Reviewerly/exHarmony huggingface dataset:
USE OF INNOVATIVE LEARNING TECHNOLOGIES IN HIGHER EDUCATIONAL IN THE CONDITIONS OF MIXED EDUCATION

The article is devoted to one of the urgent pedagogical problems of domestic science, which reveals the importance of introducing innovative technologies into the higher educational process in conditions of mixed learning. The article clarified difference between the concepts of “pedagogical technology”, “innovative technology”, “innovative teaching method”, “mixed, synchronous and asynchronous learning”, “contact and non-contact hours”. The publication presents the features of the use innovative technologies in the educational process as: information and communication technologies (video conferences in Zoom, Google Meet, Webex Meet; chats, forums, websites, multimedia, “Kahoot!”), cloud technologies (Skydrive, Sway, Prezi, Google services, Moodle), Mind Maps, Scribe and interactive technologies (Case studies, business and role-playing games). The article analyzes different approaches of scientists to the classification of innovative learning technologies. Attention is focused on the recommendations of the Ministry of Education and Science of Ukraine regarding the introduction of a mixed form of education organization into the educational process of a higher school in accordance with today’s requirements. The role of digitization of higher education and its impact on various forms of knowledge acquisition and formation of students’ abilities and skills, conducting scientific research and creative activities, forms of control, in particular the performance of individual tasks, control and independent works of students, testing, defense of qualification and course projects are described. The use of innovative learning technologies contributes to the formation of search, research, communicative, analytical abilities and skills in students of higher education, the realization of their abilities by each participant in the educational process, an individual approach to learning, the development of cognitive interests, the activation of students’ creative abilities, a successful combination of classroom and remote tasks work.
A Thorough Comparison of AI-Enabled Phishing Attack Detection Strategies

Phishing attacks have become increasingly common in recent years, affecting internet users, government institutions, and service providers. In a phishing attack, the attacker uses bogus websites and faked emails to steal sensitive data from the victim, such as login passwords and credit/debit card information. These websites are frequently used in online social engineering assaults, leading in a variety of fraudulent actions. This paper presents an overview of the existing literature on Artificial Intelligence (AI) strategies to get a full knowledge of phishing attempts. Furthermore, for each AI method, this study gives a comparative analysis of research identifying phishing assaults. This study also discusses the present issues in identifying and mitigating phishing attempts, as well as future research prospects in this topic
Narrative, interest, motivation: L.I.Petrazhitsky vs Ya. E.Golosovker

The modern philosophy of science pays special attention to the processes of social regulation of the researcher’s activity. In fact, today, the epistemologist is approaching the boundaries of the sociology of science. Rather often, his reasoning turns out to be consonant with the concepts of specialists in the field of social management. But when the discussion begins on how to build a particular “program”, sociologists, as a rule, will not include in their research the historical experience of knowledge. Nevertheless, both epistemologists and sociologists agree that meaningfulness forms the basis of the productivity of human activity (in any sphere). But how to achieve it? Under what social conditions does a person have the intent to think? The article actualizes the ideas of social programming aimed at creating such conditions. The author refers to the intellectual heritage of Russian philosophers and humanities scholars of the early 20th century, L.I.Petrazhitsky and Ya.E.Golosovker, to show the methodological effectiveness of their reasoning about “motivation” and “interest”, as well as the fundamental ideological consonance of their philosophical research programs. Petrazhitsky speaks of motivation from the philosophical and legal fields, while Golosovker proceeds from the logic of the imaginary. But both of them understand the importance of story (narrative) for the emergence and cultivation of motivation and interest in knowledge.
Creative Teaching Strategies Implemented in Teaching English to Young Language Learner

