Reviewerly/exHarmony huggingface dataset:
Hierarchical Clustering Based on Local Cores and Sharing Concept

Hierarchical clustering is an important research branch of cluster analysis that has extensive ranges of practical applications. Meanwhile, it still faces problems such as inaccurate, time-consuming, and difficulty in choosing linkage method. In this paper, we present a new Hierarchical Clustering method based on Local Cores and Sharing concept (HCLCS) which takes a "divide-and-merge" framework by first dividing a data set into several small clusters and then merging them hierarchically. To improve the accuracy, the merging process is further divided into two substeps: (1) pre-connect small clusters that belong very likely to the same category, and (2) merge the pre-connected intermediate clusters and the remaining unconnected small clusters in a classical hierarchical way. Extensive experiments on synthetic and real-world data sets show that HCLCS can achieve better performance than existing methods in dealing with data sets with complex structures and is less time-consuming than two state-of-the-art algorithms (SNN-DPC and RSC).
Review of Optimization in Improving Extreme Learning Machine

Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hiddenlayer feedforward neural networks and the kernel functions.Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation.To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications.Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons.This study will help other researchers to find out the research issues that lowering the performance of the ELM.
Active Curation of Large Longitudinal Surveys: A Case Study

In this paper we take an in-depth look at the curation of a large longitudinal survey and activities and procedures involved in moving the data from its generation to the state that is needed to conduct scientific analysis. Using a case study approach, we describe how large surveys generate a range of data assets that require many decisions well before the data is considered for analysis and publication. We use the notion of active curation to describe activities and decisions about the data objects that are “live,” i.e., when they are still being collected and processed for the later stages of the data lifecycle. Our efforts illustrate a gap in the existing discussions on curation. On one hand, there is an acknowledged need for active or upstream curation as an engagement of curators close to the point of data creation. On the other hand, the recommendations on how to do that are scattered across multiple domain-oriented data efforts. In describing the complexities of active curation of survey data and providing general recommendations we aim to draw attention to the practices of active curation, stimulate the development of interoperable tools, standards, and techniques needed at the initial stages of research projects, and encourage collaborations between libraries and other academic units.
The importance of digitalization in the education process

Ubiquitous digitization is a domain of the community that seems to be increasingly visible in the education process. It can bring many benefits not only to students but also to other entities. Knowledge is one of the most important values. It is largely the basis of the modern economy – Knowledge Based Economy (GOW). The education process is one of the most important factors influencing the civilization level of the society, and teaching is a process consisting primarily of activities that lead mainly to the organization and direct management of the learning process, aimed at creating conditions conducive to learning, and is related to the teacher's personality and work. Both the literature on the subject and observations of the education market clearly indicate that the issues related to digitization and its importance in the education process should be considered in the context of teacher's work at school. The article focuses on the teaching process in the context of organizing a teacher's workshop at school, and to a lesser extent on teacher's qualifications and professional competences. Moreover, attention was focused on issues related to the problems of equipping schools with high technology teaching aids.
Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP

This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent’s policy is a best response to the other agents’ (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with N − 1 fixed FSCs into an infinite-horizon POMDP whose solution is an N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> agent best response; (2) propose a JESP variant, called Inf-JESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach. This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent’s policy is a best response to the other agents’ (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with N 1 fixed FSCs into an infinite-horizon POMDP whose solution−is an N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> agent best response; (2) propose a JESP variant, called Inf-JESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach.
Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep learning approach to the problem, utilizing a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals, outperforming widely-used wavelet denoising techniques.
Neural Embedded Dirichlet Processes for Topic Modeling

This paper presents two novel models: the neural Embedded Dirichlet Process and its hierarchical version, the neural Embedded Hierarchical Dirichlet Process. Both methods extend the Embedded Topic Model (ETM) to nonparametric settings, thus simultaneously learning the number of topics, latent representations of documents, and topic and word embeddings from data. To achieve this, we replace ETM's logistic normal prior over a Gaussian with a Dirichlet Process and a Hierarchical Dirichlet Process in a variational autoencoding inference setting. We test our models on the 20 Newsgroups and on the Humanitarian Assistance and Disaster Relief datasets. Our models present the advantage of maintaining low perplexity while providing analysts with meaningful document, topic and word representations that outperform other state of the art methods, while avoiding costly reruns on large datasets, even in a multilingual context.
Intelligent methods for improving the accuracy of prediction of rare hazardous events in railway transportation

