Reviewerly/exHarmony huggingface dataset:
Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models

Popular language models (LMs) struggle to capture knowledge about rare tail facts and entities.Since widely used systems such as search and personal-assistants must support the long tail of entities that users ask about, there has been significant effort towards enhancing these base LMs with factual knowledge.We observe proposed methods typically start with a base LM and data that has been annotated with entity metadata, then change the model, by modifying the architecture or introducing auxiliary loss terms to better capture entity knowledge.In this work, we question this typical process and ask to what extent can we match the quality of model modifications, with a simple alternative: using a base LM and only changing the data.We propose metadata shaping, a method which inserts substrings corresponding to the readily available entity metadata, e.g.types and descriptions, into examples at train and inference time based on mutual information.Despite its simplicity, metadata shaping is quite effective.On standard evaluation benchmarks for knowledge-enhanced LMs, the method exceeds the base-LM baseline by an average of 4.3 F1 points and achieves state-of-the-art results.We further show the gains are on average 4.4x larger for the slice of examples containing tail vs. popular entities.
A Joint Learning Approach for Semi-supervised Neural Topic Modeling

Topic models are some of the most popular ways to represent textual data in an interpretable manner.Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models.We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semisupervised neural topic model.We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.
Performance-Interpretability Tradeoff of Mamdani Neuro-Fuzzy Classifiers for Medical Data

Neuro-fuzzy systems are models that incorporate the learning ability and performance of Artificial Neural Networks (ANNs) with the interpretable reasoning of fuzzy inference systems (FISs). An ANN can learn patterns from data and achieve high accuracy, while a FIS uses linguistic and interpretable rules to match inputs and outputs of the data. Two types of FISs are used the most in literature: Takagi-Sugeno-Kang (TSK) and Mamdani. The main focus of this paper is on the Mamdani neuro-fuzzy systems, notably the Hybrid Neuro-Fuzzy Inference System (HyFIS) and the Neuro-Fuzzy Classifier (NEFCLASS). It aims at evaluating and comparing the two classifiers over two medical datasets to study their performance-interpretability tradeoff. Results show that HyFIS is the best in terms of performance, while NEFCLASS is better in terms of interpretability. As for the performance-interpretability tradeoff, NEFCLASS has the best overall results; it achieves a good performance while being less complicated and more interpretable.
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations

We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG).SHRG has been used to produce meaning representation graphs from texts and syntax trees, but little is known about its viability on the reverse.In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs.Consistent results are obtained as evaluated on a collection of annotated corpora.This work reveals the ability of PSHRG in formalizing a syntaxsemantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization.
A new approach for credit card fraud detection using Machine Learning

The financial industry is growing at a rapid pace, and as a result, banking online transactions are on the rise as the government promotes digital transactions.Debit or credit cards have been used for the majority of financial transactions.As a result, the fraud associated with it is also on the rise.However, our current machine learning approach is unable to correctly detect fraudulent transactions since present fraud detection machine learning algorithms are taught and then evaluated on extremely unbalanced data sets, reducing their performance in real-world circumstances.In this paper, we proposed an algorithm that may work better after converting these imbalanced data sets into balanced data sets by using the oversampling technique so that system is not biased when the algorithm is actually implemented.The results show that the Random Forest algorithm with SMOTE performs better than the Logistic Regression machine learning algorithm.
Evaluation of LOINC semantic coverage for coding clinical observations related to environmental influenced diseases

Terminology standards ensure semantic interoperability by allowing efficient exchange and pool of data coming from many idiosyncratic sources.This is especially true when operating among multiple domains that are connected to each other through different kinds of semantic relationships.Environment and health are strictly related domains, as the first one produces a variety of effects on the second one.While terminologies exist for coding and classifying almost all the aspects of the healthcare process (actors, products, observations, etc.), a specific one focused on environmental related or caused health problems has not yet been developed.Nonetheless, concepts related to the link between the two domains can be found in different standards.Among them, Logical Observation Identifiers Names and Codes (LOINC ® ) is the most widely used for identifying parameters connected to clinical conditions.Considering this, the work presented in this paper aims to: 1) identify a subset of existing LOINC codes of clinical observations influenced or related to environmental factors; 2) search for environmental concepts not covered by LOINC in the UMLS Metathesaurus to see if they are covered and how they are expressed in other medical terminologies; and consequently 3) detect LOINC concept gaps related to the link between environment and health to be filled by proposing new LOINC terms for standardizing identified clinical parameters.
Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection

