Reviewerly/exHarmony huggingface dataset:
The role of the teacher-student relationship in the innovation of education: Interdisciplinary review of the issue

Many contemporary authors addressed digital and online education, competencies, and lifelong learning. However, less is said about the importance of interpersonal relationships, even though they are essential for preserving the sustainability of education. The research aims to examine the field of human relations. To achieve this goal, the theory of organisational and social psychology was used. Relations can only be implemented through interpersonal dialogue. From an environmental perspective, both subjective and objective factors were determined to define the strength of the relationship. It was concluded that the teacher’s personality is the key to success at all levels of education. It is justified that the result of curricula’s central regulation is that the education of tomorrow’s professionals is directed by today’s professionals, who often have an interest in providing what is needed now rather than what may be needed in the future. It is proved that it is not enough for students to be aware of the academic mission, nor for the university to be a kind of training site for the labour market. From a socio-psychological perspective, it was disclosed that teaching quality mattered most, but subject to the theory of organisational psychology. The introduced materials can be utilized by universities to work on the nature of graduates’ preparation through the advancement of their character. Considering various approaches to the organization of education participants’ activities and characteristics of their self-development, the model for the formation of the value system of future teachers will make it possible to provide conditions for the individualization of training paths.
Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs

Recovering original images from blurred images is a challenging task. We propose a new deconvolution method termed incoherent nonlinear deconvolution using an iterative algorithm (INDIA). Two inputs are introduced into the algorithm: one is a random or engineered point spread function of the scattering system, and the other is a blurred or distorted image of some object produced from this system. The two functions are Fourier transformed, and their phase distributions are processed independently of their magnitude. The algorithm yields the image of the original object with reduced blurring effects. The results of the new method are compared to two linear and two nonlinear algorithms under various types of blurs. The root mean square error and structural similarity between the original and recovered images are chosen as the comparison criteria between the five different algorithms. The simulation and experimental results confirm the superior performance of INDIA compared to the other tested deblurring methods.
McFly: Verifiable Encryption to the Future Made Practical

Blockchain protocols have revolutionized how individuals and devices interact and transact over the internet. More recently, a trend has emerged to harness blockchain technology as a catalyst to enable advanced security features in distributed applications, in particular fairness. However, the tools employed to achieve these security features are either resource wasteful (e.g., time-lock primitives) or only efficient in theory (e.g., witness encryption). We present McFly, a protocol that allows one to efficiently “encrypt a message to the future” such that the receiver can efficiently decrypt the message at the right time. At the heart of the McFly protocol lies a novel primitive that we call signature-based witness encryption (SWE). In a nutshell, SWE allows to encrypt a plaintext with respect to a tag and a set of signature verification keys. Once a threshold multi-signature of this tag under a sufficient number of these verification keys is released, this signature can be used to efficiently decrypt an SWE ciphertext for this tag. We design and implement a practically efficient SWE scheme in the asymmetric bilinear setting. The McFly protocol, which is obtained by combining our SWE scheme with a BFT blockchain (or a blockchain finality layer) enjoys a number of advantages over alternative approaches: There is a very small computational overhead for all involved parties, the users of McFly do not need to actively maintain the blockchain, are neither required to communicate with the committees, nor are they required to post on the blockchain. To demonstrate the practicality of the McFly protocol, we implemented our SWE scheme and evaluated it on a standard laptop with Intel i7 @2,3 GHz.
Recognition of Radar Compound Jamming Based on Convolutional Neural Network

