Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence for a Fair, Just, and Equitable World

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">From the 1970s</b> onward, we started to dream of the leisure society in which, thanks to technological progress and consequent increase in productivity, working hours would be minimized and we would all live in abundance. We all could devote our time almost exclusively to personal relationships, contact with nature, sciences, the arts, playful activities, and so on. Today, this utopia seems more unattainable than it did then. Since the 21st century, we have seen inequalities increasingly accentuated: of the increase in wealth in the United States between 2006 and 2018, adjusted for inflation and population growth, more than 87% went to the richest 10% of the population, and the poorest 50% lost wealth <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Following the crisis of 2008, social inequalities, rights violations, planetary degradation, and the climate emergency worsened and increased (see <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> ). In 2019, the world’s 2153 billionaires had more wealth than 4.6 billion people <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . The World Bank estimates that COVID-19 will push up to 150 million people into extreme poverty <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> .
Verified Software Units

Abstract Modularity - the partitioning of software into units of functionality that interact with each other via interfaces - has been the mainstay of software development for half a century. In case of the C language, the main mechanism for modularity is the compilation unit / header file abstraction. This paper complements programmatic modularity for C with modularity idioms for specification and verification in the context of Verifiable C, an expressive separation logic for CompCert . Technical innovations include (i) abstract predicate declarations – existential packages that combine Parkinson &amp; Bierman’s abstract predicates with their client-visible reasoning principles; (ii) residual predicates, which help enforcing data abstraction in callback-rich code; and (iii) an application to pure (Smalltalk-style) objects that connects code verification to model-level reasoning about features such as subtyping, self , inheritance, and late binding. We introduce our techniques using concrete example modules that have all been verified using the Coq proof assistant and combine to fully linked verified programs using a novel, abstraction-respecting component composition rule for Verifiable C.
Knowledge Discovery Using an Evolutionary Algorithm and Compensatory Fuzzy Logic

Database Knowledge Discovery attention has been growing last decades using different approaches as part of a new era when information is multiplied in proportion and importance. Fuzzy Logic predicates approach is one of them, fundamental because of their interpretability properties. A new concept of transdisciplinary interpretability has been introduced by using a new axiomatic approach: Compensatory Fuzzy Logic. Several ways have been used as fuzzy predicates searching techniques, notably a Genetic Algorithm, part of a Data Analysis Platform called Eureka Universe. This paper presents two Genetic Programming Algorithm Approaches, with outstanding results and illustrated by a case study.
A Customizable Multilingual Chatbot System for Customer Support

Implementing third-party services to develop a chatbot is not cost-effective for many small and medium enterprises especially considering that many services only supports one language at a time. To address this gap, this paper proposes a multilingual chatbot system that will allow companies and organizations to customize and deploy their own multilingual chatbot service with two extended features, which are live chat and ticketing system. The chatbot will also be able to understand and reply in English, Malay, and Chinese as well as customizable given the dialogue shell and knowledge base. To achieve this, the development uses TypeScript for frontend web application while Go as the backend development. The development language for mobile application is Dart and the User Interface (UI) library is React. Finally, the database management system used is MongoDB. The developed prototype is then evaluated via a survey questionnaire and the findings suggested that the proposed system would be able to assist small and medium-sized business and organizations to deploy their own chatbot system as an alternative to existing customer services.
Sentiment Analysis of Community Opinion on Online Store in Indonesia on Twitter using Support Vector Machine Algorithm (SVM)

Abstract Public opinion on the Online Shop service company in Indonesia can be seen through comments sent via tweeter. The purpose of this study is to see the public opinion sentiment towards online stores in Indonesia via Twitter using the Support Vector Machine algorithm. Public opinion and assessment on Twitter can be classified into 3 classes, namely negative, neutral, and positive. The approach used is Multiclass One Vs Rest SVM by using Kernel Sigmoid, Linear, and RBF to classify public tweets about Indonesian Online Shop services, namely Shopee, Tokopedia, Bukalapak, and JDid. The results obtained are the Multiclass (One Vs Rest) Support Vector Machine kernel algorithm which has the best accuracy is the Sigmoid kernel with 82% accuracy on the Shopee online store dataset, 94.7% on the Tokopedia dataset and 75.3% for the Bukalapak dataset, while the Linear kernel Jdid dataset provides better accuracy, namely 78%. Clarification errors can occur due to overfiting, namely the model adjusts the training data very well (accuracy can reach 100%) so that the model cannot generalize well on the testing data.
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification

