Reviewerly/exHarmony huggingface dataset:
The European Consortium of Liberal Arts and Sciences’ Manifesto for the Reform of Undergraduate Education in Europe

The conversation after the pandemic should concern the role of higher education in dealing with the social, political and environmental challenges facing Europe and the world. Universities are confronted not only by the challenges of 21th century—and AI in the clasroom seems like most urgent—but also are inflexible to respond to changes due to its inherent conservative nature and bureaucratic inertia. The question thus stands: should the ‘ivory tower’, faced with the problems of the outside world, turn itself into a fortress, oblivious to the peril outside, as has often occurred in the past? Or should it open up and actively generate analyses and instigate action? In this respect, it is important to reform the structure, function and differentiation of universities’ undergraduate and graduate programs. Unfortunately, European politicians and administrators have so far failed to reflect or act upon the unique function and potential of the bachelor’s degree, which represents a critical aspect of higher education. Currently, undergraduate study in Europe mostly comprises narrowly focused programs, taught in large classes via the lecture-exam model. The world’s top universities offer bachelor’s degrees that focus on general academic disciplines and intellectual or soft skills, with students choosing their major during their studies. Narrowly focused specialization and vocational professional education should remain the domain of postgraduate and doctoral programs. This chapter critically reviews liberal arts education in Europe and discusses the ECOLAS Manifesto, arguing for the importance of embracing the full potential of undergraduate education.
Exploring the Adaptation of Mobile GUI to Human Motion Status

The changing of display content of a GUI has an intuitive impact on readability and usability. One of the examples is when people look at the screen of a wearable device (e.g., smartphone, smartwatch) while moving, and they are particularly sensitive to the size of the display elements. In this work, we provide an initial exploration of adaptive interface for different human motion states. Specifically, we conducted an empirical study to explore the relationship between 4 mobile GUI element (text, image, icon, and hybrid) sizes and 4 different human motion states (sitting, standing, walking, and jogging), based on the select-and-click performances for 15 subjects. Our findings show the possibility of utilizing these data for GUI adaptive adjustment in different human motion states for improving selecting efficiency.
MILC 2023: 3rd Workshop on Intelligent Music Interfaces for Listening and Creation

The third edition of the Workshop on Intelligent Music Interfaces for Listening and Creation (MILC), held in collaboration with the 28th International Conference on Intelligent User Interfaces (IUI) features a half-day program addressing recent and future developments in human-centered music technology. The presented papers cover recommendation in sound libraries, the use of generative systems for composition in Digital Audio Workstations (DAWs), tools for richer means of interaction with music streaming platforms, and music personalization for Cochlear implant users.
A study on automatic face parts extraction for evaluating face similarity of Haniwa based on 3D measured point clouds

Haniwa were created for ritual use during the Kofun period and were buried with the dead as funerary offerings. Archaeologists visually observe haniwa and classify them based on archaeological findings, such as the characteristics they possess. Since observation is a subjective evaluation, an objective evaluation method is necessary for authenticity. For objective evaluation, Lu .et al.<sup>1</sup> presented a previous research by using 3D point clouds of Haniwa. Unfortunately, Lu .et al.<sup>1</sup> includes manual operation to extract target point sets. Therefore, this paper describes a method to automate the process of extracting only the Haniwa’s face from point cloud data of the whole Haniwa body, as shown in Figure 1, which was used in the previous study.<sup>1</sup>
Image denoising based on the noise prediction model using smooth patch and sparse domain priors

