Reviewerly/exHarmony huggingface dataset:
One-Hot Graph Encoder Embedding

In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC -- making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.
PHILOSOPHICAL INTERPRETATION OF THE CONCEPTS OF QUANTUM MECHANICS “STATE” AND “TIME” IN THE TREATISE OF W. HEISENBERG'S “REALITY AND ITS ORDER”

W. Heisenberg's treatise “Reality and its order” is a philosophical work of a scientist and philosopher and reflects the totality of his views on various aspects of reality. In this article, the author attempts to analyze the central concepts of quantum mechanics, “state” and “time”, as well as to determine how relevant the ideas of the treatise may be in the world of modern natural science. This point seems especially important, given the time of writing the treatise (no later than 1942), which gives reason to recall the philosophical foundations on which physics rests, and give them a new interpretation that meets modern challenges. The article shows the need to build modern physics on the principles of metaphysics, that is, to study the subject of physics from the position that the world has a metaphysical basis. Statements about the nature of time by various authors are given: from ancient natural philosophers (Aristotle) to medieval thinkers (Augustine, Grossetest), as well as modern researchers (A.Yu. Sevalnikov, Yu.S. Vladimirov, V.D. Zakharov), etc.
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning

Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced distributed samples. The random selection and manual labelling and balancing of this validation set is not only sub-optimal for meta-learning, but it also scales poorly with the number of classes. Hence, recent meta-learning papers have proposed ad-hoc heuristics to automatically build and label this validation set, but these heuristics are still sub-optimal for meta-learning. In this paper, we analyse the meta-learning algorithm and propose new criteria to characterise the utility of the validation set, based on: 1) the informativeness of the validation set; 2) the class distribution balance of the set; and 3) the correctness of the labels of the set. Furthermore, we propose a new imbalanced noisy-label meta-learning (INOLML) algorithm that automatically builds a validation set by maximising its utility using the criteria above. Our method shows significant improvements over previous meta-learning approaches and sets the new state-of-the-art on several benchmarks.
A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers Model

The volume of audio visual content produced on social networks has increased tremendously in recent decades, and this information is quickly spread and consumed by a large number of people. The disruption of false news sources and bot accounts for disseminating fake news is a possibility in this scenario. Applied research has been supported by promotional information as well as sensitive stuff over the network. Artificial Intelligence will be used to automatically assess the trustworthiness of social media accounts (AI). In this research, we describe a multilingual strategy to using Deep Learning to solve the bot identification problem on Twitter. End-users can utilise machine learning (ML) methodologies to assess the trustworthiness of a Twitter account. To achieve so, a number of tests were carried out using cutting-edge Multilingual Language Models. Construct an encoding of the user account's text-based features, which is then concatenated with the rest of the metadata to build a potential input vector on top of a Bot-DenseNet Dense Network. As a result, this article evaluates the language constraint from prior experiments where the encoding of the language was limited. Only the metadata information or the metadata information along with some other information was examined by the user account. properties of fundamental semantic text The Bot-DenseNet also generates a low-dimensional representation of the data. Within the Information Retrieval (IR) framework, a user account can be utilised for any application.
UPAYA PENINGKATAN KEMAMPUAN MENULIS SURAT UNDANGAN MELALUI METODE DRILL KELAS XII IPA 1 MA USB FILIAL MAN BATAM

