Reviewerly/exHarmony huggingface dataset:
Improved Method for Grading Bilingual Mathematics Exams Based on Computing with Words

As the Content and Language Integrated Learning approach to studying subjects such as mathematics through foreign languages gains more recognition, the problem of assessing students’ performance in bilingual settings remains largely understudied. In this work, we propose an improved method for grading mathematics exams using words that takes into account correctness, optimality, thoroughness, and tidiness of solutions written by students. We outline the general multilayer framework for grading bilingual mathematics exams, where assessment at each layer is computed as a fuzzy set. Our approach is an improvement upon previously published methods and considers four main competences that a student should obtain. Applicability of our method is illustrated using model data.
Fairness Audit and Compositional Analysis in Trusted AI Program

In this work, definitions and approaches in the analysis of fairness and bias in “black box” learning systems were examined from the perspective of unsupervised generative learning. Challenges in direct analysis of fairness and bias in “black box” systems that cannot provide suitable explanations for their decisions have several facets, including: sufficiency and representativity of marked trusted data sets for confident correlation analysis; dimensionality and processing challenges with complex real-world data; and trustworthiness of the trusted data itself. We investigated approaches to fairness audit from the perspective of generative compositional analysis that does not depend on significant amounts of prior data and for that reason can provide additional perspective to the analysis. With generative structure of informative low-dimensional representations that can be obtained with different methods of unsupervised learning, certain forms of bias analysis can be performed without prior data. As demonstrated on a practical example, the significance of the proposed method is that it can provide additional directions and valuable insights in the analysis of bias and fairness of learning systems.
Development of the Online Library of the University Department to Support Educational Activities: A Conceptual Model

The research carried out an analytical review of the sources, which showed that today the issue of information support for the activities of university libraries has been studied at a sufficient scientific and educational-methodological level. Researchers consider this process both from the point of view of creating electronic libraries (electronic resources) in the Internet environment, and from the point of view of using social networks to establish communication and spread information about the activities of book collections. The basis of the research was carried out at the Department of Social Communications and Information Activities of the Lviv Polytechnic National University. Although library service to users at the university level is organized at a high level, library service at the department level is not sufficiently regulated. The proposed solution will allow not only to automate library service at the department level, but also to establish communications with readers - students of the department. The developed website of the Department of Social Communications and Information Activities library allows users to familiarize themselves with the document fund of the department, and registered users to access full-text documents Such e-library allows not only systematizing the works of university teachers (teaching manuals, textbooks, scientific articles, theses, etc.), but also to provide students with more opportunities for online learning, including the following: constant full-text access to such materials; independent preparation for classes; abstract analysis of such documents, etc.. In addition, the study presents general proposals for the development of information activities of the department's library, the problems of implementing the online library and the possibility of avoiding and getting out of conflict situations.
A Method for Evaluating the Efficiency of the Semantic Kernel in the Internet Promotion Channel

Over the past twelve years, search engine optimization has become a classic technology for product promotion. Its tools have also become classics, such as Google AdWords advertising, HTML code optimization and building the semantic core of a WEB resource. However, recently they no longer give significant results, and their efficiency drops. We completed several WEB projects in 2020–2021, the results of which show a catastrophic drop in the efficiency of classic SEO tools by at least 50%. The main reason for this is that the online market is constantly changing due to competition. In our study, we propose to look for ways to improve efficiency based on the reengineering of existing tools. To implement the reengineering procedure, it is necessary to evaluate the effectiveness of existing SEO tools. Therefore, in this paper, we focused on the problem of evaluating the effectiveness of the semantic kernel of digital content as the most common approach in SEO. It is based on the method of analyzing the current situation and its automatic evaluation. By the situation, we mean a set of WEB documents that contain a semantic kernel. Therefore, the evaluation of the efficiency of the kernel must be performed both from the point of view of the document and from the point of view of the keyword. Further, such an assessment should also include a measure of the quality of the semantic kernel from the position of the search server. Such a server offers two evaluation tools. The first is WEB statistics, that is, how many visits this kernel attracted. Another way is the structure of the kernel itself, that is, what keywords are included in its composition. And the third component of the assessment is the budget or financial resources that need to be spent on the synthesis and implementation of the kernel on the Internet. Based on the results of this modeling, the article shows a real example (WEB project in the American online services market), in which this evaluation procedure was applied.
A Study of Implementing a Blockchain-Based Forensic Model Integration (BBFMI) for IoT Devices in Digital Forensics

