Reviewerly/exHarmony huggingface dataset:
Economic geology and genesis of kaolin resources in the East African Rift system: the case of Alemtena kaolin deposit, Ethiopia

The Alemtena kaolin deposit is located within the main Ethiopian rift. New geological, mineralogical, geochemical and physical test data provide evidence of the genesis and potential use of the kaolin. These data indicate that the deposit formed as a result of the intensive weathering of felsic volcanic rocks including rhyolitic tuffs, pumice, and rhyolite. The deposit contains elevated concentrations of Cr + Nb and Ce + Y+La and low concentration of Ba + Sr suggesting the deposit has a supergene origin. The presence of only kaolinite and quartz, and absence of high temperature minerals like pyrophyllite, dickite and pyrite, low bulk density (<2 g/cm3) and high crystallinity (HI > 0.9) strengthen the supergene origin. The presence of comparable grain size distribution, mainly white colour, high Al2O3 and kaolinite, relatively low concentrations of Fe2O3, TiO2 and alkali elements make the kaolin favourable for various industrial applications including paper, filler, ceramics, pharmaceuticals, and agricultural industries. Reserve estimation from section logging and pitting gave nearly 85 million tons of reserve.
The Effect of Using Artificial Intelligence on Performance of Appraisal System: A Case Study for University of Jeddah Staff in Saudi Arabia

Despite the interest of developed countries in relying on artificial intelligence in the performance of their work, developing countries are at the beginning of this path. The researcher has noted that the use of modern technologies, especially artificial intelligence, did not give the necessary attention in government business and many private sector companies, so it was necessary to draw attention to the importance of using it to develop all businesses. This study focuses on clarifying the importance of using artificial intelligence (AI) technology in the process of evaluating the employee’s performance to increase the effectiveness of performance appraisal for all organisational levels to make strategic decisions which affect the objectives of the organisation. This study has used a questionnaire that looked at participants’ views and attitudes towards technology and its usefulness in the appraisal process. Knowing the opinions of employees and their supervisors will enable to demonstrate the need to use AI to raise the efficiency of the performance appraisal system and develop its processes for the employee’s benefits and the general interest at the organisation. Most of the answers were between agree and strongly agree, and on the other side, there was a very small percentage that did not believe the importance of using artificial intelligence in works, and this may be due to their feeling of resisting change and the desire to work a way they used to. This paper begins by providing demographic details and background information on the participants, followed by an examination of the reliability of the extracted factors/metrics. The extracted factors will then be described individually using general descriptive statistics (such as repetition percentages) to look at their respective elements and see the level of the agreement created by the participant. Following this descriptive part of the analysis, this paper seeks to conduct an in-depth analysis of the results using various inferential statistics to test group differences using demographic and background details of the participants (e.g. age, gender, education). This study investigates the impact of the use of AI on the performance appraisal model, and to examine the effect of AI on the technology adoption.
A Systematic Review on Software Project Scheduling and Task Assignment Approaches

Software Project Scheduling and Task Assignment are important integral aspects of software project management and contributes to the overall success of software projects. Key objective of Task scheduling/ assignment is to minimize the cost and time of the project. This article i.e. a systematic literature review, is in-fact the first of its kind, conducted in the context of task scheduling and assignment in software industry. This study specifically elaborates the models used in task assignment and summarizes the techniques/ machine learning algorithms to solve the software project scheduling problem (SPSP). Our Initial search brought out 1100 research articles. However, after applying the inclusion and exclusion criteria, 23 most relevant researches were segregated and thereafter thoroughly reviewed. The review revealed that there are 2 types of basic models of Task Scheduling i.e. static and dynamic, however, static models are most widely used. For Task Scheduling, evolutionary algorithms, whereas, for Task Assignment, Support Vector Machine (SVM) algorithms are most widely used. Due to lack of real-world data in software projects, most of the researches utilized synthetic data sets for Task Assignment. Exploring the Task Assignment tools during the course of review process, 7 tools were identified, however, TAMRI has been graded as most efficient.
A Real-time Algorithm for Visual Detection of High-speed Unmanned Surface Vehicle Based on Deep Learning

