Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF BACKPROPAGATION NEURAL NETWORK IN SENTIMENT ANALYSIS ON TWITTER TO PUBLIC FIGURES

Sentiment analysis is a process for identifying or analyzing people's opinions on a topic. Sentiment analysis analyzes each word in a sentence to find out the opinions or sentiments expressed in the sentence. The opinions expressed can be in the form of positive or negative opinions. Twitter is one of the most popular social media in Indonesia. Twitter users always discuss various kinds of topics every day. One of the things discussed on Twitter and which has become a trending topic several times is about public figures. This study discusses the analysis of positive or negative sentiments towards public figures based on tweet data carried out by text processing. The results of text processing are classified using a backpropagation neural network. Tests were carried out using 69 test data, resulting in an accuracy of 62.3%, with 43 correct classification results.
The Effect of Leadership Styles on Project Implementation

The goal of all leadership styles in project implementation is to transform and lead the project to success.Transformative leadership styles utilize emotional intelligence to motivate team members in a project and create a psychologically safe environment which encourages better performances during project implementation.Leadership is important for inspiration, guidance, and defining the objectives of every project.The aim of this research is to evaluate the effects of leadership style on project implementation.Through content analysis qualitative research design utilizing secondary sources, there emerges a trait that defines good leadership and how it can be leveraged for proper outcomes in project implementation and management.The study concludes that project implementation includes the engagement of teams under one or multiple leaderships.Leaders have the responsibility of engaging with human resources more than any other resource in the organization.All the phases of project implementation are vital and must be managed with dexterity and exemplary leadership performance.
iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography

Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices' efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.
A Comprehensive Review on Cloud Computing and Cloud Security Issues

Cloud computing is a type of computing where computing services such as applications, storage, infrastructure, processing power are hosted by some vendors (cloud vendors) over the Internet and delivered to users on on-demand basis. A company can benefit from cloud computing in the sense that they can rent access to anything from applications to storage to processing power rather than owning their own IT infrastructure. By using cloud-computing services, companies can avoid the various costs and complexities associated with owning and maintaining their own IT infrastructure as everything is taken care of by the service providers. Cloud computing is a paradigm in which both the users and the service providers are benefited. Service providers are greatly benefited from cloud computing by providing cloud services to a wide range of customers. The primary objective of this paper is to give an overview on cloud computing encompassing its features, the pros and cons of cloud computing and the challenges that comes with it. Furthermore, this paper also presents a list of security concerns in cloud computing which stands in the way of wide spread adoption of cloud computing thereby motivating researchers to devise competent solutions to tackle these ongoing security issues.
Face Recognition using PCA Integrated with Delaunay Triangulation

Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research develops one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.
Knowledge Discovery in COVID-19 Research Literature

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic.In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus.Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100, 959 sentences.We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development.The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.1 https://www.kaggle.com/allen-institute-for-ai/ CORD-19-research-challenge Phylogenetic studies have shown that 2019-nCoV and SARS-CoV belong to the subgenus Sarbecovirus, but they are distantly related 5-8 , with a sequence identity of 79.6% at the whole-genome level 5 However, SARS-CoV, using the same receptor, was not detected in skeletal muscle by post-mortem examinationIn conclusion, SARS-Cov is the closest related virus to 2019-nCoV for which a significant number of epitopes has been defined in humans (and other species), and that also causes human disease with lethal outcomes Concept Concept Concept ,, Action Concept Con Concept in-context has-
A Novel Neural Model With Lateral Interaction for Learning Tasks

Abstract We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.
Modeling assembly bias with machine learning and symbolic regression

Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of these surveys. Standard techniques employed to create these mock catalogs, like Halo Occupation Distribution (HOD), rely on assumptions such as the baryonic properties of dark matter halos only depend on their masses. In this work, we use the state-of-the-art magneto-hydrodynamic simulation IllustrisTNG to show that the HI content of halos exhibits a strong dependence on their local environment. We then use machine learning techniques to show that this effect can be 1) modeled by these algorithms and 2) parametrized in the form of novel analytic equations. We provide physical explanations for this environmental effect and show that ignoring it leads to underprediction of the real-space 21-cm power spectrum at $k\gtrsim 0.05$ h/Mpc by $\gtrsim$10\%, which is larger than the expected precision from upcoming surveys on such large scales. Our methodology of combining numerical simulations with machine learning techniques is general, and opens a new direction at modeling and parametrizing the complex physics of assembly bias needed to generate accurate mocks for galaxy and line intensity mapping surveys.
A Comprehensive Study About Cybersecurity Incident Response Capabilities in Ecuador

