Reviewerly/exHarmony huggingface dataset:
Design and Application of Mapping Model for Emotion-Based Font Recommendation System

Font usage is effective in accentuating meaning and establishing the overall tone of a message. Nevertheless, the process of selecting an appropriate font can be burdensome for users as it necessitates examining all available fonts. Furthermore, users with limited font usage experience might inadvertently choose an inappropriate font. To tackle this concern, we developed a system that recommends fonts by evaluating similarity between font keyword values and emotions extracted from content through deep learning emotion analysis. Considering the disparity in criteria utilized for classifying content emotions and font keywords, the necessity arose for a mapping model to evaluate the similarity between these two sets of criteria. Accordingly we designed our mapping model constructed based on the PAD model, a framework that represents emotions along three axes on a coordinate plane. We formulated two distinct methods to assess similarity: the first converts content and font characteristics into a single PAD value, subsequently discerning the distance; The second method analyzes the Pearson correlation coefficient between the criteria for emotional classification to determine the similarity. A comparative evaluation was conducted between these two methods. The results of the evaluation affirmed that the model reflecting the correlation coefficient yielded greater efficacy. As a result, we opted for this mapping model as the approach for calculating similarity between content and font.
Realizing interoperable end-to-end systems for efficient open scholarly communication

To ensure the success of Open Science, we have to understand how it systematically changes the research system as a whole. This presentation focuses on the synthesis of the changing research landscape and the utilisation of policies, emerging tools, and practices into shaping end-to-end systems for embedding actionability in all research stages: planning, tracking, and assessing. Specifically, it will explore the designing of interconnected systems that involve Data Management Plans, Scientific Knowledge Graphs, and FAIR Assessors, all of which are essential components of research workflows.
A structural model for building a system for the development of methodological competence and methods for evaluating its effectiveness

The paper develops a structural model for building a system for developing methodological competence. The structural model is built based on a service-oriented approach to developing large software complexes and includes six subsystems. Each subsystem is implemented as a separate microservice, which allows system scaling. The paper describes a technique that allows you to build a teacher’s competency map and evaluate its eight components: cognitive, didactic, project, informational, communication, reflective, monitoring, and personal-motivational. A four-level scale is proposed for assessing the level of competencies. A methodology for evaluating the effectiveness of the methodical competence development system based on the hierarchical expert method has been developed. The system was verified and implemented. According to the results of the system implementation, the intensification of the educational process and the improvement of the training quality of students were recorded. Students’ success in studying subjects has increased significantly. Namely, the number of bad students has decreased by 15 %, the number of «excellent» students has increased by 10 %, and the number of «good» students has increased by 18 %. The paper touches upon creating information technologies adequate for improving the higher education system. The goal is the targeted development and implementation of information technologies in educational institutions and the creation and operation of integrated, flexible software to support a mixed system for organizing the educational process, as well as increasing the efficiency of implementing a mixed system for organizing the educational process.
Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.
A Reflection on the Impact and Connection of AI with Humanities

Artificial intelligence, as a phenomenon that has a wide scope in various issues and topics, is also related to humanities and plays an effective role in it. The two basic questions of this article are whether artificial intelligence is related to humanities and whether artificial intelligence has an effective role in humanities. By reflecting on each of the questions, we can see that AI is related to humanities and plays a role in it. Its relevance is due to the fact that humanities have the potential to be related to other fields, and of course artificial intelligence will also have this capability. Regarding the second question, it should be said that AI can play a role in humanities in three ways. 1. Have an active role; 2. Help humanities researchers; 3. Be raised as a subject to be investigated in humanities.
Improving a method for determining the level of wear of the mark for people with visual impairments on Ukrainian hryvnia banknotes

The object of this study is the wear resistance of marks for people with visual impairments, which are printed by the intaglio printing method on Ukrainian hryvnia banknotes to identify the banknote denominations. The subject of the study is to determine the impact of wear on the recognizability of marks for people with visual impairments and the identification of banknote denominations by people with visual impairments. The paper considers the problem of inadequate assessment of the level of banknote wear by the systems of control and rejection of protected products from the point of view of banknote accessibility for people with visual impairments. An improved methodology for determining the level of wear of the mark for people with visual impairments on Ukrainian hryvnia banknotes is given, taking into account the indicator of its accessibility for all population groups, in particular for people with absolute vision loss, throughout the entire period of circulation. According to the developed methodology, artificial wear of banknotes was carried out according to three test options. The results of changes in the mass of banknotes, the microstructure of the surface of banknotes, the compressibility of marks for people with visual impairments (elasticity level), and the level of tactile identification of the denomination of banknotes by people with total vision loss were obtained. It was determined that during the artificial wear of banknotes, the main destruction of the mark takes place in the first cycle. After the second cycle of wear, there is an increase in the elasticity of the mark for people with visual impairments, a decrease in the level of its tactile sensation and the identification of the denomination of the banknote. Therefore, the properties of banknotes after the second cycle of wear have been determined to be critical for withdrawing such banknotes from circulation. The revealed regularities make it possible to adjust the parameters of intaglio printing during the production of banknotes and to adjust the sorting equipment to determine the suitability of banknotes for further circulation, taking into account the criterion of accessibility of banknotes for all groups of people.
DEVELOPMENT OF BASIC FINANCIAL ACCOUNTING E-BOOKLETS AS LEARNING MEDIA