We proposed a high-robustness real-time visual detection algorithm based on deep learning, which is aiming at the problem that high-speed unmanned surface vehicle(USV) are difficult to detect and identify targets in complex mission scenarios. We design feature extraction network based on MobileNet, by removing average pooling layer, full connectivity layer and softmax layer. We then add 8 convolution layers to improve feature extraction capability. Additionally, we built SSD structure detection network to achieve fast multi-scale detection in fuse selected Multi-size feature map results. In the end, we implement and test algorithm on the embedded GPU. The results show that our algorithm can detect multiple types of specific targets on water in real time, with strong robustness and multi-scale characteristics. The detection time of single-frame video can be completed within 50ms. Through our video simulation experiments, the algorithm has high detection rate and strong robustness to the actual detect situation, while has important engineering practical value.
On-demand generation of entangled photons in the telecom C-band

Fiber-based quantum networks require on-demand sources of entangled photons in the telecom C-band for long distance information transfer. Historically, the field of in-fiber entanglement distribution has been dominated by photons provided via spontaneous processes. In recent years, semiconductor quantum dots have emerged as strong competitors in terms of generating single and entangled photons due to their promise of deterministic qubit generation in the NIR wavelength region. Here, we show the on-demand generation of polarization entangled photons in the telecom C-band based on InAs/GaAs quantum dots grown via metal-organic vapor-phase epitaxy. By employing a robust phonon-assisted two-photon excitation scheme, we are able to generate pairs of entangled photons with a concurrence of 91.4 &plusmn; 3.8 % and a maximum fidelity to the Bell state &Phi;+ of 95.2 &plusmn; 1.1 %.
Digitization of Intangible Cultural Heritage as a method to save and actualize it on the example of Kazakh folk dance

The author draws attention to the importance of the role of digitization in the process of preserving the intangible cultural heritage of the people of the world using the example of visualization of Kazakh folk dance samples. Currently, a lot of work is being done in the world to preserve the intangible cultural heritage (ICH) at the initiative of the UN in accordance with the Convention on the Preservation of the Intangible Cultural Heritage of UNESCO, adopted on October 17, 2003.
An Improved Q-Learning Algorithm for Path Planning in Maze Environments

The path planning is the problem of finding the optimal paths in a given environment, which has become an important way to test the intelligent learning algorithms. In AI-based path planning, the earliest and more in-depth issue is Intelligent Obstacle Avoidance, that is, an agent needs to successfully avoid all obstacles or traps in an unknown environment. Compared with other learning methods, RL (Reinforcement Learning) has inherent advantages in path planning. Unlike most machine learning methods, RL is an unsupervised active learning method. It can not only effectively achieve intelligent obstacle avoidance, but also find the optimal path from unfamiliar environment such as maze through many experiments. Q-learning algorithm is recognized as one of the most typical RL algorithms. Its advantages are simple and practical, but it also has the significant disadvantage of slow convergence speed. This paper gives a called ɛ-Q-Learning algorithm, which is an improvement to the traditional Q-Learning algorithm by using Dynamic Search Factor technology. Experiments show that compared with the existing Q-Learning algorithms, ɛ-Q-Learning can find out a better optimal paths with lower costs of searching.
Effects of the political risk on Bitcoin return and volatility: evidence from the 2016 US presidential election

Purpose This paper aims to investigate the effect of the political risk on Bitcoin return and volatility during the 2016 US pre-election and post-election periods. Design/methodology/approach A daily composite political risk index is calculated by using the principal component analysis and Google Trends. A quantile regression approach is adopted to assess the effect of the political risk index on Bitcoin return and volatility for both periods subject to market conditions. Findings Findings reveal that the political risk index tends to increase when moving from the pre-election period to the post-election one. This is mostly attributed to the new challenges faced by the new elected government. During the pre-election period, the quantiles regression shows that the political risk index negatively affects Bitcoin return when the market is bearish, whereas a positive impact on volatility is found in bearish and bullish markets. When the political situation becomes severer during the post-election period, the quantiles plots show that the increase of the political risk index leads to a significant increase of Bitcoin return, whereas Bitcoin volatility remains relatively stable. This means that Bitcoin can be adopted as a hedging tool when the political situation becomes severer. Originality/value Comparing to the existed studies in the field, this paper considers Google trends as a main source to assess the daily composite political risk index during the 2016 US presidential election.
Relaxed Rule-Based Learning for Automated Predictive Maintenance: Proof of Concept

