Reviewerly/exHarmony huggingface dataset:
EVALUATION OF GROUND WATER QUALITY IN Al-BAIDH FIELD, HODIEDA CITY, YEMEN

Ground water is the main source for drinking water in Hodieda city, Yemen. This study discusses the chemical and physical characteristics of nineteen ground water wells in the Al-baidh field. Thus, the measurement of water quality parameters such as electrical conductivity EC, total dissolved solids TDS, pH, turbidity, color, total alkalinity, total hardness TH , NO3-,SO42-, PO43-, Cl- ,Na+, K+, Ca2+ and Mg2+ in addition to Mn, Cu, Fe and Cr were expressed. The results indicate unacceptable&#x0D; levels of TDS , Cl-, Na+ , Ca2+ and Mg2+comparing with the maximum tolerable concentration given by World Health Organization (WHO) and Yemen standard (YS). Elsewhere, the measurement of SO42- and NO3- exceeded the allowable values given by WHO and Y.S for most the samples which can be attributed to the nature of the geological and chemical structure of soil in Albaidh field. In this study, measurement of the concentration for elements such as Cr, Fe, and Mn were carried out, the results indicate that the values for this elements were within the allowable ranges. In this paper the concepts of star- refinement and strongly star- refinement of covering are extended to fuzzy topological space in the sense of Chang, basic theorem for covering dimension of normal fuzzy topological space is proved . Also, the small inductive dimension function is extended to fuzzy topological space, and some results for this inductive dimension in Chang’s space are obtained.
Optimized Stream-Cipher-Based Transciphering by Means of Functional-Bootstrapping

Fully homomorphic encryption suffers from a large expansion in the size of encrypted data, which makes FHE impractical for low-bandwidth networks. Fortunately, transciphering allows to circumvent this issue by involving a symmetric cryptosystem which does not carry the disadvantage of a large expansion factor, and maintains the ability to recover an FHE ciphertext with the cost of extra homomorphic computations on the receiver side. Recent works have started to investigate the efficiency of TFHE as the FHE layer in transciphering, combined with various symmetric schemes including a NIST finalist for lightweight cryptography, namely Grain128-AEAD. Yet, this has so far been done without taking advantage of TFHE functional bootstrapping abilities, that is, evaluating any discrete function “for free” within the bootstrapping operation. In this work, we thus investigate the use of TFHE functional bootstrapping for implementing Grain128-AEAD in a more efficient base ( $$B > 2$$ ) representation, rather than a binary one. This significantly reduces the overall number of necessary bootstrappings in a homomorphic run of the stream-cipher, for example reducing the number of bootstrappings required in the warm-up phase by a factor of $$\approx $$ 3 when $$B=16$$ .
UAV Swarm Control and Coordination Using Jason BDI Agents on Top of ROS

This paper describes and evaluates the use of Jason BDI agents to make the high-level control and the coordination of a swarm of autonomous unmanned aerial vehicles (UAVs). The UAVs in the swarm work together searching and extinguishing fire spots in a simulated forest firefighting scenario. This problem was presented by the 2022 SARC–BARINET aerospace competition, which makes use of the ROS/Gazebo simulation environment. ROS is also used here to perform the low-level UAV’s control. Our solution requires a minimum of 2 UAVs and can be easily scaled. We were able to successfully complete the given mission using up to 8 UAVs. Additional experiments devoted for performance-data collection were conducted using swarms with 4, 6, and 8 UAVs. Obtained results are presented and discussed along the paper.
GeoNLPlify: A spatial data augmentation enhancing text classification for crisis monitoring

Crises such as natural disasters and public health emergencies generate vast amounts of text data, making it challenging to classify the information into relevant categories. Acquiring expert-labeled data for such scenarios can be difficult, leading to limited training datasets for text classification by fine-tuning BERT-like models. Unfortunately, traditional data augmentation techniques only slightly improve F1-scores. How can data augmentation be used to obtain better results in this applied domain? In this paper, using neural network explicability methods, we aim to highlight that fine-tuned BERT-like models on crisis corpora give too much importance to spatial information to make their predictions. This overfitting of spatial information limits their ability to generalize especially when the event which occurs in a place has evolved and changed since the training dataset has been built. To reduce this bias, we propose GeoNLPlify,1 a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.
Does Digital Balanced Scorecards Lead To The Sustainable Performance Amongst The Jordanian SMEs?

