Reviewerly/exHarmony huggingface dataset:
The Impact of Financial Compensations on Job Satisfaction of Public Service Employees

The study aims to test the direct and indirect financial compensation on job satisfaction among public service employees in five branches of the Ministry of Social Affairs in the southern Palestinian governorates. Identifying the impact of financial compensations dimensions (direct; job, salary, various bonuses, and indirect; promotion, end-of-service benefits, and retirement benefits) on the job satisfaction. It adopted descriptive-analytical approach, where a survey questionnaire was developed and distributed to a sample-representing %50 of the study consisting of 228 employees, and a recovery rate of 91%. The study results showed that there is a medium significant effect for the two dimensions; the job and end-of-service benefits, and a low significant effect for the dimensions; salary, various bonuses, promotion, and retirement benefits in the government compensation on the levels of job satisfaction. It was proven that the government financial compensation varies between medium and low impact. Moreover, there is no commitment in the regulations for granting compensation, which negatively affects the levels of job satisfaction, due to defective mechanisms for applying the direct and indirect financial compensation, and the discrepancy of standards set by the Public Service Law. Finally, the study recommends taking clear strategic measures, and addressing the deficiencies of the mechanisms related to (job, salary, various bonuses, promotions, end-of-service benefits, and retirement benefits), to develop them in order to increase effectiveness of government’s financial compensation. In addition to consolidating principles of justice, fairness and equality in granting them, and equitable the right of women to increase job satisfaction, and raise the level of performance and productivity.
Authentic Materials of Choice among English Lecturers

This study aims to reveal the indicators of lecturers in choosing materials. The method used in this research is descriptive qualitative using a narrative approach. Researchers used documentation, document analysis checklists, and interview guidelines as research instruments. The results of the study indicate that lecturers have four criteria in choosing authentic materials, including 1) authentic materials must follow the needs of students; 2) authentic material must be essential to teach; 3) authentic material must be easy to understand; 4) Authentic materials must be up-to-date. Based on the criteria, several authentic materials can be used: letterforms, order forms, signs, photos, and authentic auditory materials such as videos and songs to provide students with an enlightening and exciting learning experience. In conclusion, English lecturers have used various types of authentic materials such as pictures, videos, and songs using several criteria, such as appropriateness, excitement, understandable, and up-to-date.&#x0D; Keywords: Authentic Material, EFL, Learning, Lecturer
How do people find pairs?

Humans continuously scan their visual environment for relevant information. Such visual search behavior has typically been studied with tasks in which the search goal is constant and well-defined, requiring relatively little interplay between memory and orienting. Here we studied a situation in which the target is not known in advance, and instead, memory needs to be dynamically updated during the actual search. Observers compared two simultaneously presented arrays of objects for any matching pair of items-a task that requires continuous comparisons between what is seen now and what was seen a few moments ago. To manipulate the balance between memorizing and scanning, we ran two versions of the task. In an eye-tracking version, the objects were continuously available and could be scanned with relative ease. The results suggested that observers preferred scanning over memorizing. In a mouse-tracking version, perceptual availability was limited, and scanning was slowed. Now observers substantially increased their memory use. Thus, the results revealed a flexible and dynamic interplay between memory and perception. The findings aid in further bridging the research fields of attention and memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Dark web pedophile site users' cybersecurity concerns: A lifespan and survival analysis

This paper explores the concept of criminal expertise within the context of online pedophile community and applies rational choice theory to understand the decision-making processes of offenders. Specifically, this study aims to explore the cybersecurity concerns of dark web pedophile forum users known to be a hard-to-reach offender population. Sequential generalized linear model and Cox proportional hazard regression model were used to examine cybersecurity themes predicting both the lifespan and survival of 290 cybersecurity-related threads extracted from three pedophile forums identified on the dark web. Results showed that that risk factors for law enforcement identification-related topics were the most predictive of threads' lifespan and survival. Moreover, topics related to proactive protection strategies were more predictive than those related to reactive ones. Finally, threads with a superior skill level were more likely to survive than other types of content. This study builds upon both criminal expertise and cybercrime literature, particularly on the applicability of the criminal expertise framework to dark web pedophile forum users and provide a better understanding of the enculturation process among pedophile community.
Towards Instant Clustering Approach for Federated Learning Client Selection

In just few years, Federated Learning (FL) started to gain unprecedented attention given its ability to solve some fundamental privacy and communication challenges of traditional machine learning. Client selection is one of the main challenges in FL and is usually done in a random fashion, where the central server arbitrarily selects a certain number of clients to participate in each training round. However, given the heterogeneity of the client devices in terms of data quality and resource availability, randomly selecting clients is likely to result in long local training time and thus delayed global model’s convergence. To address this problem, in this work, we propose a real-time and on-demand client selection mechanism that employs the DBSCAN (Density-Based Spatial clustering of Applications with Noise) clustering technique from machine learning to group the clients into a set of homogeneous clusters based on a set of criteria defined by the FL task owners, such as resource availability, data quality, data size, data freshness and non-IID degree. Based on the requirements of each FL task, the server then intelligently selects the clusters of clients that best match with each task’s requirements, thus improving the performance of the overall federated learning process. Experiments suggest that our solution significantly improves the accuracy of FL compared to the Vanilla FL approach.
A Meta-Analysis of the Utilization of Computer Technology in Enhancing Computational Thinking Skills: Direction for Mathematics Learning