The rapid advancement of Internet of Things (IoT) technology has brought about new challenges in the field of digital forensics. Traditional forensic methods are often inadequate in dealing with the decentralized and distributed nature of IoT devices. IoT has several advantages that have made it appealing to consumers and attackers alike. The technology and resources available to today’s cybercriminals allow them to launch millions of sophisticated attacks. In this paper, we propose a blockchain-based forensic model (BBFMI) for investigating IoT devices. The proposed BBFMI model utilizes the immutability and tamper-proof features of blockchain technology to provide a secure and reliable way of collecting, storing, and analyzing forensic evidence from IoT devices. One of the most important benefits of BBFMI is that it provides digital forensics investigators with an immutable chain of evidence that can be used to trace the source of data and its subsequent changes. For instance, when using BBFMI, forensic investigators can easily trace the chain of events that led to the data breach of an IoT device. Our results show that the proposed blockchain-based forensic approach can provide a secure, efficient, and tamper-proof solution for investigating IoT devices in digital crime scenes.
The Extended Fredkin Gates with Reconfiguration in NCT Basis

The Fredkin gate is a universal reversible logic gate widely used in designing low-power and quantum devices of various complexities. Recently, the gate's extension was proposed based on its elementary components. It provided variety in functionality and, at the same time, preserved the main features of the gate. This paper offers additional variations of the Fredkin gate, referring to the recent findings. The versatility of logic circuits is particularly beneficial in programmable logic. Therefore, a reconfiguration for the proposed gates has been designed. The device includes 24 newly obtained gates and 8 known extensions. The reconfigurable circuit has a significant advantage in flexibility over the classic gate of 32 extended 3-bit Fredkin gates with a relatively small increase in hardware complexity and delay time. The implementation of the circuit has been tested and verified in Active-HDL, confirming the design's correctness. Reconfigurable devices synthesized in the work make it possible to create programmable fault-tolerant reversible logic circuits effectively.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images

Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
ICDAR 2023 Competition on Recognition of Multi-line Handwritten Mathematical Expressions

Mathematical expressions play an essential role in scientific documents and are critical for describing problems and theories in various fields, such as mathematics and physics. Consequently, the automatic recognition of handwritten mathematical expressions in images has received significant attention. While existing datasets have primarily focused on single-line mathematical expressions, multi-line mathematical expressions also appear frequently in our daily lives and are important in the field of handwritten mathematical expression recognition. Additionally, the structure of multi-line mathematical expressions is more complex, making this task even more challenging. Despite this, no benchmarks or methods for multi-line handwritten mathematical expressions have been explored. To address this issue, we present a new challenge dataset that contains multi-line handwritten mathematical expressions, along with a challenging task: recognition of multi-line handwritten mathematical expressions (MLHMER). The competition was held from January 10, 2023 to March 26, 2023 with 16 valid submissions. In this report, we describe the details of this new dataset, the task, the evaluation protocols, and the summaries of the results.
A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images

