Reviewerly/exHarmony huggingface dataset:
A Transfer Learning for Line-Based Portrait Sketch

This paper presents a transfer learning-based framework that produces line-based portrait sketch images from portraits. The proposed framework produces sketch images using a GAN architecture, which is trained through a pseudo-sketch image dataset. The pseudo-sketch image dataset is constructed from a single artist-created portrait sketch using a style transfer model with a series of postprocessing schemes. The proposed framework successfully produces portrait sketch images for portraits of various poses, expressions and illuminations. The excellence of the proposed model is proved by comparing the produced results with those from the existing works.
An efficient hybrid approach for medical images enhancement

Medical images have various critical usages in the field of medical science and healthcare engineering. These images contain information about many severe diseases. Health professionals identify various diseases by observing the medical images. Quality of medical images directly affects the accuracy of detection and diagnosis of various diseases. Therefore, quality of images must be as good as possible. Different approaches are existing today for enhancement of medical images, but quality of images is not good. In this literature, we have proposed a novel approach that uses principal component analysis (PCA), multi-scale switching morphological operator (MSMO) and contrast limited adaptive histogram equalization (CLAHE) methods in a unique sequence for this purpose. We have conducted exhaustive experiments on large number of images of various modalities such as MRI, ultrasound, and retina. Obtained results demonstrate that quality of medical images processed by proposed approach has significantly improved and better than other existing methods of this field.
Digital Educational Environment and Online Learning Format Through the Lens of Students: Pros and Cons

The modern transformation of all spheres of life is associated with rapid technological and digital development. The educational sphere, focusing on external demands, systematically introduces technological and digital solutions into the processes of pedagogical systems functioning, focusing on the expansion of the educational space, on increasing the availability of educational services. In order to create a comfortable and effective digital environment in universities, it is important to take into account not only the level of development of digital competencies of students and teachers, but also their basic needs and requirements in this area. We set a goal to identify the most successful digital solutions used at this stage in university education from the students’ perspective and to consider the factors inhibiting these processes. To solve the problems, we used methods of theoretical analysis and empirical processing of research data. The main method was a survey of students using the questionnaires “Digital learning environment and online learning format” and “Quality of digital educational resources and organizational aspects of e-learning” developed by the authors. More than a thousand students from Russian universities took part in the survey. The results of the survey showed what means of communication in online learning are considered by students to be the most effective, what means of increasing productivity and what means of organization and support in online learning are most useful to them. The platforms and learning systems were considered in detail to understand the important characteristics of these services for students. The analysis highlighted the main areas in the digital transformation of the educational environment, which should be targeted for their development and improvement.
Recovering and Reusing Scientific Data

Legacy scientific data relevant to current and future scientific research exist scattered across memory institutions. This data remains unexplored and often faces the potential to be destroyed. This poster focuses on data rescue initiatives within the sciences, exploring how these efforts suggest the need for greater focus on scientific thinking and collaboration between the sciences and LIS.&#x0D; This research explores the efforts of digital curators and scientists to recover library and archival records housed on obsolete formats (both physical and digital) and data within unpublished documents. The ongoing "Recovering and Reusing Archival Data for Science" project (RRAD-S) draws on semi-structured interviews with scientists from atmospheric sciences to ornithology and data curators, including metadata librarians and archivists. These interviews document the catalysts, priorities, and challenges of participants' data reuse/recovery efforts. This poster reports on initial findings regarding how scientific data rescue entails a degree of digital curation expertise and planning on the part of scientific research teams, meaning that scientists thinking proactively about data preservation or reuse might collaborate with information professionals to develop data conservation plans.&#x0D; Historically, LIS education dedicates limited attention to understanding scientific thinking, which usually emphasizes generating new data over reuse. This poster asserts that LIS education can support long-term scientific scholarship by consciously educating students to participate in scientific data preservation initiatives. Additionally, educators can utilize scientific data practices to inform LIS pedagogy by prioritizing and centering proactive data creation within scientific endeavors.
Implementasi Model Pembelajaran Cerdas Berbahasa Indonesia Fonik (CBI FONIK) dalam Menstimulus Kemampuan Menyimak Anak Usia Dini