The increase of security threats and attacks in Ecuador, motivates the implementation of security incident response teams (CSIRT) in the different Ecuadorian organizations in different domains: academic, military, financial, public sector and critical infrastructures. NIST has developed a set of steps that are necessary to establish the CSIRT. The purpose of this study is to develop an analysis of each of the steps proposed by NIST in the Ecuadorian context, to identify the current status of response capabilities to security incidents in Ecuador and analyze possible actions to improve these capabilities.
New Practical Public-Key Deniable Encryption

The primitive of deniable encryption aims to protect the privacy of communicated data in the scenario of coercion by allowing the sender (or receiver or both of them) to open the ciphertext transmitted into a different message. There are two types of deniability, namely, multi-distributional deniability and full deniability, and the later provides better security guarantees than the former one. However, all existing schemes under the framework of full deniability are less efficient. In this paper, we first propose a new public key encryption scheme in which the ciphertexts could be decrypted by the receiver depending on the decision of the sender. Additionally, building on this encryption, we construct a new public-key sender-deniable encryption scheme under the framework of full deniability. Compared with Canetti et al.’s party scheme, the proposed scheme is superior in both efficiency andeniability.
Effectiveness of MPC-friendly Softmax Replacement

Softmax is widely used in deep learning to map some representation to a probability distribution. As it is based on exp/log functions that are relatively expensive in multi-party computation, Mohassel and Zhang (2017) proposed a simpler replacement based on ReLU to be used in secure computation. However, we could not reproduce the accuracy they reported for training on MNIST with three fully connected layers. Later works (e.g., Wagh et al., 2019 and 2021) used the softmax replacement not for computing the output probability distribution but for approximating the gradient in back-propagation. In this work, we analyze the two uses of the replacement and compare them to softmax, both in terms of accuracy and cost in multi-party computation. We found that the replacement only provides a significant speed-up for a one-layer network while it always reduces accuracy, sometimes significantly. Thus we conclude that its usefulness is limited and one should use the original softmax function instead.
How to Apply Creative Thinking to Physical Education Training in Colleges and Universities

&lt;p&gt;With the general development and progress being made these days, there are higher requirements from societies on college sports teaching, as well as training results. University education is the last link to cultivate talents for the society. A talented person must have rich and in-depth professional knowledge, a strong and powerful body, and should be a creative thinker who is willing to break away from conventions, This article puts forward new requirements for college physical education, which should not only be applied with creative thinking to make students strengthen their physical fitness in physical exercise, but also guide and stimulate the creation of student's thinking while teaching.&lt;/p&gt;
A Unified Mixture-View Framework for Unsupervised Representation Learning

Recent unsupervised contrastive representation learning follows a Single Instance Multi-view (SIM) paradigm where positive pairs are usually constructed with intra-image data augmentation. In this paper, we propose an effective approach called Beyond Single Instance Multi-view (BSIM). Specifically, we impose more accurate instance discrimination capability by measuring the joint similarity between two randomly sampled instances and their mixture, namely spurious-positive pairs. We believe that learning joint similarity helps to improve the performance when encoded features are distributed more evenly in the latent space. We apply it as an orthogonal improvement for unsupervised contrastive representation learning, including current outstanding methods SimCLR, MoCo, and BYOL. We evaluate our learned representations on many downstream benchmarks like linear classification on ImageNet-1k and PASCAL VOC 2007, object detection on MS COCO 2017 and VOC, etc. We obtain substantial gains with a large margin almost on all these tasks compared with prior arts.
IMPLEMENTATION OF STEAM CONCEPT IN PREPARATION OF PRE-SERVICE MATHEMATICS TEACHERS

The article considers the current problem of improving the education system, namely the application of the concept of STEAM as an innovative approach, which is today the subject of mass interest of both researchers and teachers-practitioners. Thanks to STEAM-education, teachers have the opportunity to develop students in several subject areas - computer science, physics, technology, engineering, art and mathematics. The necessity of introducing the concept of STEAM-education into the system of training pre-service teachers, which continues the implementation of STEM ideas, caused by the transition of society to the digital age, is substantiated. STEAM approaches in the educational process are considered. The experience of such developed countries as the USA, Finland, Australia, Great Britain, Israel, Korea, Singapore was analyzed and it was found out that they implement state programs in the field of STEAM education. In Finland, a LUMA center has been opened, in Spain teachers use STEAM-Makerspace to improve the knowledge of high school students in the field of geometry, in Australia the program STEAM Professionals in Schools has been implemented that brings together teachers with STEAM professionals to improve STEAM teaching practice and provide STEAM training at Australian schools. Possibilities of realization of three main ways of introduction of STEAM-education into the learning process, such as STEAM-project, STEAM-lesson, Maker-space are covered which can be effectively introduced into the educational process of higher education institutions. The implementation of the STEAM approach using the example of training pre-service mathematics teachers is considered. The author notes the important role of teachers who are ready to implement the ideas of STEAM-education in this process. The results of experimental work on the implementation of the concept of STEAM-education within the project activities of students using the example of the project "Ukrainian embroidery" are presented. The purpose and activity of students are revealed, the research tasks which they carried out are described. The result of the study was the development of a program that "embroiders", simulates the process of embroidery in different techniques, implementing different approaches to bypass the embroidery scheme. In addition, a set of tools was created in the GeoGebra package for research and design of various ornaments based on traditional elements of Ukrainian embroidery.
Changing the role of the Russian university teacher: The route to sustainable education