It is inevitable that an image is suffered from the noise. There are several existing image denoising algorithms. Some image denoising methods, including the Wiener filter, the maximum likelihood method, and their adaptive forms, can be implemented efficiently by the linear convolution and the two-dimensional discrete Fourier transform (2D DFT). However, their performance is highly affected by the distribution of the noise. For different values of the peak signal-to-noise ratio (PSNR) and different style of the noise, the optimal parameters for image denoising are also different. Therefore, noise estimation plays a critical role in image denoising. In this work, an effective way for noise estimation is proposed. The proposed algorithm is based on the following two priors. We think that, for an image without noise, there should be some patch that has the mean gradient near to zero. We call it the smooth patch prior. Moreover, in the frequency domain, there should be some bands that has the average energy near to zero. We call it the zero-band prior. Therefore, the noise can be estimated from the patch with a relatively smaller average gradient and its sparse part in the 2D DFT domain. With the proposed algorithm, the noise distribution can be well predicted and the optimal parameter of the image denoising filter can be determined automatically.
AGG: An Automated Genogram Generator by Discovering Information in Clinical Texts

In Deep Learning, the use of pre-trained language models such as BERT has exploded within NLP for model fine-tuning due to the top performance results. We showcase AGG, an Automated Genogram Generator, capable of extracting relevant family data in clinical texts to generate genograms, which are hierarchical relationship diagrams of a family with special emphasis in the family health. The contributions are: (i) automated real-time genograms generation by family history data discovery in texts through language models fine-tuning; (ii) real-time customization of the visual representation of the genograms; and (iii) web service with user-friendly interactive UI. AGG allows the easy genogram creation to users without expertise and saves time in physicians work.
About the competence-based approach in modernization of the masters programme "computer engineering"

The article deals with the issues related to the modernization of the educational programme "Computer Engineering" for the IT-profile graduates using competence-based approach. The problems of specialists training, ways of modernizing the educational programme are considered. Particular attention is paid to the competence-based approach and the specifics of teaching at Masters level according to the new curriculum. The transformational experience of the higher education system of the Republic of Belarus is analysed from the point of view of its adaptation to the European Higher Education Area standards and traditions as well as from the point of view improving its efficiency in the conditions of dynamic labour market. Special emphasis has been made on IT-specialist social competences development as a condition for both professional self-realisation and more successful professional socialisation of future specialists.
The Media and the Development of Interpersonal Communication Competences Using the Example of "Pokemon Go!" Game and Its Possibilities

This text focuses on an attempt to indicate which communication competences can be developed thanks to the user's involvement in conducting games as part of the "Pokemon Go!" mobile application and what actions taken by the users have the greatest impact on the development of the interpersonal communication. This was done by analysing the possibilities offered by the game, and the results were supplemented by the qualitative data collected through mini focus group interviews (mini-FGI). The participants of the study were asked, among others, about how they communicate with other players, what form of communication with other players they care more about - online or in the real world, what communication skills they gain while playing "Pokemon Go!". The opinion of the users is of great importance because it can be the basis for starting research on the development of communication competences through mobile games using AR technology. On the basis of the results obtained, firstly, the areas with communication potential were identified, and secondly, the areas of interpersonal communication noted and described in qualitative terms by the players were detailed.
Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease

Corn is the second largest commodity in Indonesia after rice. In Indonesia, East Java is the largest corn producer. The first symptom of the disease in corn plants is marked by small brownish oval spots which are usually caused by the fungus Helminthoporium maydis, if left unchecked, farmers can suffer losses due to crop failure. Therefore it is important to provide treatment for diseases in corn plants as early as possible so that diseases in corn plants do not spread to other plants. In this study, the dataset used was taken from the kaggle website entitled Corn or Maize Leaf Disease Dataset. This dataset has 4 classifications: Blight, Common Rust, Grey leaf spot, and Healthy. This study uses the Convolutional Neural Network method with 2 different models, namely the EfficientNet-B0 and ResNet-50 models. The architectures used are the dense layer, the dropout layer, and the GlobalAveragePooling layer with a dataset sharing ratio of 70% which is training data and 30% is validation data. After testing the two proposed scenarios, the accuracy results obtained in the test model scenario 1, namely EfficientNet- B0 is 94% and for the second test model scenario, namely ResNet-50, the accuracy is 93%.
ChainHire: A Privacy-Preserving and Transparent Job Search Portal Using an Enterprise-Level Permissioned Blockchain Framework