We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines.Our application domain presents unique challenges that render traditional classification methods ineffective: the number of entity types is large; and many symptoms are rare, resulting in a long-tail distribution of training examples per entity type.We tackle these challenges with an autoregressive model that generates standardized names of symptoms.We introduce a data augmentation technique to increase the number of training examples for rare symptoms.Experiments on real-life patient vaccine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.
METHODOLOGICAL BASES AND PRINCIPLES FOR FORMING THE READINESS OF FUTURE ENGINEERS FOR SOCIAL AND PROFESSIONAL ADAPTATION

5.8.7)
Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept

In this research work, a novel eye-blink detection model is developed.The proposed eye blink detection model is modeled by following seven major phases: (a) video-to-frame conversion, (b) Pre-processing, (c) face detection, (d) eye region localization, (e) eye landmark detection and eye status detection, (f) eye blink detection and (f) Eye blink Classification.Initially, from the collected raw video sequence (input), each individual frames are extracted in the video -to-frame conversion phase.Then, each of the frames is subjected to pre-processing phase, where the quality of the image in the frames is improved using proposed Kernel median filtering (KMF) approach.In the face detection phase, the Viola-Jones Model has been utilized.Then, from the detected faces, the eye region is localization within the proposed eye region localization phase.The proposed Eye region localization phase encapsulates two major phases: Feature extraction and landmark detection.The features like improved active shape models (I-ASMs), Local Binary pattern are extracted from the detected facial images.Then, the eye region is localization by using a new optimized Convolution neural network framework.This optimized CNN framework is trained with the extracted features (I-ASM and LBP).Moreover, to enhance the classification accuracy of eye localization, the weight of CNN is fine-tuned using a new Seagull Optimization with Enhanced Exploration (SOEE), which is the improved version of standard Seagull Optimization Algorithm (SOA).The outcome from optimized CNN framework is providing the exact location of the eye region.Once the eye region is detected, it is essential to detect the status of the eye (whether open or close).The status of the eye is detected by computing the eye aspect ratio (EAR).Then, the identified eye blinks are classified based on the computed correlation coefficient as long and short blinks.Finally, a comparative evaluation has been accomplished to validate the projected model.
How Does the COVID-19 Impact the Cryptocurrency?

This paper aims to investigate the interaction between the Covid-19 pandemic and the prices of two representative cryptocurrencies.We analyze the conditional variance of the log rate of return for these two cryptocurrencies using the ARMAX-GARCH model and the interaction between the pandemic severity and the log rate of returns using VARbased impulse responses analysis.Our finding pointed out that the pandemic has a significant positive impact on the cryptocurrencies' prices in the short run, making the cryptocurrency market a good investment to hedge the pandemicimplanted risks in the conventional financial market.
Transformer State Evaluation Method Based on Cloud Theory and Variable Weight

In order to accurately evaluate the operating status of the key equipment transformer of the power system, a transformer status evaluation method based on variable weight analysis and cloud model is proposed. First, construct a transformer state evaluation index system; second, propose a combination of expert scores and cloud models to determine the constant weight of the indices, and use the variable weight method with an equilibrium function to change the constant weight; then, a cloud model algorithm combining normal cloud and trapezoidal cloud is proposed to obtain the degree of membership of indices to the operating state of the transformer; finally, the evaluation results of the quantitative and qualitative indexes are integrated to obtain the final state of the transformer. The example shows the effectiveness of this method and provides a new idea for transformer condition assessment.
Public participation in general educational organization management