The utilization of e-booklets as learning media offers numerous advantages, serving as a digital resource to support the teaching and learning of fundamental concepts and principles in financial accounting. This research aims to develop an e-booklet on financial accounting fundamentals, intending to create a valid and practical learning medium. The method employed in this study is Research and Development (R&amp;D) with the Rowntree development model, encompassing planning, development, and evaluation stages. The results demonstrate that the developed e-booklet on financial accounting basics has undergone rigorous validity testing and proves to be highly practical for educational purposes. This innovation presents an interactive learning medium that can be accessed conveniently online or offline through smartphones and other devices, paving the way for new opportunities in the field of education.
Application Of the Cobit 2019 Framework to Analyse the Security Of Academic Information Systems

STMIK Amik Riau implements an integrated information system to support a fast and real-time information management process where each service has its security. In this study, the analysis used to determine the maturity level of information system security governance was the COBIT 2019 framework with a CMMI scale. In COBIT 2019 the domains used were DSS05 and APO13. Based on the result of the analysis, the results of the average value of the overall maturity level on the security of the academic information system of STMIK Amik Riau were currently at level 3, which was defined. It meant that the security of the information system was running well but needed to be evaluated and optimized continuously. The value of each sub domain was 3.08 for the DSS05 sub domain (user), 3.35 for DSS 05 sub domains (maintainer), and 2.5 for APO13 sub domains (maintainer). Then the average result obtained from the gap was 0.53, meaning that the current level of maturity level with the desired maturity level is not too far away and can be increased by providing recommendations.
Blockchain-Based, Confidentiality-Preserving Orchestration of Collaborative Workflows

Business process collaboration between independent parties is challenging when participants do not completely trust each other. Tracking actions and enforcing the activity authorizations of participants via blockchain-hosted smart contracts is an emerging solution to this lack of trust, with most state-of-the-art approaches generating the orchestrating smart contract logic from Business Process Model and Notation (BPMN) models. However, compared to centralized business process orchestration services, smart contract state typically leaks potentially sensitive information about the state of the collaboration, limiting the applicability of decentralized process orchestration. This paper presents a novel, collaboration confidentiality-preserving approach where the process orchestrator smart contract only stores encrypted and hashed process states and validates participant actions against a BPMN model using zero-knowledge proofs. We cover a subset of BPMN, which is sufficient from the practical point of view, support messagepassing between participants, and provide an open-source, endto-end prototype implementation that automatically generates the key software artifacts.
Open Science in an open cloud

European Open Science Cloud (EOSC) is an European wide initiative that aims to develop a ‘Web of FAIR Data and Services’ for science in Europe that will provide researchers, students, companies and citizens with a federated and open multi-disciplinary environment where they can collaborate, share and reuse data, tools and services. EOSC Portal Catalogue and Marketplace is an entry point which improves the findability and accessibility of the scientific resources, their curation, support and training activities.In order to empower local stakeholders (researchers, policy makers, research performing organisations, etc.), NI4OS-Europe project (National Initiatives for Open Science in Europe) provides technical support for onboarding, i.e. making scientific web services compliant with the NI4OS/EOSC requirements and inclusion in catalogues. At the same time, as open science cloud initiatives emerge across the EU landscape, in the terms of infrastructure and policy development, it also supports building the Croatian Open Science Cloud (abbrev. locally HR-OOZ) Initiative. HR-OOZ is gathering prominent national institutions with an active role regarding open science covering all important stakeholder groups: research performing organisations (RPO), research funding organisations, research supporting organisations and policy makers. The Initiative aims to build a modern, high-quality, internationally relevant, and competitive science system based on the principles of open science, which is harmonised and connected with the European Research Area and relevant European initiatives. The Initiative has two main goals. The first goal is to set up the HR-OOZ which includes tasks of defining its organisational structure, defining the technological principles of services that will make the HR-OOZ, and ensuring its sustainability. The second goal of the Initiative is to draft the proposal of the National Action Plan for Open Science and propose changes to the law governing scientific activities.We will present the interconnections between national and European initiatives and how they complement each other as well as the proposed mechanisms for incentivizing and rewarding OS practices which are the vital components of the whole OS ecosystem.
The Ephemeral Public

