Reviewerly/exHarmony huggingface dataset:
Geo-Navigation in Museums: Augmented Reality Application in the Geological Museum Indonesia

Navigational challenges in large buildings with multiple rooms, such as museums, often result in inefficient visitor experiences. Traditional signage and direction plans, while common, do not always effectively convey the necessary information. This paper introduces an innovative solution leveraging Augmented Reality (AR) technology to enhance navigation in such complex environments. We developed a mobile application utilizing the Immersal Software Development Kit (SDK) to facilitate interaction with the surroundings in the Bandung Geological Museum. The application serves as a digital guide, providing clear directions and route information to various rooms within the museum. Our study's findings reveal that the application not only facilitated easier navigation through its accurate room identification and route suggestions but also enhanced the overall visitor experience by making it more interactive and immersive. Furthermore, the user engagement and experience survey, encompassing a broad demographic range, highlighted a significant increase in visitor satisfaction and interaction. The application's intuitive and user-friendly interface played a key role in this enhanced engagement. The survey results reflect the application's success in meeting its main objectives, demonstrating usability, and offering an effective user interface.
Crypto Technology - Impact on Global Economy

The last decade has been marked by the evolution of cryptocurrencies, which have captured the interest of the public through the offered opportunities and the feeling of freedom, resulting from decentralization and lack of authority to oversee how cryptocurrency transactions are conducted. The innovation in crypto space is often compared to the impact internet had on human life. There is a new term called Web 3.0 for denoting all new computing innovations arising due to the blockchain technologies. Blockchain has emerged as one of the most important inventions of the last decade with crypto currencies or financial use case as one of the domains which progressed most in the last 10 years. It is very important to research about Web 3 technologies, how it is connected to crypto economy and what to expect in this field for the next several decades.
The Decision-Support Modeling with Fuzzy Analytic Hierarchy Process (AHP) to Determine the Career Path for Bachelor Informatics Students

The pursuit of success in one's chosen profession is a universal aspiration that requires individuals to engage in competition, not just domestically but also internationally, particularly in today's era of globalization. The rapid advancement of technology presents both opportunities and challenges, necessitating proactive management to achieve professional success. In the field of Informatics Engineering, there is a wide range of professional pathways, each offering unique opportunities and requiring distinct skill sets. For university graduates, it is crucial to have a comprehensive understanding of the specific requirements and demands associated with various job paths to make informed decisions. This knowledge enables them to select a professional route that aligns with their individual aspirations and goals, thereby avoiding potential discontent in their chosen career. Lack of knowledge or awareness during the decision-making process can negatively impact productivity and overall performance. There is a growing demand among university graduates, especially at the undergraduate level, for career-related information. This has led to heightened competition among graduates in terms of skills, knowledge, and availability. To effectively navigate this competitive landscape and engage in meaningful competition with fellow graduates, individuals must possess a comprehensive understanding of their desired career trajectory. To address these challenges, the Decision Support Model (MPK) can be utilized, employing the Fuzzy Analytical Hierarchy Process methodology. This approach considers four primary criteria—Financial Compensation, Non-Financial Compensation, Soft Skills, and Hard Skills—each with several sub-criteria. These criteria are evaluated based on the perspectives of certified graduates of Informatics Engineering and industry specialists. The study successfully identified the primary criteria influencing career decisions, such as job conditions, incentives, and prospects. It also highlighted the most favorable career path, with the role of a Data Analyst being identified as particularly promising. This career path involves roles within the field of Informatics Engineering that focus on processing data according to specific requirements, highlighting the importance of understanding the evolving landscape of technology and its impact on professional opportunities. Doi: 10.28991/ESJ-2024-08-01-011 Full Text: PDF
An Adaptive Harmony Search Part-of-Speech tagger for Square Hmong Corpus

Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.
N-N-E-Trending Tectonic Systems

Introduction to Global Tectonic Systems, pp. 117-145 (2024) No AccessChapter 5: N-N-E-Trending Tectonic SystemsYuzhu Kang, Shuwen Xing, Zhihong Kang, Yue Zhao, Zhihu Ling, Zhijiang Kang, and Huijun LiYuzhu KangSinopec Petroleum Exploration and Production Research Institute, China Petrochemical Corporation, Beijing, China, Shuwen XingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihong KangChina University of Geosciences (Beijing), Beijing, China, Yue ZhaoInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhihu LingInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China, Zhijiang KangSinopec Petroleum Exploration and Production Research Institute, Beijing, China, and Huijun LiInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, Chinahttps://doi.org/10.1142/9789811285561_0005Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In this chapter, N-N-E-trending tectonic systems are introduced, including those in China, New Zealand–Tonga, the Eastern United States and the east coast of South America. Keywords: Tectonic systemN-N-E-trendingType FiguresReferencesRelatedDetails Recommended Introduction to Global Tectonic SystemsMetrics History KeywordsTectonic systemN-N-E-trendingTypePDF download
Multi-genre Digital Music Based on Artificial Intelligence Automation Assisted Composition System

