Reviewerly/exHarmony huggingface dataset:
Research on College English Teaching Path under the Background of “Internet+”

With the advent of the Internet era, various technologies have been continuously developed and applied in practice, and people’s lives have undergone earth-shaking changes. Under such a development trend, how to achieve the integration of Internet technology and college English teaching? The effective connection of various resources has become the focus of more and more colleges and universities in the reform of English teaching at this stage. With the technical support of the Internet, various online classes such as open video classes, MOOCs, and flipped classrooms have achieved great success. The development of these classes has not only explored a new direction for college English teaching reform, but also college English. The teaching reform has played a leading role. This article will explore how to better carry out innovative reforms in college English classrooms under the background of “Internet +”, and realize the innovation of teaching methods, so as to cultivate more English professionals.
The Implementation of Online Learning Policy at Tirta Kencana Elementary School

With the issuance of Circular Letter of the Minister of Education and Culture No. 4 of 2020, all schools conduct distance learning, both online and offline.The purpose of this research was to find out the implementation of online learning in Tirta Kencana Elementary School in Makarti Jaya sub-district.The type of research was qualitative research with the case study method.Data collection were interviews, observations, and documentation.From the results of the research, it can be concluded that the implementation of online learning in Tirta Kencana Elementary School went well, starting from planning, implementing, evaluating, preparing media, teaching materials, and learning resources.In the implementation, lack of signals, frequent power outages and did not have an android handphone.The solution to that problem is that teachers do offline learning for students who are constrained when learning online.
Virtual enrollment platform to fulfill social distancing: COVID-19

Worldwide, there are long queues outside educational institutions, causing them to become infected with COVID-19 due to the agglomeration of people, which generates great consternation and misunderstanding among parents during the enrollment process. Therefore, the objective of this research is to implement a web platform to optimize the enrollment process in educational institutions, taking into consideration the availability, integrity and confidentiality of the information. The system development methodology was divided into 3 phases. Start;The problem, the functional and non-functional requirements were defined, the technologies to be used for development were evaluated and the system prototypes were designed. Development;The Frontend was designed by programming the functional requirements and the architecture of the database was developed in the Backend. Transition;A feedback was made correcting errors by users through a usability test. In this sense, it was possible to carry out the registration process in a virtual way, complying with social distancing, preventing citizen insecurity, optimizing processes in a fast and efficient way, preserving information security. The web platform made it possible to reduce the digital gap for the use of information technologies (IT) between the institution and parents, reducing educational inequality with an inclusive education, taking a step towards digital transformation in an equitable and transparent way.
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression

Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian Processes but their training remains challenging. Most existing methods for inference in DGPs use sparse approximation which require optimization over a large number of inducing inputs and their locations across layers. In this paper, we simplify the training by setting the locations to a fixed subset of data and sampling the inducing inputs from a variational distribution. This reduces the trainable parameters and computation cost without any performance degradation, as demonstrated by our empirical results on regression data sets. Our modifications simplify and stabilize DGP training methods while making them amenable to sampling schemes such as leverage score and determinantal point processes.
Top 10 Most Effective UX Trends for 2021

This article details the top 10 user experience (UX) design takeaways from 2020, and how they can be applied to 2021.
Computing Frameworks for VM Migration in Cloud

Cloud Service users world wide are increasing with advancement in IT infrastructre. This leads to the demand of online storage for various operational reasons. The applications across various organizations are exploiting the cloud infrastructure offered by different service providers like Microsoft, Amazon, Twitter, IBM, and Hewlett-Packard, enable developers to distribute software through a central organization's hosting machine. Online storage is the next logical phase in the development of IT infrastructure. Cloud Storage Infrastructure has become a norm in distributed environment to host the multiple application on easily available networks. Rather than bothering about the Capital Expenditutre, the organizations are now using funds to invest in operational cost of running the services through Cloud service provider increasing the efficiency. While attempting to migrate the workload needs to be considered as imbalanced workload may result in system failure. Also the VM consolidation need to be done while the sytem is live without any failure. The security concerns for live migration are enlisted in limitations which is the largest concern for an organization whle deciding future course of action. This article reviews the cloud computing systems in the light of migrating the existing legacy systems to the cloud environment and suggest the operational features that an organization should consider before the migration.
An Effect Realization on Temporality, Aspectuality, and Capital in English Indonesian Language

