Reviewerly/exHarmony huggingface dataset:
Technology of application of educational quests in mathematics lessons

Currently, students are increasingly using various gadgets in their lives, therefore, in the educational process, along with traditional teaching methods, modern educational technologies are also used, among which we can single out the technology of using educational quests at school. The purpose of this article is to show how this technology is applied in mathematics lessons. Within the framework of a theoretical study, the article reveals the basic concepts and structure of a web quest. Mathematics is a rather difficult subject for choosing a topic for a quest, so special attention is paid to exactly how to choose a topic for it in the lesson. The article provides examples of self-development of a web quest in mathematics for a lesson on consolidating the studied material on the topic "Dot product of vectors" using hyperlinks and for a lesson on learning new material on the topic "Ordinary fractions", created on the basis of existing online platforms on the Internet . In conclusion, the course of the experimental study is described, which is aimed at confirming the effectiveness of the application of the technology of educational quests in mathematics lessons, which is confirmed using statistical methods for processing the results of the study.
Development of professional self-consciousness of students: diagnostic results and content of pedagogical maintenance

The article is devoted to rethinking the problem of psychological and pedagogical maintenance for the development of professional self-consciousness among students. Our experimental study identifies the optimal pedagogical conditions of professional self-consciousness among students. Within the framework of the article, the content and methods of pedagogical maintenance of students were revealed. The study of this problem allowed us to develop the following set of pedagogical conditions that ensure the effectiveness of pedagogical maintenance for the development of the foundations of professional self-consciousness: diagnostics; implementation of the author's methodology (the use of pedagogical technologies that initiate the reflective potential of the student as a subject of professional self-consciousness); development of an individual way of learning. Diagnostics of the structural components of professional self-consciousness of full-time and extramural students included the motivation of learning, professional directivity, self-esteem, personalitytraits. The results of the first diagnostics determined the main content of the forming stage of the experiment, namely, the conduct of a variable course. The author's methodology is aimed at the development of personally oriented educational process in the system of higher education. The results of diagnostics after a variable course allow us to confirm the improvement in the studied parameters for full-time students (statistically different in terms of self-esteem), as well as for extramural students (statistically different in terms of sociability).
Evaluation Model of Modern Network Teaching Quality Based on Artificial Intelligence E-Learning

Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society.
Development Of Digital Competences and Their Influence On Innovative Approaches In The Teaching Practice Of Students Of The Academy Of Fine Arts Of The University Of Sarajevo

The development of new technologies has a significant impact on many artistic fields, with work processes redefined and new digital tools can join analog ones to become a continuation of the creator's hand. The goal of this research is to determine the possibility of using digital and analog tools in art education. The research was conducted among students/future teachers, with the aim of determining the extent to which digital and analog tools are suitable for certain art design, and to examine the methodological specifics of the implementation of such classes. The research “Development of digital competences and their influence on innovative approaches in the teaching practice of students of the Academy of Fine Arts of the University of Sarajevo” foresees a series of activities, which require that students, through lectures, seminars and workshops, become familiar with new possibilities of digital and analog tools, and to implement the acquired knowledge in their teaching practice. The impact of the research is reflected in the strengthening of students’ output competencies, their preparation for independent activity in the primary educational context. Digital and analog tools, along with the application of the general laws of visual language, will have an empowering effect on students and future teachers, but also on the end users of the acquired knowledge, that is, on the students of Canton Sarajevo primary schools. The use of digital and analog tools for the purpose of creating an art solution will enable all participants of the research activities to develop a wide range of creative skills and expand their output digital competences, as well as easier design and construction of a personal art manuscript. The results of the research can be a real incentive for the modernization of the university education program, the lifelong training program for art teachers, and current and future teaching practices, which primarily want to respond to the challenges of social aspirations.
Exploring the Potential of Blockchain Data as Pseudorandom Numbers for Microtonal Algorithmic Composition. A Case Study on Blockchain Music 2.5

