Reviewerly/exHarmony huggingface dataset:
Digitalization approach in education based on applying the network readiness index as the universal metric

Purpose. To achieve an integrated approach to assess the readiness of a higher educational institution for digital transformation in the form of a multi-criteria analysis of the university activities and development. Methodology. The approach of educational institutions rating assessment, based on a hierarchical criterion, called the Index of Information and Communication Technologies Development (hereinafter ICT Index) is proposed in the work. The approach is considered to be an analogy of the Networked Readiness Index developed by the Information Technology Group at Harvard Universitys Center. Findings. It is proposed to follow the approach of the ICT Index calculating as a universal metric for the digital transformation level assessment of educational institutions based on the Networked Readiness Index, namely to adopt abstract categories of top-level criteria and to average the values of hierarchical nesting. Originality. The approach originality consists in the weight coefficients input at any level of the hierarchy. In addition, the approach provides a flexible choice of the Index resulting scale for the most understandable interpretation of the results, as well as the universal mathematical apparatus development for Index calculating. The proposed mathematical apparatus can be easily modified for any specificity of an educational institution and its criteria. Practical value. The use of the ICT Index for all educational institutions in Kazakhstan will solve the problems of automating processes to ensure training and expanding the technological capabilities of educational institutions for modifying the forms of education at the state level by bringing the assessment indicators of all objects to a single scale.
Application of Industry 4.0 model in Oil and Gas companies

Although the concept of Industry 4.0 was defined a decade ago, experts have only been talking about the "Oil and Gas 4.0" model in the last few years. Experts in this industry agree that the "Oil and Gas 4.0" model in use, based on the digitalization and intelligence of the oil and gas industry, can bring huge benefits to the company. However, the "Oil and Gas 4.0" model is still in its infancy, but applications in areas such as big data (BDA), industrial Internet of Things (IIoT), or analysis of typical oil and gas industry chain application scenarios through examples, such as an intelligent oil field, an intelligent pipeline, and an intelligent refinery, make today's challenges real. So the essence of the "Oil and Gas 4.0" model is a data-driven intelligence system based on high digitization. This paper provides an overview of the state and development of the Industry 4.0 concept in the Oil and Gas Industry.
Deep Reinforcement Learning in Human Activity Recognition: A Survey

&lt;p&gt; Human activity recognition is a popular research field in computer vision that has already been widely studied. However, it is still an active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep Reinforcement Learning (DRL) has recently been employed to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based human activity recognition has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this field, we have constructed a comprehensive survey on activity recognition methods that incorporate deep reinforcement learning. Towards the end of this survey, we summarize key challenges and open problems in this area that can be addressed by researchers in the future. &lt;/p&gt;
HR Development in Saudi Public Universities: The Role of Organizational Culture and Communication

Universities in Saudi Arabia are expected to improve their quality, standard, effectiveness, and efficiency to achieve their objective of becoming world-class universities. Moreover, organizational communication tends to affect human resources development performance. This study seeks to examine the organizational culture and communication influence on HRD performance among academicians. A total of 425 academicians at institutions of higher learning in Saudi Arabia responded to a questionnaire survey. The result indicated that organizational culture and communication positively impact HRD performance. Decision makers in higher education have to focus more in implementing effective communication and organizational culture to improve the HRD performance of universities.
Transfer learning based entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model for multiclass text classification and generation

&lt;p&gt;We propose a semisupervised discrete latent variable model for multi-class text classification and text generation. The proposed model employs the concept of transfer learning for training a quantized transformer model, which is able to learn competently using fewer labeled instances. The model applies decomposed vector quantization technique to overcome problems like posterior collapse and index collapse. Shannon entropy is used for the decomposed sub-encoders, on which a variable DropConnect is applied, to retain maximum information. Moreover, gradients of the Loss function are adaptively modified during backpropagation from decoder to encoder to enhance the performance of the model.&lt;/p&gt;
PENGARUH VIDEO PEMBELAJARAN TERHADAP ANTUSIASME SISWA KELAS X TKJ PADA MATA PELAJARAN SIMULASI DAN KOMUNIKASI DIGITAL DI SMK KHAMAS ASEMBAGUS

