Reviewerly/exHarmony huggingface dataset:
Plan of Load Balancing using Bees Swarm Optimization in Cloud

Lately, the resource allocation and errand planning is a fundamental possibilities of distributed computing. The ideal assignment of assets with load adjusting in distributed computing is been a significant test in the field of distributed computing. In this paper, we propose an incorporated calculation for load adjusted asset intending to give viable cloud administrations. The strategy makes a multidimensional asset arranging model situated in Bees Swarm Optimization (BSO) to accomplish asset arranging productivity in cloud framework. The client demands are chosen powerfully from the heap advancement BSO calculation planned in a multidimensional way that builds the use of virtual machines by balancing the heaps. The BSO component improves the cloud usefulness tentatively. The outcomes show ideal nature of proposed cloud structure utilizing BSO calculation with diminished computational expense, reaction time and asset booking effectiveness.
ANALISIS KETUNTASAN PEMBELAJARAN ONLINE PADA MATAKULIAH BAHASA INGGRIS

The pandemic due to the corona virus at the end of 2019 (covid-19) has caused changes in the order of life, including education. Social and physical distancing in tackling the spread of Covid-19 affect the learning model. Starting from early childhood education to the university level applying an online learning model. This study analyzes the completeness of the online model of learning English. The analysis was carried out by using profile matching method on 430 student data from three universities in Yogyakarta. Completeness of learning is divided into 5 levels based on 4 components. The results showed that the level of completeness of online learning in all areas of higher education was 89.1%. The analysis of the results shows that the level of learning completeness of IT students is better than non-IT students, respectively 90.5% (IT) and 88.6% (Non-IT).
SMS Spam Detection using Supervised Learning

Over the last decade, the growth of short message services has been rising. These text messages are more powerful for corporations than even SMS. This is because about 80 percent of sms remain unopened while 98 percent of smartphone users read theirs by the end of the day. Spam, which refers to any irrelevant text messages sent via mobile networks, has also gained popularity. For consumers, they are seriously irritating. Due to the geographical material, use of abbreviated words, the current Spam Detection techniques are more challenging than e-mail spam detection techniques , unfortunately very few of the existing research addresses these challenges. Much of the current research that has attempted to filter Spam has focused on features that were manually found. This paper aims to solve these concerns. Filtering is one of the most effective strategies among the methods developed to stop spam. Days of machine learning techniques are now used to process the spam SMS automatically at a very good rate. The goal of this research is to differentiate between ham and spam messages by developing an accurate and responsive model of classification that provides good accuracy with a low false positive rate
Phishing Webpage Detection using Hybrid Features and Deep Learning Techniques

Phishing is a sort of online assault that endeavors to cheat delicate data of organization clients. Current phishing website page discovery strategies primarily utilize manual component assortment, and there are issues that include extraction is muddled and the conceivable connection between's highlights can't be stayed away from. To tackle the issues, another phishing site page location model is proposed, among which the primary segments are programmed taking in portrayals from multi-perspectives highlights through portrayal learning and extricating highlights by mixture profound learning organization. Initially, the model treats URL, HTML page substance, and document object model design of pages as character arrangements individually, also, utilizes portrayal learning innovation to consequently get familiar with the portrayal of the pages; at that point, sends different portrayals to a half breed profound learning network made out of a convolutional neural network also, a bidirectional long and transient memory network through various channels to separate neighborhood and worldwide highlights, and utilize the consideration component to fortify the influence of significant highlights; finally, the yield of various channels is combined to acknowledge classification expectation. Through four arrangements of trials to check the recognition impact of the model, the outcomes show that the general classification impact of the model is superior to the current exemplary phishing site page discovery strategies, the exactness arrives at 98.05%, and the bogus positive rate is just 0.25%. It is demonstrated that the techniques of extricating website page highlights from all angles through portrayal learning and cross breed profound learning organization can successfully improve the discovery impact of phishing site pages..
Famous People Media Through the Use of Artificial Intelligence in Digital Marketing

Famous people though the use of artificial intelligence in digital marketing in this article were to study about the meaning and role of personal media, development trend of artificial intelligence (AI), and advantages and disadvantages of using artificial intelligence (AI) and personal in digital marketing. They showed that the people affecting persuasion; therefore, the use of people in the advertising industry by bringing both individuals with famous personal media to be (presenter) and artificial intelligence (AI). Therefore, if these people use artificial intelligence to help market in the digital age, there will definitely be more customers than at present because artificial intelligence can help to quickly respond to information instead of people, and it can screen and classify customers, but psychological persuasion still requires personal media.
School as a microcosm of society