Finding a suitable employment position is a crucial and difficult task. Nowadays, Job seekers may instantly apply for multiple job openings using job search platforms. Personal information has to be exchanged as part of every job application. Sharing personal information across unsafe channels, however, increases the risk of data theft. Apart from that, attempts to tamper with hiring data are unfortunately common. Previously, traditional databases were only used for job search platforms, which do not provide sufficient transparency or protection against tampering. The incorporation of blockchain technology in job search portals can address these issues and provide a more transparent and secure process. Our proposed solution uses Hyperledger Fabric (HLF), an open-source blockchain frame-work, to create a secure and transparent job search platform. In this platform, both recruiters and job seekers can participate in a permissioned network, where smart contracts are used to ensure a transparent and privacy-friendly hiring process. To demonstrate the feasibility of this solution, we have implemented and deployed a prototype using Amazon Managed Blockchain Service. To understand the optimal configuration for our system, we tested the performance of our network using the Hyperledger Caliper tool. Although further research is necessary to fully understand the capabilities and limitations of using blockchain technology in job search portals.
Research on Digital Curriculum Collaborative Filtering Technology based on Feature Group

With the speedy increase of the amount of data shared on the Internet, it is more important to use human feedback when finding information. Although collaborative filtering is a talented method to salvage useful information, unluckily, there is a difficulty in present collaborative filtering. Since a single system is unlikely to serve every type of object with the best performance, or at least it may be too complex in implementation, it is more practical to use various systems, each of which handles a detailed goal. Personalized recommendation system uses knowledge discovery technology to provide users with accurate and reasonable information to win customers. The recommendation routine based on the characteristics of user groups is a mode that has achieved certain success in both research and practice. However, the complexity of this algorithm increases rapidly with the growth of the number of customers. Therefore, in practical applications, facing tens of thousands of users, the service system has to bear a heavy load of computing, which indications to the decline of recommendation efficiency. This paper proposes a feature based recommendation algorithm to solve the problem of poor scalability faced by user based recommendation algorithm. The use of feature based recommendation algorithm can improve the recommendation efficiency and achieve or exceed the recommendation performance of user based recommendation algorithm.
ACTUAL ISSUES OF THE DEVELOPMENT OF SCIENCE AND ENSURING THE QUALITY OF EDUCATION

Attempts to use nanomaterials, including nanoparticles, to increase the productivity of agricultural plants, resistance to stress factors, as mineral fertilizers, are giving real results.Nanoparticles can play an important role in plant growth stimulation and protection.Experiments show that nanoparticles can migrate from the air, soil and water to the plants and affect a number of physiological processes.Recent studies have shown that attempts to use nanoparticles as mineral fertilizers to increase the yield, development, resistance to stress factors and diseases of agricultural plants are yielding real results.In the presented article, the leaves of seedlings of wheat plants grown in field conditions were used to evaluate various physiological and biochemical characteristics.The middle part of the leaf was used to study the chlorophyll content and enzyme activity.The effect of nanoparticles on seed germination, growth stimulation, morphophysiological and biochemical, metabolic processes has been studied in a large number of scientific studies.The effect of nanoparticles can be positive or negative depending on the type of plant and their composition and concentration, as well as application conditions.In our experiments, the effect of Fe 2 O 3 , ZnO and TiO 2 nanoparticles on the chlorophyll content of wheat leaves was investigated.Thus, in our experiments, the amount of chlorophyll a, b and carotenoids in the leaves of wheat seedlings was determined spectrally and the effect of nanoparticles was studied comparatively.It was determined that the values of chlorophyll a and b, as well as carotenoids at 440 nm, 665 nm and 645 nm wavelengths in wheat sprouts exposed to TiO 2 nanoparticles
The Impact of Organizational Justice on Organizational Trust: Examining the Moderating Role of Job Security: A Study at the Jordanian Insurance Companies