This study aims to determine whether or not there is an influence of learning videos on the enthusiasm of class X TKJ students in Simulation and Digital Communication subjects at Khamas Asembagus Vocational School. This study uses quantitative methods of observation, interviews, questionnaires and documentation. Based on data analysis using validity tests, reliability tests, normality tests, correlation coefficients and the results of the hypothesis (t-test) resulting in a questionnaire with 20 respondents, it is declared valid because Rcount &gt; Rtable (0.444) and the residual value is normally distributed because the significance (0.200) &gt; 0 ,05. Thus, learning videos are very influential with evidence at a high level of influence on student learning enthusiasm because they are able to provide motivation for students so that they do not feel bored and are not always monotonous in the teaching and learning process.
Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine

In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. Therefore, using machine learning technology for fuzzy research has a good research prospect. In the development of related research, the author of this study noticed that some researchers began to use tennis machine learning technology and achieved good results. However, most of the research is only for simple analysis and is related to the current work. It cannot be used to move a solid tennis ball, nor it can make small changes to the original tennis movement; thus, it cannot carry out a complete and brand-new movement. The defense of tennis first establishes visual teaching tools with the help of various courses and visual teaching techniques to improve the teaching effect. By optimizing the network data, this study constructs the corresponding data search model, which downloads a large amount of data from the network ram, so as to separate the impact of the network environment on the load. The simulation results show that the model is optimized for the high-quality 3G network environment, and the load time and energy consumption are greatly reduced. It is more efficient in WiFi and a a high-quality 4G network environment.
Graph-based representations of clarification strategies supporting automatic dialogue management

This paper aims at presenting a dialogue-oriented approach to the construction of a graph knowledge base (KB) supporting task-oriented human-machine interactions.In particular, we focus on different pragmatic scenarios, facing the Common Ground issue and arguing that knowledge bases (in the form of graphs) are needed to make a clarification and recover pieces of information when inconsistencies occur during the communicative exchange.The main contributions of this work are: 1) a flexible dialog system architecture designed to be plugged into existing service infrastructures, 2) a graph-based knowledge representation protocol to manage both dialog domain and dialog management, 3) a detailed investigation of clarification requests forms with respect to their functions.After a brief introduction (see Section 1), we present: the theoretical underpinnings of the paper and the background work (see Section 2) our system architecture (see Sections 3 and 4) and the clarification requests (CRs) issue (see Section 5); our CRs classification, and some examples in context (see Section 6).
Decision Support System For Teacher Performance Assessment at the Yayasan Pendidikan Islam Al-Islamiyah with the Analytical Hierarchy Process Method (AHP)

This study focuses on the development of a decision support system (DSS) for teacher performance assessment at the Al-Islamiyah Islamic Education Foundation. In an educational institution, schools have teachers who are assets that must be owned by schools in creating a quality education process. The problem that occurs at this foundation is that there is no proper method and application of the teacher performance appraisal system, and also the large number of teachers, so it is difficult to choose the best teacher. Therefore, appropriate methods and DSS are needed to overcome the existing obstacles. The Analytical Hierarchy Process (AHP) method is one method that can be used in determining the best teacher performance assessment in determining the selection of the best teacher. The criteria that have been set are based on presence, neatness of dress, discipline, teaching ability, and responsibility. This study aims to determine the right method and build a DSS for assessing teacher performance at the Al-Islamiyah Islamic Education Foundation school in determining the best teacher. This DSS produces an alternative priority order, which can be used as a problem-solving tool in determining teacher performance appraisals and selecting the best teacher.
VALICO-UD: Treebanking an Italian Learner Corpus in Universal Dependencies

This article describes an ongoing project for the development of a novel Italian treebank in Universal Dependencies format: VALICO-UD.It consists of texts written by Italian L2 learners of different mother tongues (German, French, Spanish and English) drawn from VALICO, an Italian learner corpus elicited by comic strips.Aiming at building a parallel treebank currently missing for Italian L2, comparable with those exploited in Natural Language Processing tasks, we associated each learner sentence with a target hypothesis (i.e. a corrected version of the learner sentence written by an Italian native speaker), which is in turn annotated in Universal Dependencies.The treebank VALICO-UD is composed of 237 texts written by non-native speakers of Italian (2,234 sentences) and the related target hypotheses, all automatically annotated using UDPipe.A portion of this resource (36 texts corresponding to 398 learner sentences and related target hypotheses)-firstly released on May 2021 in the Universal Dependencies repository-is associated with error annotation and the automatic output is fully manually checked.In this article, we focus especially on the challenges addressed in treebanking a resource composed of learner texts.In addition, we report on a preliminary data exploration that makes use of three quantitative measures for assessing the quality of the data and for better understanding the role that this resource can play in tasks lying at the intersection of Computational Linguistics and learner corpus studies.
Interval Type-2 Dynamic Fuzzy Neural Network with Tensor Inverse

