Reviewerly/exHarmony huggingface dataset:
A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Cloud computing is considered one of the most important techniques in the field of distributed computing which contributes to maintain increased scalability and flexibility in computer processing. This is achieved because it, using the Internet, provides different resources and shared services with minimum costs. Cloud service providers (CSPs) offer many different services to their customers, where the customers’ needs are met seeking the highest levels of quality at the lowest considerate prices. The relationship between CSPs and customers must be determined in a formal agreement, and to ensure how the QoS between them will be fulfilled, a clear Service Level Agreement (SLA) must be called for. Several previously-proposed models used in the literature to improve the QoS in the SLA for cloud computing and to face many of the challenges in the SLA are reviewed in this paper. We also addressed the challenges that are related to the violations of SLAs, and how overcoming them will enhance customers’ satisfaction. Furthermore, we proposed a model based on Deep Reinforcement Learning (DRL) and an enhanced DRL agent (EDRLA). In this model, and by optimizing the learning process in EDRLA, proposed agents would be able to have optimal CSPs by improving the learning process in EDRLA. This improvement will be reflected in the agent's performance and considerably affect it, especially in identifying cloud computing requirements based on the QoS metrics.
Distributed Target Tracking Using Maximum Likelihood Kalman Filter With Non-Linear Measurements

We propose a distributed method for tracking a target with linear dynamics and non-linear measurements acquired by a number of sensors. The proposed method is based on a Bayesian tracking technique called maximum likelihood Kalman filter (MLKF), which is known to be asymptotically optimal, in the mean squared sense, as the number of sensors becomes large. This method requires, at each time step, the solution of a maximum likelihood (ML) estimation problem as well as the Hessian matrix of the likelihood function at the optimal. In order to obtain a distributed method, we compute the ML estimate using a recently proposed fully distributed optimization method, which yields the required Hessian matrix as a byproduct of the optimization procedure. We call the algorithm so obtained the distributed MLKF (DMLKF). Numerical simulation results show that DMLKF largely outperforms other available distributed tracking methods, in terms of tracking accuracy, and that it asymptotically approximates the optimal Bayesian tracking solution, as the number of sensors and inter-node information fusion iterations increase.
Implementation Of Encryption And Hidden Data Using Tiny Encryption Algorithm And End Of File

Security and confidentiality of data is one important aspect of an information system. The information can be misused very large losses in high-profile cases such as vital information confidential corporate, customer data banks and etc. Information security solutions in one of them can be used with cryptography. Cryptographic algorithms used in this study is a tiny encryption algorithm. Cryptographic data security attacks can always wear can occur, with this in mind the authors added security techniques to perform data hiding with the media as a placeholder, this term is called steganography. Steganography is used in this study is the end of the file. These techniques make the process of data hiding which is located at the end of the image, so it does not affect the image quality of the reservoir. In this study, a system built using microsoft visual studio 2010 C#. This system can work well, but has a color image blur caused to the container caused by the inserted message, where the greater the size of the message was inserted then color the image blur that arises will be many more.
A Survey of Strategies, and Procedures for Detecting Drone Utilizing Sound, Radar, Thermal Infrared Sensor and Video Applications

With the exponential rise in the use of drones anywhere anytime, malicious use by outlaws is increasing as well. This calls for protective, detective, preventive measures to counter these attacks. This paper aims to review literature on drone detection and classification that utilizes a myriad of modalities ranging from using thermal infrared sensors to radar detections. In addition, there is a summary of a detailed discussion on drone classification along with recent progress and development in drone detection using machine learning, all of which is performed in an attempt to identify means to thwart such attacks. Furthermore, some future research directions, related to this new field of study, are discussed.
PROFESSIONAL SELF-DETERMINATION OF HIGH SCHOOLS AS A PURPOSE PROCESS

