Reviewerly/exHarmony huggingface dataset:
Real-time Card Fraud Detection: A Stacked Ensemble Machine Learning Approach

As the world witnesses, Card fraud detection has become more and more crucial observes a rise in new and innovative techniques used by offenders. Machine learning and artificial intelligence are at the forefront of the various fraud detection technologies that credit card firms and financial institutions are utilizing. The Nilson Report's startling statistics demonstrate the scope of the issue. Global losses due to credit and debit card fraud increased to $40.6 billion in 2022, up from $34.7 billion the year before, with the United States being responsible for a large 36.8% of these losses. Additionally, younger people, especially those between the ages of 20 and 29, were disproportionately affected by credit card fraud. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. Our study used a dataset with the transaction histories of over 300,000 people from the UCI Machine Learning Repository. We determined the best methods through a multi-step procedure that included data pretreatment, dataset division, and model selection. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. The Stacked Ensemble model, SN Algorithm (SVM + Naive Bayes), beat other models according to the results in terms of important metrics. Impressive AUC, CA, F1, accuracy, and recall ratings demonstrated the system's effectiveness in preventing card theft.
Knowledge Graph Convolutional Recommender Model with Collaborative Information and Common Neighbor Ranking Sampling

Knowledge graph can be used as auxiliary information in recommendation models. However, some models do not consider collaborative information when extracting structured information, which leads to problems in obtaining high-order representations of items. In addition, some models use fixed sampling sizes to reduce computational costs, but this sampling method cannot distinguish the importance of neighboring entities by randomly selecting samples for selective aggregation of neighborhood information. To address these issues, this paper proposes a knowledge graph convolutional recommendation model that integrates collaborative information and common neighbor ranking sampling. Experimental results show that compared with other baseline models, this model has improved performance in AUC indicators.
Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera

With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar plants with tracking devices, the operator needs to anticipate the achievement of a solar irradiance threshold to start or stop their system. In this case, binary forecasts are more relevant. Moreover, while most forecasting systems are deterministic, the probabilistic approach provides additional information about their inherent uncertainty that is essential for decision-making. The objective of this work is to propose a methodology to generate probabilistic solar forecasts as a binary event for very short-term horizons between 1 and 30 min. Among the various techniques developed to predict the solar potential for the next few minutes, sky imagery is one of the most promising. Therefore, we propose in this work to combine a state-of-the-art model based on a sky camera and a discrete choice model to predict the probability of an irradiance threshold suitable for plant operators. Two well-known parametric discrete choice models, logit and probit models, and a machine learning technique, random forest, were tested to post-process the deterministic forecast derived from sky images. All three models significantly improve the quality of the original deterministic forecast. However, random forest gives the best results and especially provides reliable probability predictions.
Prediksi Kepribadian Mahasiswa Menggunakan Naïve Bayes

College students are in a transitional phase from youth to adulthood. The transition period makes students still unstable to control their emotions. It makes his curiosity towards new things increase which then shows his personality traits. The purpose of this study was to find out how researchers collect data about personality from students, to find out how to classify personality from the data that has been collected. Research methods start from collecting data using Text Preprocessing questionnaires, Data Training, Classification, Testing, to making predictions. After applying the classification algorithm with the Naïve Bayes algorithm, the Train Score is 0.947 and the Test Score is 0.879. Trials have also been carried out to make predictions with new data whose results are correct.
Öğretmen Adaylarının Dijital Materyal Yeterliklerinin Çeşitli Değişkenler Açısından İncelenmesi

This research aims to examine pre-service science teachers' self-efficacy in developing digital teaching materials with web 2.0 tools according to various variables. The study was conducted by the quantitative research method and the survey model was utilized. The sample of the study consists of 450 pre-service science teachers studying at three different state universities in Turkey. Data were collected through the "Self-Efficacy Scale of Teachers' Digital Teaching Material Development.” While analyzing the data; descriptive statistics, t-tests, and analysis of variance tests were used. According to the findings, the pre-service science teachers' self-efficacy in developing digital materials with Web 2 tools differed significantly according to their university, year of university education, and previous content development with Web 2.0. However, no significant differentiation was observed according to their gender. In light of all these results, it was found that pre-service teachers' proficiency in producing digital material using Web 2.0 is at a moderate level and differed significantly according to various variables.
Pengembangan Model Web Interaktif Dalam Mendukung Aktivitas Pariwisata Di Kota Batam

