Reviewerly/exHarmony huggingface dataset:
Human Resource Management from an Ethical Conduct

Abstract: "Business ethics" refers to a company's moral duty to its employees to support balance, fairness, and justice. Safety in the workplace, respect, fairness, privacy, basic human rights, fair treatment of workers, and honest processes are all part of human resource management's ethical scope. For example, many companies that let people work from home think that it's important to keep an eye on their employees. Ethical time tracking lets companies keep track of what their workers are doing without going against their morals. To do this, the quality of stored voice and video is on purpose made worse. So, once Human Resources has learned the ethical problems that come up in their field, they will be better able to find and keep good people, since it is generally agreed that it is up to the person to make ethical decisions. Ethics are becoming more important in human resource management for a number of reasons. These include globalization, which has forced organizations to change their policies to work in the global economy, and rising competition, which has forced businesses to look for new ways to get ahead and make more money. The company has begun to think about how making ethical decisions could give it an edge in the market. This is because it is important to make sure that any business decisions are based on only the right information. In this paper, we will discuss about the HRM and its ethical conduct.
Textual Analysis and Identification of Spam News

Abstract: Our study aims to tackle the problem of recognizing spam news through the use of Machine Learning. To accomplish this, we devised a hybrid approach named "SpamBuster" that employs various algorithms including Bernoulli Naive Bayes, Multinomial Naive Bayes, Random Forest, and Kernel SVM. We trained the model on a large dataset of news articles and evaluated it based on multiple performance metrics such as accuracy, precision, recall, and F1 score. Our experimental findings revealed that our approach achieved outstanding performance with an accuracy of 0.9507, precision of 0.9747, recall of 0.9253, and F1 score of 0.9494. These results demonstrate that our approach is effective in identifying spam news and could have practical implications in combating the spread of false information and propaganda. In conclusion, our research showcases the potential of machine learning techniques and textual analysis for detecting spam news and emphasizes the significance of this area of research in the modern era of information.
Two-Memory Reinforcement Learning

While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of parametric neural networks. Non-parametric episodic memory, on the other hand, provides a faster learning alternative that does not require representation learning and uses maximum episodic return as state-action values for action selection. Episodic memory and reinforcement learning both have their own strengths and weaknesses. Notably, humans can leverage multiple memory systems concurrently during learning and benefit from all of them. In this work, we propose a method called Two-Memory reinforcement learning agent (2M) that combines episodic memory and reinforcement learning to distill both of their strengths. The 2M agent exploits the speed of the episodic memory part and the optimality and the generalization capacity of the reinforcement learning part to complement each other. Our experiments demonstrate that the 2M agent is more data efficient and outperforms both pure episodic memory and pure reinforcement learning, as well as a state-of-the-art memory-augmented RL agent. Moreover, the proposed approach provides a general framework that can be used to combine any episodic memory agent with other off-policy reinforcement learning algorithms.
Recurrent Transformer for Dynamic Graph Representation Learning with Edge Temporal States

The burgeoning field of dynamic graph representation learning, fuelled by the increasing demand for graph data analysis in real-world applications, poses both enticing opportunities and formidable challenges. Despite the promising results achieved by recent research leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs), these approaches often fail to adequately consider the impact of the edge temporal states on the strength of inter-node relationships across different time slices, further overlooking the dynamic changes in node features induced by fluctuations in relationship strength. Furthermore, the extraction of global structural features is hindered by the inherent over-smoothing drawback of GNNs, which in turn limits their overall performance. In this paper, we introduce a novel dynamic graph representation learning framework namely Recurrent Structure-reinforced Graph Transformer (RSGT), which initially models the temporal status of edges explicitly by utilizing different edge types and weights based on the differences between any two consecutive snapshots. In this manner, the varying edge temporal states are mapped as a part of the topological structure of the graph. Subsequently, a structure-reinforced graph transformer is proposed to capture temporal node representations that encoding both the graph topological structure and evolving dynamics,through a recurrent learning paradigm. Our experimental evaluations, conducted on four real-world datasets, underscore the superior performance of the RSGT in the realm of discrete dynamic graph representation learning. The results reveal that RSGT consistently surpasses competing methods in dynamic link prediction tasks.
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view, they still suffer from the 3D inconsistency of produced images at large camera poses and imprecise image attribute editing, like affecting unspecified attributes during editing. For more efficient image inversion, we train a shared encoder for all images. To alleviate 3D inconsistency at large camera poses, we propose two novel methods, an alternating training scheme and a multi-view identity loss, to maintain 3D consistency and subject identity. As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images. We compare the latent space and inversion manifold of GAN models and demonstrate that editing in the inversion manifold can achieve better results in both quantitative and qualitative evaluations. Extensive experiments show that our method produces more 3D consistent images and achieves more precise image editing than previous work. Source code and pretrained models can be found on our project page: https://mybabyyh.github.io/Preim3D/
FORECASTING TOURISM DEMAND DURING THE COVID-19 PANDEMIC: ARIMAX AND INTERVENTION MODELLING APPROACHES