The article presents the theoretical aspect of the problem of professional self-determination of high school students. The essence of the concepts of the presented problems is revealed. Professional self-determination of high school students is presented as a purposeful process that requires the development of effective conditions for its implementation. A procedural model of professional self-determination of high school students is proposed for consideration as one of the reasons for personal growth, which includes cognitive-reflexive, value-semantic and behavioral-modeling components. The stages of realization of purposeful professional self-determination of senior pupils as a reason for personal growth are presented: theoretical and analytical, ascertaining, formative and developmental. A number of methods are presented that create the basis for the purposeful process of professional self-determination, namely: the method of self-knowledge, the method of identifying and activating the values ​​of personal growth, the method of designing the professional "I"; the presented model activates and directs the process of professional self-determination of high school students. It is concluded that a high level of development of the cognitive-reflexive component presupposes the involvement of high school students in active self-knowledge, the result of which is a thorough knowledge of their own individual-typological characteristics, an understanding of the strengths and ways of their successful use, a constant interest in the topic of choosing a profession. The high level of development of the value-semantic component testifies to the formation of high school students' values ​​of labor and values ​​of growth, the presence of a stable desire for self-improvement. A high level of the behavioral modeling component presupposes that high school students are aware of their responsibility for choosing a future profession, the ability to make informed decisions, and a willingness to model the professional “I”.
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training

Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning tasks in text and speech. Training of such LSTM applications is computationally intensive due to the recurrent nature of hidden state computation that repeats for each time step. While sparsity in Deep Neural Nets has been widely seen as an opportunity for reducing computation time in both training and inference phases, the usage of non-ReLU activation in LSTM RNNs renders the opportunities for such dynamic sparsity associated with neuron activation and gradient values to be limited or non-existent. In this work, we identify dropout induced sparsity for LSTMs as a suitable mode of computation reduction. Dropout is a widely used regularization mechanism, which randomly drops computed neuron values during each iteration of training. We propose to structure dropout patterns, by dropping out the same set of physical neurons within a batch, resulting in column (row) level hidden state sparsity, which are well amenable to computation reduction at run-time in general-purpose SIMD hardware as well as systolic arrays. We conduct our experiments for three representative NLP tasks: language modelling on the PTB dataset, OpenNMT based machine translation using the IWSLT De-En and En-Vi datasets, and named entity recognition sequence labelling using the CoNLL-2003 shared task. We demonstrate that our proposed approach can be used to translate dropout-based computation reduction into reduced training time, with improvement ranging from 1.23x to 1.64x, without sacrificing the target metric.
Thermal Image Face Recognition through Transverse Dyadic Wavelet Transform and GLCM

Face recognition use in biometric application for authentication. Traditional face recognition methods performance is inaccurate in uncontrolled environment, such as illumination and pose. The colour image face recognition method, does not provide accurate recognition. Hence, we propose to use thermal image for face recognition. The face recognition from thermal image recognizes faces with cosmetics and illumination. The feature points of thermally active regions in face image determine by wavelet transform such as Discrete Wavelet Transform (DWT), Non Decimated Stationary Wavelet Transform (NDSWT) and Transverse Dyadic Wavelet Transform (TDyWT). The DWT and NDWT does not show accurate representation of thermal regions in face due to translational invariance and inaccurate wavelet feature coefficient. However, the accurate estimation of approximate and detail coefficient in TDyWT leads to thermal region delineation. The delineated region and feature points spatial pixel relation determine by Grey Level Co-occurrence Matrix (GLCM). The feature points spatial relation between pixels classify by Least Square Support Vector Machine (LSSVM) for face recognition. The robustness of method evaluate with facial images under different illumination, pose conditions and resolution. The TDyWT of face recognition outperforms colour image face method. The TDyWT has 94.73% accuracy for face recognition compared to traditional methods.
Perancangan Sistem Login pada Aplikasi Berbasis GUI Menggunakan Qtdesigner Python

Advances in technology are increasingly making applications have increasingly sophisticated quality.Various programming languages also take part in building various applications with various types of platforms.One type of desktop application.Currently, desktop platform applications can be built using a variety of programming languages.One of them is the python programming language.The python programming language has several advantages, namely: the python programming language is simple and flexible so that it is easy to develop (reusable), desktop applications generated using python are dynamic and eventbased which makes the application responsive.Making desktop applications using python is easier with the QtDesigner, namely classes in the python programming language that are event handlers that are suitable for desktop applications.One example of a GUI application that will be created using QtDesigner based on the python programming language is the login system in the application.
Implementasi Algoritma Rough Set Untuk Memprediksi Jumlah Pendaftar Siswa Baru Pada SMK Swasta Sinar Harapan