This study aims to describe the implementation of the Indonesian Phonics Intelligent Learning Model (CBI FONIK) in Stimulating Early Childhood Listening Ability in Pelangi Alam Kindergarten, Ponorogo. This study used a qualitative descriptive method. Observation, interview, and documentation were used to collect the data. This study used observation to see the application of listening skills using the Indonesian Phonics Smart learning model (CBI FONIK). Interviews were addressed to the principal, teachers, and students in group A. The child's achievement card served as documentation. Data analysis in this study used the Miles and Huberman model. It was carried out using the stages of data reduction, data presentation, and verification/drawing conclusions. The results showed that the Smart Indonesian Phonics (CBI FONIK) learning model used the pre-phonic level with the single-story poster and song technique. Its application is not only done in the classroom but mainly in the school environment (nature). Most children can listen well and can say any words they hear.
EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Objectives:To propose an algorithm to balance resource utilization and revenue generation in the cloud environment.Methods: This study proposes the Entropy-Based TOPSIS algorithm for task scheduling in IaaS Clouds (EBTASIC) to balance resource utilization and revenue generation using the objective-based Entropy Weighting Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).Findings/Novelty: Various performance evaluation factors are calculated using EBTASIC and compared with baseline algorithms First Come First Serve (FCFS) and Earliest Deadline First (EDF) algorithms over 12750 lease requests with hard deadlines.The actual response time of EBTASIC is 37.61 percent faster than FCFS and 47.95 percent faster than EDF.Total time spent on lease execution by EBTASIC is reduced by 3.43 percent when compared to FCFS and 3.99 percent when compared to EDF.Turnaround time of EBTASIC is lowered by 21.14 percent when compared to FCFS and 28.81 percent when compared to EDF.EBTASIC throughput is enhanced by 40.80 percent over FCFS and 54.3 percent over EDF.The average response time of EBTASIC is shortened by 9.38 percent compared to FCFS and 14.81 percent compared to EDF.Resource utilization of the proposed algorithm EBTASIC is enhanced by 25.54 percent over FCFS and 6.79 percent over the EDF algorithm.
Invisible but Understandable: In Search of the Sweet Spot between Technology Invisibility and Transparency in Smart Spaces and Beyond

Smart technology is already present in many areas of everyday life. People rely on algorithms in crucial life domains such as finance and healthcare, and the smart car promises a more relaxed driving experience—all the while, the technology recedes further into the background. The smarter the technology, the more intransparent it tends to become. Users no longer understand how the technology works, what its limits are, and what consequences regarding autonomy and privacy emerge. Both extremes, total invisibility and total transparency, come with specific challenges and do not form reasonable design goals. This research explores the potential tension between smart and invisible versus transparent and understandable technology. We discuss related theories from the fields of explainable AI (XAI) as well as trust psychology, and then introduce transparency in smart spaces as a special field of application. A case study explores specific challenges and design approaches through the example of a so-called room intelligence (RI), i.e., a special kind of smart living room. We conclude with research perspectives for more general design approaches and implications for future research.
Formation of Professionalism of Future Social Teachers in the System of Training in Russia and Kazakhstan (Comparative Aspect)

Social pedagogues are specialists representing the nurturant profession that requires high professionalism and is still in great demand. The modern society needs highly qualified social pedagogues to address the issues of younger generation’s socialization in today's complex socio-economic and political context. Future social pedagogues improve their professional skills by mastering the profession through gaining the awareness of their professional level that motivates the career development. The excellence and necessary competences are incepted at the stage of professional education. The authors of this article investigate the features of future social pedagogues’ professional formation in the training systems of Russian and Kazakhstan. Research methods are analysis, comparison, generalization, synthesis, historical and pedagogical reconstruction. The article presents the results of a comparative analytical review of professional formation specifics in the national education systems of Russia and Kazakhstan. The comparison of professional formation specifics demonstrated the common problems of social pedagogues’ training determined by the shared historical past, the similarity of socio-economic factors in the development of the education systems. The identified differences imply the analysis of the ways to improve the training of future social pedagogues. The analysis shows that the professional success of social pedagogues is defined by focusing on social competences and social responsibility development as part of the training process. According to the authors, specialists' training based on the methodological principles of axiological approach contributes to the formation of future social pedagogues’ conscious attitude both to their education and their professional development.
Shape Robust Text Detection with Progressive Scale Expansion Network

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. In this paper, we decouple images and texts for multimodal contrastive learning thus scaling the usable training data in a combinatorial magnitude with low cost. We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning. We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval. Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using around 200K data). Our code is available at https://github.com/RyanWangZf/MedCLIP.
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

Abstract The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even more so. It normally requires many trial-and-error training attempts to force a stable training and reach a reasonable fidelity. Significant tuning work has to be done to achieve the accuracy required by physics analyses. This work uses the physics-agnostic and high-performance-computer-friendly hyperparameter optimization tool HYPPO to optimize and examine the sensitivities of the hyperparameters of a GAN for two independent HEP datasets. This work provides the first insights into efficiently tuning GANs for Large Hadron Collider data. We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities. We also provide guidelines for how to go about GAN architecture tuning using the analysis tools in HYPPO.
Methodical Preparation of the Future Physics Teachers to the Formation of Schoolchildren Science literacy

