Reviewerly/exHarmony huggingface dataset:
A Personalized Academic Advisory Recommender System (PAARS): A Case Study

Recommender systems (RSs) are effective tools to reduce the number of available selections and to expose users to items that meet their needs. RSs predict the preferences of users and generate recommendations based on the interest model of users. RSs have been used in several domains including e-learning and e-library. In particular, collaborative-based filtering (CF) and content-based filtering are the common approaches used to recommend items to users. The development of CF introduces content-based filtering which does not need users' rating for items used to compute the similarity between users or items in CF. Instead, the affinity in content-based filtering RSs is computed based on the provided information of items selected by a user and then make the recommendation accordingly. The traditional system of recommending a list of courses to students is time-consuming, risky, and monotonous work. These drawbacks may negatively affect the students' academic performance and learning experience. While content-based filtering can introduce a solution to automate the process of course selection, this paper introduces a Personalized Academic Advisory Recommender System (PAARS) that recommends a list of courses based on each student's profile and similar students' profiles. The primary data mining technique used to learn profiles for students is a k-nearest neighbors (kNN) classifier. The objectives of this research are twofold. The first is to introduce a model that personalizes the learning process, since each student may have different objectives than other students. The second is to introduce PAARS web-based framework that automates the process of course recommendation. PAARS would help students to enhance their academic performance and improve their level of loyalty to their universities as well.
Fast Search of Lightweight Block Cipher Primitives via Swarm-like Metaheuristics for Cyber Security

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Symmetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarm-like normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.
Media Literacy and Media Education

Features of perception of media texts are considered, modern theories of media education are analyzed, which accumulate pedagogical theories and theories of media in themselves. Directions of development of media education are formulated, components of formation of media literacy and media competency are distinguished.
IHWC: intelligent hidden web crawler for harvesting data in urban domains

Abstract Due to the massive size of the hidden web, searching, retrieving and mining rich and high-quality data can be a daunting task. Moreover, with the presence of forms, data cannot be accessed easily. Forms are dynamic, heterogeneous and spread over trillions of web pages. Significant efforts have addressed the problem of tapping into the hidden web to integrate and mine rich data. Effective techniques, as well as application in special cases, are required to be explored to achieve an effective harvest rate. One such special area is atmospheric science, where hidden web crawling is least implemented, and crawler is required to crawl through the huge web to narrow down the search to specific data. In this study, an intelligent hidden web crawler for harvesting data in urban domains (IHWC) is implemented to address the relative problems such as classification of domains, prevention of exhaustive searching, and prioritizing the URLs. The crawler also performs well in curating pollution-related data. The crawler targets the relevant web pages and discards the irrelevant by implementing rejection rules. To achieve more accurate results for a focused crawl, ICHW crawls the websites on priority for a given topic. The crawler has fulfilled the dual objective of developing an effective hidden web crawler that can focus on diverse domains and to check its integration in searching pollution data in smart cities. One of the objectives of smart cities is to reduce pollution. Resultant crawled data can be used for finding the reason for pollution. The crawler can help the user to search the level of pollution in a specific area. The harvest rate of the crawler is compared with pioneer existing work. With an increase in the size of a dataset, the presented crawler can add significant value to emission accuracy. Our results are demonstrating the accuracy and harvest rate of the proposed framework, and it efficiently collect hidden web interfaces from large-scale sites and achieve higher rates than other crawlers.
SOLVABILITY OF THE INVERSE INITIAL-BOUNDARY VALUE PROBLEM WITH A KNOWN VALUE ON THE LINE

The definitions of either the kernel or the right-hand sides of integro-differential equations, or the values of either the initial or boundary conditions for integro-differential equations or the definition of the right-hand side for an integro-differential equation with over determination at an interior point based on additional information about the solution of the original problem is called inverse problems. Mathematical models of modern problems of geophysics, oceanology, atmosphere, physics, technology and other sciences are described using integro-differential equations with partial derivatives of the fourth order. The present article is devoted to the solvability of the inverse problem, that is, the recovery of the kernel in the initial-boundary value problem for a fourth-order integro-differential equation with partial derivatives with a known value of the desired solution on the straight line x = x0, 0 &lt; x0 &lt; 1, that is, with a new definition in the inner line. The authors have proved for the first time the existence and uniqueness of the solution of the inverse problem under consideration. Well-known methods are used to achieve this goal: the method of reducing the inverse problem to a linear integral Volterra equation of the second kind, the method of Green's functions for ordinary differential equations of the second order with homogeneous boundary conditions. When solving the formulated inverse problem, sufficient conditions for the existence and uniqueness of the solution of the inverse problem of recovering the kernel in a fourth order partial integro-differential equation are found. First, using transformations and the Green's function, the original problem is reduced to an equivalent problem, for which a theorem on the existence and uniqueness of a solution is proved. Further, using the methods of the theory of inverse problems, three Volterra integral equations of the second kind are compiled and the existence and uniqueness of the solution of systems of Volterra integral equations of the second kind are proved.
Processes of applying of quantum genetic algorithm in function optimization