In the present paper, we conduct an in-depth study on multi-styled digital music and design an artificial intelligence-based multi-styled digital theme automation-assisted composition system. In the process of system development, B/S software design architecture is selected, J2EE ecosystem-related technology is used, and the system framework is built following the principle of sub-module development. A web hosting platform is provided for the whole Internet publishing system to realise the online and real-time functions. In addition, to improve the robustness, scalability, security, and ultimately, the system's quality of service, the functional design analysis, software architecture system, development technology, and optimisation strategy are carefully studied and discussed. After learning and analysing the file format of MIDI digital music, we chose the feature extraction method for each instrument to preserve the instrument's characteristics. We used its parameter model for each device during the composition to ensure the instrument's characteristics. After optimisation of the model, composition experiments were conducted, and the compositional effect was measured; the average ratio of adjacent notes with intervals within one octave was 83.57% for the composed pieces. The composition system eliminates using only a single algorithmic composition technique method in the system platform. Instead, it adopts the direction of a hybrid system that integrates multiple processes, an inevitable trend. The composition system provides flexible human-computer interaction at all music composition levels to improve the system's usefulness and effectiveness.
Enhancing Disease Prediction with a Hybrid CNN-LSTM Framework in EHRs

This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.
Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach

Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.
Deployment of optoelectronic devices based on improved NSGA-II

In order to improve the security of the defended targets in our region and efficiently utilize various types of optoelectronic devices, the research is conducted on optoelectronic devices deployment within the region. With the objectives of maximizing the protection effectiveness and minimizing devices operational cost, a multi-objectives optimization model is established by considering constraints such as devices-target visibility conditions and device types, quantities, etc. Non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) improved by Q-learning is designed to solve the model. To address the difficulty of fixed parameter settings adapting to dynamic changes, Q-learning is adopted to adaptively adjust the crossover probability and variation probability. In order to search for Pareto front solutions that are close to the global optimal more efficiently. The correctness of the model and the effectiveness of the algorithm are verified through simulation examples.
Fuzzy-PID-based trajectory tracking for 3WIS robot

This paper presents a trajectory tracking approach for a three-wheel independent steering (3WIS) robot using a fuzzy PID controller. The kinematic model of the 3WIS robot is introduced, highlighting its capability for omnidirectional motion control. A comparison is made between traditional PID controllers and fuzzy PID controllers, emphasizing the advantages of the latter in handling nonlinear systems. The concept of fuzzy PID control is explained, and fuzzy rules for parameter adjustment are proposed. The accuracy of the kinematic model and the effectiveness of the fuzzy PID controller are verified through simulation using the Webots platform and real-world experiment. Results indicate successful trajectory tracking, although slight fluctuations in the heading angle are observed due to frictional torque and gear backlash. The findings validate the accuracy of the kinematic model and the effectiveness of fuzzy PID controller in trajectory tracking.
Supplemental Material: The Zaozigou orogenic gold-antimony deposit, West Qinling Orogen, China: Structural controls on multiple mineralization events

&lt;p&gt;Table S1: Detailed operating parameters and conditions for dolomite U-Pb dating. Table S2: LA-ICP-MS U-Pb isotope data for dolomite. &lt;/p&gt;
An all-in-one lightweight image restoration network based on polarized attention mechanism and efficient feature extraction

Removing noise from images is a challenging task in front-end computer vision applications, especially in snowy, rainy, foggy, and underwater environments, which pose significant difficulties for various visual tasks. Existing image restoration methods often struggle to achieve robust generalization and are burdened with immense computational complexity. This paper introduces a lightweight image restoration network based on polarized attention mechanisms and efficient feature extraction, capable of addressing tasks like rain removal, snow removal, fog removal, and underwater image enhancement. Initially, a polarized self-attention mechanism is proposed to intricately learn weather noise, followed by an efficient noise removal module complementing the initial network. This addition compensates for any noise information overlooked by the polarized self-attention mechanism. Furthermore, residual connections are integrated into each module of the network, along with grouped convolutions, to prevent network degradation and reduce computational load. In comparison to existing models, this network not only learns noise with similar shapes but also adapts to noise with significant shape variations. Additionally, the introduced residual structure ensures the network's stability, avoiding performance degradation before achieving expected results. The collaborative use of the efficient noise removal module and polarized self-attention mechanism sets a high standard for noise learning in this network. Experimental results demonstrate the efficacy of this algorithm on publicly available datasets including Snow100K, CSD, Rain200H, Rain200L, Rain800, RSID, and EUVP. The proposed method efficiently removes various image noises, effectively achieving the desired image restoration objectives.
The Impact and Integration of Artificial Intelligence in Human Resource Management: A Middle Eastern Perspective