This study explores the utilization of blockchain data as a set of pseudorandom numbers in the context of microtonal algorithmic composition. Conventional methods of generating indiscriminate numbers often lack the desired levels of unpredictability and uniqueness necessary for an intricate musical piece. By harnessing the hash parameter of several blockchains, we propose an innovative approach for obtaining pseudorandom numbers that offer heightened randomness and diversity. Moreover, the ensuing structure ensures the preservation of all composition data on servers, facilitating the re-generation of the piece and enabling listeners to revisit its past fragments. Additionally, leveraging blockchain technology allows the audience to actively interact with the composition, fostering a more engaging and participatory musical experience. Through a comprehensive case study of a self-written, self-generating piece (Blockchain Music 2.5, 2023), we demonstrate the practical implementation of blockchain-generated numbers, showcasing their potential in exploring new post-tonal dimensions and producing unique sonic textures based on the predetermined set of rules. We also discuss the implications, challenges, and potential future directions of incorporating blockchain-generated numbers in algorithmic music composition. The research expands the landscape of digital music composition techniques and offers new avenues for leveraging blockchain technology in the domain of creative expression.
Artificial Intelligence and Automation for the Future of Startups

This chapter examines the potential benefits of integrating artificial intelligence (AI) and automation within startups. Through a literature review, the study unveils how AI and automation reshape various aspects of startup operations, from market insights and customer engagement to efficiency enhancement. Ethical considerations and the evolution of human-AI collaboration are highlighted, emphasising responsible integration. Case studies showcase AI's role in augmenting human capabilities. The study suggests several promising research directions. These include exploring ethical implications, identifying industry-specific applications, examining scalability, and ensuring regulatory compliance. It culminates in practical recommendations for startups, advocating tailored adoption of AI tools, data-driven strategies, and fostering human-AI synergy. This research endeavour underscores AI and automation's potential to drive startups' success by offering ethical, innovative, and efficient pathways to navigate the evolving technological landscape.
Identification of Foxtail Millet Solution for Malignant Colorectal Cell Growth Reduction Using Deep Learning-Based Mobile Net Model

Millets are cereal crops, commonly grown in semi-arid regions. Millets are known to be a good source of fiber, magnesium, phosphorous, zinc, serotonin, and gluten-free product that helps to heal Cancer effectively. The active components of Foxtail millet bran (FMBP) are promoting higher levels of reactive oxygen species (ROS) that help block the STAT3 pathway by reducing the growth of malignant cells in intestine. Nowadays in medical colonoscopy, histological image analysis and MRI are some commonly adapted techniques for the identification of polyps in the colon. However, the manual approaches are very time-consuming and less accurate. There is no specific model available that can measure the effectiveness of millet in healing malignant cell growth in colon. The main goal is to design a deep learning based MoblileNet-V3 model for the detection and classification of histological images. This study will help researchers and medical practitioners to compare the malignancy status before and after the consumption of millet.
Introducing Quantum Error Correction

Abstract We briefly introduce some basic ideas of information and quantum information. We start by comparing a classical memory with a quantum memory. Simple repetition codes can protect classical memories from error and we introduce the idea of a code space, physical bits, and logical bits. However, the quantum no-cloning theorem prevents a straightforward generalization to the quantum case. Nonetheless quantum error correction is indeed possible. We show the 9 qubit Shor code as an example.
Influential Factors in the Implementation of an Educational Blockchain System in the Democratic Republic of the Congo: Case of Higher Education Institutions

Blockchain has gained significant attention and use beyond its initial purpose. It is recognized as a disruptive technology in many domains, including education. Its interest in education has grown due to its ability to prevent fraud in academic documents. This research aimed to identify influential factors in implementing a Blockchain system in DR Congo higher education using the combination of Technology Acceptance Model &amp; Technology-Organization-Environment. Data was collected through an online survey of 355 respondents from public and private institutions of the country. The findings provide insight into the factors influencing Blockchain adoption and highlight its importance in addressing the issue.
Adaptive learning point cloud and image diversity feature fusion network for 3D object detection

Abstract 3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity. However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely on consistent information from both sensors while ignoring their complementary information. In this paper, we present a novel two-stage multi-sensor deep neural network, called the adaptive learning point cloud and image diversity feature fusion network (APIDFF-Net), for 3D object detection. Our approach employs the fine-grained image information to complement the point cloud information by combining low-level image features with high-level point cloud features. Specifically, we design a shallow image feature extraction module to learn fine-grained information from images, instead of relying on deep layer features with coarse-grained information. Furthermore, we design a diversity feature fusion (DFF) module that transforms low-level image features into point-wise image features and explores their complementary features through an attention mechanism, ensuring an effective combination of fine-grained image features and point cloud features. Experiments on the KITTI benchmark show that the proposed method outperforms state-of-the-art methods.
Large-capacity information hiding method based on a chunking matrix

