Reviewerly/exHarmony huggingface dataset:
Deceptive XAI: Typology, Creation and Detection

Abstract Providing rationales for decisions can enhance transparency and cultivate trust. Nevertheless, in light of economic incentives and other factors that may encourage manipulation, the reliability of such explanations comes into question. This manuscript builds upon a previous conference paper $$^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> by introducing a conceptual framework for deceptive explanations and constructing a typology grounded in interdisciplinary literature. The focus of our work is on how AI models can generate and detect deceptive explanations. In our empirical evaluation, we focus on text classification and introduce modifications to the explanations generated by GradCAM, a well-established method for explaining neural networks. Through a user study comprising 200 participants, we demonstrate that these deceptive explanations have the potential to mislead individuals. However, we also demonstrate that machine learning (ML) techniques can discern even subtle deceptive tactics with an accuracy exceeding 80%, given sufficient domain expertise. Furthermore, even in the absence of domain knowledge, unsupervised learning can be employed to identify inconsistencies in the explanations, provided that fundamental information about the underlying predictive model is accessible.
Students' self-determination as a psychological resource for self-realization in the process of studying at a university

The article presents a study of the possibilities of developing students' self-determination as a psychological resource for self-realization in the process of studying at a university. The object of the study was the phenomenon of self-determination. The subject of the study is the development of students' self-determination as a psychological resource for self-realization in the process of studying at a university. Using a theoretical analysis of approaches to the study of students’ self-determination in the learning process, the following components were identified: autonomy, self-expression, ability to self-government, meaningfulness of life, which formed the basis for the implementation of the psychological and pedagogical program as its components. The goal of the psychological and pedagogical program was to develop the components of self-determination as a psychological and pedagogical resource for the self-realization of students’ personality. The program was implemented with small groups of students of 15 people at meetings of the psychological club. The following methods were used as diagnostic research methods: “Self-determination test” by E.N. Aspen, modification of K. Sheldon’s self-determination scale; “Test of meaning in life orientations” D.A. Leontyev; test “Ability to self-government” N.M. Peysakhova. The calculation of Student's T-test for connected and disconnected samples was also applied. As a result of testing the program for the development of self-determination on the basis of the Center for Social Adaptation and Support of Psychological Health at the Federal State Budgetary Educational Institution of Higher Education "Amur State University named after Sholom Aleichem", statistically significant indicators of its effectiveness were identified, consisting in an increase in indicators of autonomy, self-expression, ability to self-government and meaningfulness of life in students participating in its implementation.
Bibliometric mapping of theme and trends of augmented reality in the field of education

Abstract Background An innovative and interactive real‐world environment can be presented with augmented reality (AR) that comprises digital visual elements, audio, or other sensory information delivered via technology to enhance one's experience. AR has numerous potential applications in various everyday fields. The education sector is one such arena where AR has been implemented prominently globally. AR in education can impact active students' learning to memorize effectively and can persuade them to engage in a meaningful process of learning. Objectives This article presents a bibliometric analysis of some recent applications of AR, emphasizing the potential effects, challenges, and trends of AR in the education sector for the past 15 years. Methods A total of 3909 documents published during 2006–2020 were retrieved from the Scopus database for analysis. The bibliometric analysis included document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of the most cited documents. Additionally, it provides a brief account of the challenges for AR adaptation in education. Results and conclusions The findings uncover an increased use of AR to promote enhanced learning in the education field. This work will provide the survey, limitations, and drawbacks, as well as open usability and technical issues that may represent new challenges for future research in this field.
Investigation of ant colony optimization with Levy flight technique for a class of stochastic combinatorial optimization problem

The demand for efficient solutions to optimization problems with uncertain and stochastic data is increasing. Probabilistic traveling salesman problem (PTSP) is a class of Stochastic Combinatorial Optimization Problems (SCOPs) involving partially unknown information about problem data with a known probability distribution. It consists to minimize the expected length of the tour where each customer requires a visit only with a given probability, at which customers who do not need a tour are just ignored without further optimization. Since the PTSP is NP-hard, the usage of metaheuristic methods is necessary to solve the problem. In this paper, we present the Ant Colony Optimization (ACO) algorithm combined with the Levy Flight mechanism (LFACO), which is based on Levy distribution to balance searching space and speed global optimization. Experimental results on a large number of instances show that the proposed Levy ACO algorithm on the probabilistic traveling salesman problem allows to obtain better results compared with the classical ACO algorithm.
PENGGUNAAN METODE PORT KNOCKING, PORT BLOKING, DAN ARP UNTUK MELAKSANAKAN KEAMANAN JARINGAN ROUTER MIKROTIK OS