The social milieu of a person is the immediateworld for him/her. We grow up experiencingthis immediate world of ours and it shapes ourperspectives and thoughts from a young age. Schoolis an integral part of this immediate world and thisinstitution is expected to prepare us to decide whatis right or wrong, to understand the values of lifeand become better citizens. Schools also functionin this social milieu and societal practices arereflected in the school practices too. Schools do notjust train individuals for employment but also servea greater purpose of instilling democratic values,like equality, justice, and respect from a young age.I got the opportunity to teach in one such schoolwhich functions with complete awareness of thepurpose of educating the children who come there.
RFID self-service innovation of reader service in University Library. Take Baise University as an example

Taking Baise University as an example, this paper introduces in detail the University Library in order to better serve readers, keep pace with the times, implement innovative service mode, and apply RFID self-service for innovative service. RFID is a kind of wireless communication technology, which can identify specific targets and read and write relevant data through radio signals without establishing mechanical or optical contact between the identification system and specific targets. It is also called RFID self-service machine that readers use RFID machine to borrow and return books by themselves at the screen terminal of RFID reader. The self-service of university library not only facilitates readers, but also saves time, improves the level of management technology and work efficiency, and promotes the development of university library work.
Elliptical Sentence In Open File A Cliff Hardy Novel by Peter Corris

Ellipsis is a branch of syntax that study about elimination word to avoid repetition word in the sentence. This study concern to find out the types of elliptical sentence and analyze the constituent structure of sentence in the novel by Peter Corris in title Open File A Cliff Hardy. This study applied the theory proposed by Quirk and Greenbaum (1973) to find out the types of elliptical sentence and the theory from Brown and Miller (1991) to analyze the constituent structure of sentence. The data were collected by documentation method and analyzed descriptively by qualitative method. The finding shows that there are five types of elliptical sentence which the most dominant is ellipsis of subject (and auxiliary)
Hashtag Marketing and Indian brands – Sentiment Exploration

Social Media continues to impact the marketing campaign in a big way, and Indian brands are not an exception. One of the influential yet understudied tools for social media is a hashtag, which in recent times created a valuable and spellbound marketing impression. Still, less research has been done on how hashtag creates such a magnificent consumer engagement. The paper aims to answer this unfolded query by evaluating the use of the hashtag by Indian brands through sentiment analysis. There are two ways Indian brands use the hashtag – Campaign hashtag and Brand hashtag. This research is based on brand hashtags and infers quite an interesting outcome. As an experiment, we executed the hashtags for top Indian brands to identify the overall sentiment polarity of the hashtag. The outcomes were divided into two parts, wherein, in the first part, we evaluated sentiments polarities (divided into positive and negative) and computed Conversation to Engagement (C/E) Ratio and Engagement to Reach (E/R) Ratio to identify the prominence of each hashtag. In the second part of the analysis, we analyzed the responses based on demographic analysis including gender, age, interests, and occupations, which gives a clear idea of hashtag exploration. Overall, we collected 5611 texts and 154472 tokens between 01/08/2019 and 30/09/2019 from the selected brands..
Siamese Network Training Using Sampled Triplets and Image Transformation.

The device used in this work detects the objects over the surface of the water using two thermal cameras which aid the users to detect and avoid the objects in scenarios where the human eyes cannot (night, fog, etc.). To avoid the obstacle collision autonomously, it is required to track the objects in real-time and assign a specific identity to each object to determine its dynamics (trajectory, velocity, etc.) for making estimated collision predictions. In the following work, a Machine Learning (ML) approach for Computer Vision (CV) called Convolutional Neural Network (CNN) was used using TensorFlow as the high-level programming environment in Python. To validate the algorithm a test set was generated using an annotation tool that was created during the work for proper evaluation. Once validated, the algorithm was deployed on the platform and tested with the sequence generated by the test boat.
Forensic Analysis of Gurmukhi Letters based on Zone Division for Verification

