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Data owners are increasingly drawn to outsource their data due to the massive processing capabilities and scalable storage offered by cloud-based Internet of Things (IoT) solutions. To protect user privacy, the acquired data may be encrypted; however, this often presents challenges for efficiently searching the data. Although encrypted range queries are among the most commonly used techniques and have been extensively studied, several issues persist within current systems. Specifically, these systems may inadvertently disclose the order relationship between the encrypted index and the upper and lower bounds of a range query, rendering them vulnerable to inference attacks that enable an attacker to uncover the user’s attribute values. To address these challenges, we propose an effective privacy-preserving range query with leakage suppressed approach. The proposed method transforms range comparisons into set intersections, thereby successfully concealing the relationship between the encrypted index and the upper and lower bounds. Consequently, to perform range queries safely and efficiently, we construct an encrypted bloom filter. This ensures that the proposed plan can effectively thwart inference attacks. Furthermore, we incorporate a point-value function polynomial technique into the proposed protocol to facilitate straightforward verification of the query results. A comprehensive security analysis demonstrates the effectiveness of our approach in achieving the stated design goals. Performance evaluations illustrate the practical applicability of the proposed scheme by demonstrating its scalability and feasibility.

The proliferation of troll memes on social media platforms has become a pressing issue due to their potential to spread misinformation and incite conflict. Detecting troll memes is complex due to the intricate interplay between visual and textual elements. In this research, we propose MemesViTa, a novel multimodal fusion model that combines Vision Transformer (ViT) for image processing and DeBERTa for textual analysis, specifically designed to detect troll memes with high accuracy. This research uses a public dataset of 4,368 training image-text and 1092 testing image-text. The primary goal of this study is to identify the most effective approach for troll meme detection by evaluating the performance of different multimodal models. We comprehensively assess multimodal deep learning approaches for troll meme detection, leveraging visual, textual, and Large Language Models in zero-shot and few-shot scenarios. Our study integrates models including BERT, GPT-2, GPT-4, VGG16, ResNet50, and ViT to analyze their performance. Experimental results indicate that the proposed ViT+DeBERTa model, named MemesViTa, achieves superior results, with 94.287% accuracy and 95.82% F1 score, representing a 41.3% improvement over the best visual model of VGG16 and a 48.1% improvement over the best textual model of CNN. Standalone LLMs like GPT-4, even in few-shot scenarios, show promising yet inferior results, with GPT-4 in a 50-shot scenario reaching an accuracy of 80.00% and an F1 score of 37.00%. Multimodal combinations, including BERT+VGG16 and BERT+VGG16+CLIP, highlight the importance of integrating multiple data modalities. Our findings suggest that combining visual and textual data and the strategic use of LLMs can significantly improve the robustness and accuracy of troll meme detection systems. In the future, we intend to enhance our dataset by incorporating a broader spectrum of troll memes. This will help our model to work better across different social media platforms and contexts.

This study is dedicated on formulating a resilient fault-tolerant control for clock-embedded industrial internet of things systems to obtain precise time synchronization by counteracting the detrimental effects of actuator faults, external noises, gain fluctuations, uncertainties and communication delays. Notably, the time synchronization among arbitrary clock pairs is conceptualized within a state-space framework. Precisely, in this model, clock states are linked through unidirectional time-stamped packet exchanges denoted as packet coupling with frequency drift and time-lags are depicted as disturbances. Especially, the robust packet-coupled oscillators protocol is devised to transmit synchronization packets optimally by mitigating the effects of perturbations emerging from time-stamping uncertainties, delay, jitter and frequency drift. In the subsequent stages, the devised resilient fault-tolerant feedback control is implemented with the $L_{2}-L_{\infty }$ based performance to uphold the desired time synchronization precision, regardless of the deteriorating factors. Subsequently, through the adoption of Lyapunov theory and linear matrix inequality approach, we delineate the constraints necessary to validate the asymptotic synchronization of the implemented system and the gain values of configured controller are affirmed. Finally, numerical simulation outcomes are presented to validate the significance and superiority of the theoretical formulations.

Deep Learning and computer vision have become potent agricultural technologies in recent years. These technologies are essential for identifying hazardous plant leaf diseases, significantly impacting crop quality and productivity. The precise distinction between healthy and damaged palm leaves is at the core of this research. Our study marks a significant improvement in the area by introducing a novel method for identifying palm leaf disease using a hybrid model. Our hybrid model’s central component combines the Efficient Channel Attention Network (ECA-Net) with reliable transfer learning techniques utilizing ResNet50 and DenseNet201. In addition to improving disease diagnosis accuracy, this fusion sets a new performance bar compared to earlier models. Our hybrid model maintains a validation accuracy of 98.67% while achieving an amazing 99.54% training accuracy in precisely identifying diseases. Compared to its contemporaries, it also performs exceptionally well in F1 score values, highlighting its remarkable prowess in agricultural technology. This research provides a breakthrough method for disease detection in palm leaves. It will revolutionize the agriculture sector.