Purpose: This study aims to fill up a literature gap related to the sustainable performance in Jordanian SMEs, where the literature focused on limited variables related only to the human capital. Theoretical framework: The researchers provided a suggested model to be implemented in the SMEs in Jordan as one of the industries that must be activated in the country to help the economy. Management accounting system and digital balanced scorecards were the main variables contributing, while variables like capital forecasting and decision making also were playing the role of the dependents variable for the D.BSC. Design/methodology/approach: All of the mentioned variable toward sustainable performance in the Jordanian SMEs in this quantitative research tested via randomly distributing questionnaires among the employees of the industry with returned and valid for analysis of 910 surveys. Using SPSS for descriptive analysis and AMOS for the EFA and CFA. Findings: The results deducted as there is a significant mediation role for D.BSC between MAS and decision making and insignificant mediation role for D.BSC between MAS and capital forecasting. Research, Practical &amp; Social implications: This research is suggesting a model and variables to be used by the SMEs in Jordan. This model will vast the SMEs industry and support the society by affording further employment chances. Originality/value: The paper contributes to the body of knowledge and the Jordanian SMEs industry and Jordanian society. This paper targeting the most important industry in the country currently because such third world countries needed the SMEs referring to the previous studies.
The Impact of HRM Practices on Employee Performance at the Ministry of Agriculture and Fisheries in Oman the Moderating Role of Organizational Justice

Purpose: The purpose of this article is to examine the impact of HRM practices on employee performance in the Ministry of Agriculture and Fisheries in Oman, as well as the moderating role of organizational justice in this relationship. Theoretical framework: This study integrates theoretical perspectives on HRM practices, organizational justice, and employee performance. It posits that HRM practices have a direct positive impact on employee performance, while organizational justice moderates the relationship between these factors and employee performance. Design/methodology/approach: A survey questionnaire instrument was employed to collect data from 387 employees working at Ministry of Agriculture and Fisheries Wealth (MAFW) department in Oman. For analyzing the data, the SPSS v23 and Partial Least Square SEM using Smart-PLS 3.3. 9 were used to test the proposed hypotheses. Findings: The results revealed that HRM practices have a significant positive impact on employee performance. Organizational justice was found to moderate the relationship between HRM practices and employee performance, highlighting the importance of fairness and equity in the workplace. Research, Practical &amp; Social implications: The findings of this study provide valuable insights into how the Ministry of Agriculture and Fisheries in Oman can enhance its HRM practices to improve employee performance and achieve its objectives. The study's practical implications include the design of effective human resource management strategies that enhance employee performance and contribute to the organization's success. The social implications of the study include the creation of a positive work environment that promotes fairness and equity, leading to improved employee satisfaction and engagement. Originality/value: This study adds to the literature on the effectiveness of HRM practices and organizational justice in enhancing employee performance in a specific context, namely the Ministry of Agriculture and Fisheries in Oman. The study also highlights the moderating role of organizational justice in the relationship between HRM practices and employee performance, contributing to the literature on the importance of fairness and equity in the workplace.
The Effect of After School Exercise New Sports Exercise Program on Physical Fitness and Obesity Factors among Obese Girls in Elementary School

Twenty five obese elementary school students were participated in this study. The students in the exercise group participated in an exercise program that applied T-ball, sports dodgeball, and chuke ball three times a week and 60 minutes a day for 12 weeks. The following conclusions were obtained as a result of examining the effects of the exercise program on physical fitness and obesity factors. First, weight and BMI in body composition decreased after participating 12 weeks of after school new sports exercise program. Second, among physical fitness factors, flexibility, power, and muscle strength. Third, in the obesity factors, Leptin decreased and Adiponectin increased, and Ghrelin decreased in the exercise group, but increased in the control group. In the consideration of results of this study, new sport activities are found to improve body composition among obese elementary school girls and to have positive effects on physical fitness and obesity-related hormone factors. Therefor, new sport activities need to be recommended to school-age obese children for their recognition of the importance exercise and life long participation in exercise by forming exercise habits in early age.
Functional extreme learning machine