The tourism sector in Indonesia is one of the economic sectors severely impacted by the COVID-19 epidemic and its variants. The government is attempting to revive the economy by implementing numerous recovery policies. These economic recovery policies, particularly in the tourism sector, must be backed by a policy evaluation conducted using tourism demand data, such as the number of international visitors visiting Indonesia. However, official data from the BPS-Statistics Indonesia has been released with a two-month delay and is sometimes revised the following month. Consequently, it is necessary to forecast the data for the current situation. In addition, the forecasting model must be modified to account for data conditions caused by the COVID-19 pandemic. This research proposes an ARIMAX forecasting model that utilizes Google Trends and an intervention forecasting model with data sources from BPS and Google Trends. Thus, this research aims to present an overview, develop a model and identify the best forecasting model, and calculate the influence of the intervention on the number of international visitors visiting Indonesia. Compared to the ARIMAX model, the results indicated that the intervention model provided the most accurate forecasts. Not only superior in forecasting results, but the intervention model also demonstrates the magnitude of the intervention's effect on the number of international visitors visiting Indonesia.
Development of Android-Based Educational Games to Improve Students’ Learning Outcomes in Hydrocarbon

Android-based educational game is a game using a smartphone operating system with certain teaching materials as learning media in an interesting, unique, and fun. The purpose of this study was to produce educational game media product that are feasible and valid. This development research used the ADDIE model (analyze, design, developt, implementation, and evaluation). The results of the study: (1) the media expert's validity value was 96.6% in the very feasible and very valid category, (2) the material expert's validity value was 91.4% in the very feasible and very valid category, and (3) the results of the students' readability test was 93.6% in the very feasible and very valid category, (4) N-Gain score of 0.84 in the high category and an effectiveness level of 84.6% in the "effective" category. Therefore, the android-based educational game media was effectively used to improve student learning outcomes with high effectiveness. Keywords: educational game, chemistry learning, ADDIE. Abstrak: Game edukasi berbasis android merupakan permainan menggunakan sistem operasi smartphone dengan materi ajar tertentu sebagai media pembelajaran dengan menarik, unik, dan menyenangkan. Tujuan penelitian ini adalah untuk menghasilkan produk media game edukasi yang layak dan valid. Penelitian pengembangan ini menggunakan model ADDIE (analyze, design, developt, implementation, and evaluation). Hasil penelitian: (1) nilai kevalidan ahli media sebesar 96.6% dengan kategori sangat layak dan sangat valid, (2) nilai kevalidan ahli materi sebesar 91.4% dengan kategori sangat layak dan sangat valid, dan (3) hasil uji keterbacaan peserta didik sebesar 93.6% dengan kategori sangat layak dan sangat valid, (4) Skor N-Gain sebesar 0.84 dengan kategori tinggi dan tingkat efektifitas sebesar 84.6% dengan kategori “efektif”. Oleh karena itu, media game edukasi berbasis android yang diterapkan efektif digunakan untuk meningkatkan hasil belajar siswa dengan efektifitas tinggi. Kata kunci: game edukasi, pembelajaran kimia, ADDIE. DOI: http://dx.doi.org/10.23960/jpmipa/v24i1.pp14-27
Equivariant Neural Networks: A Geometric Approach to Robust and Interpretable Machine Learning

Machine learning has made significant advancements in recent years, with deep learning models achieving state-of-the-art performance in various tasks. However, these models often lack interpretability and robustness, which are crucial for real-world applications. In this paper, we explore the use of equivariant neural networks, a class of models that leverage geometric principles to improve interpretability and robustness. We provide an overview of the mathematical foundations of equivariant models, discuss their applications in machine learning, and present recent advancements in training methods and performance guarantees. Finally, we highlight open challenges and future directions in the field of geometric machine learning.
Quantum Key Distribution in Networking: Use of Photonic Circuits and Digital Signal Processing

In the present day today communication and increasing complexity of hacking we need to have a good control over our communication technologies.One of the primary concern is our cyber security and data transmission technology which is used for secret government information, defense data and other scientific reports.Quantum communication is one of the new emerging technology which is being adopted for confidential data transmission in all these areas.Quantum communication is based on Quantum Key Distribution(QKD). In this research we have discussed the basics of quantum key distribution with its mathematical analysis in DSP, simulation, control, applications and challenges.
IMPLEMENTATION OF THE FUZZY GUSTAFSON-KESSEL METHOD ON GROUPING DISTRICTS/CITIES IN KALIMANTAN ISLAND BASED ON POVERTY ISSUES FACTORS

