Search Results (12)

This article examines the 1910 philosophical dispute between Vladimir Ern and Semyon Frank in post-1905 Russia as a dispute over the criterion of philosophy itself. The controversy arose in a field where the meaning of “Russian philosophy,” the authority of neo-Kantian nauchnost’ [scientificity], the religious-ontological program of Put’, and the problem of culture had become closely interconnected. The article argues that the central issue concerned what makes a claim philosophical: participation in an antecedent order of being, or conceptual articulation, proof, and universally valid justification. Ern’s intervention is presented as an attempt to reconstitute philosophy through Logos. For Ern, modern rationalism separates the discursive-logical from the “fullness of reason,” producing ratio as an autonomous and ultimately meonic form of thought; Logos, by contrast, names the ontological principle through which thought remains inwardly bound to being. Frank’s response locates the issue in the concept of philosophy itself. While acknowledging intuition, ontologism, and the insufficiency of one-sided rationalism, he insists that every appeal to being becomes philosophical only when it enters the medium of concepts, reasons, and proof. The article argues that the controversy turns on two irreducible conditions internal to philosophy itself: thought must remain faithful to being, yet it must do so in a form through which its claims become philosophically valid. Read in this way, the Ern–Frank exchange discloses a constitutive tension between ontology and conceptual justification, and between historical embodiment and universal validity. Full article

Addressing Artificial Intelligence (AI) faculty deficiencies in higher education, this paper develops the AI+ Dual-qualified Teacher Capability Ecology Model (AI-DTCEM) based on Capability Ecology Theory. The model is developed after a thorough analysis of the current state of new engineering talent cultivation in universities and the innovative practical abilities required in the AI+ environment. This paper proposes an implementation framework characterized by “three-dimensional collaboration, four-tier progression, and five-element drive.” Additionally, it uses the collaborative education project involving Hangzhou Normal University, Zhejiang University, and Hangzhou Ruishu Technology Co., Ltd. as a backdrop to introduce a deep collaborative education model, showcasing the theoretical and practical achievements of this project. Using NetLogo as the simulation platform, this paper designs a 96-month system dynamics experiment to compare and analyze the outcomes of four scenarios: the baseline experiment, the AI-enhanced experiment, the policy-driven experiment, and the comprehensive optimization experiment. This study reveals the following findings: (1) Policy-driven initiatives are crucial for the successful construction of dual-qualified teacher teams, with the policy-driven scenario achieving the highest overall skill level (9.332). (2) The application of AI technology significantly enhances teacher skill development, resulting in AI skill improvements ranging from 116.6% to 163.4%. (3) The comprehensive optimization scenario (utilizing a collaborative mechanism) achieves systemic advantages, realizing a 100% dual-qualified teacher ratio. However, this comes with diminishing marginal returns on investment. This research provides a theoretical foundation, quantitative analysis, and practical pathways for developing dual-qualified teacher teams in the AI+ era. Full article

Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively. Full article

The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications. Full article

Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects. Full article

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach. Full article

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset. Full article

Small ruminant lentiviruses (SRLVs) represent a very heterogeneous group of ss-RNA viruses that infect sheep and goats worldwide. They cause important, deleterious effects on animal production and limit the animal trade. SRLVs show a high genetic variability due to high mutation rate and frequent recombination events. Indeed, five genotypes (A–E) and several subtypes have been detected. The aim of this work was to genetically characterize SRLVs circulating in central Italy. On this basis, a phylogenetic study on the gag-pol genetic region of 133 sheep, collected from 19 naturally infected flocks, was conducted. In addition, to evaluate the frequency of mutation and the selective pressure on this region, a WebLogo 3 analysis was performed, and the dN/dS ratio was computed. The results showed that 26 samples out of 133 were clustered in genotype A and 106 samples belonged to genotype B, as follows: A9 (n = 8), A11 (n = 10), A24 (n = 7), B1 (n = 2), B2 (n = 59), and B3 (n = 45). No recombination events were found. Mutations were localized mainly in the VR-2 region, and the dN/dS ratio of 0.028 indicated the existence of purifying selection. Since the genetic diversity of SRLVs could make serological identification difficult, it is important to perform molecular characterization to ensure a more reliable diagnosis, to maintain flock health status, and for the application of local and national control programs. Full article