The purpose of this study was to describe the ability to write invitation letters through the DRILL method for Class XII IPA 1 MA USB Filial MAN Batam. There are two kinds of data collection techniques in this study, namely: (l) Non-test Techniques, and (2) Test Techniques. The results showed an increase in the ability to write invitation letters in the first cycle the average achievement of class results was 84.00% consisting of: invitation letter systematics 29.00% sentence effectiveness 33.50% and spelling 21.50%. So in the first cycle there were 34 students or 85% completed. In the second cycle the average achievement of class results was 93.30% consisting of: invitation letter systematics 30.00% increased 1.00% from cycle I, sentence effectiveness 39.70% increased 6.230% from cycle I, while spelling 23 ,60% increased by 2.10% from the first cycle and 44 students or 100% completed. When compared from the initial data, the average achievement of class results is 63.68% consisting of: systematic 21.58%, sentence effectiveness 23.95%, and spelling 18.68%, and 8 students or 23% who completed, so It is clear that with the Drill method, the students' ability to write invitation letters has improved very well and quickly. ABSTRAKTujuan penelitian ini adalah untuk mendeskripsikan kemampuan menulis surat undangan melalui metode DRILL Kelas XII IPA 1 MA USB Filial MAN Batam. Teknik pengumpulan data dalam penelitian ini dua macam teknik, yakni: (l) Teknik Nontes, dan (2) Teknik Tes. Hasil penelitian menunjukan peningkatan kemampuan menulis surat undangan pada siklus I rata- rata pencapaian hasil kelas 84,00% yang terdiri dari: sistematika surat undangan 29,00% keefektifan kalimat 33,50% dan ejaan 21,50%. Jadi pada siklus I ada 34 siswa atau 85% tuntas. Pada siklus II rata- rata pencapaian hasil kelas sebesar 93,30% yang terdiri dari: sitematika surat undangan 30,00% naik 1,00% dari siklus I, keefektifan kalimat 39,70% naik 6,230% dari siklus I, sedangkan ejaan 23,60% naik 2,10% dari siklus I dan 44 siswa atau 100% tuntas. Kalau diperbandingkan dari data awal yang hanya rata- rata pencapaian hasil kelas 63,68% terdiri dari: sistematika 21 ,58%, keefektifan kalimat 23,95%, dan ejaan 18,68%, dan 8 siswa atau 23% yang tuntas, jadi jelas dengan metode Drill kemampuan menulis surat undangan yang pada siswa meningkat sangat baik dan cepat.
Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method

The performance of adaptive estimators that employ embedding in reproducing kernel Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers. Parameter convergence and error approximation rates depend on where and how the kernel centers are distributed in the state-space. In this paper, we develop the theory that relates parameter convergence and approximation rates to the position of kernel centers. We develop criteria for choosing kernel centers in a specific class of systems - ones in which the state trajectory regularly visits the neighborhood of the positive limit set. Two algorithms, based on centroidal Voronoi tessellations and Kohonen self-organizing maps, are derived to choose kernel centers in the RKHS embedding method. Finally, we implement these methods on two practical examples and test their effectiveness.
Development of E-Learning System Using Codeigniter Framework and Prototype Model on MTs Negeri 1 Musi Banyuasin

Based on the 1945 Law, article 31 paragraph one states that every citizen has the right to be taught. So that every educational institution is obliged to provide services under any conditions, including in emergencies such as Covid-19. One solution to overcome this problem is to develop all learning media such as e-learning. With the existence of learning media through e-learning, it is hoped that the learning process will continue to run normally. The problem as mentioned certainly occurs in MTs. Negeri Bumiayu is one of the schools in Musi Banyuasin Regency. To achieve the goal to overcome these problems, namely developing e-learning using the Codeigniter framework and Prototyope model. The results of the development show that the e-learning produced has features that can overcome the learning process well as evidenced by e-learning features such as materials and tasks in the learning process.
FRAUDULENT FINANCIAL REPORTING IN MALAYSIA: FROM FRAUD TRIANGLE THEORY PERSPECTIVE

Fraudulent financial reporting (FFR) activities have a negative impact on market value, companies' reputation, accounting profession credibility, and loss of investors' confidence in financial statements. Despite the establishment of new regulations and governance, the FFR cases in Malaysia keep on growing. The involvement of top management in fraud cases is induced by fraud triangle components, namely pressure, opportunity, and rationalisation. However, the impact of fraud triangle elements on fraudulent financial reporting is inconclusive. Hence, the objectives of this study are twofold: to determine the trend of fraudulent financial reporting in Malaysia for five years from 2016 to 2020 and to examine whether the fraud triangle elements have a significant impact on fraudulent financial reporting. This study used the Binary Logistic Regression model to achieve its second objective. The independent variables used to determine fraudulent financial reporting comprise fraud triangle elements, namely pressure, opportunity, and rationalisation. The fraudulent financial reporting is proxied by M-score of the Beneish model. This study provides empirical evidence that even though pressure and opportunity have no significant relationship with fraud likelihood, rationalisation has a significant impact on fraudulent financial reporting. Besides contributing to the literature on corporate fraud issues, this study offers imperative insights to financial information users in determining the possibility or indication of fraudulent reporting. Future research may incorporate other proxies for fraud triangle elements to augment this study or include companies with specific fraud cases, such as PN17.
CONNETION BETWEEN EDUCATION LEVEL AND SOURCE OF 3R KNOWLEDGE WITH THE PRACTICAL USE OF 3R PRINCIPLES