The modern electromagnetic environment is becoming more and more complicated, and during detection, radar may face not only single jamming but also compound jamming signals that belong to different varieties, which is more challenging to recognize. Traditional methods are difficult to extract effective features from a variety of jamming signals and their compound signals. Here, a fractional Fourier transform (FRFT)-based multifeature fusion network has been proposed, which combines the multibranch fractional features of the jamming signals and improves the recognition performance. By combining the local and global features of the fractional domain of the jamming signals and adding the attention mechanism, the attention ability of the network to the notable features of images can be further improved. Meanwhile, to make use of the correlation and complementarity between multiple types of information, the time-frequency images of jamming signals are fused based on this network model to realize a more effective and comprehensive expression of features. Simulation results show that, compared with the existing four classical network models, this algorithm has better recognition performance and generalization ability. When the jamming-to-noise ratio is −3 dB, the recognition accuracy of this algorithm can reach more than 99%.
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user’s image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of Dream-Booth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at https://github.com/VinAIResearch/Anti-DreamBooth.git.
A study on awareness of college students about cryptocurrency and Its relation to Level of Education

Technology made many changes to the payment system. Students are more familiar with the technology. After the bitcoin boom in India, the mostly young generation are attracted to cryptocurrency. Cryptocurrency is decentralized and it is based on blockchain technology. Bitcoin, Ethereum, Cardano, Dogecoin, Litecoin, Bitcoin Cash, and Potcoin are more popular cryptocurrencies in India. The majority of investors are the age of between 18 and 24 years old. This study mainly focuses on the awareness of college students about cryptocurrency. This study also focuses on various factors that affected awareness of cryptocurrency. For this study, we collected 120 students’ responses. Samples were selected based on the non-probability snowball sampling method. The finding of this study is, that most respondents are aware through friends and social media about the cryptocurrency. And male respondents are more aware than female respondents. The higher level of educational qualification also impacts the high-level awareness of cryptocurrency. The majority of respondents are interested to invest in cryptocurrency.
NOVEL IMPULSE DETECTION TECHNIQUE FOR IMAGE DENOISING

In this paper a simple noise (Salt and Pepper) detection technique is proposed by using 3 x 3 sub-windows in which the test pixel appears. A weight is assigned to the test pixel based on its position after sorting in each sub-window. Two thresholds are used to decide whether the test pixel is corrupted or not. If corrupted then only the standard median of the 3 x 3 vicinity is used to eliminate noise, other –wise no filtering is applied. The proposed scheme is simulated using standard images under different noisy conditions. The performance study shows the superiority of the proposed scheme over the existing standard median filters using 3 x 3 and 5 x 5 windows.
Bottom-Up Recognition Learning: A Compilation-Based Model of Limited-Lookahead Learning

When faced with a novel problem, people can sometimes decide what to do by imagining alternative sequences of actions and then taking the sequence that solves the problem. In many problems, however, various constraints, such as working memory capacity, limit the amount of internal lookahead that people can do. This paper describes Bottom-Up Recognition Learning (BURL), a model of limited-lookahead learning based on final first learning and knowledge compilation. In BURL, knowledge compilation of limited-looka head search over successive problemsolving trials transfers knowledge from the leaf nodes of a problem space to the top node. Two experiments test BURL’s predictions. The first compares the Soar implementation of BURL to human subjects learning to play two Tic-Tac-Toe isomorphs. This experiment shows that BURL can account for learning that occurs when subjects can perform a limited lookahead. The second experiment studies transfer between two strategy acquisition tasks for one isomorph. This experiment shows that BURL must be used in conjunction with other learning methods to fully explain skill acquisition on limitedlookahead tasks.
Geochemical Mapping in Europe

The Forum of European Geological Surveys (FOREGS) includes Geological Surveys from 34 European countries and is responsible for co-ordinating Geological Survey activities in Europe. The FOREGS Geochemistry Task Group was established in 1994 to supervise European geochemical mapping policy for environmental, legislative, resource-management and scientific purposes. The task group comprises representatives from five countries, charged initially with the compilation of an inventory of geochemical data within FOREGS countries. The preparation of European Geochemical Baseline maps will involve the integration of different national datasets following the recommendations of the International Geological Correlation Programme (IGCP) Project 259 “International Geochemical Mapping”. Results of the inventory show that most geochemical surveys in Europe conform to the IGCP 259 recommendations. Stream sediment (26% coverage), surface water (19% coverage), soil/till (21% coverage) and radiometric data (19% coverage) are the most extensive sample types, and the majority of surveys (81%) have been carried out at sampling densities of ≤ 1 sample per 100 km2. Most filtered-water surveys are based on a filter size of 0.45µm, and 83% of stream sediment surveys collect samples sieved at 100-200 µm. The collection of the Global Reference Network (GRN) samples recommended by IGCP 259 to provide internationally standardised geochemical data and the careful use of statistical and map-generation techniques should facilitate the levelling of different national datasets and preparation of a European Geochemical Baseline.
The Impact of Networked Information on Access Services

