Search Results (1,405)

SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of ‘deepfake’ movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision’s primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system’s efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era. Full article

This article examines public perceptions of virtual humans across various contexts, including social media, business environments, and personal interactions. Using an experimental approach with 371 participants in the United Kingdom, this research explores how the disclosure of virtual human technology influences trust, performance perception, usage likelihood, and overall acceptance. Participants interacted with virtual humans in simulations, initially unaware of their virtual nature, and then completed surveys to capture their perceptions before and after disclosure. The results indicate that trust and acceptance are higher in social media contexts, whereas business and general settings reveal significant negative shifts post-disclosure. Trust emerged as a critical factor influencing overall acceptance, with social media interactions maintaining higher levels of trust and performance perceptions than business environments and general interactions. A qualitative analysis of open-ended responses and follow-up interviews highlights concerns about transparency, security, and the lack of human touch. Participants expressed fears about data exploitation and the ethical implications of virtual human technology, particularly in business and personal settings. This study underscores the importance of ethical guidelines and transparent protocols to enhance the adoption of virtual humans in diverse sectors. These findings offer valuable insights for developers, marketers, and policymakers to optimise virtual human integration while addressing societal apprehensions, ultimately contributing to more effective and ethical deployment of virtual human technologies. Full article

In the evolving field of artificial intelligence, fine-tuning diffusion models is crucial for generating contextually coherent digital characters across various media. This paper examines four advanced fine-tuning techniques: Low-Rank Adaptation (LoRA), DreamBooth, Hypernetworks, and Textual Inversion. Each technique enhances the specificity and consistency of character generation, expanding the applications of diffusion models in digital content creation. LoRA efficiently adapts models to new tasks with minimal adjustments, making it ideal for environments with limited computational resources. It excels in low VRAM contexts due to its targeted fine-tuning of low-rank matrices within cross-attention layers, enabling faster training and efficient parameter tweaking. DreamBooth generates highly detailed, subject-specific images but is computationally intensive and suited for robust hardware environments. Hypernetworks introduce auxiliary networks that dynamically adjust the model’s behavior, allowing for flexibility during inference and on-the-fly model switching. This adaptability, however, can result in slightly lower image quality. Textual Inversion embeds new concepts directly into the model’s embedding space, allowing for rapid adaptation to novel styles or concepts, but is less effective for precise character generation. This analysis shows that LoRA is the most efficient for producing high-quality outputs with minimal computational overhead. In contrast, DreamBooth excels in high-fidelity images at the cost of longer training. Hypernetworks provide adaptability with some tradeoffs in quality, while Textual Inversion serves as a lightweight option for style integration. These techniques collectively enhance the creative capabilities of diffusion models, delivering high-quality, contextually relevant outputs. Full article

In contemporary society, the internet, particularly social media, has become a significant area where individuals spend considerable amounts of time engaging in various activities. Concurrently, the growing utilization of artificial intelligence (AI) has emerged as a critical component of the propaganda that is disseminated online within economic, social, and political spheres. AI encompasses a broad range of applications, including data collection for microtargeting and the dissemination of diverse forms of disinformation. Additionally, AI can be effectively employed to detect and remove content from social media platforms that contradicts democratic principles, such as disinformation or hate speech. This study reviews the existing literature on the use of AI in political propaganda, examining not only how AI has become an integral part of propaganda strategies, but also how it is utilized to counter propaganda that violates democratic values. It explores the legislation in various countries that enables (and mandates) the removal of propaganda content contrary to democratic principles from social media platforms with the assistance of AI, and it discusses perspectives that highlight the potential conflict between these practices and the principle of freedom of expression. Full article