Based on the analysis of the research, the author identifies and substantiates the ways of preparing students as future physics teachers for the formation of natural science literacy among schoolchildren. The conclusion is made about the special methodological preparation of the future physics teacher for the formation of natural science literacy among schoolchildren. It is proposed to carry out such training as a system, taking into account the level implementation (basic, advanced, advanced levels of training). The option of training carried out at the Transbaikal State University in accordance with the curriculum in the direction of training 44.03.05 Pedagogical education (with two training profiles), profile “Informatics and Physics” is being considered. The basic level of training can be implemented within the framework of the normative discipline “Methods of training and education (Physics)”. An increased level of training can be carried out when studying the discipline “Modern technologies for teaching Physics”. An in-depth level of training can be implemented within the framework of elective disciplines (“Modern Physics Lesson”, “Selected Chapters of the Methods of Teaching Physics”), when writing term papers and final qualification papers on the methodology of teaching Physics. The article provides specific examples illustrating the implementation of the developed system of methodological training. Particular attention is paid to the basic and advanced levels of complexity methodological tasks within the framework of the methodological disciplines under consideration. The special role of methods and forms of teaching students is emphasized, assuming their active participation in the discussion of problems related to the formation of natural science literacy among schoolchildren when studying physics at school. Examples of special tasks of various levels of complexity on the formation of natural science literacy among schoolchildren when teaching Physics in basic school are given and analyzed. These tasks have been developed by a student under the guidance of the author of the article as a part of the final qualifying work. The theoretical basis of the study is a systematic approach. The author views the prospects for further research in identifying and substantiating approaches to the formation of competencies of a future physics teacher in conjunction with natural science literacy and its structural elements formed in schoolchildren when teaching physics.
Pengelolaan Perpustakaan sebagai Alternatif Penguatan Budaya Literasi di SD Muhammadiyah Sudagaran Wonosobo

The library is a means of supporting teaching and learning activities in schools because it stores library materials so that there needs to be good management so that it can be used as an alternative to strengthening literacy culture. This study aims to describe library management and the form of literacy activities that exist in the library of SD Muhammadiyah Sudagaran Wonosobo. This research is a qualitative research with the subject of the principal, librarian, teachers and students. The object of the research is library management and literacy activities in the library. Data collection techniques are indepth interviews, observation and documentation. Data analysis techniques are data collection, data reduction, data presentation, and drawing conclusions. The data validity test technique is source triangulation and technical triangulation. The results of the research on library management as an alternative to strengthening literacy culture in SD Muhammadiyah Sudagaran Wonosobo have been managed properly and optimally. Library management is carried out by planning, organizing, implementing and monitoring. The literacy activities in the library are reading, writing, scientific and mathematical literacy.
The Role of Information Overload on Consumers’ Online Shopping Behavior

In today's market, there exists a variety of products and brands for creating various items based on the needs and demands of customers. As technology advances, more companies are emerging, and it is evident that multiple businesses have developed products that are comparable to one another. To expose the products to the market and attract customers, each of these businesses adopts unique description techniques. This sometimes results in information overload. The study sought to investigate the role of information overload on consumers’ online shopping behavior. Based on reviews of relevant theories and principles of the consumer decision-making process, questionnaires were used to gather data from 201 respondents. The findings revealed that as a textual description of product attributes increases, so do the perceptions of information overload, and customers become overwhelmed while trying to process the information. The findings indicated that information overload significantly causes consumers to experience stress, frustration, and perceived risk. Following the study findings, it recommended that managers realize that excessive information can potentially decrease consumers' ability to analyze attributes of products and to compare alternatives; hence, they should analyze the scope to which the amount of provided information can be processed by their target consumers without difficulty.
Fears of Backlash Underlie Gendered Participation in Question-and-Answer Sessions

Gender disparities in participation at conferences is a well-studied phenomenon, and seem to be propelled by men being more likely to initiate questions. An unexplored explanation for why this gap may occur is that women may be more likely than men to fear backlash for their participation. Across four studies, we tested fears of backlash as a mechanism to explain gender gaps in Q&amp;amp;A session participation. Fears of backlash explained the variance between gender and question-asking intentions in a hypothetical scenario. Women’s reduced fears of backlash were attenuated in friendly as compared to typical audience, but friendly audiences also emboldened men to take up more space. There was not an effect of Q&amp;amp;A formats on gendered participation. When considering why women might be less
Image Segmentation for Human Skin Detection

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article&amp;rsquo;s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.
Sentiment and Emotional Analysis of Risk Perception in the Herculaneum Archaeological Park during COVID-19 Pandemic

This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park in Italy during the period of the COVID-19 pandemic. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provided input to the risk management team with which they could respond immediately mitigating any apparent risk and reducing the perceived risk. A two-stage approach was adopted to prune a massively large dataset from Twitter. In the first phase, a social network analysis and visualisation tool NodeXL was used to determine the most recurrent words, which was achieved using polarity. This resulted in a suitable subset. In the second phase, the subset was subjected to sentiment and emotion mapping by survey participants. This led to a hybrid approach of using automation for pruning datasets from social media and using a human approach to sentiment and emotion analysis. Whilst suffering from COVID-19, equally, people suffered due to loneliness from isolation dictated by the World Health Organisation. The work revealed that despite such conditions, people's sentiments demonstrated a positive effect from the online discussions on the Herculaneum site.