Gurmukhi Language is a dominating language in northern states. Many documents written in this dialect become the subject matter of judicial scrutiny, where again much is left to conjectural analysis. It is a humble effort to devise scientific technology and tools for application to verify Gurmukhi handwriting - one disputed to be verified along with the genuine and admitted one. The rules and technology applied in English language is far less useful for the reason of its 35 original letters-consonants covered in three zones but more of them in upper and middle one; and frequent use of 11 diacritics - 9 of them being applied in the upper two zones of the writing. Accordingly, the paper makes a forensic analysis of two zones of writing because of their dominant use. Again, the relativity and aspect ratio methods of handwriting verification are worth bringing satisfactory results in determining genuineness of a handwritten document in Gurmukhi script subject to the judicial scrutiny. The researcher illustrates the application of relativity and aspect ratio along study of two zones of Gurmukhi language resulting in identifying the similarity or marked difference of the two writings.
Development of Android-based New Student Admission Application at National University: Development of Android-based New Student Admission Application at National University

Admission registration for new students will be entered or commonly known as PPMB is a route of years in every private and public university every year. Various information about the PPMB also published from: various media, among others: sources in various web campuses, as well as the following info about the registration, schedule and test location as well as information about graduation or absence. Many of the info also often forced students to always see the PPMB sites, if missed then: will always remain in the information about obtaining the latest information about PPMB. Therefore, the need to build an Android-based PPMB application into a proper alternative and with PHP programming language with PostgreSQL and Waterfall method is also a good choice. The research conducted in this phase is based on gradual, namely: the need for analysis, designing and design of the system, implement the system and also test the system. According to the test tests that have been conducted, it can be seen that: a PPMB application with the Android base could be used as an intermediary list for the new students. Other things from the analysis of the making of PPMB are also able to become intermediaries new information about the information registration with real-time so that the candidates from students have the convenience of getting the latest information on the PPMB site.
A Comparison of Different Consensus Protocols

After launching bitcoin, blockchain is gaining massive growth in the last decade. Researchers are trying to find blockchain potential in other areas, also such as healthcare, government services, data access, and many more. As we all knew, whenever technology is expanding exponentially, it is crucial to discuss all the critical components of technology. Some key elements of blockchain technology are cryptography, decentralization, P2P network, validity, consensus protocol, and distributed ledger. In this chapter the authors focus on the consensus algorithm. A consensus algorithm is a backbone and primary root of revolutionary technology. A consensus algorithm creates an agreement that decides how a block appends as a new block. The chapter discusses two types of algorithms: proof-based and voting based. At the end of the chapter we present a detailed comparison of the blockchain consensus algorithm.
IDENTIFICATION OF MIXER TRANSACTIONS IN THE BITCOIN NETWORK IN THE FRAMEWORK OF SOLVING THE PROBLEMS OF PREVENTING MONEY LAUNDERING AND TERRORIST FINANCING

This article addresses the problem of using cryptocurrencies in crimes related to money laundering and terrorist financing. The development of technologies that allow anonymizing participants of various cryptosystems not only increases their reliability and security for ordinary users, but also exposes such systems to the risk of being used by criminals, and also significantly complicates countering fraudulent or other illegal actions committed using cryptocurrencies. The authors propose algorithms that allow us to classify bitcoin transactions on the subject of whether they use mixers – the main means of hiding traces in the public blockchain and, perhaps, the main participant in most criminal schemes, whether it is money laundering or terrorist financing.
PSYCHOLOGICAL AND PEDAGOGICAL FEATURES OF FORMING LEADER QUALITIES IN FUTURE PSYCHOLOGISTS

This article discusses the psychological and pedagogical features of the formation of leadership qualities in future teachers-psychologists. Requirements for leadership qualities are provided by a combination of personality qualities, which allows future professionals to effectively carry out professional activities. In modern conditions, the future specialist must possess not only professional knowledge, skills, but also a certain set of leadership qualities that allow him to successfully realize his creative potential in practical activities. The psychological and pedagogical features of the formation of leadership qualities in future pedagogues-psychologists are determined by the development of reflection abilities, a positive impact on people, an active and responsible attitude to society. The psychological features of the development of leadership qualities of students in the process of studying at a university, as future pedagogues-psychologists, are features of socialization and adaptation of a person, the development of self-awareness, the specifics of professional activity, the interaction of students and teachers, as well as intra-group interaction of students. The pedagogical features of the development of leadership qualities are in the learning process to show such qualities as determination, determination, independence, responsibility, emotional stability, mobility, sociability
Projective MDS Codes Over GF(27)‎

MDS code is a linear code that achieves equality in the Singleton bound, and projective MDS (PG-MDS) is MDS code with independents property of any two columns of its generator matrix. In this paper, elementary methods for modifying a PG-MDS code of dimensions 2, 3, as extending and lengthening, in order to find new incomplete PG-MDS codes have been used over . Also, two complete PG-MDS codes over of length and 28 have been found.
Human Action Recognition using Multimodal CNN