The study aimed to examine the impact of Organizational justice in terms (distributive, procedural, and interactional justice) on organizational trust: the moderating role of job security at Jordanian Insurance Companies. The quantitative analytical descriptive method was used. 19 Jordanian Insurance companies participated in the study out of 22 companies operating in the Jordanian market. The sampling unit consisted of middle and first-line management, working at all the companies’ branches, a random sample of 360 was selected. For collecting the needed information and data the questionnaire was used, 360 questionnaires were distributed, and 319 were valid for statistical analysis. The main findings of the study revealed that there is a statistically significant impact of organizational justice on organizational trust, and job security as a moderator improved this impact by 0.077. The study recommended giving more attention to procedural justice and to enhance transparency and other corporate governance elements. The study recommended also fostering employees’ job security through engaging in human resources retaining strategies.
Optimized pointwise convolution operation by Ghost blocks

&lt;abstract&gt; &lt;p&gt;In the lightweight convolutional neural network model, the pointwise convolutional structure occupies most of the parameters and computation amount of the model. Therefore, improving the pointwise convolution structure is the best choice to optimize the lightweight model. Aiming at the problem that the pointwise convolution in MobileNetV1 and MobileNetV2 consumes too many computation resources, we designed the novel Ghost-PE and Ghost-PC blocks. First, in order to optimize the channel expanded pointwise convolution with the number of input channels less than the output, Ghost-PE makes full use of the feature maps generated by main convolution of the Ghost module, and adds global average pooling and depth convolution operation to enhance the information of feature maps generated through cheap convolution. Second, in order to optimize the channel compressed pointwise convolution with the number of input channels more than the output, Ghost-PC adjusts the Ghost-PE block to make full use of the features generated by cheap convolution to enhance the feature channel information. Finally, we optimized MobileNetV1 and MobileNetV2 models by Ghost-PC and Ghost-PE blocks, and then tested on Food-101, CIFAR and Mini-ImageNet datasets. Compared with other methods, the experimental results show that Ghost-PE and Ghost-PC still maintain a relatively high accuracy in the case of a small number of parameters.&lt;/p&gt; &lt;/abstract&gt;
Research on Leukocyte Recognition Method Based on Convolutional Neural Network

There are various types of leukocytes in blood cells, and various types of leukocytes play an important role in fighting fungal, bacterial, viral and other infections in the human body, so studying and classifying leukocyte types is an important task for medical researchers. Based on the low efficiency and accuracy of traditional methods for detecting leukocytes, an efficient processing of leukocyte images using convolutional neural networks is proposed, which can perform tasks such as classification and localization of leukocytes. The proposed method and previous detection networks were also compared in experiments, and the experimental results proved that the proposed Yolov5 convolutional neural network has the highest detection speed and accuracy, with mAP reaching 86.47%.
PEDAGOGICAL AGENCY IN DIGITALISED EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 1375-1384ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0397Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
PERCEPTION OF DIGITAL TEACHER COMPETENCIES AMONG MEXICAN STUDENTS IN TEACHER TRAINING INSTITUTIONS

Appears in: INTED2023 Proceedings Publication year: 2023Page: 7611 (abstract only)ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.2079Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
DESIGNING A BIOCHEMICAL ESCAPE ROOM FOR UNDERGRADUATE STUDENTS

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 8140-8146ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.2206Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
THE IMPACT OF DISTANCE-BASED EDUCATION DURING COVID -19 ON MATHEMATICS EDUCATION AT THE UNIVERSITY OF ŽILINA

THE IMPACT OF DISTANCE-BASED EDUCATION DURING COVID -19 ON MATHEMATICS EDUCATION AT THE UNIVERSITY OF ŽILINA
EDUCATION DOCTORAL PROGRAMS THAT TAKE AN EXPERIENTIAL AND INTEGRATIVE APPROACH HAVE THE CAPACITY TO CONTRIBUTE TO THE KNOWLEDGE ECONOMY