ABSTRACT&#x0D; Where the number of new student registrants at the Sinar Harapan Private Vocational School is unstable every year and the school has difficulty predicting how many new student registrants will be in the coming year and preparing extracurricular activities such as English/computer training or the best facilities for new students such as chairs, tables, classrooms and so on, as well as the competitiveness of private schools in the district. banyan. So we need a system that can dig up data and help predict the number of new student registrants, using data mining techniques, namely the Rough Set Algorithm which aims to get a short rule estimate from a table, in this study using student registration data in 2018, 2019, 2020. By applying data mining with the rough set algorithm and carrying out the rough set stages, it can produce decisions in the form of generating rules (knowledge), and the results of the calculation analysis are 152 students to predict the number of students in the coming year.
FUTURE MASTERS’ PROFESSIONALISM DEVELOPMENT IN THE PROCESS OF PRACTICAL TRAINING IN THE INSTITUTIONS OF OUT-OF-SCHOOL EDUCATION

The article is devoted to the problem of future masters’ professionalism development in the process of practical training in the institutions of out-of-school education. Based on the analysis of psychological and pedagogical literature, the understanding of the concept of professionalism has been clarified, the peculiarities of practice in a multidisciplinary institution of out-of-school education have been revealed, the requirements to the bases of practical training of future masters of education, educational program 011 Educational, pedagogical sciences (Out-of-school education) It has been found out that the future teachers’ practical training acts as an integrating main component of personal and professional development of the specialist, the link between theoretical training and independent work in the establishments and institutions of out-of-school education. Providing performance of various tasks, which include: acquaintance with the general directions of work in a multidisciplinary institution of out-of-school education; creation of an individual educational trajectory of the applicant of out-of-school education according to abilities of talents and gifts of children and youth on the basis of psychological and pedagogical diagnostics; development of curricula, calendar and lesson plans for group and club work, development of scenarios and script plans for collective and mass events, which are included in the annual work plan of the institution of out-of-school education. It has been specified that the professionalism of the future master will be reflected in the following results of practical training: the ability to recognize and take into account the diversity of students, the ability to provide individual support in the educational process; awareness of different contexts in which training, upbringing and development of students of out-of-school education can take place; ability to implement subject-subject interaction with different participants of the educational process at different levels; the ability to contribute to the formation of a general culture of personality, socialization, conscious choice and development of additional educational programs, using a variety of forms, techniques, methods and means of teaching; to master the norms of pedagogical relations in the process of designing and implementing educational activities, search, sharing, application of innovations and creativity in the educational process.
SISTEM INFORMASI PEMBERITAHUAN EVENT BERBASIS FRAMEWORK CODE IGNITER

Data Management System of Event Organizer for notification of an event to the community based on the Igniter Framework code is done by utilizing the social media features. These data are stored in the form of recorded documents. This research seeks to build an event information system so that data management is easier so information access is faster. This system was developed by the waterfall method. Evaluation of the system in the aspect of appearance by the user shows the value which states that most people want this system to be made so that the community and event organizers are helped a lot. Software test results show that the functions of the Event Organizer Information System For Notification of an Event have been running well according to requirements. The conclusion is that this system is appropriate to be used to assist the work of organizers, event organizers, and users in holding events, finding events, and successfully organizing events.
Uranium Recovery Focuses

Aspects in Mining & Mineral Science Uranium Recovery Focuses Hamid Kheyrodin* Semnan University, Iran *Corresponding author: Hamid Kheyrodin, Assistant professor, Semnan University, Iran Submission: April 10, 2021;Published: October 07, 2021 DOI: 10.31031/AMMS.2021.07.000667 ISSN 2578-0255Volume7 Issue4
Batavia asked for advice. Pretrained language models for Named Entity Recognition in historical texts.

Pretrained language models like BERT have advanced the state of the art for many NLP tasks.For resource-rich languages, one has the choice between a number of language-specific models, while multilingual models are also worth considering.These models are well known for their crosslingual performance, but have also shown competitive in-language performance on some tasks.We consider monolingual and multilingual models from the perspective of historical texts, and in particular for texts enriched with editorial notes: how do language models deal with the historical and editorial content in these texts?We present a new Named Entity Recognition dataset for Dutch based on 17th and 18th century United East India Company (VOC) reports extended with modern editorial notes.Our experiments with multilingual and Dutch pretrained language models confirm the crosslingual abilities of multilingual models while showing that all language models can leverage mixed-variant data.In particular, language models successfully incorporate notes for the prediction of entities in historical texts.We also find that multilingual models outperform monolingual models on our data, but that this superiority is linked to the task at hand: multilingual models lose their advantage when confronted with more semantical tasks.
Auto Question Tagging for Health Care Using Machine Learning Technique