Information hiding is a crucial technology in the field of information security. Embedding capacity and stego-image quality are two key performance metrics in information hiding. In recent years, many information-hiding methods have been proposed to enhance embedding capacity and stego-image quality. However, through the study of these methods, we found that there is still room for improvement in terms of performance. This paper proposes a high-capacity information-hiding method based on a chunking matrix (CM). CM divides a 256×256 matrix into blocks, where each block contains k×k corresponding secret numbers. A pair of pixels is extracted from the original image and used as the coordinates for the matrix. In the search domain at that coordinate position, the corresponding secret number is found, and the matrix coordinates of the secret information are used as the pixel value for the stego-image. This paper evaluates the security and effectiveness of CM through measures such as embedding capacity, peak signal-to-noise ratio (PSNR), and bit-plane analysis. CM achieves a maximum embedding capacity of 4.806 bits per pixel (bpp) and maintains a PSNR value of more than 30 dB. Furthermore, the bit-plane analysis fails to detect the presence of the information hidden using CM method.
Oblivious network intrusion detection systems

A main function of network intrusion detection systems (NIDSs) is to monitor network traffic and match it against rules. Oblivious NIDSs (O-NIDS) perform the same tasks of NIDSs but they use encrypted rules and produce encrypted results without being able to decrypt the rules or the results. Current implementations of O-NIDS suffer from slow searching speeds and/or lack of generality. In this paper, we present a generic approach to implement a privacy-preserving O-NIDS based on hybrid binary gates. We also present two resource-flexible algorithm bundles built upon the hybrid binary gates to perform the NIDS's essential tasks of direct matching and range matching as a proof of concept. Our approach utilizes a Homomorphic Encryption (HE) layer in an abstract fashion, which makes it implementable by many HE schemes compared to the state-of-the-art where the underlying HE scheme is a core part of the approach. This feature allowed the use of already-existing HE libraries that utilize parallelization techniques in GPUs for faster performance. We achieved a rule encryption time as low as 0.012% of the state of the art with only 0.047% of its encrypted rule size. Also, we achieved a rule-matching speed that is almost 20,000 times faster than the state of the art.
Predicting Maps Using In-Vehicle Cameras for Data-Driven Intelligent Transport

Bird’s eye view (BEV) semantic maps have evolved into a crucial element of urban intelligent traffic management and monitoring, offering invaluable visual and significant data representations for informed intelligent city decision making. Nevertheless, current methodologies continue underutilizing the temporal information embedded within dynamic frames throughout the BEV feature transformation process. This limitation results in decreased accuracy when mapping high-speed moving objects, particularly in capturing their shape and dynamic trajectory. A framework is proposed for cross-view semantic segmentation to address this challenge, leveraging simulated environments as a starting point before applying it to real-life urban imaginative transportation scenarios. The view converter module is thoughtfully designed to collate information from multiple initial view observations captured from various angles and modes. This module outputs a top-down view semantic graph characterized by its object space layout to preserve beneficial temporal information in BEV transformation. The NuScenes dataset is used to evaluate model effectiveness. A novel application is also devised that harnesses transformer networks to map images and video sequences into top-down or comprehensive bird’s-eye views. By combining physics-based and constraint-based formulations and conducting ablation studies, the approach has been substantiated, highlighting the significance of context above and below a given point in generating these maps. This innovative method has been thoroughly validated on the NuScenes dataset. Notably, it has yielded state-of-the-art instantaneous mapping results, with particular benefits observed for smaller dynamic category displays. The experimental findings include comparing axial attention with the state-of-the-art (SOTA) model, demonstrating the performance enhancement associated with temporal awareness.
Semi-White-Box Strategy: Enhancing Data Efficiency and Interpretability of Convolutional Neural Networks in Image Processing

Data-hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data-hunger in machine-learning-based image-processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data-hunger. Based on these findings, this paper introduces a novel semi-white-box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all-in-one model, a semi-white-box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well-established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi-white-box strategy over the traditional all-in-one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi-white-box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi-white-box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all-in-one approach. In conclusion, this paper contributes to mitigating data-hunger challenges in machine-learning-based image processing through the introduction of a novel semi-white-box model construction strategy, backed by empirical evidence of its effectiveness.
Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.
Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.