In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use of deep learning-based computer vision techniques for automated disease diagnosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized ($\sim$100K pixels), making them infeasible to be used directly for training deep neural networks. Also, often only slide-level labels are available for training as detailed annotations are tedious and can be time-consuming for experts. Approaches using multiple-instance learning (MIL) frameworks have been shown to overcome these challenges. Current state-of-the-art approaches divide the learning framework into two decoupled parts: a convolutional neural network (CNN) for encoding the patches followed by an independent aggregation approach for slide-level prediction. In this approach, the aggregation step has no bearing on the representations learned by the CNN encoder. We have proposed an end-to-end framework that clusters the patches from a WSI into ${k}$-groups, samples ${k}'$ patches from each group for training, and uses an adaptive attention mechanism for slide level prediction; Cluster-to-Conquer (C2C). We have demonstrated that dividing a WSI into clusters can improve the model training by exposing it to diverse discriminative features extracted from the patches. We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution. The framework is optimized end-to-end on slide-level cross-entropy, patch-level cross-entropy, and KL-divergence loss (Implementation: https://github.com/YashSharma/C2C).
The Use of Kalman Filter Techniques for Ship Track Estimation

Unscented Kalman Filter (UKF) is a technique used in non-linear applications and dynamic systems identification (e.g. tracking marine vessels and ships) that require state and parameter estimation. This paper studies Kalman Filter (KF) based techniques for tracking ships using Global Positioning System (GPS) data. The present work proposes to exploit information from GPS sensors in order to track a ship in real-time. The absence and presence problem of a ship is handled by a applying KF theory to analyze GPS coordinates and compare current marine vessel routes to previously recorded ones. To study tracking performance, the system was implemented in C++ and simulation results demonstrate the feasibility and high accuracy of the proposed tracking method
An extensible equality checking algorithm for dependent type theories

We present a general and user-extensible equality checking algorithm that is applicable to a large class of type theories. The algorithm has a type-directed phase for applying extensionality rules and a normalization phase based on computation rules, where both kinds of rules are defined using the type-theoretic concept of object-invertible rules. We also give sufficient syntactic criteria for recognizing such rules, as well as a simple pattern-matching algorithm for applying them. A third component of the algorithm is a suitable notion of principal arguments, which determines a notion of normal form. By varying these, we obtain known notions, such as weak head-normal and strong normal forms. We prove that our algorithm is sound. We implemented it in the Andromeda 2 proof assistant, which supports user-definable type theories. The user need only provide the equality rules they wish to use, which the algorithm automatically classifies as computation or extensionality rules, and select appropriate principal arguments.
Post-Quantum Network Security:McEliece and Niederreiter Cryptosystems Analysis and Education Issues

The paper is aimed at analyzing of the classical McEliece and Niederreiter cryptosystems as well as theQuasi-Cyclic MDPC McEliece cipher in a context of the post-quantum network security. Theoretical foundations ofthe aforesaid cryptographic schemes are considered. The characteristics of the given cryptosystems and otherasymmetric encryption schemes are analyzed. The cipher metrics, which are considered in the paper, includecryptographic strength, performance, public key size and length of ciphertext. The binary Goppa codes are describedin the context of their role for the cryptanalytic resistance of the classic McEliece and Niederreiter schemes. Thecrucial advantages and drawbacks of the aforementioned cryptosystems are analyzed. The prospects for applicationof these ciphers to the network security protocols are outlined. The investigations, which are aimed at finding waysto reduce the public key sizes and improve the energy efficiency of the given ciphers, are briefly described. A neweducational module “Introduction to Post-Quantum Cryptography” is presented
MEOD: Memory-Efficient Outlier Detection on Streaming Data

In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points defined by a given radius. The radius value detection problem is converted into an optimization problem. The radius value is determined using a particle swarm optimization (PSO)-based approach. The results of the MEOD technique application are compared with existing approaches in terms of memory, time, and accuracy, such as the memory-efficient incremental local outlier factor (MiLOF) detection technique. The MEOD technique finds outlier points similar to MiLOF with nearly equal accuracy but requires less memory for processing.
A Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation

In this paper we propose using a novel differentiable convolutional distance transform layer for segmentation networks such as U-Net to regularize the training process. In contrast to related work, we do not need to learn the distance transform, but use an approximation, which can be achieved by means of the convolutional operation. Therefore, the distance transform is directly applicable without previous training and it is also differentiable to ensure the gradient flow during backpropagation. First, we present the derivation of the convolutional distance transform by Karam et al. [6]. Then we address the problem of numerical instability for large images by presenting a cascaded procedure with locally restricted convolutional distance transforms. Afterwards, we discuss the issue of non-binary segmentation outputs for the convolutional distance transform and present our solution attempt for the incorporation into deep segmentation networks. We then demonstrate the feasibility of our proposal in an ablation study on the publicly available SegTHOR data set.
Sign Language Recognition using Principal Component Analysis and Support Vector Machine

The World Health Organization (WHO) estimates that over five percent of the world's population are hearing-impaired. One of the communication problems that often arise between deaf or speech impaired with normal people is the low level of knowledge and understanding of the deaf or speech impaired's normal sign language in their daily communication. To overcome this problem, we build a sign language recognition system, especially for the Indonesian language. The sign language system for Bahasa Indonesia, called Bisindo, is unique from the others. Our work utilizes two image processing algorithms for the pre-processing, namely the grayscale conversion and the histogram equalization. Subsequently, the principal component analysis (PCA) is employed for dimensional reduction and feature extraction. Finally, the support vector machine (SVM) is applied as the classifier. Results indicate that the use of the histogram equalization significantly enhances the accuracy of the recognition. Comprehensive experiments by applying different random seeds for testing data confirm that our method achieves 76.8% accuracy. Accordingly, a more robust method is still open to enhance the accuracy in sign language recognition.
Block Mining reward prediction with Polynomial Regression, Long short-term memory, and Prophet API for Ethereum blockchain miners

The Ethereum blockchain is an open-source, decentralized blockchain with functions triggered by smart contract and has voluminous real-time data for analysis using machine learning and deep learning algorithms. Ether is the cryptocurrency of the Ethereum blockchain. Ethereum virtual machine is used to run Turing complete scripts. The data set concerning a block in the Ethereum blockchain with a block number, timestamp, crypto address of the miner, and the block rewards for the miner are explored for K means clustering for clustering miners with a unique crypto address and their rewards. Linear regression and polynomial regression are used for the prediction of the next block reward to the miner. The Long ShortTerm Memory (LSTM) algorithm is used to exploit the Ether market data set for predicting the next ether price in the market. Every kind of price and volume for every four hours is taken for prediction. The root mean square error of 34.9% is obtained for linear regression, the silhouette score is 71% for K-means clustering of miners with same rewards, with the optimal number of clusters obtained by Gap statistic method.
SOCIAL EDUCATIONAL INFRASTRUCTURE FOR RUSSIAN SCHOOL STUDENTS: BASELINE STUDY IN AVAILABILITY AND ACCESSIBILITY UNDER REGULAR AND CRITICAL CONDITIONS

Information about the paper titled "SOCIAL EDUCATIONAL INFRASTRUCTURE FOR RUSSIAN SCHOOL STUDENTS: BASELINE STUDY IN AVAILABILITY AND ACCESSIBILITY UNDER REGULAR AND CRITICAL CONDITIONS" at IATED Digital Library
A Model DevOps Framework for SaaS in the Cloud

The corporateOjo-Gonzalez, Karina organizational structure and culture should evolve in the development of software technology as the company grow;Prosper-Heredia, Rene otherwise,Dominguez-Quintero, Luis it will present weaknesses that hinder the implementation of any software methodology.Vargas-Lombardo, Miguel In this paper, the beneficial characteristics acquired by software products that have been developed under a DevOps conception have been identified, which has given DevOps a greater degree of relevance, promoting its study in greater detail, and assessing the growing interest of companies in having software as a quality that does not require additional costs for updating and maintenance. Additionally, the paper presents a systematic literature review, obtaining a conceptual schema of the relationship of DevOps with service-oriented software development, mitigating the lack of formalization of the concepts involved in each stage through an ontology. The result approximates a Model DevOps-based framework for SaaS development which can contribute as a guide for enterprises interested in the adoption of DevOps in their software production process for the first time.
Compliance Monitoring and Testing Seismometer to Detect Compliquake

Effective corporate compliance function has been the focus of all the financial institutions (FI) and the regulators across the globe in this fast-changing and dynamic business landscape. The compliance function is a key component of financial institutions' (FIs) lines of defence in terms of managing and mitigating the risks. Compliance monitoring and testing by the compliance departments can act as an effective “seismometer” in detecting the “compliquakes” in the FIs if implemented effectively. If FIs across the globe internalize this philosophy, the author opines that the overall financial services and the financial institution's ecosystem will be a much better world in terms of identification and strengthening of the compliance control framework.
The Realization of Intelligent Algorithm of Knowledge Point Association Analysis in English Diagnostic Practice System