The increasing number of electronic serials, books, and other documents being produced, and the opportunity for wide distribution of information over the Internet has raised new issues regarding library access to information. Few libraries currently provide access to such information sources, nor do most network users think to turn to a library to find these documents. This article concentrates on the possibilities for integrating existing networked electronic information into current library structure by exploring both the variety of electronic documents currently available on the network and the present means of access, followed by a discussion of the possibilities for providing library access and implications for access services
User’s Activity Classification And Domain Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.
Real-Time Monitoring and Tracking of Intrusions in Warehouses Using Video Analytics Techniques

Automated intrusion detection in the warehouse is a more challenging task in real-time era. The algorithms available in literature are suitable for stored videos but not for real-time videos. Very few algorithms are work in real-time but they took more computation power so that is not adapted by cyber-physics hardware. In this chapter, some video analytics techniques are discussed, compared the results, and explained suitable selection of algorithm for an application. In video surveillance, all algorithms are not suite for all application. Most of the algorithms are environment specific which means algorithm design for a specific environment and it acts best in that situation. Some algorithms are work better on most of the environment in the change detection dataset 2014. The dataset consists of 12 categories of videos and each category has more videos for evaluation of the proposed algorithm. The universal algorithm for motion detection is a visual background extractor. It gives the best result in most of the videos in the change 30detection dataset 2014. And also some cyber-physical hardware with their use and how to interface with algorithms has been discussed in this chapter.
The Importance of the Enviroment in Indonesia Learning

The environment has an important role in learning Indonesian. Environmentally friendly materials can help students develop skills and interests and can foster enthusiasm for learning Indonesian. Environmental materials can also make it easier for students to understand Indonesian language material well, add insight, and can build students' linguistic intelligence. Then Indonesian language subjects can provide comprehensive knowledge and skills about protecting the environment.
FOX: Fooling with Explanations : Privacy Protection with Adversarial Reactions in Social Media

Socia1 media data has been mined over the years to predict individual sensitive attributes such as political and religious beliefs. Indeed, mining such data can improve the user experience with personalization and freemium services. Still, it can also be harmful and discriminative when used to make critical decisions, such as employment. In this work, we investigate social media privacy protection against attribute inference attacks using machine learning explainability and adversarial defense strategies. More precisely, we propose FOX (FOoling with eXplanations), an adversarial attack framework to explain and fool sensitive attribute inference models by generating effective adversarial reactions. We evaluate the performance of FOX with other SOTA baselines in a black-box setting by attacking five gender attribute classifiers trained on Facebook pictures reactions, specifically (i) comments generated by Facebook users excluding the picture owner, and (ii) textual tags (i.e., alttext) generated by Facebook. Our experiments show that FOX successfully fools (about 99.7% and 93.2% of the time) the classifiers, outperforms the SOTA baselines and gives a good transferability of adversarial features.
Diversity, Equity, and Inclusion in Optometric Education

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Ruth Shoge, LaShawn Sidbury Duckett, Carol Brubaker; Diversity, Equity, and Inclusion in Optometric Education. The Advisor 1 December 2021; 41 (4): 9. doi: https://doi.org/10.47578/0736-0436.41.4.9 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
FEATURES OF THE LESSON OF INFORMATICS IN PRIMARY SCHOOL