To achieve high-precision calculation of the electromagnetic field of layered media and to ensure that the apparent resistivity calculation and sensitivity are not affected by numerical errors, this paper implements high-precision calculation of the layered electromagnetic field based on the weighted average (WA) extrapolation method. Firstly, the 1D electromagnetic field expression of an arbitrary attitude field source is obtained by using the magnetic vector potential; then, the WA extrapolation technique is introduced to achieve the high-precision and fast solution of the Hankel transform, and the effects of the number of Gaussian points and the number of integration intervals on the accuracy are investigated. The theoretical model test shows that, compared with the open-source Dipole1D, the algorithm proposed in this paper has wider adaptability, and can achieve high-precision calculation of electric and magnetic dipole sources with higher efficiency. Compared with the epsilon algorithm studied by previous researchers, the WA extrapolation method proposed in this article can improve the convergence rate by approximately 20% under the same conditions. It can obtain high-precision numerical solutions with less integration time. The relative accuracy can reach the order ${10}^{-10}$, and its computational efficiency is significantly better than the existing epsilon algorithm. Finally, we used two cases of marine controlled source electromagnetic method to show the application. The sensitivity and Poynting vectors are calculated, which provides a technical tool for a deep understanding of physical mechanisms in layered media. Full article

The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news. This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text. Through a series of experiments on diverse datasets, applying transfer and incremental learning techniques, we demonstrate the effectiveness of our hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Furthermore, our findings emphasize the importance of dataset alignment and balance in transfer learning, as well as the utility of incremental learning in maintaining high detection performance while reducing runtime. This research offers promising avenues for further advancements in fake news detection methodologies, with implications for future research and development in this critical domain. Full article

To reveal the effect of sucrose concentration on the production of secondary metabolites, a metabolome and transcriptome joint analysis was carried out using callus induced from grape variety Mio Red cambial meristematic cells. We identified 559 metabolites—mainly flavonoids, phenolic acids, and stilbenoids—as differential content metabolites (fold change ≥2 or ≤0.5) in at least one pairwise comparison of treatments with 7.5, 15, or 30 g/L sucrose in the growing media for 15 or 30 days (d). Resveratrol, viniferin, and amurensin contents were highest at 15 d of subculture; piceid, ampelopsin, and pterostilbene had higher contents at 30 d. A transcriptome analysis identified 1310 and 498 (at 15 d) and 1696 and 2211 (at 30 d) differentially expressed genes (DEGs; log2(fold change) ≥ 1, p < 0.05) in 7.5 vs. 15 g/L and 15 vs. 30 g/L sucrose treatments, respectively. In phenylpropane and isoflavone pathways, DEGs encoding cinnamic acid 4-hydroxylase, chalcone synthase, chalcone isomerase, and flavanone 3-hydroxylase were more highly expressed at 15 d than at 30 d, while other DEGs showed different regulation patterns corresponding to sucrose concentrations and cultivation times. For all three sucrose concentrations, the stilbene synthase (STS) gene exhibited significantly higher expression at 15 vs. 30 d, while two resveratrol O-methyltransferase (ROMT) genes related to pterostilbene synthesis showed significantly higher expression at 30 vs. 15 d. In addition, a total of 481 DEGs were annotated as transcription factors in pairwise comparisons; an integrative analysis suggested MYB59, WRKY20, and MADS8 as potential regulators responding to sucrose levels in flavonoid and stilbene biosynthesis in grape callus. Our results provide valuable information for high-efficiency production of flavonoids and stilbenes using grape callus. Full article

Immersive journalism, utilizing virtual reality (VR) and augmented reality (AR) technologies, offers a transformative approach to storytelling by creating deeply engaging news experiences. This study examines the acceptance of immersive journalism by people working in news media industries focusing on different demographic groups. Using qualitative focus groups, participants from varied age groups experienced VR news stories and discussed their perceptions and responses. Key findings revealed high emotional engagement across all demographics, with younger participants showing greater enthusiasm and ease with the technology, while older participants expressed interest but faced technological barriers. Trust in the media and perceived realism were crucial factors influencing acceptance. Concerns about accessibility and inclusivity were also significant, particularly among older and less technologically proficient participants. This study concludes that, for immersive journalism to gain widespread acceptance, media organizations must address technological barriers, enhance user education, and ensure ethical standards in storytelling. These insights contribute to understanding how immersive journalism can be integrated into mainstream media to enhance audience engagement and trust, highlighting the importance of accessibility and emotional resonance in the adoption of innovative media technologies. Full article

