Search results

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

The purpose of this paper is to examine the behavior of self-disclosure among Saudi women and men in an attempt to understand gender differences in language. The study also gives insights about the cultural norms and how they affect language production.

The author utilized Levi-Belz and Kreiner's (2019) three-dimensional tool of self-disclosure; namely, the HOW MUCH dimension, the WHAT dimension and the HOW dimension. The HOW MUCH dimension is measured through word count of self-disclosure and the duration of self-disclosure in spoken discourse. The WHAT dimension is measured through analyzing the topics, emotions and social actors that are discussed during self-disclosure episodes. The HOW dimension is measured through examining the acoustic features of self-disclosure such as intonation, loudness and fluency.

Saudi women tend to engage in more self-disclosure than Saudi men, and their self-disclosure tends to be longer and more detailed. Women also tend to use more intonation variability and softer loudness, reflecting the cultural norms of politeness and reservation. Both genders tend to use similar frequencies of positive and negative emotion words in their self-disclosure, with positive emotion words correlating more with personal self-disclosure and negative emotion words with self-disclosures about loss, failure, conflict, rejection and uncertainty. The data also show that the use of reflective verbs leads to more authentic and empathetic communication and that pronoun use correlates with the type of emotional experience being discussed.

This study has limitations due to a small sample size. Future research should use larger and diverse samples to explore self-disclosure in Saudi televised interviews comprehensively. The study focused solely on televised interviews; future research can examine self-disclosure across various media platforms. Findings have practical implications for Saudi media and policymakers. Understanding self-disclosure in interviews can guide content creation, fostering open communication. Presenters consciously act as role models, influencing Saudi youth, emphasizing the role of positive self-presentation.

This study utilized Levi-Belz and Kreiner's (2019) three-dimensional tool of self-disclosure in a way that could be used for other languages and cultures. The study examines the Saudi cultural norms in self-disclosure, which has never been tackled before.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This paper aims to present a new Islamic product called gambling-free lottery, which is inspired by ideas of Musharakah, Takaful and Al-qard Al-hasan, where the winner of the lottery receives the prize as an interest-free loan, and buyers of tickets get their money back after the winner’s repayment of the loan.

The paper reports the religious opinions of three Islamic scholars who were interviewed for the purpose of this study. The results of a questionnaire to survey the mood of 430 persons about the new product are also reported.

The paper concludes that although the proposed product is still at an exploratory stage and not a definitive product acceptable to all Muslim society, it could be a successful Islamic financial product, provided that it was put into practice with some modifications to accommodate all Islamic views.

Main limitations of this study are the number of Islamic scholars interviewed does not reflect all the Islamic views regarding the new product and the lack of more information about the religious side of this type of product.

By introducing such a product, the gambling-free lottery could become not only a means of credit provision but also a new method of playing a “game” while lending money to someone who is more likely to be poor. Converting the poor into the rich could overcome many problems, particularly in poor countries.