Search Results (43)

Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from the textual form of an equation into the textual representation of its analytical solution for a broad class of equations. More precisely, we introduced a benchmark and fine-tuning protocol for differential equation solving with pre-trained LLMs. We curated a dataset of 300,000 differential equations and corresponding solutions to fine-tune T5-small, Phi-4-mini, DeepSeek-R1-Distill-Qwen, and two Mamba variants (130M and 2.8B parameters). Performance was evaluated using BLEU and TeXBLEU metrics. Phi-4-mini achieved the best results, with average BLEU > 0.9 and TeXBLEU > 0.78 across all considered equation classes, which shows the strong generalization abilities of the model. Therefore, this model should be further investigated on a broader class of differential equations and potentially can be used as a part of mathematical agents for solving more complex particular tasks, for example, from physics or engineering. Based on our results, DeepSeek-R1-Distill-Qwen consistently underperformed, while T5 showed strong results for the most frequent equation type but degraded on less common ones. Mamba models achieved the highest TeXBLEU scores despite relatively low BLEU, attributable to their production of lengthy outputs mixing correct expressions with irrelevant ones. Full article

Driven by industry–education integration and digital technology, higher vocational education textbooks are transitioning from traditional formats to an integrated “loose-leaf + digital” model. Combining the flexibility of loose-leaf textbooks with digital technology, these new materials enable real-time updates and align closely with industry practices. We explored the era connotations of integrated textbooks and proposed a development process based on cognitive psychology, interdisciplinary integration, and synergy theory. Continuous optimization through robust evaluation systems and digital platforms is required to provide modernized and informatized vocational education. Full article

Steganography serves a crucial role in secure communications by concealing information within non-suspicious media, yet traditional methods often lack resilience and efficiency. Distributed steganography, which involves fragmenting messages across multiple containers using secret sharing schemes, offers improved security but increases complexity. This paper introduces a novel two-phase embedding algorithm that mitigates these issues, enhancing both security and practicality. Initially, the secret message is divided into shares using Shamir’s Secret Sharing and embedded into distinct media containers via pseudo-random LSB paths determined by a unique internal stego key. Subsequently, this internal key is further divided and embedded using a shared stego key known only to the sender and receiver, adding an additional security layer. The algorithm effectively reduces key management complexity while enhancing resilience against sophisticated steganalytic attacks. Evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), demonstrate that stego images maintain high quality even when embedding up to 0.95 bits per pixel (bpp). Additionally, robustness tests with StegoExpose and Aletheia confirm the algorithm’s stealthiness, as no detections are made by these advanced steganalysis tools. This research offers a secure and efficient advancement in distributed steganography, facilitating resilient information concealment in sophisticated communication environments. Full article

This paper describes a new steganographic method for QR codes. Unlike most information-hiding techniques in this field, it does not rely on the error correction property. Instead, it uses the segmentation feature of QR codes. Encoding of data in a QR code is achieved by creating segments of specific modes, chosen according to data type in order to save space. However, the segmentation does not have to be optimal. A secret message may be embedded into a QR code by selecting an alternative segment type. The presented method generates valid QR codes that may be decoded with standard readers. The solution has been tested using several QR decoders, and it has been confirmed that only the regular message was returned. Additionally, the error correction quality of produced codes is not diminished. The described algorithm is suitable for either digital or printed media, and in both cases, QR codes retain secret data. Full article

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education. Full article

Threshold schemes are used among cryptographic techniques for splitting visual data. Such methods allow the generation of a number of secret shares, a certain number of which need to be assembled in order to reconstruct the original image. Traditional techniques for partitioning secret information generate equal shares, i.e., each share has the same value when reconstructing the original secret. However, it turns out that it is possible to develop and use partitioning protocols that allow the generation of privileged shares, i.e., those that allow the reconstruction of secret data in even smaller numbers. This paper will therefore describe new information sharing protocols that create privileged shares, which will also use visual authorization codes based on subject knowledge to select privileged shares for secret restoration. For the protocols described, examples of their functioning will be presented, and their complexity and potential for use in practical applications will be determined. Full article

This paper presents research on determining individual perceptual thresholds in cognitive analyses and the understanding of visual patterns. Such techniques are based on the processes of cognitive resonance and can be applied to the division and reconstruction of images using threshold algorithms. The research presented here considers the most important parameters that affect the determination of visual perception thresholds. These parameters are the thematic knowledge and personal expectations that arise at the time of image observation and recognition. The determination of perceptual thresholds has been carried out using visual pattern splitting techniques through threshold methods. The reconstruction of the divided patterns was carried out by combining successive components that, as information was gathered, allowed more and more details to become apparent in the image until the observer could recognize it correctly. The study being carried out in this way made it possible to determine individual perceptual thresholds for dozens of test subjects. The results of the study also showed strong correlations between the determined perceptual thresholds and the participants’ accumulated thematic knowledge, expectations and experiences from a previous recognition of similar image patterns. Full article

Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used for image analysis and classification, including semantic analysis of medical images, aimed at supporting diagnostic processes and determining the severity of lesions visualized by diagnostic imaging methods. This paper will describe various types of cognitive information systems designed for lesion recognition in selected abdominal and coronary structures, as well as skeletal parts of the human body, made visible by the application of various modalities in medical diagnostic imaging procedures. In this paper, a new generation of cognitive systems will also be described, and when compared to existing systems, will have the ability to perform extended cognitive resonance processes. Inference based on extended resonance inference allows the system to acquire additional knowledge, as well as expand the knowledge base used for semantic analysis. This paper will also propose the implementation of new efficient formal grammars, which increase the efficiency of lesion recognition in selected medical images to over 90%. Full article

Background: Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value. Objective: Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users. Methods: This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience. Results: More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals. Conclusions: VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience. Full article

In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very poplar file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier. Full article

Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by hand is both time consuming and error prone. Deep learning (DL) techniques can offer more effective methods for detecting fractures, and with the increasing availability of ubiquitous cloud resources, implementing such systems worldwide is becoming more feasible. This study aims to evaluate the effectiveness of ViT for detecting cervical spine fractures. Data gathered during the research indicates that ViT models are suitable for large-scale automatic detection system implementation. The model achieved 98% accuracy and was easy to train while also being easily explainable. Full article

This paper defines new classes of algorithms for securing and sharing visual information. Algorithms offering data protection against unauthorised access are cryptographic protocols for data sharing and splitting. These protocols ensure the division of information among a trusted group of secret holders, with every protocol participant being allocated a specified number of shares in the executed algorithm. Proposing and defining new solutions in the field of cryptographic algorithms for data sharing constitutes the main topic of this paper. This paper discusses a new class of algorithms for secret sharing with the use of linguistic formalisms dedicated to the processes of meaning interpretation and linguistic data sharing. Linguistic threshold schemes serve the processes of data protection in distributed systems; they are also used to distribute the shared secret parts in an optimum way, and to perform the meaning analysis and interpretation of various data sets. Semantic analysis as an element of the impact assessment of the meaning of the interpreted and analysed data will make it possible to take into consideration a much wider aspect of description and interpretation of the analysed phenomenon or data set; it will also enable the assessment of the core of the characterised sets in respect to other information with related meaning. The proposed protocols enhance the security of shared data, and allow the generation of any number of secret shares, which is greater than traditional secret sharing methods. Full article

The IoT is a specific type of network with its own communication challenges. There are a multitude of low-power devices monitoring the environment. Thus, the need for authentication may be addressed by many available sensors but should be performed on the fly and use the personal characteristics of the device’s owner. Thus, a review and a study of the available authentication methods were performed for use in such a context, and as a result, a preliminary algorithm was proposed as a solution. The algorithm utilizes a variety of independent factors, including the user’s personal characteristics, knowledge, the context in which the authentication is taking place, and the use of steganography, to authenticate users in the dispersed environment. This algorithm encodes all of these factors into a single data vector, which is then used to verify the user’s identity or as a digital signature. Using this personalized context-aware protocol, it is possible to increase the reliability of authentication, given the emphasis on usability in low-computing-power but highly sensor-infused environments and devices. Although more testing is needed to optimize it as an industry solution, personalized protocols seem to have a future in the IoT world. Full article

This paper describes a multi-secret steganographic system for the Internet-of-Things. It uses two user-friendly sensors for data input: thumb joystick and touch sensor. These devices are not only easy to use, but also allow hidden data entry. The system conceals multiple messages into the same container, but with different algorithms. The embedding is realized with two methods of video steganography that work on mp4 files, namely, videostego and metastego. These methods were chosen because of their low complexity so that they may operate smoothly in environments with limited resources. It is possible to replace the suggested sensors with others that offer similar functionality. Full article

In recent years, the number of personal accounts assigned to one business user has been constantly growing. There could be as many as 191 individual login credentials used by an average employee, according to a 2017 study. The most recurrent problems associated with this situation faced by users are the strength of passwords and ability to recall them. Researchers have proven that “users are aware of what constitutes a secure password but may forgo these security measures in terms of more convenient passwords, largely depending on account type”. Reusing the same password across multiple platforms or creating one with dictionary words has also been proved to be a common practice amongst many. In this paper, a novel password-reminder scheme will be presented. The goal was that the user creates a CAPTCHA-like image with a hidden meaning, that only he or she can decode. The image must be in some way related to that individual’s memory or her/his unique knowledge or experience. With this image, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a number. If the image is selected properly and strong association with a person’s visual memory has been linked to it, the chances of recalling a lengthy password he/she created should not present a problem. Full article