Reviewerly/exHarmony huggingface dataset:
Visual Diagrammatic Queries in ViziQuer: Overview and Implementation

Knowledge graphs (KG) have become an important data organization paradigm.The available textual query languages for information retrieval from KGs, as SPARQL for RDFstructured data, do not provide means for involving non-technical experts in the data access process.Visual query formalisms, alongside form-based and natural language-based ones, offer means for easing user involvement in the data querying process.ViziQuer is a visual query notation and tool offering visual diagrammatic means for describing rich data queries, involving optional and negation constructs, as well as aggregation and subqueries.In this paper we review the visual ViziQuer notation from the end-user point of view and describe the conceptual and technical solutions (including abstract syntax model, followed by a generation model for textual queries) that allow mapping of the visual diagrammatic query notation into the textual SPARQL language, thus enabling the execution of rich visual queries over the actual knowledge graphs.The described solutions demonstrate the viability of the model-based approach in translating complex visual notation into a complex textual one; they serve as "semantics by implementation" description of the ViziQuer language and provide building blocks for further services in the ViziQuer tool context.
THE IMPACT OF MACROECONOMIC INDICATORS ON BITCOIN: A CASE STUDY ON PAKISTAN

Modern-day revolution is brought on by cryptocurrencies. Both the terms cryptocurrency &amp; currency describe means of exchanging goods &amp; services. In system that uses cryptocurrencies, every transaction is preprogrammed. Underlying blockchain technology governs transactions by using consensus mechanisms, hash algorithms, and public and private key encryption, doing away with the need for user to have faith in the counterparty. In this study, impact of macro-factors on bitcoin in Pakistan was examined. The relevance of this study is that the majority of people are ignorant of bitcoin to spread awareness of bitcoin and how to use them among the general audience. The macroeconomic indicators (GDP, INF, Exchange, &amp; Gold) were monitored on a quarterly basis between JAN 2016 and DEC 2021. The E-view program and numerous tests on secondary data were also used in this inquiry. In Pakistan, there is no long-term link. Bitcoin's short-term relationships with Pakistan's GDP, INF, ER, and gold. In short term, there is a link between bitcoin and the exchange rate.
An ontology-based approach to support the knowledge management of software quality standards

Nowadays, the quality of software systems is crucial for companies to provide high-quality services and products. However, a wide number of software projects still fail. To increase the success probability of projects, it is suitable to adopting software quality standards to guide the process. However, standards are commonly described by means of natural language making difficult its analysis. For example, it is not easy to choose the most suitable standard according to the characteristics of a project. Furthermore, the usage of natural language hinders the automatic detection of inconsistencies and ambiguities. On the other hand, ontologies are an artificial intelligence technique that has been successfully used to represent and analyze knowledge in numerous domains because its capacity to enable the automatic validation and consistency checking of the represented information. This paper aims to present an ontology-based approach to describe and analyze software quality standards. Since this ontology can represent the knowledge of several standards, a reasoner may automatically validate the information and infer new knowledge. This ontology might support the reduction of conceptual ambiguity of standards descriptions and improve its understanding.
Toward a Spectral Foundation Model: An Attention-Based Approach with Domain-Inspired Fine-Tuning and Wavelength Parameterization

Astrophysical explorations are underpinned by large-scale stellar spectroscopy surveys, necessitating a paradigm shift in spectral fitting techniques. Our study proposes three enhancements to transcend the limitations of the current spectral emulation models. We implement an attention-based emulator, adept at unveiling long-range information between wavelength pixels. We leverage a domain-specific fine-tuning strategy where the model is pre-trained on spectra with fixed stellar parameters and variable elemental abundances, followed by fine-tuning on the entire domain. Moreover, by treating wavelength as an autonomous model parameter, akin to neural radiance fields, the model can generate spectra on any wavelength grid. In the case with a training set of O(1000), our approach exceeds current leading methods by a factor of 5-10 across all metrics.
What Makes ImageNet Look Unlike LAION

ImageNet was famously created from Flickr image search results. What if we recreated ImageNet instead by searching the massive LAION dataset based on image captions alone? In this work, we carry out this counterfactual investigation. We find that the resulting ImageNet recreation, which we call LAIONet, looks distinctly unlike the original. Specifically, the intra-class similarity of images in the original ImageNet is dramatically higher than it is for LAIONet. Consequently, models trained on ImageNet perform significantly worse on LAIONet. We propose a rigorous explanation for the discrepancy in terms of a subtle, yet important, difference in two plausible causal data-generating processes for the respective datasets, that we support with systematic experimentation. In a nutshell, searching based on an image caption alone creates an information bottleneck that mitigates the selection bias otherwise present in image-based filtering. Our explanation formalizes a long-held intuition in the community that ImageNet images are stereotypical, unnatural, and overly simple representations of the class category. At the same time, it provides a simple and actionable takeaway for future dataset creation efforts.
Peculiarities of the Digital Environment of the University Libraries on the Modern Stage