In this paper we propose a novel approach of rule learning called Relaxed Separate-and- Conquer (RSC): a modification of the standard Separate-and-Conquer (SeCo) methodology that does not require elimination of covered rows. This method can be seen as a generalization of the methods of SeCo and weighted covering that does not suffer from fragmentation. We present an empirical investigation of the proposed RSC approach in the area of Predictive Maintenance (PdM) of complex manufacturing machines, to predict forthcoming failures of these machines. In particular, we use for experiments a real industrial case study of a machine which manufactures the plastic bottle. We compare the RSC approach with a Decision Tree (DT) based and SeCo algorithms and demonstrate that RSC significantly outperforms both DT based and SeCo rule learners. We conclude that the proposed RSC approach is promising for PdM guided by rule learning.
RETRACTED: Applications of Internet of things on Smart Grid

Abstract Internet of Things devices in public and private spaces has changed our lifestyle. For example, home automation applications that support smart devices, locks, keys, surveillance systems, and Internet-connected devices has change the way we interact with our living spaces. These systems are widely adopted; the Internet of Things digitally expresses concern on safety and security. This technology makes life easier, safety as its priority. The firewall is also very important for the Internet of objects. The algorithms designed for users should be unique and safety. The basic principle of the Internet of Things is based on the integration of information networks to make people’s lives easier. In this way, the objects can connect to each other and share information. The development of the Internet of Things has triggered by technology. A smart grid is an electricity network based on digital technology that is used to supply electricity to consumers via two-way digital communication. This system allows for monitoring, analysis, control and communication within the supply chain to help improve efficiency, reduce energy consumption and cost, and maximize the transparency and reliability of the energy supply chain. The smart grid was introduced with the aim of overcoming the weaknesses of conventional electrical grids by using smart net meters. Smart Grid technologies contribute to manage IoT energy efficiently, which the present ones lack in the existing framework. The two-way communication between connected devices and hardware which can sense and respond to user demands makes the IOT smart grid better. A smart Grid with the help of these technologies is more resilient and less costly than the current power infrastructure
Possibilistic Approach For Novelty Detection In Data Streams

In many real-world applications data arrive continuously, in the form of streams. Such data can be used for the acquisition of knowledge by machine learning methods. In data streams learning, novelty detection is a relevant topic, which aims to identify the emergence of a new concept or a drift in the known concept in real time. Most approaches in the literature that focus on the novelty detection problem, make assumptions that limit the method usefulness. For instance, some methods are designed lying on the supposition that labeled data will be available at some time in the stream, while others restrict the proposed algorithm to one-class problems. Some recent approaches aim to overcome the limitations mentioned, considering multiclass problems and unlabeled data streams. In addition, there are also proposals that explore concepts of fuzzy set theory to add more flexibility to the learning process, although restricted to labeled streams. In this paper, we propose a method for novelty detection in data streams called Possibilistic Fuzzy multiclass Novelty Detector for data streams (PFuzzND). Our methods generates models based on a proposed summarization structures named SPFMiC (Supervised Possibilistic Fuzzy Micro-Cluster), which provides flexible class boundaries, allowing the identification of different types of novel information, i.e, novel classes, extension of classes or outliers more efficiently. Experiments show that our approach is promising in dealing with the changes in data streams and presents improvements in comparison to the non-fuzzy version.
Particle swarm optimization with adaptive elite opposition-based learning for large-scale problems

A novel particle swarm optimization with elite opposition-based learning algorithm is proposed in an attempt to improve the performance on solving large-scale optimization problems (LSOP) in maximum power point tracking (MPPT) of photovoltaic system. The standard particle swarm optimization (PSO) algorithm shows its weakness on LSOP, such as easily falling into local optimum, slow convergence and low accuracy at later evolution process. Therefore, this paper develops a modified PSO algorithm based on elite opposition-based learning mechanism and adaptive multi-context cooperatively coevolving (AM-CC) framework. In every iteration, the current high-priority individuals execute dynamic generalized opposition-based learning to generate their opposite solutions which enhance the ability of local exploration and help the particles escape from local optimum. The simulation experiments are conducted on a comprehensive set of benchmarks (up to 2000 real-valued variables), as well as on a large-scale MPPT application. Compared with some state-of-the-art variants of PSO and differential evolution (DE), the results show that the improved algorithm has higher convergence speed and accuracy. Besides, it can avoid premature phenomenon effectively and is suitable to solve the large-scale optimization problem.
Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store

In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.
A study on dynamic aspects variability in the SOLAR educational software ecosystem

