Reviewerly/exHarmony huggingface dataset:
Breast abnormalities classification using deep learning feature extraction

In this study, the main goal is to improve the performance of existing computer diagnostic systems by proposing new processing methods. We use the public CBIS-DDSM dataset for training and validation. The dataset consists of normal screenings with benign tumors and malignant tumors, with all pathologies carefully selected and checked by a radiologist. The data set also includes ROI masks and pathology bounding boxes, as well as labels corresponding to the class of each pathology diagnosis. To achieve better results on the dataset, we transform the data for their more efficient representation using autoencoders in order to obtain features with low intraclass and high interclass variance, and apply LDA to the encoded features to classify pathologies. Methods for automated pathology detection are not considered in this article, since it is mainly focused on the classification task itself. The entire pipeline of the system consists of the following steps: first, feature extraction using pathology segmentation; dividing the data into two clusters; feature transformation using linear discriminant analysis to minimize intra-class variance; finally, the classification of pathologies. The results of this study for the classification of pathologies using various deep learning methods are presented and discussed.
Investigation of Drawbacks of the Software Development Artifacts Reuse Approaches based on Semantic Analysis

Development of reliable software systems requires effective activities for implementing of software development lifecycle processes. One of the activities, which helps to raise an effectiveness of software development lifecycle processes (requirement analysis, software designing, development and testing), is reusing of software development artifacts. Organization of quick and successful reuse of software development artifacts for different problem domains requires: (i) considering peculiarities of functional and non-functional requirements for software; (ii) using of quality (full and non-contradictable domain models), obtained after domain analysis; (iii) involving effective approaches for estimation of reuse risks. Many existing reuse approaches and practices do not guarantee effective reusing of software development artifacts because there are no well-working tools supporting full reuse process including a semantic analysis of requirement specification and processing requests to repository of software components. Paper presents the analysis of implementing software assets reusing approach based on domain engineering. Key feature of reuse is a semantic analysis of software development artifacts [2, 12]. Analysis is organized in the next way: (i) case studies for software artifacts reuse approach based on domain engineering are considered; (ii) steps of reuse approach are described and corresponding peculiarities and drawbacks are outlined. Conclusions contain the results of analysis and recommendations for implementation of approaches for development of software systems in information defense area.
Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization

An algorithm for solving nonconvex smooth optimization problems is proposed, analyzed, and tested. The algorithm is an extension of the trust-region algorithm with contractions and expansions (TRACE) [F. E. Curtis, D. P. Robinson, and M. Samadi, Math. Program., 162 (2017), pp. 1–32]. In particular, the extension allows the algorithm to use inexact solutions of the arising subproblems, which is an important feature for solving large-scale problems. Inexactness is allowed in a manner such that the optimal iteration complexity of for attaining an -approximate first-order stationary point is maintained, while the worst-case complexity in terms of Hessian-vector products may be significantly improved as compared to the original TRACE. Numerical experiments show the benefits of allowing inexact subproblem solutions and that the algorithm compares favorably to state-of-the-art techniques.
Automatic summarization of long text of Chinese patents based on PatBertsum model

Aiming at the problems of traditional text abstract extraction algorithms for processing long text of Chinese patents and unsatisfactory results of long abstract generation, the PatBertSum algorithm is proposed, which enables the algorithm to process long (more than 1500 words) patent text with high efficiency and generate high-quality long (more than 200 words) text summaries. The method is based on the improved BertSum algorithm model, using the new CLTPDS patented text dataset, processing long texts by Head-Tail, transforming Chinese input representations, generating sentence vectors using a pre-trained model, and capturing internal text features and text structure features to extract summaries. Experimentally, this paper demonstrates that the method has improved the recall and F-value of ROUGE by more than 8 percentage points compared with existing methods.
Benchmarking of the European and Ukrainian Practice of Applying a Personalised Approach to Learning

The Report is prepared on the basis of the information received from three European universities – the Project’s partners (including information from the universities’ websites) about the practices of personalised learning (PL); and the information from six Ukrainian universities – the Project’s partners (including surveys of staff and students) to describe the current state/situation, to understand the existing gaps and to define the tasks for the process of implementing the Project. Information about the European universities presents their achievements in the realisation of PL in higher education, including the following directions: PL model; Institutional policies and PL; Infrastructure, environment, tools etc. for PL; QA system and PL; Students and PL; University teachers and PL; Management and PL; Inclusion in the education process. Information on Ukrainian universities has two parts: the 1st – General questions about implementing PL in the university; the 2nd – Questionnaire for surveying staff and students. The Conclusions that are finalising the publication have a practical orientation.
ColDBin: Cold Diffusion for Document Image Binarization