The aim of the article is to single out and analyze the main components of the digital environment of the university library, as well as related to it advantages and demands to librarians. Research methods are based on general scientific principles of the theory and practice unity, the knowledge complexity and comprehensiveness, the applying of the system and structural approach, as well as analytical and synthetic scientific texts processing. Scientific novelty. The given in the article analysis of the digital environment of the university libraries and obtained results is a contribution to the theoretical background of the new generation forming process of the library establishments, which are oriented on the advanced technological potential of the digital epoch, that can strengthen their subjectivity in the context of communication non-linear model. Main conclusions. The research results testified the proclivity to the modification of the university library digital landscape under the influence of the technological evolution accelerated pace in the XXI century. In this article, structural peculiarities of the digital environment of the university libraries, that consists of “old” components (times of Internet revolution) and “new” ones (innovative 4IR), were analyzed. It is emphasized that its transformation and technological evolution in the XXI century is connected with the transition from the first ones to others. To “old” digital components belong OPAC, CD-ROM and flash-drives, and to “new” ones AI, Cloud Computing, blockchain, robotics and Big Data are related. The analysis of the digital components of the renewed environment of the university library proves that the usage of advanced technologies demand from the librarians to work on the development of requested skills, constantly improve practice in using tools and options that reveal their potential, form leading qualities and necessary experience of work in conditions of anthropo- and technospheres co-evolution.
Stabilizer codes for open quantum systems

The Lindblad master equation describes the evolution of a large variety of open quantum systems. An important property of some open quantum systems is the existence of decoherence-free subspaces. A quantum state from a decoherence-free subspace will evolve unitarily. However, there is no procedural and optimal method for constructing a decoherence-free subspace. In this paper, we develop tools for constructing decoherence-free stabilizer codes for open quantum systems governed by the Lindblad master equation. This is done by pursuing an extension of the stabilizer formalism beyond the celebrated group structure of Pauli error operators. We then show how to utilize decoherence-free stabilizer codes in quantum metrology in order to attain the Heisenberg limit scaling with low computational complexity.
Pengembangan LKPD Project Based Learning Pembelajaran IPAS Kelas IV SD Negeri 40 Pontianak Utara

This research generally aims to develop and produce student worksheet teaching material (SWTM) that are valid, interesting, and effective to be used to support the learning of SD Negeri 40 North Pontianak fourth grade student. This research use the method of research and development (RND) with the approach of mixed borg and gall method, that contain the ten steps of research and that is information gathering, planning, prototype developing, early testing , product revision, field testing, field testing revision, operational field testing, final product revision , deployment and implementation. At the product testing stage done at SD negeri 40 north Pontianak fourth grade with the amount of 20 student, the result of this research is a project based learning the IPAS learning material "How I Become More Orderly". The result of validity test material shows a percentage as big as 92% with the interpretation "Very Deserved". And the result of design validity test shows a percentage as big as 91% with the interpretation "Very Deserved". The response of student shows a percentage as big as 88% with the interpretation "Very Deserved" and the teacher respone shows the percentage as big as 94% with the interpretation “Very Deserved" to be used in the process of IPAS learning material "How I Become More Orderly" in the SD Negeri 40 North Pontianak fourth grade.
DEVELOPMENT OF WORDPRESS AND LEARN PRESS-BASED LEARNING MANAGEMENT SYSTEM (E-LEARNING)

Development of Learning Management System (e-Learning) based on Wordpress and Learnpress. This research was conducted to generate and determine the feasibility of web-based e-learning using CMS (Content Management System) WordPress and LearnPress at PT Indo Karya Solusi. This research is a development research with the ADDIE method (Analyze, Design, Develop, Implementation, Evaluation). The steps in this research are the analysis stage, design stage, development stage, implementation stage and evaluation stage. validation of media experts, material experts, one-to-one trials, small group trials and field trials using questionnaires. The data analysis technique for feasibility uses quantitative descriptive analysis. According to the assessment of media experts, this e-learning is feasible to use with a score of 88.8% this is the very good category. According to the material expert's assessment, this e-learning is feasible to use with a score of 93.3% this is the very good category. according to the one-to-one trial, this e-learning is feasible to use with a score of 90.6% this is the very good category. According to the small group trial, this e-learning is feasible to use with a score of 96.13% this is the very good category and according to field trials with a larger number of respondents (50 e-learning training participants), it is feasible to use it with a score of 92,76% this is the very good category. From these tests, it can be concluded that web-based e-learning using CMS (Content Management System) WordPress and LearnPress at PT Indo Karya Solusi is feasible to use.
Application of Market Basket Analysis on Beauty Clinic to Increasing Customer’s Buying Decision