This article describes optimization processes of functions and their solutions using quantum genetic algorithms. These are continuous heuristic optimization methods based on simulated genetic mechanisms, i.e., on dynamic processes in a population such as mutation, crossover, selection, and so on. This process leads to the emergence of a new class called quantum genetic algorithms. In this article, we have presented a discussion of new classes of quantum genetic algorithms, future capabilities, and benefits.
A digital pathology tool for quantification of color features in histologic specimens

Abstract In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin‐embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue‐based analyses.
Continuous Library and Information Education in the Era of Digital Transformation

Various modifications of the system of continuous library and information education are associated with the tasks of digitalization, determined by the national development goals of the Russian Federation. The purpose of this study is to identify the main transformational trends of this system in the context of the digitalization of culture. The authors reveal the concepts of “digitalization of education”, “lifelong education”. The article analyses the reports of the track “Professional education of specialists in library and information sphere” conducted in the framework of international scientific and practical conference “Science, Technology and Information in Libraries (LIBWAY — 2020)”, held on September 14—17, 2020. The authors identified the main problematic issues of continuous library and information education: personnel potential of libraries in the era of large-scale and accelerating technological changes; diversification of additional professional education for library staff as one of the links in continuous education; regulatory support for the development of system of continuous library education; new vectors in educational activities aimed at the formation of professional and supra-professional competencies as a factor of competitiveness of the library specialists in the context of multitasking and efficiency of the cultural sector. The article emphasizes that the continuous library and information education of specialists in the era of digitalization of culture is progressing due to the new information technologies, implementation of innovative library experience, as well as the formation of new competencies of library personnel. The authors state that the development of culture and the growth of the country’s potential depend on the success of building unified library and information knowledge space as a valuable resource and the basis for economic success in the future, part of which is the system of continuous professional education.
Robustness Study of Edge Segmentation and Completion by CNN Based Method for Tessellation Images

In this paper, the robustness of tessellation images segmentation using Convolutional Neural Network (CNN) is presented. Particularly, this paper aims to study the effect of the quality and number of the images used for training on the robustness of the edge segmentation and completion. Three kinds of image deterioration are considered: the absence of a part of the tessellation, the discontinuity of the edges constituting the tessellation and the presence of a background noise. Five CNNs are trained with several kinds of deteriorated images and the trained models are then compared according to the results obtained with four classes of images.
Data-driven identification of complex disease phenotypes

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from network science, machine learning and computational phenotyping to assess interactions between two or more diseases in a transparent way across the full diagnostic spectrum. We demonstrate that health states of hospitalized patients can be better characterized by including higher-order features capturing interactions between more than two diseases. We identify a meaningful set of higher-order diagnosis features that account for synergistic disease interactions in a population-wide ( N = 9 M) medical claims dataset. We construct a generalized disease network where (higher-order) diagnosis features are linked if they predict similar diagnoses across the whole diagnostic spectrum. The fact that specific diagnoses are generally represented multiple times in the network allows for the identification of putatively different disease phenotypes that may reflect different disease aetiologies. At the example of obesity, we demonstrate the purely data-driven detection of two complex phenotypes of obesity. As indicated by a matched comparison between patients having these phenotypes, we show that these phenotypes show specific characteristics of what has been controversially discussed in the medical literature as metabolically healthy and unhealthy obesity, respectively. The findings also suggest that metabolically healthy patients show some progression towards more unhealthy obesity over time, a finding that is consistent with longitudinal studies indicating a transient nature of metabolically healthy obesity. The disease network is available for exploration at https://disease.network/.
On Inclusions Between Quantified Provability Logics

We investigate several consequences of inclusion relations between quantified provability logics. Moreover, we give a necessary and sufficient condition for the inclusion relation between quantified provability logics with respect to $$\Sigma _1$$ arithmetical interpretations.
Competency Formation of a University Graduate: Model for Assessment

The authors of the article propose a model describing the characteristics of a university graduate. The discussed model comprises a set of competencies formed in the learning process. A tiered approach to assessing the parameters of the model is considered. The authors analyse the criteria for identifying model tiers. Logical links between the parameters and levels of the model are determined.
A hybrid fuzzy modeling method to improve the strategic scenarios design. : Integrating Artificial Intelligence algorithms and the field of Futures Studies methods

The aim of this article is to investigate the role of fuzzy cognitive maps (FCM) and foresight techniques in supporting strategic foresight to reduce uncertainty in the field of Futures Studies. The combination of these approaches helps to better understand the future alternatives available to decision-makers by combining storytelling with trend extrapolation using a proposed new hybrid process called FLighTS (Future Lighthouse Trending Scenarios). Thus, in the process of determining the most appropriate future, it is possible to reduce the uncertainty due to vague and ambiguous value judgments in the process of inference of decision makers.The work presented in this article aims to improve the combination of qualitative and quantitative approaches to facilitate further exploration of the possibilities of a new non-linear way of thinking about the future. The article includes a conceptual model and an experimental example that develops a case for a higher education institution (HEI).The article ends with a reflection on conceptual modeling and suggests ways for use in future research.
Estimation and computations for Gaussian mixtures with uniform noise under separation constraints