Document images, when captured in real-world settings, either modern or historical, frequently exhibit various forms of degradation such as ink stains, smudges, faded text, and uneven illumination, which can significantly impede the performance of deep learning-based approaches for document processing. In this paper, we propose a novel end-to-end framework for binarization of degraded document images based on cold diffusion. In particular, our approach involves training a diffusion model with the objective of generating a binarized document image directly from a degraded input image. To the best of the authors’ knowledge, this is the first work that investigates diffusion models for the task of document binarization. In order to assess the effectiveness of our approach, we evaluate it on 9 different benchmark datasets for document binarization. The results of our experiments show that our proposed approach outperforms several existing state-of-the-art approaches, including complex approaches utilizing generative adversarial networks (GANs) and variational auto-encoders (VAEs), on 7 of the datasets, while achieving comparable performance on the remaining 2 datasets. Our findings suggest that diffusion models can be an effective tool for document binarization tasks and pave the way for future research on diffusion models for document image enhancement tasks. The implementation code of our framework is publicly available at: https://github.com/saifullah3396/coldbin .
ICDAR 2023 Competition on Born Digital Video Text Question Answering

This paper presents the final results of the ICDAR 2023 Competition on Born Digital Video Text Question Answering (i.e., BDVT-QA) which contains two major task tracks: 1) End-to-End Video Text Spotting, and 2) Video Text Question Answering. BDVT-QA aims to spot texts and answer questions from born-digital videos. The proposed competition introduces a brand new dataset consisting of 1,000 video clips fully annotated with manually-designed question/answer pairs, where the answers are based on the text captions presented in the video clips. A total of 23 final submissions were received for this competition. The top-3 performances of each track are as follows: 1)T1.1 - 57.53%, T1.2 - 53.3%, T1.3 - 52.35%, and 2) T2.1 - 31.2%, T2.2 - 28.84%, T2.3 - 21.19%. We summarize the submitted methods and give a deep analysis. Besides, this paper also includes dataset descriptions, task definitions and evaluation protocols. The dataset and the final ranking of submissions are publicly available on the challenge’s official website: https://tianchi.aliyun.com/specials/promotion/ICDAR_2023_Competition_on_Born_Digital_Video_Text_QA .
What will we leave our future? Data accessibility challenges and solutions

This is a talk about open science, primarily open data, and how it is important that we get organized to meet an uncertain future. The talk was recorded in Bergen, Norway at a conference titled "The Effects of Climate Change on the World's Ocean (ECCWO)" in April of 2023.
Generalized framework for image and video object segmentation using affinity learning and message passing GNNS

Despite significant amount of work reported in the computer vision literature, segmenting images or videos based on multiple cues such as objectness, texture and motion, is still a challenge. This is particularly true when the number of objects to be segmented is not known or there are objects that are not classified in the training data (unknown objects). A possible remedy to this problem is to utiize graph-based clustering techniques such as Correlation Clustering. It is known that using long range affinities (Lifted multicut), makes correlation clustering more accurate than using only adjacent affinities (Multicut). However, the former is computationally expensive and hard to use. In this paper, we introduce a new framework to perform image/motion segmentation using an affinity learning module and a Message Passing Graph Neural Network (MPGNN). The affinity learning module uses a permutation invariant affinity representation to overcome the multi-object problem. The paper shows, both theoretically and empirically, that the proposed MPGNN aggregates higher order information and thereby converts the Lifted Multicut Problem (LMP) to a Multicut Problem (MP), which is easier and faster to solve. Importantly, the proposed method can be generalized to deal with different clustering problems with the same MPGNN architecture. For instance, our method produces competitive results for single image segmentation (on BSDS dataset) as well as unsupervised video object segmentation (on DAVIS17 dataset), by only changing the feature extraction part. In addition, using an ablation study on the proposed MPGNN architecture, we show that the way we update the parameterized affinities directly contributes to the accuracy of the results.
Introduction to the special section on causal inference and agent-based modeling.