Introduction Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. However, the ELM also has some shortcomings, such as structure selection, overfitting and low generalization performance. Methods This article a new functional neuron (FN) model is proposed, we takes functional neurons as the basic unit, and uses functional equation solving theory to guide the modeling process of FELM, a new functional extreme learning machine (FELM) model theory is proposed. Results The FELM implements learning by adjusting the coefficients of the basis function in neurons. At the same time, a simple, iterative-free and high-precision fast parameter learning algorithm is proposed. Discussion The standard data sets UCI and StatLib are selected for regression problems, and compared with the ELM, support vector machine (SVM) and other algorithms, the experimental results show that the FELM achieves better performance.
Probabilistic Deep Learning for Validation of Emergent Structures in Simulated Images

Abstract Deterministic integrated metrics for quantitative comparison of simulated images and experimental images, e.g., RMS error, are agnostic to structures that can emerge in highly nonlinear complex systems. Similarly, simple probabilistic metrics, such as direct comparisons of image data distributions, also do not explicitly account for salient structures. Normalizing flow architectures are probabilistic generative deep learning algorithms that leverage the nonlinear pattern recognition capacity of neural networks with variational Bayesian methods to assign likelihood values to images with respect to a “target” probability density learned from training images. If a normalizing flow is trained on simulation image data, then it can be used to quantify the probability that an experiment image could have been sampled from the unknown high dimensional distribution that describes the simulated images and vice versa. We demonstrate this validation method using the real non-volume-preserving (RealNVP) normalizing flow architecture and MNIST, corrupted MNIST, Wingdings, and blurred Wingdings data sets. Normalizing flows, and consequently our validation method, are not limited to two-dimensional data and may be applied to higher dimensions with appropriate modifications. Applications include, but are not limited to, turbulent flow simulations, proton radiography simulations, multi-phase flow simulations, and medical radiology.
AUTOMATED WHEELCHAIR FOR DIFFERENTLY ABLED PERSON WITH FALL DETECTION AND MANEUVERABILITY

While many people with disabilities can benefit from power wheelchairs, some people with disabilities find it difficult or impossible to use a basic power wheelchair. To serve this group, several researchers have developed "smart wheelchairs" that use technologies originally developed for mobile robots to reduce the physical, perceptual, and cognitive skills needed to operate a motorized wheelchair. Smart Wheelchairs are mechanically controlled devices that can move on their own with the help of a user command. This reduces the user's human effort and force required to push the wheelchair wheels. It also allows visually or physically disabled people to move from one location to another. The wheelchair also has an obstacle detection system, which reduces the likelihood of a collision while traveling.
A Systematic Review on Recommendation Systems Developed for the Field of Adult Education

Today, the changing job performances and the desire of individuals to improve themselves have created the desire of people to quickly reach the content that is suitable for them. The use of suggestion systems designed to identify the needs of individuals and present the most appropriate content is considered as a solution method in this regard. The aim of this systematic study is to determine the trends in the field by making a comprehensive analysis about what kind of suggestions are given in the studies on the recommendation systems used and designed in the field of adult education, in which year the studies were conducted, the research method used, the filtering methods used and the algorithms used, and to identify the trends in this field to establish an up-to-date basis for new entrants. As a result of the review made in various databases, 113 studies were reached, and a systematic analysis of 75 studies that met the inclusion criteria was carried out. As a result of the review, it was seen that the most content suggestions were made, the most publications were made in 2020, the research method focused on determining the system performance and promoting, and a limited number of experimental studies were included. It has been determined that the most collaborative filtering method is used, and content-based and hybrid filtering methods are less preferred. It has been concluded that the K nearest neighbor algorithm is used much more than other algorithms, and besides this algorithm, artificial neural networks, support vector machines, decision trees and newly proposed algorithms by the researchers are also included. In line with the results obtained, investigations were made and suggestions were made for practice and research for future studies.
Retracted: Application of Digital Information Technology in Book Classification and Quick Search in University Libraries

[This retracts the article DOI: 10.1155/2022/4543467.].
Retracted: Biomechanical Analysis of Volleyball Players’ Spike Swing Based on Deep Learning

[This retracts the article DOI: 10.1155/2022/4797273.].
A fast stereo matching network based on temporal attention and 2D convolution