Along with the decline in the covid pandemic in the city of Batam, the government has reopened the tourism faucet to the Batam area. The growth of tourist visits from month to month has increased. However, when compared to the level of tourist arrivals to Batam in the year before the Covid pandemic, it was very much different. Even though the government has improved tourism supporting infrastructure, service levels, better security conditions, the growth of visits has not met expectations. There is one aspect that needs to be developed, namely the aspect of information technology as a communication mouthpiece, travel guide and as an assistant in tourism travel. Information technology is able to solve existing problems in tourism activities in Batam City. For this reason, it is necessary to develop an integrated tourism portal between visitors, tour service providers consisting of the government, the private sector as tourism business managers in the city of Batam and other interested parties. The development of tourism portals is still individual in nature which is still not effective for visitors, because every visitor who needs information and tourism services must open various websites. For this reason, it is necessary to have an interactive web portal that is integrated from various aspects of needs and is expected to become a communicator from stakeholders in the Batam city tourism sector, and is also able to provide precise information on various types of activities in Batam City.
Improvements and Shortcomings in The First Stage of Scrum Implementation in Medium-Sized IT Companies

The application of the SCRUM procedure in hardware projects is of great importance because the material costs and the high risk of irreversible costs caused by changes negatively affect the business of IT companies that produce hardware. After the implementation of the pilot project in the software company, the introduction of agile methods was started in medium-sized companies that produce electronic equipment. General improvements and current problems that occur in the first stages of the implementation of agile method and scrum framework in a medium-sized IT company are presented in this paper. In order to see the workload of the team, Velocity Chart of the amount of work done after each spring was used. The team's work is shown on the Sprint Burndown Chart.
Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.
Explainable AI for Communicable Disease Prediction and Sustainable Living: Implications for Consumer Electronics

Communicable diseases are transmitted through water, food, contaminated surfaces, bodily fluids, air. In such a situation, staying in home isolation for fewer chronic health problems and monitoring health status frequently through Medical Sensors (MSs) is recommended. The use of Artificial Intelligence (AI) in smart consumer electronics and sustainable healthcare has recently demonstrated remarkable results. However, the healthcare domain requires high levels of accountability and transparency for communicable disease prediction and sustainable life in edge networks. This paper aims to present an intelligent healthcare prototype that can identify risk factors according to monitoring parameters by analyzing the Explainable XGBoost (XXGB) model. Using edge networks for sustainable living, we explore the intersection between healthcare and consumer electronics. Initially, the prototype has been trained using the XXGB model over one publicly available dataset related to communicable diseases. Next, the prototype identifies patient risk factors by analyzing real-time monitoring parameters. Simulation results illustrate the efficiency of the proposed XXGB model up to 84.2% accuracy, which is higher than existing models.
Evaluation of Image Support Resolution Deep Learning Technique based on PSNR Value

Machine learning is a subset of deep learning. Deep learning techniques have become increasingly popular in recent years for a variety of applications. Many techniques are used in image processing for image enhancement, object detection, and a variety of other purposes. Many deep learning techniques are important in single-image super-resolution (SISR). Many models are used for SISR, and they produce good perceptual and adversarial results. The procedure involves creating a high-resolution (HR) image from a low-resolution (LR) image, including some popular SISR methods: "Super-Resolution with Convolutional Neural Network" (SRCNN), "Super-Resolution Residual Network" (ResNet), "Super-Resolution Generative Adversarial Network" (SRGAN), and "Enhanced Super-Resolution Generative Adversarial Network" (ESRGAN). These deep learning models are layered, with each layer performing a different task for an LR image. These methods convert a single LR image into an HR image. A single LR image yields many high-resolution images. This paper creates a dataset of DSLR images for use with the SISR method. Deep learning technology is used to convert an LR image to an HR image. The HR images are then compared to the DSLR images. The "peak signal-to-noise ratio" (PSNR) and the "Structural Similarity Index" (SSI) are used to compare them (SSIM). When compared to the old method, the proposed method produces identical results. SSIM and PSNR are shown for both old and proposed approaches. Both approaches possess almost comparable results. The higher the PSNR value, the better the image quality. The same is the case with the SSIM value
The Impact of Leader-Leader Exchange on Project Success in Non-profit Organisations