Technological developments to date continue to grow. These developments have an impact on the security of the system in it. So that the Organization becomes more vulnerable to threats or network attacks or information security caused by various sources, both from internal personnel activities or hacker attacks. Therefore, this research will carry out security on the Mikrotik router by using the port knocking, port blocking and arp methods which are a special program made for the security of the Mikrotik Router OS network, in this study the aim is to make security on the Mikrotik router in a network by making 3 rules Port knocking, Port blocking, and ARP and making port knocking steps so that only clients who know the authentication code can access the port service. Based on this research, it can be concluded that the port knocking, port blocking and arp methods have succeeded in creating a network security where port services can only be accessed by specified users by carrying out several steps that have been made.
The Influence of Time Pressure and Persuasion Principles on Phishing Detection

Phishing is a pervasive threat with annually growing costs. Research has explored how users may be susceptible due to individual differences, email content, and situational factors. However, the influences of persuasive strategies and time pressure on phishing susceptibility remain unclear. The present study explored how the use of Cialdini’s six persuasion principles and time pressure (high vs low vs control) affect users’ abilities to classify 60 emails (50% phishing). Results indicated that time pressure influenced the pace at which users completed the task but was otherwise uninfluential. Rather, participants were generally insensitive to emails in all three conditions, indicating a potential floor effect. Additionally, persuasion principles influenced classifications. Users struggled to classify emails using the authority and commitment/consistency principles the most, and emails using the social proof, liking, and reciprocity principles the least. Overall, our findings suggest that users struggle to identify persuasive phishing attacks, regardless of time pressure.
Chaos and DNA coding technique for image cryptography

Abstract In today's cybersphere, cryptography plays a vital role in various fields. Image encryption is an integral part for securing information because of its vast application areas such as military (defense), multimedia, healthcare and so forth. In this article, an image encryption algorithm for both grayscale and color image is proposed based on Tangential Delay‐Ellipse Reflecting Curve System (TD‐ERCS) chaotic map system and deoxyribonucleic acid (DNA) coding. Chaotic map is used to scramble the pixel positions; to achieve confusion and for creation of mask image, and DNA coding is used for changing the pixel values; to achieve diffusion. Upon experimental analysis, proposed work achieved significantly high mean square error and low peak signal to noise ratio, almost zero correlation, high number of pixel change rate and unified averaged changed intensity values, and resistance to noise and data loss attacks. In addition, the decryption is possible without loss in quality of image.
Experience in Implementing Personal Support for Teachers Using in House Software

The activities of a university teacher are extremely rich and varied, so there are no general-purpose software products or IT (Information Technologies) solutions that specifically focus on the person of the teacher. The typical teacher lectures, makes assessments, publishes, collaborates on research, performs knowledge management, creates educational content, and performs a plethora of other related activities, including self-study in his area of expertise. All these activities of a teacher have one shared element: working with educational content or knowledge. The authors address the digital transformation of education as an interdisciplinary problem with a focus on the synchronization of pedagogical and IT activities. The basis of the research approach is that educational processes are based on educational knowledge, so they can be automated (technically, knowledge is a parameter of educational processes). As part of their long-term research, the authors have developed the WPad educational software, which uses so-called virtual knowledge. This virtual knowledge takes the form of a simple database table, and an individual (teacher, researcher, student) can fill this table with educational content in natural language. This software was initially applied to teaching in the classroom and creating e-learning content for undergraduates. It has gradually spread to academic research (e.g., computer-assisted collaborative learning, knowledge management, learning analytics). This article describes the multipurpose use of WPad as an all-in-one teaching tool on a combined offline and online IT infrastructure using examples from teaching and research. Further development will be focused on the mass creation of educational packages and multilingual support within the framework of international cooperation (a new educational porta is planed).
Parsing millions of URLs per second

Abstract URLs are fundamental elements of web applications. By applying vector algorithms, we built a fast standard‐compliant C++ implementation. Our parser uses three times fewer instructions than competing parsers following the WHATWG standard (e.g., Servo's rust‐url) and up to eight times fewer instructions than the popular curl parser. The Node.js environment adopted our C++ library. In our tests on realistic data, a recent Node.js version (20.0) with our parser is four to five times faster than the last version with the legacy URL parser.
Metaverse: Trend, emerging themes, and future directions