This is an introduction to the special section "Causal Inference and Agent-Based Modeling" in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
A high-resolution image dehazing GAN model in icing meteorological environment

In this paper, we propose a high-resolution GAN model for image dehazing in icing meteorological environment, which strictly follows a physics-driven scattering strategy. First of all, the utilization of sub-pixel convolution realizes the model to remove image artifacts and generate high-resolution images. Secondly, we use Patch-GAN in the discriminator to drive the generator to generate a haze-free image by capturing the details and local information of the image. Furthermore, to restore the texture information of the hazy image and reduce color distortion, the model is jointly trained by multiple loss functions. Experiments show the proposed method achieves advanced performance for image dehazing in icing weather environment.
Hearing Cyber-Attacks: A Novel Model for Bridging Network Security Situation and Music

With the continuous evolution of network attacks, the risks brought by the network are also increasing. In order to enable network administrators to monitor the network situation more easily, quickly and efficiently, and discover security risks such as network anomalies or attacks in a timely manner, this paper proposes a solution about network situation auralization. The novelty of this method lies in that it solves the problems, including high professional requirements, frequent human-computer interaction, and poor situation presentation effect in traditional network security situation visualization, and instead displays the network situation in the form of music. The solution provides two models to be chosen from: one is to use the extracted network traffic data as notes to directly generate music and display it; the other consists of the following procedures: The first step is to calculate and classify the acquired MIDI music collection based on emotional color. The second step requires to slice the extracted network traffic data, performing sampling statistics in different intervals, the network situation of this section is mapped to the corresponding MIDI music collection for music display after analysis.
The effect of image resolution on convolutional neural networks in breast ultrasound

<h2>Abstract</h2><h3>Purpose</h3> The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to 512×512 pixels, for diagnosing breast cancer. <h3>Materials and methods</h3> During the period of June 2015 to November 2020, two hospitals were involved in the collection of two-dimensional ultrasound breast images for this retrospective multicenter study. The diagnostic performance of the computer models MobileNet and DenseNet 121 was compared at different resolutions. <h3>Results</h3> The results showed that MobileNet had the best breast cancer diagnosis performance at 320×320pixel REZ and DenseNet121 had the best breast cancer diagnosis performance at 448×448pixel REZ. <h3>Conclusion</h3> Our study reveals a significant correlation between image resolution and breast cancer diagnosis accuracy. Through the comparison of MobileNet and DenseNet121, it is highlighted that lightweight neural networks (LW-CNNs) can achieve model performance similar to or even slightly better than large neural networks models (HW-CNNs) in ultrasound images, and LW-CNNs' prediction time per image is lower.
K.S. Petrov-Vodkin: from Clairvoyance to Philosophy

In the article the author carries out the philosophical analysis of the artistic work of K.S. Petrov- Vodkin. The author examines the process of the artist’s formation and connects its culmination with the use of «spherical perspective» in painting. Revealing the philosophical meaning of the «spherical perspective», the author shows its fundamental difference from the linear perspective and the reverse perspective, and also dispels the popular idea of Petrov-Vodkin being a cosmist. Analyzing the «Bathing of the Red Horse» painting, the author shows that Petrov-Vodkin was a revolutionary not in politics, but in painting, offering his theory of color and advocating the creation of the Russian school of painting. Comparing the «Bathing of the Red Horse» with the «Black Square» by K. Malevich, on the one hand, and with the Russian icon, on the other hand, the author refers the canvas to the archeoavantgarde. The image of A.S. Pushkin constitutes the quintessence of the artist’s inner path for Petrov-Vodkin. When referring to it, the artist in Petrov-Vodkin gives way to the thinker. The idea of the intrinsic value of painting is replaced by the idea of the intrinsic value of Russian culture.
The Ideological Zigzags of the Journalist A.V. Amfi teatrov

The article deals with the unknown pages in the biography of Alexander Valentinovich Amfi teatrov, a popular publicist at the turn of the 19th and 20th centuries. The article analyzes the creative periods of his work in mass periodicals of diametrically opposite ideological orientation. His views, including those on the editorial policy of periodicals, and political aspirations are revealed. The article is based on the materials of previously unpublished documents from the Manuscript Department of the Pushkin House and the archive of the Amphitheaters in the Lilly Library in Bloomington, USA.
ON THE TECHNOLOGY OF IMPLEMENTING AN INTEGRATIVE APPROACH IN AN INNOVATIVE SCHOOL

The scholastic year 2022/2023 includes the final stage of the joint initiative of 125. Secondary School – Sofia and the Institute of Mathematics and Informatics at the BAS for the pilot implementation of an educational model for integrated learning in an innovative school. The technological scheme follows Altshuler’s TRIZ methodology. The article briefly describes the moments of the individual stages – specific problems and their solutions at the school level are presented. The results of the conducted research are analyzed. Identified are measures that should be taken by the teaching and management team of the school in order to move from the pilot implementation to regular pedagogical practice. Examples of project-oriented initiatives presented by students at school and regional forums are provided.
Virtual reality-based virtual lab product development in developing students' spatial abilities using the van hiele theory approach