We present a new hybrid document layout analysis approach to simultaneously detecting graphical page objects, group text-lines into text regions according to reading order, and recognize the logical roles of text regions from heterogeneous document images. For graphical page object detection, we leverage a state-of-the-art Transformer-based object detection model, namely DINO, as a new graphical page object detector to detect tables, figures, and (displayed) formulas in a top-down manner. Furthermore, we introduce a new bottom-up text region detection model to group text-lines located outside graphical page objects into text regions according to reading order and recognize the logical role of each text region by using both visual and textual features. Experimental results show that our bottom-up text region detection model achieves higher localization and logical role classification accuracy than previous top-down methods. Moreover, in addition to the locations of text regions, our approach can also output the reading order of text-lines in each text region directly. The state-of-the-art results obtained on DocLayNet and PubLayNet demonstrate the effectiveness of our approach.
Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Purpose Using the data set about the restaurants from different countries and their customer's feedback, the purpose of this paper is to address the following issues: in the restaurant industry, how have user behavior and preferences changed during the COVID-19 restrictions period, how did these changes influence the performance of recommendation algorithms and which methods can be proposed to improve the quality of restaurant recommendations in a lockdown scenario. Design/methodology/approach To assess changes in user behavior and preferences, quantitative and qualitative data analysis was performed to assess the changes in user behavior and preferences. The authors compared the situation before and during the COVID-19 restrictions period. To evaluate the performance of restaurant recommendation systems in a non-stationary setting, the authors tested state-of-the-art collaborative filtering algorithms. This study proposes and investigates a filtering-based approach to improve the quality of recommendation algorithms for a lockdown scenario. Findings This study revealed that during the COVID-19 restrictions period, the average rating values and the number of reviews have changed. The experimental study confirmed that: the performance of all state-of-the-art recommender systems for the restaurant industry has significantly degraded during the COVID-19 restrictions period; and the accuracy and the stability of restaurant recommendations in non-stationary settings may be improved using the sliding window and post-filtering methods. Practical implications The authors propose two novel methods: the sliding window and closed restaurants post-filtering method based on the CatBoost classification model. These methods can be applied to classical collaborative recommender algorithms and increase the value of metrics under non-stationary conditions. These methods can be helpful for developers of recommender systems and massive aggregators of restaurants and hotels. Thus, it benefits both the app end-user and business owners because users honestly rate restaurants when they receive good recommendations and do not downgrade because of external factors. Originality/value To the best of the authors’ knowledge, this paper provides the first extensive and multifaceted experimental study of the impact of COVID-19 restrictions on the effectiveness of restaurant recommendation systems in different countries. Two novel methods to tackle restaurant recommendations' performance degradation are proposed and validated.
Security Evaluation of Software by Using Fuzzy-TOPSIS through Quantum Criteria

Abstract: Quantum computer development attracts security experts in software. Software developers need to pay attention to the development of quantum computers in terms of software security. The security of software is at risk with the computation speed of quantum mechanisms in computing. Background: Software security evaluation focuses on the fundamental security features of the soft-ware as well as the quantum enable security alternatives. The rapid development of a number of qubits in quantum computers makes the present security mechanism of software insecure. The soft-ware security evaluation is the most crucial part of surveying, controlling, and administering security in order to further improve the properties of safety. Objective: It's crucial to understand that performing a security assessment early on in the develop-ment process can help you find bugs, vulnerabilities, faults, and attacks. In this quantitative study, the definition and use of the quantum computing security approach in software security will be cov-ered. The cryptographic calculations had to secure our institutions based on computers and networks. Methods: The Fuzzy Technique for Order Preference by Similarity to Ideal Situation (Fuzzy-TOPSIS) to quantitatively assess the rank of the quantum enable security alternatives with security factors. Results: The Quantum Key Distribution [A2], the quantum technique of security approach, has got the top priority and quantum key distribution in GHz state [A6] got the least in the estimation of software security during the era of quantum computer by the neural network method of Fuzzy-TOPSIS. Conclusion: The quantum mechanism of computing makes classical computing insecure. The secu-rity estimation of software makes developers focus on the quantum mechanism of security. The quantum mechanism of quantum key distribution is to make software secure.
Interpretable Feature Extraction for the Numerical Particle System

Particle system analysis is important but costly in solving many physical problems since particles are the basic composition of almost everything, and machine learning is increasingly used in optimization for numerical simulations.The most important and difficult job is to make the particle system understandable by the machine learning model, which usually called feature extraction.In this paper, a novel method and an accurate physical interpretation for feature extraction of cascade defects data, an important particle system in reactor pressure vessel analysis, was proposed.Four strategies were designed to extract features from cascade defects data in which the DAPP shows the best performance.This study shows that feature extraction based on physical information has a positive significance for the analysis of particle systems and provide a theoretical support for improving the physical interpretability of machine learning models on particle systems.
Research on the Application of Mind Map in the Teaching of "E-commerce"