Abstract A Software Ecosystem (SECO) refers to a collection of software products with some degree of symbiotic relationship. SOLAR is a Virtual Learning Environment (VLE) that enables the publication of courses and interaction with them among its various users. In this context, SOLAR SECO emerges, where diverse situations of software evolution and maintenance are part of its development process. The aim of this paper is to discuss the dynamic variability of SOLAR educational software ecosystem and software modeling. As an example, dynamic variability aspects of the feature model of SOLAR VLE discussion forum functionality were discussed, one of the most widely used services within SOLAR SECO. As a major conclusion of this work, we identified that the use of the contextual feature diagram allows the study of the dynamic aspects of a system, even more supported by tools to support automatic measurement collection.
Feature Selection Using Ant Colony Optimization and Weighted Visibility Graph

Feature selection technique has an important role in the elimination of unrelated features and noises from the high-dimensional data. It simplifies and enhances the quality of dataset by selecting salient features. Good feature selection algorithm leads to accurate classification. Feature selection of high-dimensional dataset addresses the problem with redundancy, accuracy, and computational complexity. Ant colony optimization (ACO) is a modern algorithm for feature selection. It is an evolutionary algorithm inspired by the foraging behavior of ants. This paper proposes the technique of weighted visibility graph and ACO method for feature extraction and feature selection. In this method, high-dimensional dataset is converted into the complex network and after extracting eight well-suited features from the dataset, feature selection is performed. Naive Bayes method is utilized to classify the selected features. Experimental results indicate that the classification accuracy is more accurate using the proposed method.
Sequence-to-Sequence Predictive Model: From Prosody to Communicative Gestures

Communicative gestures and speech acoustic are tightly linked. Our objective is to predict the timing of gestures according to the acoustic. That is, we want to predict when a certain gesture occurs. We develop a model based on a recurrent neural network with attention mechanism. The model is trained on a corpus of natural dyadic interaction where the speech acoustic and the gesture phases and types have been annotated. The input of the model is a sequence of speech acoustic and the output is a sequence of gesture classes. The classes we are using for the model output is based on a combination of gesture phases and gesture types. We use a sequence comparison technique to evaluate the model performance. We find that the model can predict better certain gesture classes than others. We also perform ablation studies which reveal that fundamental frequency is a relevant feature for gesture prediction task. In another sub-experiment, we find that including eyebrow movements as acting as beat gesture improves the performance. Besides, we also find that a model trained on the data of one given speaker also works for the other speaker of the same conversation. We also perform a subjective experiment to measure how respondents judge the naturalness, the time consistency, and the semantic consistency of the generated gesture timing of a virtual agent. Our respondents rate the output of our model favorably.
Development, validation and reliabilty of a questionnaire to evaluate the changes on the level of physical exercises and in daily life habits due to COVID-19 pandemic social distancing.

Physical inactivity is expected to happen during the COVID-19 pandemic through home quarantine measures. The aim of this study was to develop, validate and perform the reliability of the questionnaire "Physical exercise (PE) level before and during social isolation (PEF-COVID19)" to evaluate the level of PE during the social distancing due to the COVID-19 pandemic and to try to identify changes in the daily life of the individuals.This transversal study was developed to measure psychometric properties of the questionnaire PEF-COVID19. The survey was divided into 4 sections including subjects' characterization, social isolation update and physical exercise performed, pain, anxiety and stress before and during COVID-19 pandemic. After the survey construction in Portuguese language (Brazil), the survey was transferred to an online digital platform (Google® forms). The Construct, Clarity and Relevance Validation strategy was judged by a panel of experts and the validity index (VI) were calculated. The reliability was evaluated through the test-retest interrater reliability and measured through the intraclass correlation coefficient (ICC) and Kappa coefficient (KC).Twenty-five experts participated of the survey validity and 34 respondents from the target population participated of the test-retest reliability. The general average measures for VI were all above 0.84 and test-retest ICC and KC were 0.89 and 0.88, respectively.This survey was considered valid and reliable to be applied to the general population over 18 years-old to investigate the PE practice and psychological aspects during the social distancing due to the COVID-19 pandemic, a public health problem.
Improve RRT Algorithm for Path Planning in Complex Environments

This paper proposes an improved RRT algorithm, which overcomes the problems of non-optimal path and low planning success rate in complex environments. First, the algorithm introduces a memory strategy that avoids the problem of oversampling or falling into local minimum in the non-convex environment during the process of expanding random tree. Then, the heuristic function is used to guide the growth of random tree and neighborhood expansion strategy is used to avoid the "rewiring" process of RRT* algorithm, which improves the real-time planning performance. Finally, simulation experiments are performed in different types of maps to illustrate the effectiveness of the proposed algorithm.