Femtosecond laser engraving offers appealing advantages compared to regular laser engraving such as higher precision and versatility. In particular, the inscription of deep patterns exhibits an increasing interest in industry. In this work, an optimization protocol based on constraining overlap ratio and scan number is demonstrated. The proposed method allows changing overlap ratio while maintaining depth in the same range, which reduces the sampling number. This study WAS applied to stainless steel 316 L and sapphire for engravings deeper than 100 $\mathsf{\mu }$m. Results exhibit overall depths higher than threshold values and allowed to determine optimized engraving quality, for instance, roughness in steel can be reduced while maintaining depth and taper angle by reducing overlap ratio. The optimized laser parameters such as roughness and taper angle factors for sapphire were also found to be as follows: 200 kHz, 86% overlap and 12 J/cm${}^2$. As a demonstration, a logo engraving is illustrated at the end. Full article

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies. Full article

The study is devoted to the analysis of the efficiency of use and the effectiveness of disclosing the intellectual capital (IC) of a trading company operating in the market of the Russian Federation. The subject of the research is an assessment of the quality of disclosure of information about the IC company involved in the creation of financial results of activities and the growth of the company’s value. The study examines the assessment of IC and the search for links between the involvement of IC in the formation of the financial result of a trading company and the degree of its reflection in the company’s annual reports. Methods of using intellectual value-added coefficients (VAIC) such as the trademark logo (written as ™), Calculated Intangible Value (CIV) and content analysis of the company’s annual reports are used to assess the IC and its elements. The influence of IC and its components, on the financial results of a trading company are also investigated and calculated using various methods. It is shown that there are no statistically significant relationships between the assessments of IC and its elements obtained using financial ratios, and those obtained using content analysis. This indicates that the opinions and assessments of the heads of a trading company regarding IC are formed regardless of the size of IC involved in the formation of economic results and testifies to the absence of an effective investment management policy in the studied company. Some of the results obtained confirm the trends in IC structures previously observed for companies in other industries. The results also indicate that the existing system for preparing annual reports does not sufficiently consider the size, dynamics and efficiency of using the intangible assets of a trading company. The results of this study are likely to be useful to management and academics. Full article

Histone acts as the core for nucleosomes and is a key protein component of chromatin. Among different histone variants, histone H3 (HH3) variants have been reported to play vital roles in plant development. However, biological information and evolutionary relationships of HH3 genes in cotton remain to be elucidated. The current study identified 34 HH3 genes in Gossypium hirsutum. Phylogenetic analysis classified HH3 genes of 19 plant species into eight distinct clades. Sequence logos analysis among Arabidopsis, rice, and G. hirsutum amino acid residues showed higher conservation in amino acids. Using collinearity analysis, we identified 81 orthologous/paralogous gene pairs among the four genomes (A, D, At, and Dt) of cotton. Further, orthologous/paralogous and the Ka/Ks ratio demonstrated that cotton HH3 genes experienced strong purifying selection pressure with restricted functional divergence resulting from segmental and whole genome duplication. Expression pattern analysis indicated that GhHH3 genes were preferentially expressed in cotton ovule tissues. Additionally, GhHH3 gene expression can be regulated by abiotic stresses (cold, heat, sodium chloride (NaCl), and polyethylene glycol (PEG)) and phytohormonal (brassinolide (BL), gibberellic acid (GA), indole-3-acetic acid (IAA), salicylic acid (SA), and methyl jasmonate (MeJA)) treatments, suggesting that GhHH3 genes might play roles in abiotic and hormone stress resistance. Taken together, this work provides important information to decipher complete molecular and physiological functions of HH3 genes in cotton. Full article