After many years of research on non-profit organisations (NPOs), the success rate of the projects they manage remains low. This is particularly due to the fact that the awareness of the need for project management competences to achieve project success is poor. This paper aims to close this awareness gap. The purpose of this paper is to examine the relationships between leader-leader exchange (LLX), sharing values, result orientation and personal communication as critical project management competences and their impact on project success in NPOs. The research which is based on social exchange and leadership theories, includes a testing of the research model and hypotheses using the structural equation modelling and bootstrap method. Data were collected during a survey organized in March-May 2023 that targeted more than 100 NPOs in order to apply the quantitative research method. The research findings indicate that the mediating variable “Result-Orientation” has a stronger impact on project success than the positive effect “leadership” and “sharing values” have on project success. However, the significance of leadership and sharing values for project success is increased by personal communication as an important moderating variable. The contribution to the science of this paper lies in the fact that the quantitative method was applied for the first time for the impact study with the chosen project management competences on project success. The practical relevance of the research is to encourage the adoption of social exchange and leadership theories in NPOs, to increase project contribution to civil society.
Specification of the Development of a Program for Transmitting Information to Users and Technical Systems

This article demonstrates how to implement an application for the exchange of information, both between people and between man and technical equipment. The Flask library facilities the creation of such a program by simplifying the syntax of the program. An analysis of the application functionality was carried out in terms of its architecture, using decorators for more convenient display. Various technical aspects, the interaction architecture of program modules, as well as the questions of code writing, are discussed. It is shown that the use of Flask and some other libraries allow users with elementary and intermediate level of programming knowledge to create a full-fledged program to display information, not necessarily of technical nature, for its further application in various projects.
EVOLUTION OF ‘ARBITRARINESS’ IN ‘SIGN’ IN LANGUAGE TO ‘REVELATION’ IN ‘SIGN’ IN MANTRIC POETRY (WITH SPECIAL REFERENCE TO SRI AUROBINDO’S MANTRIC POETRY)

‘Sign’ in language, is, admittedly, arbitrary in nature. Having studied in the light of yogic revelations made by Sri Aurobindo of the hierarchy of consciousness, arbitrariness is found to be operative on the intellectual plane of Mind and the planes below it. The intellectual plane of Mind and all the planes below it are under the gravitational pull of the separative principle of consciousness operating on the plane of Mind as revealed by him. It is on account of the pull that the sign comes to be arbitrary. As one evolves, as a result of the realisation of the existence of the One Transcendent Divine, to the spiritual plane of Mind, the separative principle of consciousness operating on the plane of Mind tends decisively to decay, hence, the arbitrariness, too, in sign tends to decay, with the rise of a new movement on the plane of Mind, the movement towards the integrality of the Consciousness of the One Transcendent Divine. Here, one enters into the realm of spiritual consciousness. Sri Aurobindo reveals four planes of spiritual consciousness on the plane of Mind. On the two uppermost planes of the spiritual consciousness, the ‘Intuitive Mind’ and the ‘Overmind’, as he terms them, the separative principle decays to the extent that the poetry written from these planes, the poetry which, according to Sri Aurobindo, is ‘Mantra’ or Mantric Poetry, comes to be revelatory. Hence, the ‘sign’ in it comes to be revelatory.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0061/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Examining the impact of classroom teaching reforms and virtual learning environments