Public participation in the management of an educational organization, which has a tradition in Russian educational practice since the XVIII century, is an integral part of its management activities. One of the fundamental principles of the relationship between the state and its citizens is the right of society to participate in governance. The state in educational policy, the National project “Education” considers the formation of state-public management of an educational organization as a priority in its activities, understanding the importance of public participation in education management. At the federal level, the rights and obligations of participants in educational relations are defined. The article gives several definitions of the concept of state-public management of education, which reveal the essence of this process. Most Russian scientists understand the state-public management of education as constant participation in the management and interaction of two subjects expressing the interests, policies of both the state and the interests of society and the population. The author analyzes the historical experience of managing education with public participation, noting that the insufficient development of democratic institutions in Russia until the second half of the XIX century prevented the establishment of a dialogue between the authorities and society in matters of education. The Zemstvo reform of 1864 became the catalyst for the process of public participation in the management of education. Further, the author characterizes the activities of public governing bodies of educational organizations, such as the School Council, the governing council, the board of trustees, etc. The article presents a theoretical understanding of the forms of public administration, examples of successful educational practices are given. The federal law “On Education in the Russian Federation” defined the requirements for taking into account public opinion when making a number of managerial decisions by the heads of educational organizations, granted the right to teachers, students and their parents to participate in management. Emphasis is placed on a list of a number of reasons that impede the development of public participation in the management of a general educational organization, and directions for optimizing the management process are proposed.
E-Books in Foreign Language Teaching

Numerous research studies have indicated that e-books contribute to improving the quality of teaching and increasing students' achievements.As e-books imply various possibilities, the application of multimedia, and additional tools, they undoubtedly affect the interaction and dynamics of classes.The study aim of this paper is to shed light on the potential of e-books and to explore and present the advantages and disadvantages of their use.Based on the results of research (attitudes towards the use of e-books in foreign language teaching from both students of different ages and teachers' perspectives), a better and more effective implementation of e-books has been indicated.
Teaching Speaking through Picture Series Media at the Second Grade of SMP Negeri 4 Natar

The objective of this research is to find out whether there is an improvement in students’ speaking ability after they are taught by using picture series at SMPN 4 Natar. This research was conducted at the second-grade students of SMPN 4 Natar in the academic year 2020/2021. There were five classes in the second grade of SMPN 4 Natar. Each class consisted of 23 students. The researcher took class VIII D. This research used one group pretest-posttest design by giving the pre-test, treatments, and the post-test. The instrument used to get the data in this research was speaking tests. Speaking tests in the forms of pre-test and post-test were done to collect the data. Then, the t-test was used to test the hypothesis. The result of the hypothesis test proves that using picture series can improve students’ speaking ability. It can be seen from the mean score of the pre-test and post- test which was analyzed through SPSS 16.00. The mean score of the pre-test was 53.7 and it was improved to 70 in post-test and the gain was 16.3. The result of the t- test shows that the level of significance is p &lt; 0.05 and the significant two-tail is p=0.000. It can be said that using picture series csn improve students’ speaking ability.
Comparative Study of Chaos-Embedded Particle Swarm Optimization

Particle swarm optimization (PSO) is a population-based stochastic search algorithm that has been widely used to solve many real-world problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when addressing complex multimodal problems. In this paper, we propose a chaos-embedded particle swarm optimization algorithm (CEPSO). In CEPSO, the chaos-based swarm initialization is first applied to yield high-quality initial particles with better stability. Afterwards the chaotic inertia weight and the chaotic sequence based random numbers are introduced into the velocity update scheme for PSO to improve its global and local search capabilities. In addition, two different mutation strategies (chaos and levy) are utilized to enhance the swarm diversity without being trapped in local optima. Finally, the CEPSO proposed in this work is compared with several classical PSOs on a set of well-known benchmark functions. Experimental results show that CEPSO can achieve better performance compared to several other PSO variants in terms of the solution accuracy and convergence rate.
Offline Transaction System