Modern economic and technological developments have long been recognised as drivers of intense impact on the future potential of our planet and its sustainability for the long term.In recent decades the scientists and a society as a whole have been trying to find a solution for minimizing the impact on the planet and the people.Some answers seem to emerge in recent developments of technology which enables transformation of the old ways and implementation of the new economic concepts such as social entrepreneurship, doughnut economy, circular economy, sharing economy etc.The practical implications are reflected in accepting the principles (the pillars) of sustainability known as the 3R: Reuse, Reduce and Recycle.Applying these in practice, leads to decrease in the usage of energy and resources thus lessening the impact on the environment.To achieve the results, the societies should take twofold actions, on the producers' side, as well as on the side of the consumers.In this paper the author focuses on the consumers' side of the equation trying to identify what motivates the consumers to engage in responsible and sustainable consumption and could the education be the key factor that enables turning the "green" concepts into their everyday practice.The consumers play an extremely important role where the waste would go, thus giving the companies to recollect the items that can be used as a new input.On the other hand, the consumers, who apply responsible and green choices when buying products and services, can also convince the producers to shift towards more sustainable resources and ways of doing their business, due to the economic motivation.After the literature review of the latest research in this area, the authors present a part of a larger research project which explores the Croatian consumers' awareness and their engagement in reducing the waste, reusing the items that still can be used and separating the waste before the disposal to make the recycling possible in their everyday life.This preliminary communication seeks the connection between the sources of knowledge about the circular economy and the 3R concepts and the consumers practicing such a behavior.On the other hand, it also tries to check if the education level can be the important channel to create more aware and responsible consumers that apply the 3R principles in practice.
Impact of mobile technology in library services

Now Days Mobile technologies and its application are playing an increasingly vital role in phone age people. Devices such as like smart phones, tablets, and e-book readers connect users to the world immediately, Advancements in networking technologies made it possible for mobile devices and application to be used in the field of education and Library services. It will be very easy access to information via mobile etc. It will also be effective and economic. Such technologies can have a great impact on learning by providing a rich, collaborative and conversational experience for Library. Therefore, this Mobile technology has become a necessary and important in Library Services. So this article seeks to explain the meaning of mobile technology and how this type technology is used for library services as well as explicate the advantages and disadvantage this technology.
Direct domain adaptation through reciprocal linear transformations

We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear operations on the input features to the NN model, whether they are from the source or target domains, as follows: 1) A cross-correlation of the input data (i.e. images) with a randomly picked sample pixel (or pixels) of all images from that domain or the mean of all randomly picked sample pixel (or pixels) of all images. 2) The convolution of the resulting data with the mean of the autocorrelated input images from the other domain. In the training stage, as expected, the input images are from the source domain, and the mean of auto-correlated images are evaluated from the target domain. In the inference/application stage, the input images are from the target domain, and the mean of auto-correlated images are evaluated from the source domain. The proposed method only manipulates the data from the source and target domains and does not explicitly interfere with the training workflow and network architecture. An application that includes training a convolutional neural network on the MNIST dataset and testing the network on the MNIST-M dataset achieves a 70% accuracy on the test data. A principal component analysis (PCA), as well as t-SNE, show that the input features from the source and target domains, after the proposed direct transformations, share similar properties along with the principal components as compared to the original MNIST and MNIST-M input features.
Implementation of Efficient Hybrid Encryption Technique

Security troubles of restricted sources communications are vital. Existing safety answers aren't sufficient for restricted sources gadgets in phrases of Power Area and Ef-ficiency‘. Elliptic curves cryptosystem (ECC) is area efficent for restricted sources gadgets extra than different uneven cryp-to systems because it gives a better safety degree with equal key sizes compared to different present techniques. In this paper, we studied a lightweight hybrid encryption technique that makes use of set of rules primarily based totally on AES for the Plain text encription and Elliptic Curve Diffie-Hellman (ECDH) protocol for Key encryption. The simplicity of AES implementation makes it light weight and the complexity of ECDH make it secure. The design is simulated using Spyder Tool, Modelsim and Implemented using Xilinx Vivado the effects display that the proposed lightweight Model offers a customary security degree with decreased computing capacity. we proposed a key authentication system for enhanced security along with an Idea to implement the project with multimedia input on FPGA
THEORETICAL FUNDAMENTALS OF THE DEVELOPMENT OF EMOTIONAL INTELLIGENCE IN SCHOOLCHILDREN WITH DISABILITIES OF INTELLECTUAL DEVELOPMENT