Two-dimensional human pose estimation aims to equip computers with the ability to accurately recognize human keypoints and comprehend their spatial contexts within media content. However, the accuracy of real-time human pose estimation diminishes when processing images with occluded body parts or overlapped individuals. To address these issues, we propose a method based on the YOLO framework. We integrate the convolutional concepts of Kolmogorov–Arnold Networks (KANs) through introducing non-linear activation functions to enhance the feature extraction capabilities of the convolutional kernels. Moreover, to improve the detection of small target keypoints, we integrate the cross-stage partial (CSP) approach and utilize the small object enhance pyramid (SOEP) module for feature integration. We also innovatively incorporate a layered shared convolution with batch normalization detection head (LSCB), consisting of multiple shared convolutional layers and batch normalization layers, to enable cross-stage feature fusion and address the low utilization of model parameters. Given the structure and purpose of the proposed model, we name it KSL-POSE. Compared to the baseline model YOLOv8l-POSE, KSL-POSE achieves significant improvements, increasing the average detection accuracy by 1.5% on the public MS COCO 2017 data set. Furthermore, the model also demonstrates competitive performance on the CrowdPOSE data set, thus validating its generalization ability. Full article

A general theory for solving electromagnetic diffraction problems with impenetrable/penetrable wedges immersed in/made of an arbitrary linear (bianistropic) medium is presented. This novel and general spectral theory handles complex scattering problems by using transverse equations for layered planar and angular structures, the characteristic Green function procedure, the Wiener–Hopf technique, and a new methodology for solving GWHEs. The technique has been proven effective for analyzing problems involving wedges immersed in isotropic media; in this study, we extend the theory to more general cases while providing all necessary mathematical tools and corresponding validations. We obtain generalized Wiener–Hopf equations (GWHEs) from spectral functional equations in angular regions filled by arbitrary linear media. The equations can be interpreted with a network formalism for a systematic view. We recall that spectral methods (such as the Sommerfeld–Malyuzhinets (SM) method, the Kontorovich–Lebedev (KL) transform method, and the Wiener–Hopf (WH) method) are well-consolidated, fundamental, and effective tools for the correct and precise analysis of electromagnetic diffraction problems constituted by abrupt discontinuities immersed in media with one propagation constant, although they are not immediately applicable to multiple-propagation-constant problems. To the best of our knowledge, the proposed mathematical technique is the first extension of spectral analysis to electromagnetic problems in the presence of angular regions filled by complex arbitrary linear media, thereby providing novel mathematical tools. Validation through fundamental examples is proposed. Full article

Children who enter the US education system as emerging bilinguals (or English Learners) show significant gaps in test scores and graduation rates compared to their English monolingual peers. Dual-language education programs may provide an instructional context that capitalizes on emerging bilingual children’s strengths and supports their academic performance while they acquire English. However, prior research has shown that integrating semantic knowledge across language systems poses a challenge. Supports may mitigate these challenges. Thus, across two studies, we examined instructional practices that may facilitate the integration of academic content across English and Spanish in elementary-age children enrolled in dual-language education programs. In Study 1, we examined whether facts presented through reading-while-listening or children’s silent self-paced reading more effectively supported learning the facts and subsequent integration for dual-language students in grades 3 and 4 (n = 56; M_age = 9.54 years). In Study 2, we examined whether combining graphics with text was more effective in supporting fact learning and cross-language integration compared to facts presented as text alone in dual-language students in grades 4 and 5 (n = 67; M_age = 10.48 years). Overall, the studies replicate the benefits of reading-while-listening and graphics for learning directly taught facts, but underscore the difficulty in integrating semantic knowledge across lessons and languages. Full article