This paper aims to identify the various actions and expressions portrayed by a human in the input video stream. Firstly, we extract frames from the input video stream and then perform background subtraction. The frames are then pre-processed and fed to the trained CNN model (AlexNet). The proposed method aims to build two CNN models where in the first model identifies human action and the second model identifies human expression. Finally, as a result, we integrate both the models and use the soft-max classifier to classify the identified actions and expressions accordingly to their classes.
Embedding Virtual Objects into the Physical World: Student-Centred Augmented Reality Concept Design and Development

In the contemporary world where almost, everything has moved towards digitalisation, the combination of virtual and physical worlds has proved to be an efficient approach in an educational context. Since the application of augmented reality (AR) and virtual reality (VR) in education is on the rise, we recommend a conceptual framework for technology education. However, little is known about how students mix virtual and physical objects in solving everyday problems through prototyping and envisioning the potential solution. This study is based on the gathering and analysis of qualitative and quantitative data on students in higher education. In the first study, we collected (N = 100) information about potential problem-solving contexts for augmented and virtual reality applications. In addition, this study examines student (N = 62) projects in the AR and VR fields to generate new knowledge about co-creation activities in design processes. The results reveal that when students design and develop virtual objects in connection to the physical world, they learn more than just using digital technology alone. Combining the virtual with the physical world provides an extensive learning environment not only for digital problem solving but also for materialistic and physical surroundings. This paper contributes a new theoretical understanding for academicians about the role of AR in technology education and a conceptual framework for practitioners to enhance student learning through AR-based design projects.
Active Feature Acquisition with Generative Surrogate Models

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we propose models that perform active feature acquisition (AFA) to improve the prediction assessments at evaluation time. We formulate the AFA problem as a Markov decision process (MDP) and resolve it using reinforcement learning (RL). The AFA problem yields sparse rewards and contains a high-dimensional complicated action space. Thus, we propose learning a generative surrogate model that captures the complicated dependencies among input features to assess potential information gain from acquisitions. We also leverage the generative surrogate model to provide intermediate rewards and auxiliary information to the agent. Furthermore, we extend AFA in a task we coin active instance recognition (AIR) for the unsupervised case where the target variables are the unobserved features themselves and the goal is to collect information for a particular instance in a cost-efficient way. Empirical results demonstrate that our approach achieves considerably better performance than previous state of the art methods on both supervised and unsupervised tasks.
Professional and cognitive level of the journalist in science propaganda

Journalism branches arise in accordance with development of society and its needs. Society, people, and professions are undergoing systematic transformation. Scientific journalism performs with its distinctive characteristics. First, it changes and organized by the achievements of science and education. Second, the success factors of science journalism have a normative, legal, and practical basis. Third, scientific journalism has a combined function of connecting the scientific community and public. The promotion of scientific achievements is jointly carried out by professional journalists and the scientific community. Therefore, the elaboration of scientific information is important for a good perception of the content by a wide audience. The cognitive level of the scientific journalist helps him to freely use scientific theories along with other sources. The author considers that such activities should not end with the publication of scientific results by a journalist. For a journalist, high-quality publication of research results is an integral part of the success of scientific communication. The study concludes that the degree of success in scientific communication depends on several factors, including the cognitive and professional level of a journalist.
Optimal Testing of Discrete Distributions with High Probability.

We study the problem of testing discrete distributions with a focus on the high probability regime. Specifically, given samples from one or more discrete distributions, a property P, and parameters 0with probability at least 1−δ whether these distributions satisfy P or are є-far from P in total variation distance. Most prior work in distribution testing studied the constant confidence case (corresponding to δ = Ω(1)), and provided sample-optimal testers for a range of properties. While one can always boost the confidence probability of any such tester by black-box amplification, this generic boosting method typically leads to sub-optimal sample bounds. Here we study the following broad question: For a given property P, can we characterize the sample complexity of testing P as a function of all relevant problem parameters, including the error probability δ? Prior to this work, uniformity testing was the only statistical task whose sample complexity had been characterized in this setting. As our main results, we provide the first algorithms for closeness and independence testing that are sample-optimal, within constant factors, as a function of all relevant parameters. We also show matching information-theoretic lower bounds on the sample complexity of these problems. Our techniques naturally extend to give optimal testers for related problems. To illustrate the generality of our methods, we give optimal algorithms for testing collections of distributions and testing closeness with unequal sized samples.