Break through the identity characteristics of the virtual teaching and research office at present and provide ideas for how the virtual teaching and research office can become the platform for theoretical and practical teaching. Without considering insufficient funds, this paper analyzes and discusses the ideal construction of virtual teaching and research offices from three aspects: team building, platform construction, sustainable development construction, etc. Theoretical and practical instructors should enter the classroom in a variety of forms together, directing students as they engage in professional learning without relying on rigidly constructed platform resources. As long as the team members have the same ideas, can cooperate sustainably, and truly consider students, the construction goal of the virtual teaching and research room can be achieved. The Virtual Teaching and Research Room is a cross-professional and cross-regional collaborative teaching model that has emerged in recent years with the continuous growth of the demand for teaching specialization and practice and the continuous improvement of Internet technology. Although the virtual teaching and research office theoretically has an unmatched advantage over traditional physical teaching and research rooms, However, in practice, it is too difficult to implement it universally due to the limited funds of colleges and universities. This article provides a reference path for implementing and applying virtual teaching and research rooms in teaching process.
Application of CLH.com Program in Teaching Nursing Process

This chapter aims to identify knowledge, practice and satisfaction while learning Nursing process among student nurses after attending CLH.com, a mixed teaching method program. Mastering everything from a simple nursing care skill to a complicated Nursing Process necessitates the commitment of both teachers and learners. Nursing education is not a one-day endeavor for any student. This study intended to address the learning need of beginner Nursing student in drafting Nursing care plan by using mixed teaching methods during Covid -19 pandemic. On the basis of consecutive sampling, 80 student nurses were chosen for this chapter. To teach nursing process and developing standard nursing care plans, a planned program with a mixed mode of instruction was created and given the name CLH.Com. A knowledge questionnaire was administered, and practice was assessed through the creation of an individual Nursing care plan. Students' satisfaction was measured using a three-point Likert scale. Following a knowledge questionnaire and practice evaluation, the mean knowledge of the experimental and control groups was compared. With combined teaching strategies, there was a significant improvement in the mean score for knowledge (9.7±1.66) and practice (11.42), respectively.
Qubit recycling and the path-counting problem

Recently, it was shown that the qudits used in circuits with a convolutional form (e.g., matrix product states and the multiscale entanglement renormalization Ansatz) can be reset unitarily [Anikeeva et al., Phys. Rev. A 103, 042613 (2021)], even without measurement. We analyze the fidelity of this protocol for a family of quantum circuits that interpolates between such circuits and local quantum circuits, averaged over Haar-random gates. We establish a connection between this problem and counting directed paths on a graph, which is determined by the shape of the quantum circuit. This connection leads to an exact expression for the fidelity of the protocol for the entire family that interpolates between a convolutional circuit and a random quantum circuit. For convolutional circuits with a constant window size, the rate of convergence to unit fidelity is shown to be $\frac{{q}^{2}}{{q}^{2}+1}$, independent of the window size, where $q$ is the local qudit dimension. Since most applications of convolutional circuits use constant-size windows, our result suggests that the unitary reset protocol will likely work well in such a regime. We also derive two extra results in the convolutional limit, which may be of independent interest. First, we derive exact expressions for the correlations between reset qudits and show that they decays exponentially in the distance. Second, we derive an expression for the fidelity in the presence of noise, expressed in terms of the quantities that define the property of the channel, such as the entanglement fidelity.
Kockázatok és mellékhatások

In recent years, several judicial and constitutional court decisions have been handed down worldwide on the legality and constitutionality of the fundamental rights restrictive measures (including compulsory vaccination) imposed during the pandemic. Aside from Austria, Italy has imposed compulsory vaccination more widely than any other European country; moreover, the lack of vaccination has made it impossible for citizens to live their daily lives to such an extent that some scholars have even written of de facto compulsory vaccination. In December 2022, the Italian Constitutional Court ruled in three judgments against the petitions related to compulsory vaccination. After outlining the legal context and the scholars’ positions on mandatory Covid vaccination, this paper examines these decisions, focusing on the arguments on which the Court saw justification for compulsory vaccination.