The aim of this research is to summarize and observe how interventions affect students' computational thinking (CT) skills through computer technology.Some common factors predicted to cause students' heterogeneous CT skills would also be examined.A meta-analysis was chosen to conduct this study.Finding and selecting the relevant literature resulted in 43 documents published in the period of 2011 -2021 which involved 5.088 samples.The formula of Hedges' g was used to measure the effect size.The Z test and the Q Cochrane test were also used to analyze the data.The results of this study revealed that the interventions involving computer technology on students' CT skills had modest positive effect in which computer technology interventions significantly enhanced students' CT skills.It indicates that the learning process utilizing computer technology is effective to enhance students' CT skills.Furthermore, educational level, geographical location, group size of intervention, learning tool, and subject did not moderate students' heterogeneous CT skills.It means that the level of students' CT skills intervened by using computer technology is not affected by the factors.To improve students' CT skills, this study suggests teachers to utilize computer technology into their mathematics teaching-learning process.Specifically, this study suggests teachers to utilize computer technology in implementing mathematics learning process.
Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.
Quality Challenges in Deep Learning Data Collection in Perspective of Artificial Intelligence

With reinforcement learning powered by big data and computer infrastructure, data-centric AI is driving a fundamental shift in the way software is developed.To treat data as a first-class citizen on par with code, software engineering must be rethought in this situation.One surprise finding is how much time is spent on data preparation throughout the machine learning process.Even the most powerful machine learning algorithms will struggle to perform adequately in the absence of high-quality data.Advanced technologies that are data-centric are being used more frequently as a result.Unfortunately, a lot of real-world datasets are small, unclean, biased, and occasionally even tainted.In this study, we focus on the scientific community for data collecting and data quality for deep learning applications.Data collection is essential since modern algorithms for deep learning rely mostly on large-scale data collecting than classification techniques.To enhance data quality, we investigate data validation, cleaning, and integration techniques.Even if the data cannot be completely cleaned, robust model training strategies enable us to work with imperfect data during training the model.Furthermore, despite the fact that that these issues have gotten less attention in conventional data management studies, bias and fairness are significant themes in modern application of machine learning.In order to prevent injustice, we investigate controls for fairness and strategies for doing so before, during, and after model training.We believe the information management community is in a good position to address these problems.
Pedagogical Practice as a Foundation Course for the Development of Professional Skills

The study aims to identify the factors that have contributed in the student teacher's professional preparation through the students' attitudes towards pedagogical practice.The study is quantitative based on the descriptive-correlative design.The instrument used for data collection is the questionnaire.The respondents that participated in the research are students from two study programmes (Primary and Preschool) in the University of Prishtina "Hasan Prishtina" and also students of two programmes (Preschool and Primary) in the University of Gjilan "Kadri Zeka".The total of the respondents in the research is N=280 students.The results from Pearson's correlation show very strong positive correlations between students' opportunities to practice their knowledge with preschool and primary students, favourable school environment and their support from mentor teachers.The results also reflect other factors that contribute to the professional preparation of student teachers such as: the sufficiency of the days/hours of pedagogical practices, the subjects that have contributed to their professional knowledge, the challenges they have encountered during the pedagogical practices, as well as the satisfaction they have experienced during their studies.The research is important as it identifies and describes the factors that contribute to the professional preparation of student teachers.
Classification of Credit Card Transactions Using Machine Learning

In the finance domain the main difficulty faced by the customers is the fraudulency in crediting the amount. Since the evolution of credit cards increased, the frauds on the other hand joined its hand. Previously, many rule based methods were to detect the fraudulent which were not efficient in handling the wide range of variables. But it is necessary to identify the fraud to avoid customer paying unnecessary credit. Because it can be more fascinating and crucial in the security sector, identifying fraud in the payment of credit cards system. To avoid this problem of fraudulent activity in credit card system machine learning model is developed with the three algorithms. Those three algorithms indulged in the model include logistic regression, random forest classifier, bernoulli naive bayes classifier. The efficiency obtained using logistic regression is 96% and with random forest classifiers is about 98% and with naive bayes it is 95%. Thus, the model analyses the best among the three machine learning algorithms.
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.
Exploring Deepfakes - Creation Techniques, Detection Strategies, and Emerging Challenges: A Survey

Abstract: Deep learning, integrated with Artificial Intelligence algorithms, has brought about numerous beneficial practical technologies. However, it also brings up a problem that the world is facing today. Despite its innumerable suitable applications, it poses a danger to public personal privacy, democracy, and corporate credibility. One such use that has emerged is deepfake, which has caused chaos on the internet. Deepfake manipulates an individual's image and video, creating problems in differentiating the original from the fake. This requires a solution in today's period to counter and automatically detect such media. This study aims to explore the techniques for deepfake creation and detection, using various methods for algorithm analysis and image analysis to find the root of deepfake creation. This study examines image, audio, and ML algorithms to extract a possible sign to analyze deepfake. The research compares the performance of these methods in detecting deepfakes generated using different techniques and datasets. As deepfake is a rapidly evolving technology, we need avant-garde techniques to counter and detect its presence accurately.
Frequency and morphological behaviour of nouns in Czech and Russian