EDUCATION DOCTORAL PROGRAMS THAT TAKE AN EXPERIENTIAL AND INTEGRATIVE APPROACH HAVE THE CAPACITY TO CONTRIBUTE TO THE KNOWLEDGE ECONOMY
INNOVATING PEDAGOGICAL PRACTICES IN HIGHER EDUCATION: THE IMPLEMENTATION OF AN ACTIVE AND AUTHENTIC LEARNING APPROACH IN TWO STUDY CYCLES

INNOVATING PEDAGOGICAL PRACTICES IN HIGHER EDUCATION: THE IMPLEMENTATION OF AN ACTIVE AND AUTHENTIC LEARNING APPROACH IN TWO STUDY CYCLES
UNIVERSITY MEDIATION IN SPAIN: AN INNOVATIVE TOOL TO BUILD SKILLS TO IMPROVE WORKING RELATIONSHIPS AND CONFLICT RESOLUTION IN THE UNIVERSITY DOMAIN

UNIVERSITY MEDIATION IN SPAIN: AN INNOVATIVE TOOL TO BUILD SKILLS TO IMPROVE WORKING RELATIONSHIPS AND CONFLICT RESOLUTION IN THE UNIVERSITY DOMAIN
APPROACHING ATONAL-MUSIC ANALYSIS THROUGH COMPUTER-AIDED STRATEGIES: TWO REPORTS FROM UNIVERSITY STUDENTS’ DATA

APPROACHING ATONAL-MUSIC ANALYSIS THROUGH COMPUTER-AIDED STRATEGIES: TWO REPORTS FROM UNIVERSITY STUDENTS’ DATA
DESIGN OF ADAPTIVE MICRO-MODULES FOR AI-RELATED COURSES USING 2D DEPTH MATRIX

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 7203-7207ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1972Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
EDUCATIONAL PLATFORM AS A MEANS OF DISTANCE LEARNING FOR SCHOOL AND UNIVERSITY STUDENTS

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 6936-6940ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1880Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
CURRICULAR GOALS RELATING TO DIGITAL LITERACY AND DIGITAL COMPETENCES IN UPPER SECONDARY SCHOOL IN DENMARK

CURRICULAR GOALS RELATING TO DIGITAL LITERACY AND DIGITAL COMPETENCES IN UPPER SECONDARY SCHOOL IN DENMARK
MOTIVATING STUDENTS OF EARLY SCHOOL EDUCATION TO SOLVE MATHEMATICAL PROBLEMS INDEPENDENTLY

The paper presents a method of motivating students of the School Pedagogy majors in order to make them discover and solve mathematical problems on their own. The research was carried out in the form of interactive classes, during which students were to solve mathematical problems. They were working at individual thematic stands. Objects manipulation could help students learn. The research was conducted by means of the action research procedure. The results of the study were described in four areas: preparation of the learning environment (research organization), observation of respondents, communication with students (interviews with students), and use of the scientific research results.
A Big Data Platform for Real-Time Video~Surveillance

Nowadays, smart house facilities are strongly developed with the support of multiple security cameras to protect not only a house but also a building.A large amount of video data is produced by these cameras every day.Therefore, traditional data management systems face challenges in collecting, storing, and analyzing big video data.In such systems, it is difficult to find objects and their actions from video surveillance in the building because of either the consuming time or the lack of intelligent technology support.In this paper, we propose a novel big data platform for real-time video surveillance analysis based on the combination of distributed data frameworks and intelligent video processing libraries.The proposed platform is able to collect both real-time video streams and historical video data by using Kafka and Spark Structured Streaming frameworks.Furthermore, the proposed platform provides an intelligent video processing module for object detection by using OpenCV, YOLO, and Keras libraries.To evaluate the proposal, we deploy the proposed big data platform and implement a web interface to support end-user to analyze video surveillance.Through the results of the initial video querying implementation, we show the viability of the proposed platform.