Tagging is a machine learning technique that provides tags to the information that the user can easily identify the related information. Manual tagging is widely used for constructing question banks; but, this approach is time-consuming and it would create pathway to consistency issues. Semi-manual tagging which is time-consuming and people must be experts in that domain have the ability to identify the question and tag them it is not possible in real-time and high in cost. The proposed associate degree automatic tagging exploitation information processing that mechanically tags automatic question tagging. In this paper, step up with a keywords-based model to automatically tag questions with information units. With regard to multiple-choice questions, the proposed models use mechanisms to capture helpful information from keywords to boost tagging performance. Automatic tagging method using NLP automatically tag questions where users can get information regarding to his search which overcomes earlier methods. In our experiments, the result shows that the model is credible and outperforms various existing models.
General And Educational Philosophy Of Sri Aurobindo Ghosh

The present paper is an attempt to know the general and educational philosophy of great philosopher Sri Aurobindo Ghosh. In this study Philosophical and Historical method was used. Books written by Sri Aurobindo Ghosh, speeches ofSri Aurobindo Ghosh, articles, newspapers, internet sources, etc. are the main source of data. Aurobindo proposed that education is nothing but bringing out and nurturing the latent potentialities; integrate oneself with self, harmonious living of individual with society, country and humanity to make oneself a complete being or integral human being. He believed that purpose of education is not merely cramming of facts or information because this will lead to temporal achievement of goals in life but education is making of character, personality and values. He placed high importance to the indigenous education, language as it will help the child to understand his /her past and connect with the present so that he /she can progress in the future. He was not against any western education but he believed that we should learn from western education about their advancement in knowledge. He believed that chief aim of education is awakening of divinity in the individual. This can be done through yoga, meditation. He had given five secondary aims of education- physical, spiritual, moral, mental and psyche development which he later called it as Integral Education. According to him, process of education is the development of one’s physical, vital, spiritual, mental and psychic aspects of human. He was of the opinion that role of teacher is very minimal in the teaching-learning process as teaching will corrupt the mind of the child. So he believed that let the child explore the world around us and blossom on the basis of his/her innate abilities. He firmly believed that each country has a set of own values, culture and history and it is the responsibility of teachers to make students aware of it and develop them in line with the values, ideals and traditions.
The future of psychology: from natural science to the field of thought-activity

The future of psychology: from natural science to the field of thought-activity.
The Influence of Learning Media on Student Activity

In the era of 4.0 where technology is increasingly clothing and developing, it is easier for an educator to find information and float learning media. An educator is required to be able to keep up with the times. Technology is increasingly sophisticated and changing, but there are still many educators who have not been able to develop learning media. Learning media has a big influence on the activity of students in the classroom for the material conveyed by the teacher. Attractive learning media will make students easily accept the material presented by the teacher and appropriate learning media will make children not easily bored during learning activities. For this reason, the teacher determines the appropriate learning media for students. By choosing the right learning media, it will attract students' interest in learning and be able to make students active in learning.
THE ANALYSIS OF RENEWABLE ENERGY MANAGEMENT TO GENERATE ELECTRICITY IN LAMPUNG PROVINCE INDONESIA

Electricity installed in Lampung is still a power plant with fossil fuels such as steam power plants, diesel power plants, gas engine power plants, and gas power plants. For this reason, it is necessary to make a revolution in the use of fuel in Indonesia, especially in Lampung, by reducing the use of fossil fuels and increasing the use of new fuels or renewable energy. Renewable energy sources are energy sources that are produced from sustainable energy resources if managed properly, including geothermal, wind, bio-energy, sunlight, water flows, and falls, as well as movement and differences in sea layer temperature. Lampung Province itself has a lot of potential renewable energy. With the existing potential, to preserve the environment, it is necessary to increase electricity production using renewable energy. The minimum addition from the current one is 50% for fossil energy and 50% for renewable energy. However, the existing obstacles need to be resolved with the support of the Government and the general public. So that in the future electricity production can increasingly focus on the use of renewable energy.Keywords: Energy Management, Renewable Energy, ElectricityJEL Classifications: Q2, Q40, Q42DOI: https://doi.org/10.32479/ijeep.11549