The paper Aims to examine various approaches to the ways of improving the quality of predictions and classification of unbalanced data that allow improving the accuracy of rare event classification. When predicting the onset of rare events using machine learning techniques, researchers face the problem of inconsistency between the quality of trained models and their actual ability to correctly predict the occurrence of a rare event. The paper examines model training under unbalanced initial data. The subject of research is the information on incidents and hazardous events at railway power supply facilities. The problem of unbalanced data is expressed in the noticeable imbalance between the types of observed events, i.e., the numbers of instances. Methods. While handling unbalanced data, depending on the nature of the problem at hand, the quality and size of the initial data, various Data Science-based techniques of improving the quality of classification models and prediction are used. Some of those methods are focused on attributes and parameters of classification models. Those include FAST, CFS, fuzzy classifiers, GridSearchCV, etc. Another group of methods is oriented towards generating representative subsets out of initial datasets, i.e., samples. Data sampling techniques allow examining the effect of class proportions on the quality of machine learning. In particular, in this paper, the NearMiss method is considered in detail. Results. The problem of class imbalance in respect to the analysis of the number of incidents at railway facilities has existed since 2015. Despite the decreasing share of hazardous events at railway power supply facilities in the three years since 2018, an increase in the number of such events cannot be ruled out. Monthly statistics of hazardous event distribution exhibit no trend for declines and peaks. In this context, the optimal period of observation of the number of incidents and hazardous events is a month. A visualization of the class ratio has shown the absence of a clear boundary between the members of the majority class (incidents) and those of the minority class (hazardous events). The class ratio was studied in two and three dimensions, in actual values and using the method of main components. Such “proximity” of classes is one of the causes of wrong predictions. In this paper, the authors analysed past research of the ways of improving the quality of machine learning based on unbalanced data. The terms that describe the degree of class imbalances have been defined and clarified. The strengths and weaknesses of 50 various methods of handling such data were studied and set forth. Out of the set of methods of handling the numbers of class members as part of the classification (prediction of the occurrence) of rare hazardous events in railway transportation, the NearMiss method was chosen. It allows experimenting with the ratios and methods of selecting class members. As the results of a series of experiments, the accuracy of rare hazardous event classification was improved from 0 to 70-90%.
Strictifying and taming directed paths in Higher Dimensional Automata

Directed paths have been used by several authors to describe concurrent executions of a program. Spaces of directed paths in an appropriate state space contain executions with all possible legal schedulings. It is interesting to investigate whether one obtains different topological properties of such a space of executions if one restricts attention to schedulings with "nice" properties, eg involving synchronizations. This note shows that this is not the case, ie that one may operate with nice schedulings without inflicting any harm. Several of the results in this note had previously been obtained by Ziemianski. We attempt to make them accessible for a wider audience by giving an easier proof for these findings by an application of quite elementary results from algebraic topology; notably the nerve lemma.
Digital bibliography: The libraries in search of innovative bibliographic tools

The author discusses application of innovative digital technologies in library bibliographic services. The digitalization cannot be limited to collection digitization, development of digital collections and provision of digital access to these collections. Its essential task is to find integrated solutions in bibliographic activities based on innovative system comprised primarily of the big data technology, machine learning and artificial intellect. The author examines the potential of artificial intellect systems implemented in many foreign libraries. Their successful experience is very promising. The artificial intellect is used for retrieving relevant and reliable information, mining bibliographic metadata, creating standard bibliographic records and reference lists, designing chat-bots, automatic in formation distribution on user request, selecting key values from document array. With digital technology advances, the bibliographers will have to be directly in volved in designing systems, services, programs and apps to provide bibliographic and information products and services so that traditional bibliographical principles, values and ethics lay the foundation for and are preserved in innovative artificial intellect technologies.
Classification Hoax News of Covid-19 on Instagram Using K-Nearest Neighbor

The world was shocked by the outbreak of a virus known as COVID-19 in Wuhan, China. The spread of the virus is increasingly spreading to all countries in the world, including Indonesia. In Indonesia, information about the COVID-19 virus can be found on various social media, one of which is Instagram. Starting from scratch are found positive cases of COVID-19 in Jakarta. Until now, many news provider's accounts found COVID-19 uploaded on Instagram allows the spread of false news. The spread of fake news has greatly affected the handling of this COVID-19 pandemic. The number of hoax news COVID-19 spread on Instagram makes this study using Instagram as a media data retrieval. Therefore research is needed on classifying hoax news about COVID-19 on news accounts on Instagram. This study uses the K-Nearest Neighbor method to classify COVID-19 news on news accounts on Instagram. This research will classify Instagram based on valid news posts on 30 news accounts on Instagram containing news about COVID-19. The news reference used for categorizing hoaxes is from the @turnbackhoaxid account. In using the K-Nearest Neighbor method, the TF-IDF modeling is applied in conducting assessments and weighting. The research results by using the K-Nearest Neighbor method have a presentation accuracy of success 90%, where the precision value is 83%, recall 100%, and f1-score 91% can solve the problem spread of fake news in Indonesian about COVID-19 on news accounts on Instagram.
A Partially Random Trotter Algorithm for Quantum Hamiltonian Simulations