Beauty care and the need for cosmetics have become the lifestyle of modern women, especially in big cities. Public awareness to look beautiful makes modern women competing to take care of themselves to be more beautiful. The body care industry in Indonesia continues to grow. The growth has reached 6% and is predicted to continue to grow along with the high concern of Indonesian women in caring for their skin. To win the competition, companies need to know the market and consumer situation. One strategy that can be applied by the company is to use a promotional or advertising strategy. This research was conducted at the Ariana Audy beauty clinic in 2022 with the aim of identifying customer buying patterns which will then be used as reference material in the development of promotional menus for products and services offered by the beauty clinic. The approach used to design components on the promotional menu is Market Basket Analysis by applying the fp-growth algorithm using rapid miner software. Market basket analysis is focused on finding relationships between products based on customer purchases. The market basket analysis conducted resulted in 5 association rules that define consumer purchasing patterns for products and services provided by the Ariana Audy clinic. Through the 5 association rules formed, 3 promotional menus were produced, namely menu 1 consisting of baby skin crystal and oxy blue cream, menu 2 consisting of brightening cream products and sunscreen brightening, and menu 3 consisting of oxy jet peel and photodynamic therapy.
Facial Expression Recognition: Machine Learning Algorithms and Feature Extraction Techniques

Facial expression recognition (FER) systems accurately identify facial expressions by extracting facial features. The extraction of robust facial features comes after automatic face detection in this procedure. On the FAR2013 dataset, a five-step system developed to assess the performance of machine learning algorithms. Components of the system include preprocessing, feature extraction, model training and testing, classification, and evaluation. Three machine-learning algorithms utilized in this study: logistic regression (LR), random forest (RF), and AdaBoost (ADA). The RF algorithm achieved the highest degree of precision with a 61% success rate. The purpose of the study was to evaluate the performance of machine learning algorithms on the FAR2013 dataset. The study highlights the importance of facial feature extraction in FER systems and the precision of machine learning algorithms in facial expression recognition.
A Novel Algorithm for Monitoring Field Data Collection Officers of Indonesia's Central Statistics Agency (BPS) Using Web-Based Digital Technology

This study focuses on the creation of a novel algorithm for monitoring field data collection activities by field data collection officers from Indonesia's Central Statistics Agency (BPS) using web-based digital technology.This study aims to check the accuracy and veracity of data collected by the BPS data collection officer.In this research, 200 respondents were collected by 10 data collection officers of Indonesia's Central Statistics Agency (BPS) and monitored by 5 supervisors' officers from BPS.Then the Head of BPS supervises process monitoring at a certain regional BPS office.We propose a novel algorithm that can be used with web-based digital technologies.The algorithm is developed by comparing data from the BPS office in the form of three variables that are a list of names of respondents, a list of questioners for respondents, and the location (area) of respondents with data from the respondent's NIK (ID Number), answers to respondents' questions entered into the system, and the GPS location of field data collection officers detected by the web-based system.This research employed a novel algorithm on web-based digital technology, increasing the accuracy and veracity of collected data.The score value of the respondent's name variable is 92 percent, the respondent's answer to the questionnaire variable is 93 percent, and the respondent's location variable is 95 percent.The data was evaluated by all field data collection officers, supervisory officers, and the BPS head.
Solar Energy Success: Mastering the Parameters that Matter

Solar energy is a rapidly growing sector, and solar farms are playing an increasingly important role in meeting the world&amp;#039;s energy needs. However, as the size and complexity of these farms increase, so do the challenges associated with managing them efficiently. This article presents a comprehensive analysis of the fundamental parameters that underpin solar energy systems. Focusing on the latest research, we examine the challenges and opportunities intrinsic to the implementation of solar energy systems, paying particular attention to the various parameters that contribute to their performance. These parameters encompass a range of factors such as thermal performance, atmospheric boundary layer, solar energy meteorology, heat islands, cloud influences, agrivoltaic systems, shading factors, and surface energy budget. The review underscores the importance of considering a diverse array of parameters when developing solar energy systems to optimize their efficiency and effectiveness. Ultimately, by unraveling these challenges and opportunities, we can work towards creating more sustainable and reliable renewable energy sources and reducing our dependence on non-renewable alternatives.
Design an Intelligent Balanced Control of Quadruped Legs Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