In learning during the current pandemic, there are many problems, one of which students cannot meet face to face on campus, one of the learning problems that often occurs is learning geometry in universities, geometry is a scourge lesson before the pandemic both at Universitas PGRI Semarang and other private universities so far, students are required to use a compass and ruler in making flat shapes and building spaces so they have to face to face with the lecturer, but now it can't because it is still online, therefore it is necessary to make learning media that aims to help students sketch flat shapes and build spaces virtually with practice making it online with a van hiele theory approach, so that the resulting product is truly capable of elaborating between technology, the right learning approach and the content of lecture materials on an ongoing basis, then a virtual media lab for geometry courses is created that is able to display material virtually and practice making material designs virtually with the van hiele theory approach, the research method used using the ADDIE R & D model ( Analysis, Design, Develop, Implementation and Evaluation), the result of this study is a virtual geometry lab application with van Hiele theory that is valid according to material and media experts with an average score of 93 and 95 can be used as an online geometry learning media solution.at Universitas PGRI Semarang, for student responses below 40% very agree, 59% agree and 1% disagree using virtual lab geometry it is was able to improve students' spatial abilities, Results The effectiveness of the media after being tested in the experimental class obtained an N-gain value of 0.39 which was better than the control class which only obtained an N-gain value of 0.02, then a virtual media lab for geometry courses is created that is able to display material virtually and practice making material designs virtually with a van hiele theory approach.The five steps in van Hile's theory have been well implemented consisting of (1) Visualization level, (2) Analysis level, (3) Abstraction level, (4) Formal deduction level, (5) Rigor level.
PROJECT FORM OF WORK FOR DESIGN AND DEVELOPMENT OF ELECTRONIC GAMES FOR STUDENTS AS A WEB-BASED SERVICE

The development of information technologies helps to build modern games that support learning. Computer games are typically executable programs or web-based applications in which the software platform runs the game content. The characteristics of serious computer games that make them effective for educational purposes, such as being motivating, engaging and fun, are often associated with active learning. Active learner participation is one of the inherent strengths of computer games. Serious computer games provide an environment for active learning. Computer games are integrated into learning in the form of an innovative educational paradigm. This is the so-called game-based learning, where computer games are a medium for delivering learning content.
Malicious Web Links Detection - A Comparative Analysis of Machine Learning Algorithms

"One of the most challenging categories of threats circulating into the online world is social engineering, with malicious web links, fake news, clickbait, and other tactics. Malware URLs are extremely dangerous because they represent the main propagating vector for web malware. Malicious web links detection is a challenging task because the detection mechanism should not influence the consumers’ online experience. The proposed solutions must be sensitive enough, and fast enough to perform the detection mechanism before the user accesses the link and downloads its content. Our paper proposes three goals. The main purpose of this paper is to refine a methodology for malicious web links detection that may be used to experiment with machine learning algorithms. Moreover, we propose to use this methodology for training and comparing several machine learning algorithms such as Random Forest, Decision Tree, K-Nearest Neighbor. The results are compared, justified, and placed in the malicious web links literature. In addition, we propose to identify the most relevant features and draw some observations about them. 2010 Mathematics Subject Classification. 68T99, 68U99. 1998 CR Categories and Descriptors. 68T99 [Artificial Intelligence]: Applications and Expert Systems – Experiments for Malicious Web Links Detection; 68U99 [Management of Computing and Information Systems]: Security and Protection – Malicious Web Links. Key words and phrases. malicious web links detection, web-malware, artificial intelligence."
Inadequacies of SDRT and Suggested Solutions

Whereas SDRT is more effective and advanced than other theories to handle abstract entity anaphora, it still has some inadequacies which debilitate its application.
MODEL ASSESSMENT OF THE NATIONAL HIGHER SCHOOL FUNCTIONING

The main functionality of modern higher education is reduced to educational and scientific activities. Moreover, the latter is more aimed at improving the scientific and methodological level of teaching educational disciplines than at providing scientific and consulting services to external economic agents. The article builds an original model of the internal dynamics of a modern university as a typical element of a national higher school, eloquently reflecting the noted functions of its activities, on the basis of which forecast estimates of its state for the foreseeable future are made. Based on the results of a systematic study, it was concluded that the national higher school is in a state of bifurcation. At the very beginning of market reforms, wanting to immediately integrate into the world educational space, it was taken out of the highly productive stable state achieved in the Soviet period, and has not yet been brought anywhere. As the analysis showed, both archetypes (feedback loops) that make up the model are reinforcing in nature and cannot provide a controlled drift of the system to the desired state. The article developed recommendations, the implementation of which will allow to systematize (restore consistency) the national higher school in a new highly productive state in the near future, and ensure its further development in accordance with the requirements of the current moment.
Appendix: Vector calculus

Subject Astronomy and Astrophysics Mathematical and Statistical Physics Computational Physics Collection: Oxford Scholarship Online