Abstract In this paper we study a finite Gaussian mixture model with an additional uniform component that has the role to catch points in the tails of the data distribution. An adaptive constraint enforces a certain level of separation between the Gaussian mixture components and the uniform component representing noise and outliers in the tail of the distribution. The latter makes the proposed tool particularly useful for robust estimation and outlier identification. A constrained ML estimator is introduced for which existence and consistency is shown. One of the attractive features of the methodology is that the noise level is estimated from data. We also develop an EM-type algorithm with proven convergence. Based on numerical evidence we show how the methods developed in this paper are useful for several fundamental data analysis tasks: outlier identification, robust location-scale estimation, clustering, and density estimation.
Robust Training of Deep Neural Networks with Noisy Labels by Graph Label Propagation

Recent developments in technology, such as crowdsourcing and web crawling, have made it easier to train machine learning models that require big data. However, the data collected by non-experts may contain noisy labels, and training a classification model on the data will result in poor generalization performance. In particular, Deep Neural Networks (DNNs) tend to over-fit to the noisy labels more significantly due to the large number of parameters. In this study, we propose a novel method to train DNNs robustly against the noisy labels by updating the network parameters with the labels corrected by graph label propagation on the similarity graph of training samples. The effectiveness of the proposed method is confirmed by comparing it with baseline MLP and CNNs on the noisy MNIST and CIFAR-10 datasets. Experimental results prove that the proposed method successfully corrects the noisy labels and trains DNNs more robustly than the baseline models.
A Blockchain Framework for Counterfeit Medicines Detection

The emergence of counterfeit medicines has given rise to a significant setback globally. These drugs may be contaminated, contain the wrong ingredient, or have no active ingredient at all, endangering approximately a million lives per year. The World Health Organization (WHO) estimates that 73 billion euros (79.26 billion USD) worth counterfeit medicines are traded annually. The imperfect supply chain is one of the primary causes of this issue. The present system does not have a proper record of the drug or vaccine that has been manufactured, produced, distributed, and finally reached to the consumer. There is no record of change in ownership of the drugs from manufacturer to consumers. Due to the lack of transparency of the system, the data are not shared between the systems. These loopholes play a significant role in producing and distributing counterfeit medicines. Blockchain is an emerging technology that can help in solving this issue of counterfeit medicines. Using Blockchain, tracking the drugs from its manufacturing until its delivery to the consumer can be possible. Trusted people can use an authorized Blockchain to store transactions and allow trusted parties to join the system and push all the records to Blockchain.
Prediction and Detection of car accidents in video by deep learning

Road accidents are considered as one of the leading causes of human fatality in recent decade. Using machine vision in automobile sensors and through warning even a second before accidents occur, a human disaster can be prevented. In order to predict accident at the stage, it is needed to learn temporal and spatial characteristics. In this paper, a primary real-time autonomous accident detection system has been proposed which is established on object detection deep learning in video prediction techniques. The events in video will be the accidents which are predicted several frames before the event. In this technique, 5000 accident images were labeled, and the model was classified using all the existing classes. Then, the special events were detected using object detection technique. The proposed model shows an accuracy of 92.98 and a speed of 200 images per second in video. The model can predict the event 0.92 second before accident. This model can be used in inter-city cameras with immediate analysis of meta data, and it has been piloted.
Detecting Research from an Uncurated HTML Archive Using Semi-Supervised Machine Learning

The Internet Archive seeks to provide "universal access to all knowledge" through their digital library, which includes a digital repository of over 475 billion crawled web documents in addition to other content. Of particular interest, to those who use their platform, is the preservation and access to research due to its inherent value. Research or scholarly work outside of mainstream institutions, publishers, topics, or languages is at particular risk of not being properly archived. The Internet Archive preserves these documents in its attempts to archive all content, however, these documents of interest are still at risk of not being discoverable due to lack of proper indexing within this uncurated archive. We provide a preliminary classifier to identify and prioritize research, to include long tail research, which circumvents this issue and enhances their overall approach. Classification is complicated by the fact that documents are in many different formats, there are no clear boundaries between official and unofficial research, and documents are not labeled. To address this problem, we focus on HTML documents and develop a semi-supervised approach that identifies documents by their provenance, structure, content, and linguistic formality heuristics. We describe a semi-supervised machine learning classifier to filter crawled HTML documents as research, both mainstream and obscure, or non-research. Because the HTML datasets were not labelled, a provenanced approach was used where provenance was substituted for label. A data pipeline was built to deconstruct HTML website content into raw text. We targeted structural features, content features, and stylistic features which were extracted from the text and metadata. This methodology provides the ability to leverage the similarities found across differing subjects and languages in scholarly work. The optimal classifier explored, XGBoost, predicts whether a crawled HTML document is research or non-research with 98% accuracy. This project lays the foundation for future work to further distinguish between mainstream and long tail research, both English and non-English.