This paper first conducts knowledge point association analysis on a large amount of data collected in practical applications. Data mining includes data collection, data preprocessing, actual mining, and result analysis, establishes knowledge point association rules table, and develops college English diagnostic practice system. Then, starting from the existing paper composition mode of the system, the knowledge point association rule table is introduced, and the knowledge point association relationship mining model is constructed using the association rule algorithm to explore the potential influence relationship between different knowledge points that affect the improvement of learning quality. Finally, the data collected when the system is used is preprocessed, and the three dimensions of learning status evaluation, question-type association analysis, and college English score prediction are, respectively, modeled. Finally, after combining these submodels, a relatively complete and reliable diagnosis is obtained by evaluation model and related verification.
The New Generation National Research Computer Network: Current Status and Concept for the Development

The paper systematized up-to-date information on the status of work to ensure the functioning and development plans of the new generation National Research Computer Network (NIKS), created in 2019 according to the results of integration of RUNNet and RASNet — telecommunication networks in the fields of higher education and science of Russia. The normative grounds and prerequisites for the creation of a unified research and education network in the country are presented. The key characteristics of leading foreign national research and education networks are given. The parameters of the telecommunications infrastructure and network connectivity of NIKS as of the current moment, examples of services developed in the interests of the Russian R&amp;E community are indicated. Special attention is paid to the presentation of the main directions of the accelerated development of NIKS for 2021-2024 and expected results.
Russian academic journals (the case study of journals in physics)

The article analyzes the pool of Russian scientific journals in physics presented in the Russian Science Citation Index. The stages of this study are described and presented in detail. A preliminary general list of journals (173) has been formed and a list of journals (113) has been prepared according to the main selection parameters, which make it possible to identify the leading publications, both in their subject heading and in the section "Physics" in general. Based on the current version of the "State rubricator of scientific and technical information", the distribution of scientific journals by subject headings was carried out. Analysis of scientific journals in 12 thematic areas of physics made it possible to draw the main conclusions: on the presence in the VAK List (42 journals); on indexing in scientific citation databases – Web of Science (29 journals), Scopus (34 journals), RSCI (95 journals); for inclusion in the Russian Science Citation Index (54 journals); on the availability of free publication of manuscripts (40 journals); on the availability of royalties for the publication of manuscripts (4 journals). The article is prepared within the framework of the State Order “Development and improvement of the system of Open Archive of integrated information and library resources of Russian National Public Library for Science and Technology as modern knowledge management system in digital environment: on the way to Open Science” № 075-01300-20-00 for 2020–2022.
Unextendible entangled bases and more nonlocality with less entanglement

We consider a general version of the phenomenon of more nonlocality with less entanglement within the framework of the unambiguous (i.e., conclusive) quantum state discrimination problem under local quantum operations and classical communication. We show that although the phenomenon was obtained before for two qutrits, it can also be observed for two qubits, whereas still being at the single-copy level. We establish that the phenomenon is intrinsically connected to the concept of unextendible entangled bases in the two-qubit case. In the process, we demonstrate a hierarchy of nonlocality among sets of two-qubit orthogonal pure states where the ``nonlocality'' is in the sense of a difference between global and local abilities of quantum state discrimination. We present a complete characterization of two-qubit pure orthogonal state sets of cardinality three with respect to their nonlocality in terms of unambiguous local distinguishability, the status for other cardinalities being already known. The results are potentially useful for secure quantum communication technologies with an optimal amount of resources.
Book Genre Categorization Using Machine Learning Algorithms (K-Nearest Neighbor, Support Vector Machine and Logistic Regression) using Customized Dataset﻿

Text classification is playing a vital role in current era. Its requirement is increasing day by day because of increase of text data as number of digital users are increasing rapidly. As a result, machine learning algorithms are used to classify certain text data, resulting in better predictions and accuracy. By constructing a data set with proper structure and data, the genre is predicted by the title and abstract of the book. The dataset will consist books which are translated to English from Guajarati or Hindi originate books.In this paper, some weaknesses in text classification techniques are analysed and worked on to improve the accuracy of structured data.The main focus here was to classify a book by genre using machine learning algorithms.