In typological linguistics, the terms “kala,” “aspect,” “mode/modality” are used and are known as verb paradigms or verbal systems (morphosyntactically) and verb semantic aspects (semantically) in describing language characteristics. However, these terms are treated as parallel, even though all three leave theoretical problems. Kala and aspect are grammatical categories, while modality is semantic notation. In addition, there is also the term mode which is often assumed to be a synonym for modality even though the two are related different concepts. Therefore, this study attempts to answer these theoretical problems by referring to temporality, aspectuality, and modality and describing their realization in English and Indonesian to gain a comprehensive understanding. This research is a contrastive analysis that compares English and Indonesian. Data were collected from two synchronic corpora, namely the Corpus of Contemporary American English (COCA) for English data and Wortschatz Leipzig Corpora Collection for Indonesian data. The analysis results show that English and Indonesian have different realizations of these notes; English is more varied in realizing temporality, while Indonesian is more varied in learning aspectual. The manifestations of English modalities are more diverse, which include core and quasi-modal. Based on this analysis, it can be concluded that English is more affluent in morphosyntax and syntax, while Indonesian is richer in morphology.
Markov Equivalence of Max-Linear Bayesian Networks

Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian networks behaves differently than for classical Gaussian Bayesian networks. We establish the parallel between the two theories via tropicalization, and establish the surprising result that the Markov equivalence classes for max-linear Bayesian networks coincide with the ones obtained by regular CI. Our paper opens up many problems at the intersection of extreme value statistics, causal inference and tropical geometry.
Improving the Skill of Recognizing Poetry Text Building Elements Through the Think Pair Style Cooperative Learning for Students Class VIII-A at SMP N 24 OKU

The goal of this study is to explain an increase in the ability to recognize the building blocks of poetry text by applying the Form Cooperative Learning Model Think Pair to Class VIII-A students at Class VIII-A at SMP N 24 OKU.Data collection techniques: (1) observation, (2) checking.The data analysis methodology shall be processed using the percentage formula.The results of this study show that there is an improvement in the ability to recognise the building blocks of poetry text through the cooperative learning model think pair share in Class VIII-A at SMP N 24 OKU in each cycle can be clarified that the results obtained in action cycle 1 are 1610 with an average of 64,40.There were 11 students who completed the course with an average percentage of completeness of 44%.There was an improvement in Cycle 2 where the findings reached a value of 1775 with an average of 71 %.Students who complete the course are 17 students with an overall percentage of completeness of 17 percent.Then the action cycle 3 increased again where the results obtained were 1855 with an average of 74,20.Students who complete the course are 23 students with an overall percentage of completeness of 92%.
An analogy-based intelligent requirement engineering system

Missing requirements or incomplete requirements about the project is making delay in projects and that affect customer satisfaction. This problem is caused due to customer or requirement engineer. If it is due to customer then customer may not give it during requirement elicitation. If it is due to requirement engineer then requirement engineer may not collect it during elicitation. Or it is caused the inefficiency of both the parties. To avoid the missing requirements problem, this paper is expressing an intelligent approach.
Motivation/Demotivation Factors among School Teachers in Public and Private Schools of District Sialkot

Present research mainly focuses on demotivated factors among language teachers in public and private sectors.With detailed definition of motivation and demotivation, this article reviews the previous study being held in different countries.Therefore, few researches have been done on teachers' demotivation in Pakistan.So, this empirical research has specifically chosen Sialkot teachers' both from public and private school.With the help of mixed method, teachers were asked to fill online questionnaire and some interviews taken from private and public teachers.Cluster sampling and Bronfenbrenner's Ecosystems Model is used as a framework.The findings of this study explained that teachers are highly dissatisfied with administration of their school.They are facing a lot of workload with little amount of salary.Moreover, with scheduled meetings, workshops and paper checking make them psychologically ill and their personal life is also affected.Therefore, it is the duty of govt to provide teachers with facility of technology while private school administration must give relaxation to teachers and consider them human not slaves.
Temporal Variability of Soil Reaction Among Surface and Near-Surface Horizons of Soils of Dissimilar Lithology in a Humid Tropical Environment

