Reviewerly/exHarmony huggingface dataset:
Lithological mapping of northern Kohat Plateau’s limestone outcrops using integrated remote sensing and reflectance spectroscopy techniques

Remote sensing (RS) has emerged as a valuable technology for precisely demarcating the extent of lithological units in rugged terrains. It offers valuable insights into the spatial distribution and structural dynamics of complex topographies. In this study, RS technology manifested by Sentinel-2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery is used to discriminate carbonaceous strata in various parts of the Kohat Plateau region, northwest Pakistan. Various spectral image transformation techniques, including principal component analysis (PCA), minimum noise fraction (MNF), band ratio (BR), and carbonate index (CI), were utilized after atmospheric correction modules. The resultant red-green-blue (RGB) color composites obtained from Sentinel-2A, incorporating PCA (PC1, PC2, PC4) and MNF (MNF1, MNF2, MNF3), demonstrated its applicability to accurately discriminate the target features. The derived RGB color composites from the BR on ASTER data (BR5/1, BR5/7, BR3/4 × 5/4) and CI B13/B14 effectively identified the limestone bodies, providing a better approach to differentiate limestone from other lithological units. Also, the integration of spectroscopic field data with satellite-based resultant images represented a meaningful contribution to rectifying previous geological maps. The derived maps from aforementioned techniques exhibited a notable attribute to record the target units and enhance limestone differentiation from other lithologies.
Study on the perception of generation Z in relation to robotized selection processes

Born in the mid-1990s, Generation Z emerged in the midst of a society whose advances in digital and technological media have been shaping the way companies act, especially in hiring people. This is an emerging generation, and studies and research on it are incipient. The aim of this study was to identify and analyze the perception of Generation Z regarding the robotization of selection processes and the application of artificial intelligence. The study used a survey with online questionnaires addressing how Generation Z has behaved in robotic selection processes. Contradicting what is disclosed in the media in general, a representative part of the sample is reticent in accepting a robotic selection process conducted by artificial intelligence as a mediator; besides that, there is a certain distrust with personal data made available technologically, as there are several platforms that adhere to this technology as a method of filtering suitable candidates for a given vacancy. Although the research had a reduced number of respondents (108) and a sample of only one program from a single university, which is a limitation of the study, the analyses of the results indicate the need for companies to review the use of artificial intelligence to ensure greater humanization, starting from the selection process in organizations. Furthermore, the data reveal that Generation Z may not represent a group of people with a completely homogeneous profile as shown by the media in general.
Ultra-short-term forecasting of global horizontal irradiance (GHI) integrating all-sky images and historical sequences

Accurate minute solar forecasts play an increasingly crucial role in achieving optimal intra-day power grid dispatch. However, continuous changes in cloud distribution and coverage pose a challenge to solar forecasting. This study presents a convolutional neural network-long short-term memory (CNN-LSTM) model to predict the future 10-min global horizontal irradiance (GHI) integrating all-sky image (ASI) and GHI sequences as input. The CNN is used to extract the sky features from ASI and a fully connected layer is used to extract historical GHI information. The resulting temporary information outputs are then merged and forwarded to the LSTM for forecasting the GHI values for the next 10 min. Compared to CNN solar radiation forecasting models, incorporating GHI into the forecasting process leads to an improvement of 18% in the accuracy of forecasting GHI values for the next 10 min. This improvement can be attributed to the inclusion of historical GHI sequences and regression via LSTM. The historical GHI contains valuable meteorological information such as aerosol optical thickness. In addition, the sensitivity analysis shows that the 1-lagged input length of the GHI and ASI sequence yields the most accurate forecasts. The advantages of CNN-LSTM facilitate power system stability and economic operation. Codes of the CNN-LSTM model in the public domain are available online on the GitHub repository https://github.com/zoey0919/CNN-LSTM-for-GHI-forecasting.
Multi-Agents System for Game Development Through Unreal Engine