Cluster analysis is an analysis that is useful in summarizing data by grouping objects based on certain similarity characteristics. One of the group analysis is Fuzzy Gustafson-Kessel (FGK) which is the development of the Fuzzy C-Means (FCM) method. The FGK method has a good way in adjusting the form of cluster membership function correctly for a data. This study aims to determine the results of the optimal number of groups based on the Partition Coefficient (PC) and Classification Entropy (CE) validity indexes and to find out the results of grouping 56 districts/cities on the island of Kalimantan based on poverty issue factors in 2021. The optimal number of groups using the FGK method based on the validity indexes of PC and CE are two groups. The first group and the second group each consist of 28 districts/cities in Kalimantan Island.
HYBRID K MEANS-MULTIVARIATE ADAPTIVE REGRESSION SPLINES FOR DISTRIBUTION OF DENGUE FEVER RISK MAPPING IN BOJONEGORO DISTRICT

Dengue Hemorrhagic Fever (DHF) is a dangerous disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes’ bites. WHO data shows that almost half of the world's humans are exposed to Dengue Hemorrhagic Fever. The number of mortality caused by dengue disease is around 20,000 every year. In East Java, Bojonegoro District has the highest number of dengue hemorrhagic fever cases (416). To reduce this number, the causative factors need to be known. Additionally, it's important to pinpoint the region or cluster where the variables driving the spread are located so that prevention and treatment efforts are effective. Based on the elements contributing to the transmission of Dengue Hemorrhagic Fever, this study seeks to identify and categorize locations at risk for the spread of the illness. This study uses Hybrid K Means-Multivariate Adaptive Regression Splines (MARS) which is a combination of K-Means and MARS methods in the hope of providing better analytical results. This is because the data was divided into simpler parts by considering the Oakley distance. The results obtained from the K Means-MARS hybrid shows the relationship between response variables and predictor variables for each cluster. There are three clusters of risk for the spread of dengue hemorrhagic fever in Bojonegoro district with categories: high risk cluster, medium risk cluster and low risk cluster. The high risk cluster consists of 7 sub-districts (Baureno, Kepohbaru, Balen, Sumberrejo, Kedungadem, Bojonegoro and Dander). The variables affecting the DHF Sufferer in the high risk cluster were population density (X2), Altitude (X3) and Health Worker (X6). Meanwhile, the medium risk cluster consists of 10 sub-districts (Kalitidu, Kanor, Kapas, Ngasem, Ngraho, Padangan, Sugihwaras, Sukosewu, Tambakrejo, and Trucuk). The variables that affect the DHF Sufferer in the medium cluster are Number of Dead (X1), Population Density (X2) and Health Facility (X5). The low risk cluster consisted of 11 sub-districts (Bubulan, Gayam, Gondang, Kasiman, Kedewan, Malo, Margomulyo, Ngambon, Purwosari, Sekar, and Temayang). The variables affecting the DHF Sufferer rate in the low risk cluster were number of dead (X1) and population density (X2).
Differentiable Relaxations for Discrete Structures in Machine Learning

Discrete structures and operations are prevalent in many machine learning applications, but their non-differentiable nature poses challenges for gradient-based optimization. This paper presents an overview of differentiable relaxations for discrete structures, focusing on continuous and stochastic relaxations of discrete operations and algorithms. We discuss various techniques for making discrete structures differentiable, such as smoothing and gradient estimation methods, and explore their applications in weakly- and self-supervised learning, optimization with differentiable algorithms, and differentiable simulators. The paper also highlights the potential of differentiable relaxations in learning-to-rank, computer vision, and differentiable architecture search. Furthermore, we delve into the challenges and limitations of differentiable relaxations and discuss potential future research directions.
CERTAINTY FACTOR METHODS IN IDENTIFYING INTERESTS AND TALENTS OF ELEMENTARY SCHOOL CHILDREN AL IKHLAS TAQWA