The study investigated changes in soil reaction among surface and near surface horizons of four soil groups as affected by wet and dry seasons in Southeastern Nigeria. A geological map of the area guided soil sampling. Free survey approach was used in locating soil profiles. Soil samples were collected based on horizon differentiation and samples were collected from the AB-horizon (near-surface) and the A-horizon (Surface). Routine laboratory analyses were conducted on these soil samples after sieving through a 2-mm sieve. Data were subjected to analysis of variance (ANOVA) using SAS Statistical Computer Package. There were significant (p&lt;0.05) changes in soil reaction in A- and AB-horizons in the dry seasons of 2016 and 2017. Similarly, in the wet season, soil pH varied significantly (p&lt;0.05) in 2016 and 2017. Soil reaction significantly (p&lt;0.05) differed in AB-horizons in both 2016 and 2017 irrespective of the season. All soil samples were acidic irrespective of lithologic material and season with pH values ranging from 4.20-5.60 and 3.31-5.42 in the A- and AB-horizons, respectively.
SIMULATION OF PERMANENT MAGNET SYNCHRONOUS MOTOR FIELD ORIENTED VECTOR CONTROL SYSTEM

This template illustrates the control system of permanent magnet synchronous motor(PMSM) which uses field oriented vector control(field oriented vector control). PMSM is a complex, strong coupling and nonlinear system. And field oriented vector control could provide good performance as well as the PI controller setted with well parameter matching. Whereas limited by the number of voltage vector, the other control method of PMSM, direct torque control, could not satisfy accurate control when the machine running with a low speed. So modulation of the whole system is built here to realize closed-loop field oriented vector control control by keeping id 0 , and the machine model and the transformation among different coordinate system are discussed. The system is verified effective and feasible.
Patient Remote Monitoring System using IOT and ML

The advancement of the Internet of Things (IoT) technology plays a major role in developing the health sector by making it much more reachable and desirable. The integration of IoT-cloud can play a vital purpose in the smart healthcare by offering deep understanding the healthcare content to support inexpensive and standard person care. Hence the combination of IoT and cloud helps in the betterment of quality of patient health care on a regular base integrated, processed the patient-data. Many physical devices capture transmit data, and provides data to various interoperability methods in IoT. The basic functionalities of IoT is for storage, display and communicate the information. Hence the e-Health monitoring system with IoT is adapted for distant patient monitoring on a continual basis and aggregated, analyzed the data. It can bring about a massive positive transformation in the field of eSmarthealth management for the rural or urban patients. This may help the people who wants or having good opinion on the technology diagnosis. This system can bring enormous variation in the Digital based health maintenance for patients. The Patient Remote Monitoring System involves a LM35 (temperature sensor),MAX-2000(Blood Pressure sensor),MAX30100(Oxygen level sensor),SEN11574(Pulse rate sensor) and ESP32 bit microcontroller to collect the biotic parameters from the patient's body and transmit the data to the Thingspeak via ESP8266 Wi-Fi module and that can be accessed by the doctor from the remote location for diagnosis and medication.
Solar Flower for Power Generation with Ability to Generate at Night

Today energy is the principle idea for socio-economic development. But because of incremental charge of environmental difficulty renewable strength offer a tremendous interest. This opportunity strength supply is constantly achieving more recognition because of non-stop discount in fossil fuels. It is the strength comes from solar, wind, rain etc. Among the non-conventional, renewable sources, sun power presents notable capability for conversion into electric power. Maximizing strength output from a sun is appropriate to growth efficiency. In order to maximize strength output, wishes to hold the panels aligned with the solar. This paper focuses with the generation capacity of solar energy during day time and as well as during night time. The proposed model guarantees the optimization of the conversion of sun energy into electrical energy via means of properly setting up the panel according with the input of artificial light source in night time and at the same time to that of the sun position during day time. An experimental prototype was built and results have proven the good performance. In future higher efficiencies of solar panels will prove a golden way in generation of solar power 4 times the input in the night time.
Hybrid Chaotic Grey Wolf Optimizer for Task Scheduling in Cloud Computing