Fuzzy neural network is a research hotspot in the field of artificial intelligence, for the dynamic fuzzy neural network, it dynamically prunes the nodes according to the performance index, which proves that it can enhance the training model with dynamic parameters. However, due to over-fitting, the test error is large and the applicability in practical application is low. Therefore, this paper applies interval type 2 fuzzy sets to dynamic fuzzy neural networks with new consequent learning algorithm, and proposes an interval type 2 dynamic fuzzy neural network with tensor inversion, the test error is reduced, and the antiinterference ability of the network is improved with the hybrided strategy, comparable results are obtained among many intelligent algorithms.
Multi-robot Task Allocation and Rescue for Mowing

Multi-mowing robots have an advantage over a single robot for a large mowing task. The task allocation of the multi-mowing robots' system needs to solve several problems, such as minimizing mowing time, minimizing duplicate areas, etc. In particular, when one or more mowing robots break down in the multi-mowing robots' system, the uncompleted tasks need to be allocated again. There are two algorithms to accomplish the multi-robot mowing in this paper. One is the multi-mowing robots task allocation(MMRTA) algorithm based on the modified genetic algorithm, the other is the filling rescue algorithm. The task allocation was simulated with 4–32 robots to mow the grass field divided into 64 task blocks. The simulation result shows that the tasks and paths of each mowing robot are approximately equal, and proves that the task allocation algorithm proposed in this paper is feasible and effective. The filling rescue algorithm is simulated with 4 or 8 robots that surround the breakdown robot to complete the unfinished tasks of the breakdown robot. The simulation result testifies that the remaining tasks of the breakdown robot are reasonably and efficiently allocated.
Analisis Text Mining pada Sosial Media Twitter Menggunakan Metode Support Vector Machine (SVM) dan Social Network Analysis (SNA)

Online loans are growing rapidly in Indonesia in the last two years. This is because the online loan administration requirements are easier compared to bank financial service loans. Online loans are financial services that provide online-based services. Along with the development of online loans, many illegal online loans have sprung up and often commit violations, such as leaking customer personal information and abusing data by carrying out extreme actions such as terrorizing customers who make online loan transactions. This certainly gets a lot of comments from the public, especially on social media twitter. This study aims to conduct a sentiment analysis to see what phenomena are happening among the public regarding online loans. The data used are tweets or retweets from Twitter social media with #pinjamanonline #pinjol. Twitter social media was chosen because an incident can become a phenomenon if it gets a lot of attention from the community, especially on Twitter social media. In this study, using text mining techniques by applying the Support Vector Machine algorithm to classify sentiments on twitter users regarding online loans. This study also looks at the interactions that occur on social media Twitter using social network analysis (SNA). the results of research and testing of the Support Vector Machine method to classify online loans with an Accuracy value level of 86.6%, with a positive precision of 86%, neutral of 1.00% and negative of 87%, positive recall of 90%, neutral 87% and negative of 26 % and positive F1-Score of 88% neutral 42% and negative 86%. Then at the Social Network Analysis stage there is the most influential account, namely influencer @alvinline21 with 1402 nodes.
Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN)

Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. Lack of employee knowledge about the types of diseases in oil palm seedlings resulted in errors in handling them. In the selection of seeds to be planted in plantations, sick will cause unstable palm growth and even die. This study aims to find the best solution according to experts when the emergence of pests or diseases is identified through the pattern and color of the leaves. The data used in this study comes from image data of PT.Gatipura Mulya which is conducting a nursery as many as 612 images of oil palm seedlings and can be divided into 4 classes. The method that can be used in this identification is Convolutional Neural Network (CNN) which can study objects in image patterns. The result of this research is that the accuracy of image recognition is very good. So that this research can be recommended in the introduction of oil palm image patterns.
Reviewer Acknowledgements for Earth Science Research, Vol. 11, No. 1

Earth Science Research wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated.&#x0D; &#x0D; Earth Science Research is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please contact us for the application form at: esr@ccsenet.org.&#x0D; &#x0D; Reviewers for Volume 11, Number 1&#x0D; &#x0D; Ahmet KARAKAŞ, Kocaeli University, Turkey&#x0D; &#x0D; Angelo Paone, Pusan National University, Italy&#x0D; &#x0D; Ann Godelieve Wellens, Universidad Nacional Aut&amp;oacute;noma de M&amp;eacute;xico (UNAM), Mexico&#x0D; &#x0D; Fehmi ARIKAN, General Directorate of Mineral Research and Exploration Company, Turkey&#x0D; &#x0D; Kaveh Ostad-Ali-Askari, Isfahan University of Technology, Iran&#x0D; &#x0D; Pedram MASOUDI, Geovariances, France&#x0D; &#x0D; Saumitra Misra, University of KwaZulu-Natal, South Africa
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation

The trajectory probability hypothesis density filter (TPHD) is capable of producing trajectory estimates in first principle without adding labels or tags. In this paper, we propose a new TPHD filter referred as MM-TPHD for jump Markov system (JMS) model that the highly dynamic targets movement switches between multiple models in multi-trajectory tracking. Firstly, we extend the concept of JMS to the multi-trajectory scenario of maneuvering target and derive the TPHD recursion for the proposed JMS model. Then, we develop the linear Gaussian Mixture (LGM) implementation of MM-TPHD recursion and also consider the L-scan computationally efficient implementations. Finally, simulation results in maneuvering multi-trajectory tracking demonstrate the performance of the proposed algorithm.
Identifikasi Masker pada Face Detection dengan Menggunakan Metode Haar Cascade dan CNN

The World Health Organization (WHO) provides standard recommendations for the prevention and spread of COVID-19 with the importance of face masks for protection from the virus. Technology can be used to help prevent viruses from spreading quickly through direct contact. Utilizing Internet of Things (IoT) based technology tools for public place entrances can be designed to perform rapid inspections. This can be done using image classification methods, namely Haar Cascade and Convolutional Neural Network (CNN). This study aims to detect masks on the face using the python programming language and input data via a webcam camera. A total of 500 datasets of photos of faces without masks and 500 datasets of photos of faces with masks were used as training data. The test results using the Haar Cascade method have an accuracy of 80% while using CNN the accuracy is 100%. So that this study becomes a reference in identifying obedience in wearing masks.
Modelling Right-Censored Data with Partially Linear Model and Feed Forward Neural Networks: A Methodological Study

Censored data arises in different fields such as medical, biology, public health, epidemiology, engineering, economics and, so on.Observations obtained from mentioned fields are usually incomplete, especially in medical studies.For instance, some patients may still be alive, disease-free, or die at the completion of a medical study.Thus, analysis of censored survival data becomes a common problem in the literature.
Disagreement-based Active Learning in Online Settings

We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner either queries the label of the current instance or predicts the label based on past seen examples. The objective is to minimize the number of queries while constraining the number of prediction errors over a horizon of length $T$. We develop a disagreement-based online learning algorithm for a general hypothesis space and under the Tsybakov noise. We show that the proposed algorithm has a label complexity of $O(dT^{\frac{2-2\alpha}{2-\alpha}}\log^2 T)$ under a constraint of bounded regret in terms of classification errors, where $d$ is the VC dimension of the hypothesis space and $\alpha$ is the Tsybakov noise parameter. We further establish a matching (up to a poly-logarithmic factor) lower bound, demonstrating the order optimality of the proposed algorithm. We address the tradeoff between label complexity and regret and show that the algorithm can be modified to operate at a different point on the tradeoff curve.
Treatment Pattern of Patients with Narcotics and Psychotropic Abuse (Case Study at the Bandung Rehabilitation Clinic)

Narcotics, psychotropics, and addictive substances abuse can cause addiction, which is a chronic disorder characterized by compulsive drug seeking and use despite adverse consequences. This is a concurrent descriptive study carried out by evaluating medical records and MMAS-8 questionnaire for assessing patients’ adherence. In addition, therapeutic success and quality of life were also evaluated using WHOQOL-BREF questionnaire. The study took place in the period of September-November 2018 at Medika Antapani Clinic. Data collected from patients under drug maintenance was used to identify the types of drug mostly used by the patients and their treatment, assess the impact of the management, and identify factor influencing therapeutic success. The results showed that there were 40 drug abusers at Medika Antapani Clinic. The abused drugs were amphetamine (80%), cannabis (72%), heroin (35%), benzodiazepine (25%), and nitrazepam (20%). Therapeutic measures used to treat drug abuse were alprazolam in 38 patients (95%), clonazepam in 33 patients (82.5%), nitrazepam in 29 patients (72.5%), lorazepam in 22 patients (55%), and diazepam in 14 patients (35%). Drug rehabilitation patients had low adherence of only 50%. Measurement of quality of life revealed social relation as the dimension with the highest score of 69 + 20.44, with most patients categorized as having moderate quality of life. Therapy with benzodiazepine has potential to cause dependence so it must be practiced with caution.