Declensional morphology of nouns in Czech and Russian is investigated and compared. It is shown that, in general, word forms which are more similar to their lemmas are preferred, but there are differences between animate and inanimate nouns and also among grammatical genders. The frequency distribution of grammatical cases is also studied, with animacy and gender being again important factors.
Political, economic, and governance attitudes of blockchain users

We present a survey to evaluate crypto-political, crypto-economic, and crypto-governance sentiment in people who are part of a blockchain ecosystem. Based on 3,710 survey responses, we describe their beliefs, attitudes, and modes of participation in crypto and investigate how self-reported political affiliation and blockchain ecosystem affiliation are associated with these. We observed polarization in questions on perceptions of the distribution of economic power, personal attitudes towards crypto, normative beliefs about the distribution of power in governance, and external regulation of blockchain technologies. Differences in political self-identification correlated with opinions on economic fairness, gender equity, decision-making power and how to obtain favorable regulation, while blockchain affiliation correlated with opinions on governance and regulation of crypto and respondents’ semantic conception of crypto and personal goals for their involvement. We also find that a theory-driven constructed political axis is supported by the data and investigate the possibility of other groupings of respondents or beliefs arising from the data.
Truncated generalized coherent states

A generalization of the canonical coherent states of a quantum harmonic oscillator has been performed by requiring the conditions of normalizability, continuity in the label, and resolution of the identity operator with a positive weight function. Relying on this approach, in the present scenario, coherent states are generalized over the canonical or finite dimensional Fock space of the harmonic oscillator. A class of generalized coherent states is determined such that the corresponding distributions of the number of excitations depart from the Poisson statistics according to combinations of stretched exponential decays, power laws, and logarithmic forms. The analysis of the Mandel parameter shows that the generalized coherent states exhibit (non-classical) sub-Poissonian or super-Poissonian statistics of the number of excitations, based on the realization of determined constraints. Mittag-Leffler and Wright generalized coherent states are analyzed as particular cases.
Mapping Hydrothermal Alteration Zones Associated with Copper Mineralization using ASTER Data: A Case Study from the Mirjaveh Area, Southeast Iran

In this research, ASTER satellite images and the combined algorithm of band ratios with the method of logical operators with the determination of the threshold limit based on ground, laboratory and experimental studies have been used in order to highlight hydrothermal alterations. Image transformation techniques such as specialized band ratio and principal component analysis are used to map lithologic units and alteration minerals. Supervised classification technique, i.e. Spectral Angle Mapper (SAM) is applied to detect subtle differences between index alteration minerals associated with actual copper localities in the region. The results show that the integration of image transformation techniques and supervised classification of ASTER data with field studies and geochemical exploration is highly effective in targeting new copper mineralization prospects. Copper mineralization is found in siliceous veins that strike from north-south to northeast-southwest across the region. Remote sensing evidence supports the presence of propylitic and argillic alteration, which can be useful for searching for copper-gold type deposits. Kaolinite and pyrophyllite play a role in identifying arzilic alteration zones, and muscoite, epidote and chlorite minerals are very important in identifying pyropilitic and phyllic areas and in mineral exploration. Based on remote sensing processing, according to the detection of various alterations (phyllic, pyrophyllite and argillic) in this area, possible copper mineralization was detected in the central part of the studied area. The approach used in this research provides a quick and cost-effective means of initiating comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar areas.
Atom-level electronic physicists are needed to develop practical engines with a quantum advantage

Theoretical research into quantum information engines that surpass the classical Carnot limit has exploded these past 10 years, but experiments so far have almost exclusively been the realm of the optics community. To help advance the field, and to develop solutions that might help our energy-dependent global society navigate the planetary crises, it’s high time that physicists working on atom-level electronics join the game.
A method of soccer-team identification by histogram feature vector and support vector machine

Statistic of an individual player, tactical analysis in soccer-team, and offside event in a soccer game impacts to match results. An important step to analyze information of the individual player is a soccer team classification. In this paper, we proposed a method for classifying the soccer player-team in a match by histogram features and distance classification technique. This method consists of three steps: 1) player detection 2) player segmentation 3) feature extraction and team classification. Firstly, to segment foreground objects, the soccer ground field is removed by comparing the green color level with a threshold. Morphological technique is used to remove noise in foreground image. Region of objects which is smaller than criterial is also removed. Therefore, remain object is defined as players. Player can be detected. For soccer-player-team classification, histogram in RGB layers are used as feature vectors. Using a distance classification and feature vectors in database, team is classified finally. To test the performance of methods, three videos of soccer match are used. 393 player images are selected. These player images are used for classification. The linear support vector machine are used as classifier. Accuracy rate is 98.47%. Sensitivity rate is 100%. Specificity rate is 96.03%. Based on the results, the proposed method shows excellent performance.