Cloud computing is a collection of distributed computers that allow users to access resources and services over the Internet. The optimal use of resources for various applications depends on how tasks are scheduled in the cloud environment, which has a significant impact on the efficiency of cloud service providers. Task scheduling is a key process in the cloud computing environment, which aims to execute user requests on resources in an efficient manner, taking into account other features of the cloud environment. The main goal of task scheduling is minimizing the makespan time and maximizing throughput as well as minimizing the cost. Since task scheduling is considered as an NP-complete problem, the necessity of using nondeterministic and meta-heuristic algorithms to optimize task scheduling is evident at a logical time. The major part of this problem is to design an efficient intelligent searching pattern to schedule the tasks in available virtual machines. In this paper, we propose a meta-heuristic algorithm called hybrid chaotic grey wolf optimizer (HCGWO) to tackle the problem of task scheduling in various heterogeneous virtual machines. This paper focuses on minimizing overall makespan and cost by modeling the swarm intelligence. The proposed algorithm prevents the local convergence and explores the global intelligent searching in finding the best optimized virtual machine for the user task among the set of virtual machines. We have made the simulation and performance evaluation using Cloudsim toolkit and compared the results with other swarm intelligent based algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search. The evaluation results show that there is a major improvement in minimizing the makespan and cost.
Research Guides: Text and Data Mining: Tools and support for learning more advanced TDM

A brief guide to tools and resources (including datasets) for getting started with computational approaches to textual analysis.
Pallet-level Classification Using Principal Component Analysis in Ensemble Learning Model

In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models.
Mobile Healthcare in Action: Digital Self-tracking Practices of Students in Russia and Europe

The article contains the results of an empirical study of digital selftracking practices of students in Russia and Europe in the context of mobile healthcare.Today, digital self-tracking is positioned as the frontier of the trend of digitalization of the medical sphere, and therefore the study of self-monitoring practices in the context of health care is of particular relevance.Students were chosen as the target group of the study, since this social category is the most open to innovation and integrates them faster and easier into their daily lives.The research method was an in-depth interview, the data collection covered the period of 2019-2020.Despite the fact that the focus of our attention is students with different socio-cultural backgrounds, there was no purpose of comparison.On the contrary, the diverse material allowed us to identify universal practices of digital self-tracking.As a result, contrary to expectations, the involvement in the collection and analysis of personal information about various aspects of lifestyle was not directly related to the practices of preserving and maintaining health.The key category for describing the experience of self-tracking is "activity", which is not limited to physical activity, but also involves awareness and the desire to manage their own lives.There is an increase in the importance for young people of those aspects of life that are the objects of observation; they become criteria for assessing the quality of life in general.The presence of chronic diseases of the informant or his or her relatives turns out to be the only factor in the use of digital self-tracking as a way to purposefully take care of their wellbeing.As a result of the research, the concept of a "digital partner" was proposed, the social functions of tracking devices were described, and conclusions about the features of users" interaction with gadgets were drawn.The obtained data allows us to conclude that it is not yet necessary to talk about the significant role of digital selftracking in the process of actual transformations of healthcare.
Smartphone and Smartwatch for Human Activity Recognition

Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.
Developing Guidelines for Assessment and Student Learning Outcomes Through Online Systems High School Level in Penukal Abab Lematang Ilir

Assessment and student learning outcomes are routine annual activities carried out by schools, but there are always problems every time they implement it and there are no clear guidelines on how to carry out assessments and student learning outcomes online.This study aims to develop a valid, practical and effective online assessment and learning outcome guide module for students at the high school level and test the effectiveness of this module as a school guide in PALI Regency.This study uses the ADDIE development model which consists of analysis, design, development, implementation and evaluation.The module feasibility test was carried out by means of interviews, observation, and documentation which was developed by the researcher.