This study discusses the 12 DoF stability control algorithm for Quadruped robot legs to adjust the balance on irregular terrain. The source of the instability is the irregularity of the ground surface and external forces. Therefore, dynamic stability criteria are needed to plan the robot's movement and restore balance for the movement of a four-legged robot with a dynamic gait over an irregular terrain. The novelty of this study is the use of 12 ANFIS at once to manage the 12 DoF of each leg, which are grouped into four sections, and each section consists of 3 ANFIS. The ANFIS method is used as an algorithm to move the 12-DoF robot legs by training some robot leg movement data based on the slope angle of the surface. The results of training with the ANFIS method can be optimal if the number of rules is close to the given training data. From 29 body tilt angle position data and 12-DOF robot legs, good results will be obtained if the 5x5 number membership function is used for each input which will produce 25 ANFIS rules and combined using the Gaussian type so that it can produce RMSE = 0.068233. The next research is to develop reliable methods such as Zero Moment Point (ZMP) combined with the BPNN or ANFIS methods so that it is expected to get a reliable robot body balance.
Choosing the path to metaverse in HR

Metaverse is increasingly attracting the attention of scholars and practitioners seeking to understand its theoretical foundations and business implications better. This article provides an overview of what has been studied and researched in the academic analysis of the metaverse. It adopts a systematic literature review and bibliometric analysis. The research is a thematic map of metaverse research. Four metaverses of current research, Metaverse in Hr, Metaverse in future and Is India ready for it?
One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction

Recommender systems play a key role in every online platform to provide users a better experience. Many classic recommendation approaches might find issues, mainly modeling user relations. Graphs can naturally model these relations since we can connect users interacting with items. On the other hand, when we model user-item relations through graphs, we do not have interactions between all users and items. In addition, there are few non-recommendation interactions, which makes it challenging to cover this scope. Also, the scope of what will not be recommended for the user is greater than what will be recommended. An alternative is One-Class Learning (OCL) which is able to recommend or not an item for a user only to train with recommendations, mitigating the needing to cover the scope of non-recommendations. However, OCL and Recommender Systems need appropriate, adequate, and robust representations to perform the recommendations in the best possible way. Therefore, we propose the one-class recommendation via representations learned by unsupervised graph neural networks (GNNs) for link prediction to generate a more robust and meaningful representation of users and items. In the results, our GNNs for link prediction outperform other methods to represent the users and items in the one-class recommendation. Furthermore, our proposal also outperforms a GNN for link prediction. Thus, our proposal recommended better and learned more robust representations.
Algorithms of statistical anomalies clearing for data science applications

The paper considers the nature of input data used by Data Science algorithms of modern-day application domains. It then proposes three algorithms designed to remove statistical anomalies from datasets as a part of the Data Science pipeline. The main advantages of given algorithms are their relative simplicity and a small number of configurable parameters. Parameters are determined by machine learning with respect to the properties of input data. These algorithms are flexible and have no strict dependency on the nature and origin of data. The efficiency of the proposed approaches is verified with a modeling experiment conducted using algorithms implemented in Python. The results are illustrated with plots built using raw and processed datasets. The algorithms application is analyzed, and results are compared.
A Hybrid Domain Medical Image Encryption Scheme Using URUK and WAM Chaotic Maps with Wavelet – Fourier Transforms

Image encryption is one of the most important techniques to maintain data confidentiality against illegal access and fraudulent usage. In this study, a new medical image encryption technique was developed by combining the discrete wavelet transform, the fast Fourier transform, the Arnold transform, and two multidimensional chaotic systems. The medical image is subjected to a discrete wavelet transform before the magic square shuffles the image sub-bands. Confusion operations are performed on each scrambled subdomain using the Uruk 4D chaotic system. To increase randomness and unpredictability, a second stage of confusion is implemented in the domain of the Fast Fourier transform using the Arnold transform. The final encrypted image is obtained utilizing secret keys derived from the WAM 3D chaotic system. In particular, the initial conditions for chaotic systems are derived from grayscale values, thereby increasing the algorithm’s sensitivity to the input image. The results of the tests and the security analysis indicate that the proposed algorithm is exceptionally reliable and secure.