Nowadays, the changes in the university context confront educators with challenges and dilemmas, which make them seek new approaches to training graduates that will be employable in the national labour market. One of most essential issues is if faculty can meet the demands of the changing educational environment. Thus, in the present paper the authors aim to reflect on the content of teachers’ training in the Russian HE; to identify the existing weaknesses, needs and emerging issues in teaching practices and to match them with the best EU teaching practices, as well as to work out some recommendation for developing a sustainable teachers’ training model that targets to improve qualification of university teaching staff in educational methods and pedagogical approaches that are considered in the outcome-based and quality assurance context, which impart sustainable education to their students for better employability..
Neural Prototype Trees for Interpretable Fine-grained Image Recognition

Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design. Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this learned prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off using ensemble methods, pruning and binarizing. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 learned prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200- 2011 and Stanford Cars data sets. Code is available at this https URL
Blockchain and Bitcoin Security: Threats in Bitcoin

We as a whole realize that blockchain is a procedure of bitcoin which is one of the most well-known technique for digital money that permit circulated add just open record all exchanges to be recorded. Appropriated agreement convention which depends on motivating force good evidence of work gives abnormal state of security this all framework and is controlled by a system hub called miners. Miners sincerely keep up the blockchain so as to get motivators. Bitcoin dispatch in 2009, its economy has developed at a tremendous rate, and it is presently worth about170 billions of dollars. This widespread rise in the market estimation of bitcoin incite enemies to achieve bad marks for benefit, and specialists to find new presentation in the framework, propose cure, and anticipate up and coming procedure. In this chapter, we present a dealt with examination that covers the security and assurance parts of bitcoin. Our strategy initially started from chart of Bitcoin Protocol, second critical portions of bitcoin, third parts convenience and last sections interchanges inside the system. The present chapter in bitcoin and its fundamental noteworthy progressions, for instance, blockchain-based agreement tradition lead to the execution of various security perils to the normal handiness of bitcoin. We discuss about the handiness and soundness of the front-line security courses of action. Moreover, we present current assurance and absence of definition examinations in bitcoin and analyze the security-related threats to bitcoin customers close by the examination of the 224present security shielding courses of action. Finally, we format the essential open troubles and propose rules for future research toward furnishing firm security and assurance frameworks for bitcoin.
Domain engineering approach of software requirements analysis

Requirement analysis is one of the important processes in software development lifecycle management. In Agile approach requirements software models are the basic of generating other software development artifacts. Improving requirements approaches and techniques allows avoiding mistakes in other software development artifacts. Domain engineering fundamentals is the basic for “template oriented” approaches of software development artifacts designing. Reusing domain models and knowledge allows adding details in vertical “model to model” transformation operations, refine generated software development artifacts, organize systematic software reuse and perform many other activities. Paper proposes an approach of requirement analysis based on UML Use Case diagrams transformations into communication ones and the next refinements of them by means of information from domain models. The advantages of the proposed approach is the next: proposed transformation method involves ”many to many” transformation in order to save the semantic of initial model. Domain knowledge are used to complete communication diagram by means of adding details after transformation to them. In order to perform Use case to communication transformation graph representation of software models is chosen.
An Algebraic Graph Transformation Approach for RDF and SPARQL

We consider the recommendations of the World Wide Web Consortium (W3C) about RDF framework and its associated query language SPARQL. We propose a new formal framework based on category theory which provides clear and concise formal definitions of the main basic features of RDF and SPARQL. We define RDF graphs as well as SPARQL basic graph patterns as objects of some nested categories. This allows one to clarify, in particular, the role of blank nodes. Furthermore, we consider basic SPARQL CONSTRUCT and SELECT queries and formalize their operational semantics following a novel algebraic graph transformation approach called POIM.