Nowadays the video game market is the highest in the entertainment industry. There are many architectures supporting the creation of a game, such as Unreal Engine and Unity. Multi-agent system is a popular and suitable solution to design platform games. It is employed to place streets, buildings and other items, resulting in a playable video game map. The system utilizes computational agents that act in conjunction with the human designer to produce maps that exhibit desirable characteristics. This paper proposes a new architecture for organizing in game development based on the multi-agents system (MAS). This architecture includes the structure of an agent with its attributes and internal behaviors, and the structure of relations between agents for action coordination in a determined strategy This structure of MAS is also applied to build a demonstration video game as an action game through Unreal Engine, which is a complete suite of development tools made for anyone working with real-time technology.
RANCANG BANGUN E-PERPUSTAKAAN DI SMA NEGERI 16 BATAM DENGAN CODEIGNITER

Technology development growth globally has continue to strive to work more effectively and efficiently. One solution is to computerize systems that were previously conventional. In this case, the use of the website can help the library to be more effective. In this study a web-based library application was built using the CodeIgniter Framework. With this system, it is hoped that it can meet various user needs, such as searching for books, recording member data, recording book data, returning books, calculating fines, borrowing books and preparing reports. The system development methodology used in this study is the software development method with the Extreme Programming model. The system was built using the Hypertext Pre Processor (PHP) programming language and MySQL database. The results of this research are that the web-based library information system is intended for library students and staff with a user-friendly web interface, users can search for books, borrow, return, managing library members becomes more practical. In designing a library information system, there are additional features that are quite important, such as data reports on books, members, transactions and fines that can assist in monitoring and making decisions related to library management.
Assessment and quantification of heavy metals in cultivated soils in Kafin Hausa Jigawa State Nigeria using Nigerian research reactor‐1

Abstract Anthropogenic activities that cause heavy metal contamination of agricultural soils pose a significant health risk to humans who consume contaminated agricultural foods such as rice and vegetables. The purpose of this study was to access the level of heavy metal contamination in agricultural soils in Kafin Hausa by analyzing soil samples from five different sampling sites for heavy metals including aluminum (Al), titanium (Ti), vanadium (V), manganese (Mn), chromium (Cr), iron (Fe), cobalt (Co), and zinc (Zn). A total of 15 soil samples were collected using a soil auger from various locations within Kafin Hausa area, and the concentrations of the collected soil samples were estimated using the Instrumental Neutron Activation Analysis (INAA) technique via the Nigeria Research Reactor‐1. The results of the study showed that the mean concentrations of these heavy metals in the sampled soils were within the limits set by the World Health Organization. In addition, pollution indexes such as enrichment factor (EF), contamination factor (CF), and geo‐accumulation index (Igeo) were adequately determined. The sampling sites were identified to have significant enrichment, except for BR3 which was moderately enriched with Al and Ti, followed by Fe, and had an EF value of 4.87. Mean Igeo values indicated that all the sampling sites were uncontaminated. Furthermore, multivariate statistics revealed a strong positive correlation ( p &lt; 0.01) and ( p &lt; 0.05) among the investigated metals, suggesting that the major sources of those metals are similar.
APLIKASI PENGENALAN PERANGKAT KERAS KOMPUTER BERBASIS ANDROID MENGGUNAKAN AUGMENTED REALITY

In the Information and Communication Technology (ICT) learning system in elementary schools there are various kinds of material, namely regarding the introduction of computers in general. There is something that often becomes a problem when teaching, namely at this time in the current era of development, children have been introduced as early as possible to laptops, which are lighter and more practical than computers. Augmented Reality (AR) is a technology that is combined with a 3D object into a real form using an intermediary medium, namely the camera on Android. The markerless method is a method with the latest features that almost resemble Markerless based tracking so that users are not required to print markerless books to be able to display digital elements. Markerless development is influenced by the development of mobile-based supporting devices in the form of sensors and cameras. Android is an operating system based on mobile Linux and provides an open platform for developers to create their own applications so that they can be used by a variety of mobile devices. The results of this study are to produce computer hardware recognition applications in the form of 3D Argumented Reality.
Dynamic sparse coding-based value estimation network for deep reinforcement learning