This research objective is to find out the creative teaching strategies implemented in teaching English to Young Language Learner. The method used in this research is qualitative research method. The participant is the teachers and young language learners or the children in Sunday School Jemaat Buntu Pasele Rantepao. The instruments used to collect the data are interview and observation. The data analyzes by Miles and Huberman theory. The creative teaching strategies implemented in teaching English to Young Language Learner at English tuition class at Sunday School Jemaat Buntu Pasele Rantepao are (1) Play games: memory games, Hangman, Simon Says and board games. (2) Use storytelling. (3) Use music and songs. (4) Used technology such as educational apps, videos, and interactive games to teach English vocabulary and grammar. (5) Make it interactive: role-plays, group discussions, and other activities that require the children to use English. The last is Sunday Meeting in the tourist object.
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors.
Reduction between Aristotelian Modal Syllogisms Based on the Syllogism ◊I�A◊I-3

In order to provide a consistent explanation for Aristotelian modal syllogistic, this paper reveals the reductions between the Aristotelian modal syllogism ◊I�A◊I-3 and the other valid modal syllogisms. Specifically, on the basis of formalizing Aristotelian modal syllogisms, this paper proves the validity of ◊I�A◊I-3 by means of the truth value definition of (modal) categorical propositions. Then in line with the symmetry of Aristotelian quantifiers some and no, the definition of inner and outer negations of Aristotelian quantifiers, and some rules in classical propositional logic, this paper deduces the other 47 valid Aristotelian modal syllogisms from the modal syllogism ◊I�A◊I-3. The reason why these syllogisms are reducible is that: (1) any of Aristotelian quantifier can be defined by the other three Aristotelian quantifiers; (2) the Aristotelian quantifiers some and no have symmetry; (3) the possible modality ◊ and necessary modality £ can be mutually defined. This formal study of Aristotelian modal syllogistic not only conforms to the needs of formalization transformation of various information in the era of artificial intelligence, but also provides a unified mathematical research paradigm for other kinds of syllogistic.
A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

Causal inference is a fundamental research topic for discovering the cause–effect relationships in many disciplines. Inferring causality means identifying asymmetric relations between two variables. In real-world systems, e.g., finance, healthcare, and industrial processes, time series data from sensors and other data sources offer an especially good basis to infer causal relationships. Therefore, many different time series causal inference algorithms have been proposed in recent years. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. As a consequence, different algorithms often generate different causal relationships for the same input. In order to achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in a first phase. To achieve this, the data are initially divided into several partitions and the base causal inference algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which have been specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results.
MAIN COMPONENTS OF SCHOOL MANAGEMENT MODEL USING INFORMATION AND COMMUNICATIONS TECHNOLOGIES

This article is an attempt to cover the main components of ICTs’-based school management model, with special reference to the respective principles, styles, strategies, competencies, as well as their implementation and application mechanisms. An attempt is made to show how the content and functional characteristics of the mentioned components change during and as a result of the use of ICTs. The use of ICT in schools has become increasingly prevalent, and effective school management requires a comprehensive approach that integrates these tools into all aspects of operations. The article identifies the key components of this model, including communication systems, information management systems, learning management systems, and administrative management systems. The authors explore the benefits and challenges of implementing such a model and provide examples of successful applications. The efficiency of the proposed model is substantiated from the point of view of ensuring the organic connections of its components.
Credit Card Fraudulent Transaction Detection and Prevention

Abstract: In recent years, online payment methods have been used widely as an outcome of the rapid increase in non-cash and digital electronic transactions. Credit cards represent one of the electronic payment methods. With the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. The credit card frauds can be detected by evaluating the credit card purchasing patterns using the historical data in order to detect the frauds. This data evaluation can help the banks or other organizations offering credit cards to minimize their losses due to the credit card frauds. The historical data evaluation with the current purchasing patterns requires statistical modeling, which can automatically evaluate the fraudulent patterns and alarm the banks about the transactions. This helps the banks for early detection of the frauds, where they can easily eliminate the credit card frauds by declining the suspected transactions. And also blockchain technology is applied to prevent the hacker to view customers details so that fraudsters can't use stolen credit card information to open new accounts, obtain loans, and engage in other illegal activities. Credit card fraud detection and prevention have become essential for banks and other financial institutions to safeguard their customers' financial transactions. This paper presents an overview of credit card fraud detection and prevention techniques.
Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks

Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants--such as the Lyapunov exponent spectrum and fractal dimension--in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using the recurrent neural network architecture of reservoir computing. Results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model, both typical test cases for numerical weather prediction.
Linear-Scaling Quantum Circuits for Computational Chemistry

We have recently constructed compact, CNOT-efficient, quantum circuits for fermionic and qubit excitations of arbitrary many-body rank [I. Magoulas and F.A. Evangelista, J. Chem. Theory Comput. 19, 822 (2023)]. Here, we present approximations to these circuits that substantially reduce the CNOT counts even further. Our preliminary numerical data, using the selected projective quantum eigensolver approach, demonstrate that there is practically no loss of accuracy in the energies compared to the parent implementation while the ensuing symmetry breaking is essentially negligible.
Towards Fully Anonymous Integrity Checking and Reliability Authentication for Cloud Data Sharing

Cloud storage services improve the efficiency and popularity of data sharing. These services allow groups of participants to jointly maintain the shared data. As important security properties of cloud data sharing, the integrity and the reliability of the shared data have been studied recently. However, the existing research cannot sufficiently solve the issue of participant identity anonymity in the scenario of data modification. In this paper, we propose a novel approach to achieve fully anonymous integrity checking and reliability authentication for cloud data sharing. We design a predicate for the shared data, and construct the Lagrange interpolation polynomials for all participants to compute the secret keys based on the designed predicate. When modifying the shared data, the participants compute the authenticators of the modified data using the secret key associated with the designed predicate instead of their identities. In this way, the integrity checking and reliability authentication of the shared data modified by different participants can be performed while the identities of the corresponding participants remain fully anonymous. In addition, the traceability and revocation of participant identity are considered. The performance analysis demonstrates the efficiency of the proposed approach, and the security analysis shows that the proposed approach satisfies the desired properties.
UCM-Net: A U-Net-like tampered-region-related framework for copy-move forgery detection

Copy-move forgery causes a big challenge to copy-move forgery detection (CMFD) due to that the photometrical characteristics of genuine and tampered regions in the same image remain highly consistent. A novel U-Net-like architecture with multiple asymmetric cross-layer connections associated with self-correlation and atrous spatial pyramid pooling (ASPP) between feature extraction module (FEM) and tampered region localization module (TRLM), called UCM-Net, is proposed in this paper. Different from existing deep learning based CMFD networks which indiscriminately process large or small tampered regions without considering the statistical characteristics of regions, FEM differentially treats large or small tampered regions by exploiting deep backbone networks to extract high-level features with rich semantic information for large tampered regions while utilizing lightweight backbone networks to extract low-level features for small tampered regions. Multiple cross-layer connections between two modules utilize the self-correlation calculation and ASPP to remove as much irrelevant semantic information as possible while retaining multi-scale tampered features from shallow to deep convolutional layers of FEM. Unlike the previous CMFD networks, which cannot capture multi-scale features because of simply stacking convolution blocks in the upsampling step, TRLM exploits multiple U-shaped residual U-block modules with different depths to change the receptive field of each point in the tampered feature maps so as to capture global and local information, greatly improving the localization accuracy of tampered regions. Experimental results on three publicly available databases demonstrate that UCM-Net outperforms several state-of-the-art algorithms in terms of various evaluation metrics.
Kooperative Fabrikplanung mit Mixed Reality/Cooperative factory planning based on mixed reality