Abstract Metaverse is going to change human life in a profound way because it offers an opportunity to merge our physical world with the digital/virtual worlds. Yet, how much effort has the research community across the world and disciplines contributed and what are the emerging themes of metaverse? The study aims to answer these important research questions using a bibliometric approach. Using a search of (“metaverse” OR “metaverses”) in “Article Title, Abstract, Keywords” and the date range up to 2022 on March 15, 2023 in Scopus, 1031 journal articles, reviews, and conference papers were identified. Among the 1031 identified documents, 816 (i.e., around 80%) were published in 2022. Feiyue Wang of the Institute of Automation, Chinese Academy of Sciences was found to be the most productive author with 17 metaverse publications, followed by Elif Ayiter of Sabancı Üniversitesi with 11 metaverse publications. The Chinese Academy of Sciences (with its Institute of Automation) was found to be the most productive affiliation with 29 publications. China, the United States, South Korea, and the United Kingdom were the most productive countries that contributed to over 58% of metaverse publications. Co‐occurrence of keywords analysis revealed that seven clusters were emerged. The clusters included “artificial intelligence and human perception,” “metaverses and blockchain,” “e‐learning and students,” “metaverse, avatar and immersive,” “virtual reality, virtual worlds, and Second Life,” “three dimensional computer graphics and deep learning,” and “augmented reality, mixed reality, and human computer interaction.” Implications and future directions are given.
Deep network with double reuses and convolutional shortcuts

Abstract The authors design a novel convolutional network architecture, that is, deep network with double reuses and convolutional shortcuts, in which new compressed reuse units are presented. Compressed reuse units combine the reused features from the first 3 × 3 convolutional layer and the features from the last 3 × 3 convolutional layer to produce new feature maps in the current compressed reuse unit, simultaneously reuse the feature maps from all previous compressed reuse units to generate a shortcut by an 1 × 1 convolution, and then concatenate these new maps and this shortcut as the input to next compressed reuse unit. Deep network with double reuses and convolutional shortcuts uses the feature reuse concatenation from all compressed reuse units as the final features for classification. In deep network with double reuses and convolutional shortcuts, the inner‐ and outer‐unit feature reuses and the convolutional shortcut compressed from the previous outer‐unit feature reuses can alleviate the vanishing‐gradient problem by strengthening the forward feature propagation inside and outside the units, improve the effectiveness of features and reduce calculation cost. Experimental results on CIFAR‐10, CIFAR‐100, ImageNet ILSVRC 2012, Pascal VOC2007 and MS COCO benchmark databases demonstrate the effectiveness of authors’ architecture for object recognition and detection, as compared with the state‐of‐the‐art.
Blockchain Fog-based scheme for identity authentication in smart building

This paper presents a proposal for an authentication scheme for smart building systems and environments based on blockchain, its positive features, and fog computing. The most important feature that can be distinguished in the submitted proposal is its adoption of the principle of decentralization in contrast to traditional centralized documentation protocols, i.e. the proposed authentication system in which users and smart devices are implemented in a distributed and decentralized manner on the blockchain, that will provide a solution to a significant problem of low overall efficiency of the authentication process caused by a bottleneck in such important areas as computing capacity as well as centralized storage of a single authentication authority in the traditional model. There are also benefits from adding fog computing, and because it has higher computing and storage capabilities, it makes the data processing process more efficient, faster, more streamlined, and in line with the common necessities of the real-time IoT environment. The proposed scheme also provides solutions to protect the privacy of user data and increase the level of confidentiality, protection, and security, since a mysterious extractor was used to increase the confidentiality of the proposed model of the authentication system. Comparing a set of security schemes and conducting a security and performance analysis of the proposed scheme, the comparisons showed that the scheme can be characterized as having a good security level and an important efficiency level. The paper focused on specific aspects design of the authentication system, such as the registration and authentication process of all network entities, regardless of the specifics of the implementation of blockchain smart contracts.
Machine Learning for Visualization and Prediction of Spatiotemporal Spread of COVID-19 in India