Deep Reinforcement Learning (DRL) is one powerful tool for varied control automation problems. Performances of DRL highly depend on the accuracy of value estimation for states from environments. However, the Value Estimation Network (VEN) in DRL can be easily influenced by the phenomenon of catastrophic interference from environments and training. In this paper, we propose a Dynamic Sparse Coding-based (DSC) VEN model to obtain precise sparse representations for accurate value prediction and sparse parameters for efficient training, which is not only applicable in Q-learning structured discrete-action DRL but also in actor-critic structured continuous-action DRL. In detail, to alleviate interference in VEN, we propose to employ DSC to learn sparse representations for accurate value estimation with dynamic gradients beyond the conventional ℓ1 norm that provides same-value gradients. To avoid influences from redundant parameters, we employ DSC to prune weights with dynamic thresholds more efficiently than static thresholds like ℓ1 norm. Experiments demonstrate that the proposed algorithms with dynamic sparse coding can obtain higher control performances than existing benchmark DRL algorithms in both discrete-action and continuous-action environments, e.g., over 25% increase in Puddle World and about 10% increase in Hopper. Moreover, the proposed algorithm can reach convergence efficiently with fewer episodes in different environments.
A Study on the Use of Reflexive Pronoun by Chinese Korean Learners: Focusing on ‘자기’, ‘자신’, ‘스스로‘

The purpose of this study is to review how Chinese learners use the reflexive verb ‘자기, 자신, 스스로' and what errors occur based on corpus data from the learning resources of the National Institute of the Korean Language, and to find the cause of errors from the point of view of contrastive linguistics. It is expected that this study can be used as basic data to suggest a solution to the Reflexive Pronoun error or an educational plan. As a result, Chinese learners show errors when using antecedent constraints and reflexive functions. When educating Korean reflexives, it is necessary to explain the difference between reflexives and pronouns and specify the difference between reflexives. In addition, it is necessary to educate about the antecedent constraint of the reflexive and the reflexive function. (Sungkyunkwan University)
Design and application of intelligent fitness system based on android system

Abstract The purpose of this study is to make use of the features of mobile smart phones to create a simple step by step fitness application that can exercise anytime and anywhere. A third‐party open‐source library based on the Android platform was adopted in the entire technology selection, which greatly reduces the writing of a lot of pattern code and enables developers to focus more on the development of fitness system functions. The system has basic functions such as “Fitness data” and “Fitness video courses,” and collects fitness movements through sensors. Based on the extracted eigenvalues and support vector machines (SVM) algorithm, the system determines whether the fitness actions are standard or not, and uses back propagation (BP) neural network to classify and count fitness actions, to achieve recognition of fitness movements. The experimental results show that, the response time, running speed, and load of the system meet daily needs, the system can give the correct instruction probability within 3 s is 74.2%, the correct instruction probability within 3 ˜ 4 s is 17.5%, and the recognition probability after 4 s is only 6.67%, which proves that the intelligent fitness system designed in this study based on the Android system meets basic usage requirements in terms of performance and functionality.
A model of planned and unplanned information-seeking behaviour

The main purpose of this article is to present a model for information-seeking behaviour with an emphasis on unplanned and planned behaviour of users in using library resources and services. The working method was that, reviewing the literature and previous information behaviour models, such as Wilson, Ellis, Kuhlthau, and Dervin models, this article proposes a novel model of information-seeking behaviour for library users. Our model of information-seeking behaviour was developed by combining the existing models of planned information-seeking behaviour with the focus on the factors affecting unplanned rather than planned behaviour of users in accessing resources or services. Our proposed model for information-seeking behaviour of clients has two main parts. The first part planned behaviour resulting from a problem or a certain information need according to which the user seeks to find information in a planned manner. The second part deals with unplanned behaviour shaped by a hidden or uncertain information need. Finally, both types of behaviour can result in the discovery, extraction, collection and use of information.
Effect of Agriculture on Sustainable Financial Sector’s Growth in Nigeria