In the article an author is carry out the short analysis of psychologicalsources from the study of problem of emotional intellect. On the basisof theoretical analysis generalized given in relation to the features ofemotional intellect, that includes ability to watch the own feelings andfeelings of other people, distinguish them and use this information fora management thinking and activity. The basic theoretical and experimentalachievements of scientific researches that touch the aspects ofproblem of emotional intellect are outlined. Scientific and methodological,domestic and foreign sources on special pedagogy and psychology aresystematized.Theoretical analysis of the problem of emotional intelligence suggeststhat EI in general is considered as a cognitive-personal education withthe most pronounced cognitive component, as a set of mental abilities tounderstand and manage emotions, as well as knowledge, skills, operationsand strategies of intellectual activity. associated with the processing andtransformation of emotional information.Theoretically, it has been found that students with intellectual disabilitieshave an immaturity of the emotional sphere and primitive emotionalresponse.The obtained data are the basis for the development of a system ofmeasures aimed at the successful adaptation of these children to societyand will contribute to the effective acquisition of emotional competence.Thus, it can be argued that the problem in question is relevant in severalrespects: theoretical, because, as noted above, it is underdeveloped,social — since human civilization may be in a state of self-destruction if the emotions are further considered. as opposing the mind, as being inaccessibleto conscious control and regulation, in psycho-pedagogical — ifon the basis of experimental study of the peculiarities of the course ofemotional processes and states and stable properties of the personalitynot scientifically substantiated approaches to the targeted formation intraining and education emotional reasonable business training.
Preasymptotic analysis of the generalization of the classical scheme of particle allocation by sets with conditions

Generalization of the classical scheme of allocating particles into cells by sets implies extending its conditions to allow different sizes of sets. This allocation scheme is studied separately for cases without empty cells and by selected cells. The study is carried out by the enumeration method based on the construction and analysis of an iterative random process of direct non-repetitive numbered enumeration of its outcomes in the pre-asymptotic region of parameter change in the following directions: solving the numbering problem in direct and inverse formulations which consist in finding one-to-one correspondence between the numbers and types of outcomes of the scheme, determining the probabilistic distribution of the number of empty cells and on the set of outcomes of the scheme according to the introduced probabilities of iterative transitions of the process of their enumeration and developing a procedure for their modeling
VAP Risk Index: Early prediction and hospital phenotyping of ventilator-associated pneumonia using machine learning

Abstract Background: Ventilator-associated pneumonia (VAP) is a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of patients at risk of VAP enables early intervention, which in turn improves patient outcomes. We developed a predictive model for individualized risk assessment utilizing machine learning to identify patients at risk of developing VAP. Methods: The Philips eRI database, a multi-institution electronic medical record (EMR), was used for model development. For adult (≥18y) patients, we propose a set of criteria using indications of the start of a new antibiotic treatment temporally contiguous to a microbiological test to mark suspected infection events, of which those with a positive culture are labeled as presumed VAP if 1) the event occurs at least 48 hours after intubation, and 2) there are no indications of community-acquired pneumonia (CAP) or other hospital-acquired infections (HAI) in the patient charts. The resulting VAP and no-VAP (control) cases were then used to build an ensemble of decision trees to predict the risk of VAP in the next 24hrs using data on patient’s demographics, vitals, labs, and ventilator settings. Results: The resulting model predicts the development of VAP with an AUC of 76% and AUPRC of 75% at a lead time of 24h in advance. Additionally, we group hospitals that are similar in healthcare processes into distinct clusters and characterize VAP prediction for the identified hospital clusters. We show inter-hospital (teaching status and healthcare processes) and cohort-specific (age groups, gender, early vs late VAP, ICU mortality status) differences in VAP prediction and associated symptomologies. Conclusions: Our proposed VAP criteria provide a temporal context for the infection event and enable studying the disease course. Using the proposed criteria, we curated a patient cohort and used it to build a model to predict an impending VAP event before any clinical suspicions. We present a clustering approach to tailor the VAP prediction model for different hospital types based on their EMR data characteristics. The developed VAP prediction model provides instantaneous risk score that allows early interventions and confirmatory diagnostic actions.