Based on data from the 2022 Chinese Minors’ Digital Life and Online Protection Survey, this study investigated the status quo of social media use and its influencing mechanism on academic performance among Chinese children and adolescents. The statistical results indicate that the average level of Chinese students’ social media use was generally low, with their academic performance varying across socio-demographic and schooling characteristics. After controlling for other variables, it was found that the frequency of social media use could exert a significant positive impact on students’ academic performance. Moreover, the mechanism analysis revealed that online learning behavior and prosocial behavior served as chain mediators linking social media use to academic performance. Specifically, students could transfer their social media behavioral patterns to the internet-based learning context, and then effectively utilize remote learning resources. Meanwhile, engagement with social media would cultivate individuals’ prosocial personality, thereby stimulating intrinsic motivation for learning and ultimately enhancing academic performance. The heterogeneity analysis further confirmed that the impact of social media use on students’ academic performance was stronger in lower-class families, underscoring the moderating role of family socioeconomic status in the relationship between social media use and academic performance. The findings suggest that if academic performance is regarded as an integral part of individual capacity development, then the rational utilization of social media resources might be a pivotal approach to alleviate the predicament of developmental inequality faced by students from low socioeconomic backgrounds. Full article

Discovering the Representative places (RPs) of a city will benefit the understanding of local culture and help to improve life experiences. Previous studies have been limited in regard to the large-scale spatial identification of RPs due to the vagueness of boundaries and the lack of appropriate data sources and efficient tools. Furthermore, human perception of these places remains unclear. To address this gap, this research adopts a novel approach to identify and evaluate the RPs of a city from the perspective of human perception. Our methodology involves the utilization of deep learning systems, text semantic analysis, and other techniques to integrate multi-source data, including points of interest (POIs), street view images, and social media data. Taking Nanjing, China, as a case, we identified 192 RPs and their perceptual ranges (PRRPs). The results show the following: (1) Comparing RPs to non-RPs, RPs show higher average scores across four perceptual dimensions (positive indicators): Beautiful (7.11% higher), Lively (34.23% higher), Safety (28.42% higher), and Wealthy (28.26% higher). Conversely, RPs exhibit lower average scores in two perceptual dimensions (negative indicators): Boring (79.04% lower) and Depressing (20.35% lower). (2) Across various perceptual dimensions, RPs have utilized 15.13% of the land area to effectively cover approximately 50% of human perceptual hotspots and cold spots. (3) The RPs exhibit significant variations across different types, levels, and human preferences. These results demonstrate the positive perceived effects that RPs have, providing valuable insights to support urban management, the transformation of the built environment, and the promotion of sustainable urban development, and provide guidance for urban planners and designers to make improvements in urban design and planning to make these sites more attractive. Full article

This study investigated the effects of pure and methyl-β-cyclodextrin loaded forms of resveratrol (10 µg/mL, 20 µg/mL, and 40 µg/mL) on ram sperm functions post-thawing. Semen samples were pooled and divided into ten groups: Control, RES10, RES20, RES40, CD10, CD20, CD40, RLC10, RLC20, and RLC40. The groups were pre-diluted with media containing the group-specific chemicals, followed by 15 min of incubation, dilution, and freezing. To assess the effects of the chemicals, a post-thaw sperm quality assessment was conducted. Motility and other velocity parameters were evaluated using computer-assisted semen analysis. The functional integrity of spermatozoa membranes was assessed with the hypo-osmotic swelling test, and the capacitation status of spermatozoa was determined through fluorescent microscopic evaluation. Additionally, flow cytometry was used to evaluate mitochondrial activity, oxidative stress, and the integrity of the sperm membrane and acrosome. The results indicated that cyclodextrin adversely affected sperm functions following freezing–thawing, notably increasing the rate of spermatozoa exhibiting pre-capacitation and mitochondrial activity by approximately 34% and 16%, respectively (p < 0.05). It was found that 20 µg/mL resveratrol prevented pre-capacitation (p < 0.05). Both resveratrol and resveratrol-loaded cyclodextrin groups improved post-thaw sperm qualities overall, demonstrating their utility for freezing ram semen. However, higher concentrations of resveratrol were found to negatively impact sperm functions. Full article

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison. Full article