness perspective, UltRank ranking list has a competing network coverage results against the other benchmark approaches.