Nowadays, the teacher is called to solve the important task of preparing young people for life in the information society. This necessitates the formation of strong basic knowledge&nbsp;in the field of informatics, the ability to search and use the necessary information. The&nbsp;article considers the specifics and features of the lesson of computer science in primary school. The author emphasizes that the formation of information and communication&nbsp;competence is actively taking place in computer science lessons. Nowadays, the lesson&nbsp;remains an important form of organization of educational and cognitive activities of&nbsp;students (including younger students). This form has many positive aspects that allow you&nbsp;to achieve effectiveness in mastering key competencies. The author notes that the&nbsp;specifics of computer science lessons are the active use of computer technology and a&nbsp;significant amount of practical work. In addition, it is important to choose the right type of&nbsp;lesson depending on the location of the lesson within the topic, its content and objectives,&nbsp;the age of students, their own experience. Each type of lesson corresponds to a structural&nbsp;sequence, which today may vary in some way. The structural elements of the lesson havetheir tasks, logically interconnected and aimed at achieving the overall goal of the lesson.&nbsp;Features of each stage of the lesson of computer science require the use of appropriate&nbsp;methods and techniques that should be appropriate for this stage of the lesson. The mainmethodological approaches to the lesson of computer science in primary school, the author identifies: operational, technological, task and problem. The article notes that the&nbsp;lesson of computer science in primary school must meet certain requirements: structure,&nbsp;content, process, training, organization, techniques. Adherence to certain requirements&nbsp;provides a motivated mastery of educational material and mastery of skills and abilities to&nbsp;use computer technology to solve various situations of their own lives.
Content Thinking as an Image of Internet Technologies 3.0, 4.0

In the article, the authors note that at present, especially in the field of education, the already existing processes of the development of new content thinking under the influence of Internet technologies are being actualized, as well as the need to develop reflection, starting from an early age of children and in artificial intelligence. The purpose of this article is an analytical study of the changes taking place in the thinking of consumers of Internet technologies. The authors note that in the current conditions of accelerating scientific and technological progress and the development of global information networks, qualitative changes are taking place in the mental activity of a modern person. The article examines topical issues related to the development of a new type of thinking – content thinking, conditioned by web technologies. The authors examined in detail the Internet technologies Web 3.0, Web 4.0 and their influence on the psychological content of the ontological foundations of human life on the basis of the methodological principles of the consistency of determinism and development. The authors identified the main characteristics of Web 3.0 and Web 4.0 content: semantic structure, cooperativity, clustering, ample opportunities for consumer self-expression, self-developing basic personal content, self-correcting system, effective and convenient information management, accessibility, simplicity and maximum convenience, development and use additional opportunities, human resource management in the current time mode, crystallization, the presence of the maximum possible consumer protection. The authors have shown the influence of Internet technology 3.0, 4.0 on the psychological content of the ontological foundations of human life, including new content thinking. The method of such thinking is synthetic deduction and the method of developing such thinking is the method of “synergetic deduction 3.0” developed by us.
Machine Learning

Machine learning, a sub-field of artificial intelligence, is the study of computer algorithms that improve automatically through experience. Although the term was coined in 1959, machine learning builds on questions/methods that were developed earlier in linear algebra, mathematical analysis, optimization, and mathematical statistics. In this chapter we will give a brief overview of machine learning. First a few basic regression and classification techniques are introduced with examples. Then we introduce a few key concepts, such as Bayes theorem and prior and posterior distributions. Some machine-learning algorithms are based on estimating the likelihood function and on using Bayes theorem to obtain the posterior distribution, but most algorithms can be viewed as trying to model the posterior distribution directly. This leads to linear logistic regressions, the perceptron, shallow neural networks, artificial neural networks and, finally, convolutional neural networks. Machine-learning methods involve estimation of parameters, often using optimization. Determining what method to use and determining the so-called hyper-parameters require additional consideration in terms of over- and under-fitting and the need to divide data into three parts denoted as training, validation, and test. The progress within machine learning has been swift during the last decade. The chapter ends with a few examples of computational architectures for solving different types of machine-learning problems and also some dimensionality-reduction techniques.