This big study looks into how Artificial Intelligence (AI) and Human Resource Management (HRM) connect in the Middle East. In the area where technology is changing very quickly, the study wants to find out how AI integration is changing HR practices in this place. The main part of the study is carefully created surveys for HR people and experts in AI/CS. These aim to find out how AI is used now, what more can be done with it, and ethical questions around its use in HRM. The main results show that AI is drastically making human resources jobs like hiring new workers, managing their paychecks, and checking their performance easier. This lets people working in HR avoid boring tasks and concentrate on big roles. The study also looks into the problems and limits that come with using AI. These include thinking about what's right, keeping your data secret, and needing a human link in HR jobs. This study is very important because it looks at how AI makes HR better. It also shows the careful mix needed between using new technology and keeping people in mind. It gives a future outlook on how AI can change HRM in the Middle Eastern business scene. It offers helpful information for both people who work there and those studying it.
Large Language Models and Artificial Intelligence for Police Report Writing

Large Language Models (LLMs), such as ChatGPT, are advanced artificial intelligence systems capable of understanding and generating human-like text. They are trained on vast amounts of textual data, enabling them to comprehend context, answer questions, generate summaries, and even engage in meaningful conversations. As these models continue to evolve, their potential applications in various industries, including law enforcement, are becoming more apparent, as are the potential threats <https://www.europol.europa.eu/publications-events/publications/chatgpt-impact-of-large-language-models-law-enforcement> . One particularly promising area of application for LLMs in policing is report writing. As many police executives know, not all officers possess strong writing skills, which can lead to inaccurate or incomplete reports. This can have serious consequences for criminal prosecutions, as well as expose departments to civil liability concerns. Implementing LLMs like ChatGPT for report-writing assistance may help address these issues. Even if not fully implemented at the agency level, officers across the country are already using these tools to help in their report generation. Given the stakes, it is wise for agencies to have a sophisticated view and policy on these tools. This paper introduces practitioners to LLMs for report writing, considers the implications of using such tools, and suggests a template-based approach to deploying the technology to patrol officers.
Simulation of exchange processes in multi-component environments with account of data uncertainty

The goal of the work. Proposals for methods of solving systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients that defined in interval form and intended for modeling exchange processes in multicomponent environments. Research subject: systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients defined in interval form. Research method: interval analysis. The obtained results. Systems of linear homogeneous and non-homogeneous differential equations, which are used in modeling exchange processes in multicomponent environments, are considered. Such systems can be considered, for example, in problems of chemical kinetics, materials science, and the theory of Markov processes. To obtain the solution of these equations, specialized calculators of analytical transformations were used and tested. The Matlab system (ode15s solver) was used for numerical analysis of systems of differential equations. It is shown that the application of interval methods of numerical analysis at the initial stage of system modeling has some advantages over probabilistic methods because they do not require knowledge of the laws of distribution of the results of the system state parameter measurements and their errors. It is shown that existing methods of solving systems of linear differential equations can be divided into two groups. Common to these groups is the use of interval expansion of classical methods for solving differential equations given in interval form. The difference between these two groups of methods is as follows. The methods of the first group can be used for all types of differential equations but require the creation of special software. The peculiarity of the methods of the second group is that they can be used to solve equations analytically or using numerical analysis packages. The application of the methods of the second group is shown on the example of solving a system of differential equations, the coefficients of which are determined in interval form. The system of these equations is intended for modeling the processes of exchange with the external environment of the elements of the model of a specific physical system. In the case when the coefficients of these equations are variables, their piecewise-constant approximation is applied and a criterion that determines the possibility of its application is given. The technique proposed in the paper can be applied to solve systems of linear homogeneous and non-homogeneous differential equations with constant and variable coefficients if they are given by slowly varying functions. In the case when the coefficients of the equations are determined in the interval form, the technique allows obtaining their solution also in the interval form and does not require the creation of special software.
ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION

The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder.
Horizontal Learning Approach to Discover Association Rules

Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case).