As an efficient thinking tool, The Mind Map has been widely used in many industries. This teaching reform of E-commerce is based on the educational concept of "true education is a heart-to-heart activity, only from the heart can it touch the depths of the soul". During this process, students choose projects independently and apply mind maps in the teaching design of E-commerce. The teaching part adopts "mind map + optional project" and "mind map + word process document". The review part adopts "mind map + ppt Lectures" etc. After teaching, the evaluations of learning effort are made through the observation method and the survey method by the teachers and students. The results of the teaching design integrated with mind map found that this method is conducive to improving the students' notes and contributing to exchanges among group members. this reform achieved a result with better learning effects and performances in competition. Therefore, the mind map is a tool worthy of applying in note management, team collaboration, reporting and summarizing.
Track-to-track fusion with known correlations

This chapter introduces the problem of distributed fusion and presents first solutions. Since tracks from distributed sensors are often correlated, the cross-covariances cannot be ignored. This chapter reveals the nature of correlations in distributed state estimation and target tracking by providing a recursive computation. Optimal methods will be derived, where the full knowledge of the cross-covariances is assumed. This, however, as we will see is infeasible in most applications. Still, it is important to understand the approach of exact fusion with known cross-covariances in order to derive methods, which relax this harsh assumption. The chapter closes with a distributed fusion method, where the cross-covariances are reconstructed in the fusion center by means of a transmission of additional parameters.
Introduction

This book is designed to provide state-of-the-art approaches and algorithms to the interested engineer for data fusion in distributed systems such that a profound understanding of the constraints, assumptions, benefits, and drawbacks are becoming evident to the reader. To this end, the derivation of each method will be given in great detail without omitting auxiliary calculations or intermediate steps. Since this comes at the expense of readability to some degree, having the correct orientation in mind as while navigating the contents of this book is important.
Track-to-track association and distributed detection

In Chapter 8, methods for track-to-track association are addressed. While in the previous chapters, it had been assumed that all sensors observe the same target, this assumption might not be given in multi-target scenarios. If targets are closely spaced and a fusion center receives a set of tracks from each sensor, sophisticated information metrics have to be used in order to avoid miss-associations. Then, the focus will be on track detection and confirmation. The sequential likelihood ratio test provides the means to decide on track existence in a stochastically optimal way, if all measurements are available. In this chapter, it will be shown that the likelihood ratio score can well be reconstructed based on transmitted scalar values from each sensor in a distributed computation. The method is based on the information representation of the distributed Kalman Filter and has the same assumptions. However, we will show that this approach can be well extended to the general case of distributed filtering in ambiguous measurement scenarios, if the unnormalized weights from the data, association step are available. While this leads in general to an approximation of the exact method, it works astonishing well in real-world applications. This is mainly due to the fact that the likelihood ratio may well be estimated in an approximative manner in order to provide the correct decision on track existence.
Attentional factorization machine with review-based user–item interaction for recommendation

In recommender systems, user reviews on items contain rich semantic information, which can express users' preferences and item features. However, existing review-based recommendation methods either use the static word vector model or cannot effectively extract long sequence features in reviews, resulting in the limited ability of user feature expression. Furthermore, the impact of different or useless feature interactions between users and items on recommendation performance is ignored. Therefore, we propose an attentional factorization machine with review-based user-item interaction for recommendation (AFMRUI), which first leverages RoBERTa to obtain the embedding feature of each user/item review, and combines bidirectional gated recurrent units with attention network to highlight more useful information in both user and item reviews. Then we adopt AFM to learn user-item feature interactions to distinguish the importance of different user-item feature interactions and further to obtain more accurate rating prediction, so as to promote recommendation. Finally, we conducted performance evaluation on five real-world datasets. Experimental results on five datasets demonstrated that the proposed AFMRUI outperformed the state-of-the-art review-based methods regarding two commonly used evaluation metrics.