Given the Hamiltonian, the evaluation of unitary operators has been at the heart of many quantum algorithms. Motivated by existing deterministic and random methods, we present a hybrid approach, where Hamiltonians with large amplitude are evaluated at each time step, while the remaining terms are evaluated at random. The bound for the mean square error is obtained, together with a concentration bound. The mean square error consists of a variance term and a bias term, arising respectively from the random sampling of the Hamiltonian terms and the operator splitting error. Leveraging on the bias/variance trade-off, the error can be minimized by balancing the two. The concentration bound provides an estimate on the number of gates. The estimates are verified by using numerical experiments on classical computers.
SubjectivITA: An Italian Corpus for Subjectivity Detection in Newspapers

We present SubjectivITA: the first Italian corpus for subjectivity detection on news articles, with annotations at sentence and document level. Our corpus consists of 103 articles extracted from online newspapers, amounting to 1,841 sentences. We also define baselines for sentence- and document-level subjectivity detection using transformer-based and statistical classifiers. Our results suggest that sentence-level subjectivity annotations may often be sufficient to classify the whole document.
Scaling TensorFlow to 300 million predictions per second

We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we successfully tackled them; notably, implementing the models in TF and serving them efficiently with low latency using various optimization techniques.
Verified Functional Programming of an Abstract Interpreter

Abstract interpreters are complex pieces of software: even if the abstract interpretation theory and companion algorithms are well understood, their implementations are subject to bugs, that might question the soundness of their computations.While some formally verified abstract interpreters have been written in the past, writing and understanding them requires expertise in the use of proof assistants, and requires a non-trivial amount of interactive proofs. This paper presents a formally verified abstract interpreter fully programmed and proved correct in the F* verified programming environment. Thanks to F* refinement types and SMT prover capabilities we demonstrate a substantial saving in proof effort compared to previous works based on interactive proof assistants. Almost all the code of our implementation, proofs included, written in a functional style, are presented directly in the paper.
“Rumyantsev Readings — 2021”: Traditional Directions of Research and Search for the New Forms of Library Activity

On April 21—23, 2021, the Russian State Library (RSL) hosted the International scientific and practical conference “Rumyantsev Readings”, which is one of the largest annual library events. The conference traditionally raises issues of preserving and studying the world cultural heritage, promotes intercultural interaction. In 2021, it covered a wide range of issues of theory and practice of library science, bibliography science, and bibliology. The participants are specialists of libraries of all levels, from federal to municipal, and various departmental subordination, museums, archives, higher educational institutions and research institutes from 19 regions of Russia, as well as from the Republic of Belarus, the Republic of Kazakhstan, the Kyrgyz Republic, the Republic of Lithuania and the Republic of Uzbekistan. Broadcasting of the meetings, as well as holding a number of sections and round tables both in face-to-face and zoom-conference format, significantly increased the audience coverage. The Rumyantsev Readings attracted the highest number of remote participants (2,427 connections), that determined the relevance of the conference in modern conditions. Three hundred and one participants attended the conference personally. The rich program included a plenary session, 13 round tables, seminars, and sections. The following sections were organised at the Rumyantsev Readings: “Library in the modern world: accessibility in the period of restrictions”; “Information and bibliographic activity: theory and practice”; “Collectors, researchers, curators. Libraries and museums in the context of history”; “Art Publications in the collections of libraries. Creation, existence, study”; “Specialized collections (printed music and maps) in the library holdings”; “Manuscript sources in the collections of libraries”; “Rare books and book monuments”; “Theory and practice of library science”; “Library classification systems”. As part of the pre-session meeting of the 32nd Section on Library Management and Marketing of the Russian Library Association, there was a round table “Social networking activity of libraries: how to record and report (methodological approaches and solutions)”. There were also the following events: a round table a “The Register of book monuments: government database of recording and registration and scientific research resource”, an expert session “Actual problems of formation of digital competencies of learners in the library and information sphere” and a scientific and practical seminar “Actual heritage” devoted to the 150th birth anniversary of L.B. Khavkina. The publishing house of the Russian State Library “Pashkov Dom” has released the conference proceedings.
A Delay-Tolerant Data Congestion Avoidance Algorithm for Enterprise Cloud System Based on Modular Computing

Abstract Traditional large-scale enterprise cloud system has the problem of large delay. Therefore, a delay tolerant data congestion avoidance algorithm based on modular computing is designed. The resource allocation rules of rbfr strategy in traditional enterprise cloud are improved to change the traditional interactive overall mode and make it modular.Using modular thinking to construct the data congestion control algorithm, mainly in the control layer to achieve the model, through the introduction of ant colony algorithm in modularization, the design of delay tolerant data congestion avoidance algorithm is realized. In order to verify the algorithm’s congestion avoidance effect, comparative experiments are designed. Experimental results show that the packet loss rate of large enterprise cloud system under this algorithm is lower than that of traditional delay tolerant data congestion avoidance algorithm.
A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.