Anwendungen aus dem Bereich „Mixed Reality“ (MR) bieten große Potenziale bei der Lösung komplexer Planungsaufgaben. In diesem Beitrag wird die MR-Anwendung „HoloLayouts“ zur Fabrikplanung mit der Microsoft „HoloLens 2“ vorgestellt. Diese erlaubt die direkte Zusammenarbeit bei der Entwicklung und Validierung von 3D-Produktionslayouts im Team. Im Rahmen der digitalen Transformation der Produktion bietet die Integration von 3D-Visualisierungswerkzeugen eine effiziente Unterstützung bei gemeinsamen Planungsentscheidungen. Mixed reality (MR) tools offer great potential for solving complex planning tasks in production. This article presents the MR application „HoloLayouts“ for factory planning with Microsoft „HoloLens 2“. The application enables the collaborative development and validation of production layouts in 3D. Within the context of the digital transformation of manufacturing, the integration of 3D visualization tools offers efficient support for collaborative planning decisions.
Writing Summary as An Alternative Strategy To Comprehend The Text

This paper is purposed to explain the use of Writing Summary as an alternative strategy to comprehend the text in teaching reading. Some researchers have applied writing the summary strategy to increase students' reading comprehension. Based on the results of those researches, showed that writing summary could effectively encourage students in comprehending the text well. Writing summary can help students enhance understanding of what is read. The strategy requires that students rewrite or outline only the important parts of the passage. Writing summary encourages students look for the most important parts and determine what parts of a passage are less important and what should be ignore. By using this strategy, the students need to read the whole text in order to make a summary using their own words. So, by writing summary based on what they read, they can comprehend a passage.
Service Innovations, Knowledge Management, Advance Technology and Organizational Performances

The purpose of this paper is to examine the impacts of knowledge management and advance technology on organization performance through service innovation.A survey of 268 SMEs in the service industry was conducted.Partial least squares of structural equation modelling (PLS-SEM) was used to test the mediation role of the service innovation on the relationships between knowledge management, advance technology and the organizational performance.The results showed there were links between knowledge management and organizational performance.In addition, it was discovered that service innovation mediated the relationship between the knowledge management and cost and financial performance, and between advance technology and cost and financial performance.The paper shows a way forward of how to measures organisational performance in such a way that they are led from the development of innovation capability generated through knowledge management and the advance technology in the service industry.
EFEKTIFITAS PENGGUNAAN BAHAN AJAR BERBASIS WEB TERHADAP HASIL BELAJAR MAHASISWA PADA MATA KULIAH EVALUASI PROSES DAN HASIL PEMBELAJARAN UNIVERSITAS BATURAJA

The purpose of this study was to determine the effect ivenes of web-based teaching materials on student learning achievement in the evaluation of learning process and achievement course of Baturaja University. This experimental study used one group pretest postest design. There were 32 of fifth semester students participated in this study. Testing was used to collect the data. The data was analyzed using t test and computed through SPSS 22. The results showed that the significant value was 0.000 which were less than 0,05 (0,000 &lt; 0,05). This means that there was significant difference between students’ pretest postest scores. From the result, it could be conclude that web based teaching materials were effectively used to improve students’ learning achievement.Keywords: Web-based teaching materials, learning achievement, evaluation of learning process and achievement course
Securing E-Governance Services Based on Two Level Classification Algorithms

Due to the expansion of cybercrime and cyberwarfare, the necessity for cyber security has recently expanded substantially.There are several trends in cyber security, but the most important is e-governance.E-government is regarded as one of the most essential platforms for transmitting data and services over the internet that frequently contain valuable and confidential data, making them subject to threats.The majority of egovernance systems rely on public-use services.The paper proposed a framework to detect threats in internet traffic flows.This paper uses a famous dataset that was collected from internet traffic called the UNSW-NB15 dataset, which consists of 307,099 instances.The framework consists of several steps, including pre-processing, identifying a correlation between features, and selecting the best ones.Finally, different machine learning algorithms are used to distinguish the normal traffic from the malware traffic.The findings uncover that SVM achieved very high accuracy (99.16%).Additionally, in the second part, which is called multi-class and consists of two stages, in the first one the study classified the abnormal flows into nine attacks with a lower accuracy of 77.80%.In the second stage with binary classification, the dataset contained both normal and abnormal, and the accuracy improved significantly to 97.48% for SVM.