The application of GIS for disaster management and emergency response has provided the ease of producing meaningful information products that can otherwise be time and resource-intensive. Modeling, simulation, and visualization of GIS data provides disaster management decision-makers with the ease of using embedded information in effective knowledge generation and decision support processes, based on modeling geospatial data. Data science has emerged as a very promising interdisciplinary science that integrates various components that are related to data through a cycle that starts with problem definition, data acquisition, data processing, data exploration, and model selection and ends with product deployment. This data science life cycle is essentially the GIS modeling life cycle, which makes the process of using machine learning in GIS modeling a step forward. Artificial intelligence techniques, such as machine learning where the user applies a specific algorithm, allow the machine to learn from examples and experience to perform such complicated tasks. This makes the process of data exploration and product delivery worthwhile. This paper will provide a general overview of how supervised learning, unsupervised learning, as well as reinforced learning can be effective for disaster and emergency management applications, highlighting the autoregression integrated moving average (ARIMA) model for the prediction.
Robust Face Verification Using Deep Learning in Uncontrolled Environment

Face recognition in an unconstrained environment has progressively become prevalent in various applications, such as intelligent visual surveillance and identity verification. Recently, convolutional neural networks (CNNs) have been widely used in the field of face recognition. One of the basic elements for the success of deep learning methods is the accessibility of a huge amount of data. In fact, CNNs were able to learn robust face embedding from training data on several subjects. In this paper, we propose a CNN framework to learn faces in an uncontrolled environment based on data augmentation. We use different data augmentation methods such as flipping, noisy, and occluded faces, which use significant variations in facial appearance. The experimental results on the LFW (Labeled Faces in the Wild) dataset demonstrate the effectiveness of the suggested approach in wild face verification tasks with an accuracy of 99.3%. Thus, the accuracy increases by 4.8%, compared to the model without data augmentation. In fact, compared to existing face recognition systems, the proposed model attains important results in face recognition tasks.
Now Your Manuscript Is Accepted…What’s Next?

There is a need for researchers and authors to actively disseminate their scholarly work for it to have a meaningful impact. This chapter provides guidelines and steps for dissemination, specifically using web-based technologies. Strategies include signing contractual agreements, knowing your audiences, providing research summaries, identifying channels for dissemination, sharing your data, publicizing your research, and tracking your research paper. The sections of this chapter contain tips for increasing the reach of one’s work as well as suggestions for web-based technologies to use for successful dissemination.
DIGITAL TECHNOLOGIES IN IMPROVING THE EFFICIENCY OF TRAINING YOUNG PLAYERS ON THE EXAMPLE OF IMPLEMENTATION ELECTRONIC WORKBOOK (ERT)

The theoretical and methodological foundations of the design and introduction of an electronic workbook in the educational and training process of young players, as one of the educational and disabilities of training, are considered. The value of the use of an electronic workbook in the educational and training process of young football players is shown. The structural components are identified and examples of tasks from the electronic workbook in the discipline “Football” are presented. The results of the questionnaire survey of young players are 8–9 years old, aimed at identifying interest in the performance of the proposed tasks of an electronic workbook. A comparative analysis of the showers characterizing the level of achievement of educational results in the implementation of the tasks of the “Football” discipline at the end of the experiment (in points on a 12‑point score of assessment) is given.The ability to include in the content of the Electronic Working Notebook of the Young Football player we have developed in the contents of the modern presentation of information, interactive training programs, new means of monitoring knowledge for verification and self -testing contributes to an increase in educational results to the “optimal level” and performing all the proposed tasks by 8 % of young players experimental group.
Analysis on Implementation Paths of Digital Empowerment of the Teaching Links

At present, the development of digital technology continues to promote the transformation of education and teaching. The digital empowerment of teaching models prompts teachers to comprehensively transform into student-centered teaching concepts. All aspects of teaching are designed around students. The digital construction of teaching resources provides a basis for personalized teaching for students. Through digital empowerment of various teaching links such as teacher teaching process organization, student learning methods, and academic evaluation methods, we can truly realize personalized training of students and teach students in accordance with their aptitude.
The failure of cut-elimination in cyclic proof for first-order logic with inductive definitions

Abstract A cyclic proof system is a proof system whose proof figure is a tree with cycles. The cut-elimination in a proof system is fundamental. It is conjectured that the cut-elimination in the cyclic proof system for first-order logic with inductive definitions does not hold. This paper shows that the conjecture is correct by giving a sequent not provable without the cut rule but provable in the cyclic proof system.