The usage of online payment systems is increasing multifold these days due to their transaction reliability and ease of completion in real-time. The most preferred service is UPI service, which is majorly available in Metropolitan cities, in some semi-urban and rural areas which have a proper communication infrastructure. However, there is a major issue to expand these services in rural areas, which lack proper communication infrastructure. Even after the internet revolution, it is ensured that all Indians are connected to data at all times but there might be situations where the user could not able to connect to the Internet owing to many reasons like High Altitude, Low Network availability, in-flight, etc. As an individual, such situations cannot be avoided, and there it comes to the applicability of offline transaction systems. We plan to change that by introducing an offline transaction system. We plan to expand on the idea of an offline payment system that will ensure that a reliable &amp; secured digital payment system is available to all individuals.
Consistency Regularization for Unsupervised Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation is a promising technique for computer vision tasks, especially when annotating large amounts of data is very costly and time-consuming, as in semantic segmentation. Here it is attractive to train neural networks on simulated data and fit them to real data on which the models are to be used. In this paper, we propose a consistency regularization method for domain adaptation in semantic segmentation that combines pseudo-labels and strong perturbations. We analyse the impact of two simple perturbations, dropout and image mixing, and show how they contribute enormously to the final performance. Experiments and ablation studies demonstrate that our simple approach achieves strong results on relevant synthetic-to-real domain adaptation benchmarks.
On modeling the development of the region in conditions of high uncertainty and lack of data

The preprint is an example of the practical implementation of a modified modeling methodology using fuzzy cognitive maps, which was presented by the author earlier in the publication in "M.V. Keldysh IPM Preprints". A variant of the model of the development of the RF region in conditions of high uncertainty of the external environment and with a lack of initial data is described. This situation is typical, for example, for the moments of the outbreak of epidemics or conflict confrontation.
PROJECTING AS A VECTOR OF COMPREHENSIVE SCHOOL DEVELOPMENT

Specifics of projecting as a vector of comprehensive school development is discussed. It is noted that projecting is the basis of communicative activity core realized via practice oriented school teaching, where school students form inter-disciplinary skills and universal competences in real activities. Practical experiences of projecting organization in a private comprehensive school are described. Model of project workshops of “7 keys” school is presented.
Improving students' vocabulary achievement through picture series at the second grade of SMP Negeri 8 Bandar Lampung

The objectives of this research were to see whether any significant improvement of students’ vocabulary achievement and to find out which type of vocabulary has the most significant improvement. The design used was the one group pre-test post-test. Vocabulary test was the instrument to measure the pre-test and post-test. The result shows that the mean score of pretest is 64.33 while the mean of posttest is 75.20, with the N-gain number is 0.3047. It can be concluded that there is an improvement in students’ vocabulary achievement after implementing picture series in the learning process. The students’ scores in all types of vocabulary were increased. However, the most improved aspect was noun. The mean of this aspect in posttest (11,3) was higher than the mean in pretest (7,3) with the gain of 0,45977.
BMT Management Efficiency in Bandung City Mosques

The many developments of Islamic cooperatives or BMT in The City of Bandung is not supported by factors that can support an Islamic cooperative to be able to continue to develop and run well.Facts in the field show that many Islamic cooperatives sink and disband due to a lack of balanced with knowledge, experience, and professional skills regarding management of management well.The research method used is quantitative research, with data collection techniques using purposive sampling and the sample consists of 7 mosque-based Islamic cooperatives.Data processing techniques using data envelopment analysis (DEA) method, VRS assumption (outputoriented).Efficiency measurement uses variable inputs of total assets, operating expenses and cooperative capital, while the output variable consists of Income and Remaining Operating Results.The results of the DEA calculations show that in 2017 only MUI Bandung city has achieved perfect efficiency, year 2018 kopsyah cipaganti and MUI Bandung City, and Year 2020 kopsyah MUI Bandung City, Nurul Hikmah, Cipaganti and Silih Aping.The level of kopsyah that has been efficiency constantly during the period 2017-2020 is the MUI Islamic Cooperative Bandung.
Android Chat Application

Communication through internet is becoming vital these days. An online communication allows the users to communicate with other people in a fast and convenient way. Considering this, the online communication application must be able to share the texts or images or any other files in a faster way with minimum delay or with no delay. Our system is an Android Application. We have implemented our own server using PHP. Android provides better platform to develop various applications for instant messaging compared to other platforms. The main objective of this application is to present a software for the launching of a real time communication between operators/users. The system developed on android will enable the users to communicate with another users through text messages with the help of internet. The system requires both the device to be connected via internet. This application is based on Android with the backend provided by google Firebase for Authentication and PHP server for overall Application.