Psychologists and child education experts always remind the importance of knowing interests and talents from an early age to provide a stimulus to children from an early age, because the provision of the stimulus affects the future of children. This study aims to (1) To make calculations in mathematical models to calculate and analyze the interests of children's talents using applications. (2) To create a web-based information system that can facilitate teachers and parents in determining the interests of children's talents at the Al-Ikhlas Taqwa Plus Elementary School using the Certainty Factor method. The method used in this research is the research and development (R&amp;D) method using the certainty factor. The population in this study were all students of SD Plus Al Ikhlas Taqwa Medan T.P 2021/2022 starting from grades 3-6. Sampling was done by purposive sampling technique. Data collection was carried out by interviews, material expert test questionnaires, and media experts, and the results of the children's talent interest questionnaire were processed using the Certainty Factor. The results of this study are the results of interest and talent analysis based on 7 intelligence criteria and also the highest summary results from several criteria with one of the tests yielding a percentage of 93.58% in the field of linguistics.
Quantum Computing using Superconducting Qubits: A Review

Quantum computing aims at overcoming the limitations of conventional computers.Over the last few decades quantum computing has seen significant advances, particularly in superconducting quantum processor architecture based on superconducting qubits.In our work, we have provided a review over the superconducting qubits, their types, and the advantages these possess over other forms of qubits.Besides the theoretical aspects, we have also discussed different challenges and recent advancements that have been made till now in dealing with those challenges.With this review we lay out some future opportunities in this field and motivate further research in this field.
The Impact of Cryptocurrency on Global Trade and Commerce

This study aimed to investigate the impact of cryptocurrency on global trade and commerce. The research objectives included examining the extent to which the adoption of cryptocurrency has disrupted traditional financial systems and affected cross-border transactions, as well as investigating the potential benefits and challenges of using cryptocurrency for global trade and commerce. A mixed-methods research design was used, incorporating both quantitative and qualitative data collection and analysis techniques. The study involved an extensive literature review, a survey of businesses involved in international trade, and interviews with key stakeholders. The results showed that cryptocurrency offers benefits such as reduced transaction costs, faster settlement times, and increased transparency in transactions. However, there are also challenges such as regulatory and legal hurdles, security concerns, and limited understanding of cryptocurrency. The study also highlights the factors that influence the adoption of cryptocurrency in international trade, including regulatory and legal frameworks, security concerns, awareness and understanding of cryptocurrency, transaction costs, and integration with existing systems. Finally, the attitudes and perceptions of businesses towards cryptocurrency are discussed, with the study showing that confidence in the reliability and security of cryptocurrency is a significant factor for adoption. Overall, the study provides insights into the potential opportunities and challenges presented by cryptocurrency in global trade and commerce, and the implications for policymakers, businesses, and investors.
ALTERNATIVE PROOF OF THE INFINITUDE PRIMES AND PRIME PROPERTIES

Prime numbers is one of kind number that have many uses, one of which is cryptography. The uniqueness of prime numbers in their divisors and distributions causes prime numbers to be widely used in digital security systems. In number theory, one of famous theorem is Euclid theorem. Euclid theorem says about infinitely of prime numbers. Many alternative proof has been given by mathematician to find new theory or approximation of prime properties. The construction of proof give new idea about properties of prime number. So, in this study, we will give an alternative proof of Euclid theorem and investigate the properties of prime in distribution.
CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING

Mathematical decision-making ability is a complex cognitive process of finding problems solutions which is continuously explored and optimized for undergraduate students. The current research only focus on categorization on class score average into high, medium and low abilities. As a result, the lecturers do not have any standard categories to classify students’ abilities as a reference in planning supplementary learning that could optimize undergraduate students’ abilities. Therefore, the purpose of this study is to determine a classification model for undergraduate students' mathematical decision-making abilities that require supplementary learning based on the identification of the shortest distance of a new data from an existing data directory. The research method involved data mining techniques with the KNN classification model through the Knowledge Discovery in Database (KDD) process starting from data selection, pre-processing, transformation, data mining and interpretation/evaluation [1]. A total of 100 data were used as research samples which were divided into training data and testing data. Based on the test results, it is obtained that the accuracy of the classification model is 95% for the parameter value k = 15, meaning that each predicted testing data for the classification class is close to the actual condition with the number of neighbors 15 data from the training data.
Comparative analysis of image classification with retrieval system

Currently, the term 'content-based image retrieval' seems to be a highly attentive system for handling the broad image datasets since the data storage mechanisms and image acquisition are becoming the most empowered logic in image processing. The previous CBIR system has been proposed under nonlinear similarity matching measure in a logarithmic scale and informative pattern descriptor has quantified the range of similarity content. This article implements a novel CBIR system that emphasises the classification concept using a deep belief network (DBN) classifier. In this concept, apart from the image retrieval, the used classifier classifies the respective classes of retrieved images. Finally, the proposed local vector pattern (PLVP) with DBN classifier (PLVP-DBN) compares its performance over other conventional retrieval concepts: PLVP-with log similarity, PLVP-without log similarity, and also with neural network (NN) classifier.