Plethora of studies has been concluded on the relationship between agriculture and economic growth, including a few other sectors of the economy. However, no attention has been accorded the relationship between agriculture and the financial sector in line with Demand-Following Hypothesis. Against this backdrop, this study examined the effect of agriculture on the sustainable growth of the Nigerian financial sector from 1999 to 2020. Data were collected from the secondary source and extracted from the Central Bank of Nigeria’s Statistical Bulletin. Analysis of the data was carried out using Autoregressive Distributed Lags Model (ARDL). The result revealed that the variables were co-integrated in the long-run; in addition, credits granted to agricultural sector by the financial institutions (GCA), amount granted to agricultural sector under Agricultural Credit Guarantee Scheme (ACGS) were positively but insignificantly associated with the growth of the financial sector in the long-run with the coefficients: 0.008GCA (p-value = 0.1511&gt;0.05), 0.002ACGS (p-value = 0.4452&gt;0.05). The productivity of the agricultural sector (AGRPROD) however, with coefficient 1.002AGRPROD (p-value = 0.0001&lt;0.05), was predominantly positive and statistically significant in influencing the sustainability of the Nigerian financial sector’s growth in the long-run; this thus amounted to confirmation of Demand-Following Hypothesis. Furthermore, the lending interest rate maintained negative but insignificant effect on the growth of the financial sector in Nigeria. Based on the foregoing result, the study concluded that agriculture positively and significantly promoted the sustainability of the Nigerian financial sector, and recommended accordingly, that agricultural sector should be given top priority in terms of funds and other resources allocation as this culminates in stimulating financial sector’s sustainable growth in Nigeria.
IMPLEMENTASI DATA MINING PADA PENGATURAN DATA INVOICE DISTRIBUTOR MENGGUNAKAN ALGORITMA FP GROWTH

A research was conducted at PT. Wyssa Artha Sejahtera a distribution company, to explore the processing of sales transaction data using the FP Growth algorithm to support better decision-making. The company deals with a large volume of daily sales transaction data involving various types of products, both wet and dry. The study aimed to extract useful information from the sales data, such as sales trends and the most popular products among customers. Data was collected through observations and direct interviews with the company owner from January 2022 to December 2022, using the proactive method for more comprehensive and accurate information. Upon collecting the sales transaction data, the research identified certain products with higher sales than others. RapidMiner software was utilized for processing the sales data, which proved to be suitable for implementing the FP Growth algorithm, especially for wet and dry product types with increasing sales transaction data. In the testing phase, RapidMiner successfully discovered item sets 1, 2, 3, and 4, along with their corresponding support and confidence values.The results of this research carry significant implications for PT. Wyssa Artha Sejahtera in making more informed decisions. By analyzing the sales transaction data, the company can devise more effective strategies to boost sales and meet customer demands. Furthermore, the findings can serve as a valuable reference for other companies in similar industries, helping to enhance their decision-making processes. The FP Growth algorithm analysis revealed that item sets 2, 3, and 4 had a minimum support of 20% and a minimum confidence of 70%.
Phase space load balancing priority scheduling algorithm for cloud computing clusters

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A parallel optimization and transfer learning approach for summarization in electrical power systems

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Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.
GAGPT-2: A Geometric Attention-based GPT-2 Framework for Image Captioning in Hindi

Image captioning frameworks usually employ an encoder-decoder paradigm, with the encoder receiving abstract image feature vectors as input and the decoder for language modeling. Nowadays, most prominent architectures employ features from region proposals derived from object detection modules. In this work, we propose a novel architecture for image captioning. We employ the object detection module integrated with transformer architecture as an encoder and GPT-2 (Generative Pre-trained Transformer) as a decoder. The encoder utilizes the information of the spatial relationships among detected objects. We introduce a unique methodology for image caption generation in Hindi, which is widely spoken in South Asia and India and is the world’s third most spoken language as well as India’s official language. In terms of BLEU scores, the proposed approach’s performance is comparable to those of other baselines, and the results illustrate that the proposed approach outperforms the other baselines. The efficacy of the proposed approach in generating correct captions is further determined by human assessment in terms of adequacy and fluency.
Job Portal

"Job Portal” System is a student project that aims to create a website to facilitate the process of finding and applying for job opportunities. The project will develop a platform that allows job seekers to search for and apply to job openings, upload and manage their resumes and cover letters, and receive alerts for new job opportunities that match their preferences. The website will also provide resources and support for job seekers, including information about different industries and career paths, tips for preparing for job interviews and negotiations, and guidance on professional development. The goal of the project is to make it easier and more efficient for individuals to find and pursue job opportunities and to help employers connect with qualified candidates. By providing a comprehensive and user-friendly tool for managing the job search process, the Job Portal System hopes to contribute to a more dynamic and successful job market. The website utilizes various technologies and frameworks such as React, NodeJS, Express, MongoDB.