We propose a fast stereo matching network based on temporal attention and 2D convolution (TANet). Due to the high similarity of the disparity between consecutive frames in an image sequence, we propose a temporal attention (TA) module that uses the disparity map of the previous frame to guide the disparity search range in the current frame, thus significantly improving the efficiency of disparity calculation in the cost volume module. Additionally, we propose a hierarchical cost construction and 2D convolution aggregation module that constructs a pyramid cost volume by fusing edge cues to establish detail constraints. This overcomes the problem of difficult convergence caused by information loss when replacing 3D convolution with 2D convolution. Experimental results show that the TA module effectively optimizes the cost volume and, together with 2D convolution, improves the computational speed. Compared with state-of-the-art algorithms, TANet achieves a speedup of nearly 4x, with a running time of 0.061s, and reduces the parameter count by nearly half while decreasing accuracy by 1.1%. Code is available at https://github.com/Y0uchenZ/TANet.
Improving Junior High School students' vocabulary achievement by using WhatsApp

Mobile messaging applications are increasingly widespread among the new generation of students in Indonesia. They use michat, line, WhatsApp and the other messaging applications. This study aimed to improve students' vocabulary achievement by using WhatsApp of VII C students of SMPS Panca Setya Sintang in the academic year 2022/2023. In one cycle, the writer used classroom action research on 19 male and 11 female students. The writer sent vocabulary instructions electronically for two meetings. The multiple-choice test was conducted after each meeting. The results showed that 63,33 % of the students could improve their achievement in the second meeting. It means that the use of WhatsApp in teaching vocabulary can improve students' achievement. Referring to the writer's experience in conducting action research, the writer offers a suggestion. It is suggested that English teachers can use WhatsApp as an alternative application to improve students' vocabulary achievement.
SAIN: A Community-Wide Software Architecture INfrastructure

Software Architecture is the most important determinant of the functional and non-functional attributes of a system [1]–[3]. Put simply, software systems “live and die” by their architectures [4]. Despite the importance, the architecture of a software system is often not explicitly documented, especially in the prevalent Agile methods in the past decades. Instead, the architecture of a system often becomes hidden in the myriad system implementation details, and gradually decays and accumulates grime-causing significant challenges to its long-term evolution and maintenance [5]–[8]. Recovering, understanding, and updating a system's architecture is an important facet of overcoming this challenge to support the evolution and maintenance of long-lived software systems.
The ABLoTS Approach for Bug Localization: is it replicable and generalizable?

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.
Prediksi Pergerakan Saham BBRI ditengah Issue Ancaman Resesi 2023 dengan Pendekatan Machine Learning

The economic recovery after the Covid-19 pandemic is becoming increasingly challenging. According to several experts, a global recession is expected to occur in 2023, necessitating contributions from various fields of knowledge to address this situation. Machine learning is one method that can contribute by forecasting stock price movements. This research attempts to address the issues faced by traders in observing the potential movement of BBRI stock under the recession issue in 2023. Furthermore, this study uses linear regression and Bayesian regression methods to find the best model. By using six-month stock data of BBRI, with attributes such as open, high, low, and close as prediction targets, it is found that the model built using linear regression outperforms Bayesian regression. Based on the testing results, the linear regression model achieved a Dstat of 80% and an RMSE of 595.30, while the Bayesian regression model obtained a Dstat of 80% but a higher RMSE of 660.58. Based on the modeling results in this study, it is concluded that in the first semester of 2023, BBRI stock is still moving upward and is not affected by the recession issue in 2023.
Obstacle avoidance and formation transformation of multi-agent groups based on six-wheeled robot

Based on SolidWorks software, the system model of six-wheeled robot is built. Aiming at the problem of robot localization, the localization of six-wheeled robot in known environment is realized based on AMCL algorithm. Aiming at the formation control problem of multi-robot system, the controller based on Lyapunov theorem and the controller based on Pid control are designed by using the Leader-Following algorithm. The control effects of the two controllers are simulated based on Ros Operating System (ROS). Aiming at the problem of group obstacle avoidance in multi-robot system, a multi-robot formation as a whole obstacle avoidance strategy is proposed based on A* and DwA algorithm, and the navigation and group obstacle avoidance of multi-robot system are realized. Considering that the multi-robot system needs to adapt to different environments, different formation formations are selected by setting priority to improve the environmental adaptability of the multi-robot system.