Under the conditions of a pandemic, we have recognised a form of public we identify as ‘ephemeral’. In this paper, we identify the constitutive components of the public pedagogical encounter that give rise to this theorisation. We address the place and time of the encounter, the knowledge that is central to this encounter, the educative agent as pedagogue, and the public as learner—in this case, it is an ephemeral public. Our reading focusses on the structure and nature of each of these. As the term ‘educative agent’ is a recent theorisation (Charman and Dixon, 2021) we have given particular attention to that. Our intent in this paper is to further understand the nature and dynamic of the public that is present in this encounter.
Counterdiabatic optimized driving in quantum phase sensitive models

State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by phase transitions: the axial next-nearest neighbor Ising (ANNNI), XXZ, and Haldane-Shastry (HS) models. We perform quantum optimal control protocols by optimizing the energy cost function, which can always be evaluated as opposed to the fidelity one requiring the exact state. Moreover, we incorporate Bayesian optimization within a code package for computing various adiabatic gauge potentials. This protocol consistently surpasses standard annealing schedules, often achieving performance improvements of several orders of magnitude. Notably, the ANNNI model stands out as a notable example, where fidelities exceeding 0.5 are attainable in most cases. Furthermore, the optimized paths exhibits promising generalization capabilities to higher-dimensional systems, allowing for the extension of parameters from smaller models. This opens up possibilities for applying the protocol to higher-dimensional systems. However, our investigations reveal limitations in the case of the XXZ and HS models, particularly when transitioning away from the ferromagnetic phase. This suggests that finding optimal diabatic gauge potentials for specific systems remains an important research direction.
The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games

Video games demand is constantly increasing, which requires the costly production of large amounts of content. Towards this challenge, researchers have developed Search-Based Procedural Content Generation (SBPCG), that is, the (semi-)automated creation of content through search algorithms. We survey the current state of SBPCG, reporting work appeared in the field between 2011-2022 and identifying open research challenges. The results lead to recommendations for practitioners and to the identification of several potential future research avenues for SBPCG.
Contextualizing the Limits of Model &amp; Evaluation Dataset Curation on Semantic Similarity Classification Tasks

This paper demonstrates how the limitations of pre-trained models and open evaluation datasets factor into assessing the performance of binary semantic similarity classification tasks. As (1) end-user-facing documentation around the curation of these datasets and pre-trained model training regimes is often not easily accessible and (2) given the lower friction and higher demand to quickly deploy such systems in real-world contexts, our study reinforces prior work showing performance disparities across datasets, embedding techniques and distance metrics, while highlighting the importance of understanding how data is collected, curated and analyzed in semantic similarity classification.
Tuning-less Object Naming with a Foundation Model

We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the system's quality, mainly how many objects it can handle in this way.
Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis

The growing use of digital communication platforms has given rise to various criminal activities, such as grooming and drug dealing, which pose significant challenges to law enforcement and forensic experts. This paper presents a supervised keyphrase extraction approach to detect relevant information in high-volume chat logs involving grooming and drug dealing for forensic analysis. The proposed method, JointKPE++, builds upon the JointKPE keyphrase extractor by employing improvements to handle longer texts effectively. We evaluate JointKPE++ using BERT-based pre-trained models on grooming and drug dealing datasets, including BERT, RoBERTa, SpanBERT, and BERTimbau. The results show significant improvements over traditional approaches and demonstrate the potential for JointKPE++ to aid forensic experts in efficiently detecting keyphrases related to criminal activities.
LooGLE: Can Long-Context Language Models Understand Long Contexts?

Large language models (LLMs), despite their impressive performance in various language tasks, are typically limited to processing texts within context-window size. This limitation has spurred significant research efforts to enhance LLMs' long-context understanding with high-quality long-sequence benchmarks. However, prior datasets in this regard suffer from shortcomings, such as short context length compared to the context window of modern LLMs; outdated documents that have data leakage problems; and an emphasis on short dependency tasks rather than long dependency tasks. In this paper, we present LooGLE, a Long Context Generic Language Evaluation benchmark for LLMs' long context understanding. LooGLE features relatively new documents post-2022, with over 24,000 tokens per document and 6,000 newly generated questions spanning diverse domains. Human annotators meticulously crafted more than 1,100 high-quality question-answer pairs to meet the long dependency requirements. These pairs underwent thorough cross-validation, yielding the most precise assessment of LLMs' long dependency capabilities. The evaluation of eight state-of-the-art LLMs on LooGLE revealed key findings: (i) commercial models outperformed open-sourced models; (ii) LLMs excelled in short dependency tasks like short question-answering and cloze tasks but struggled with more intricate long dependency tasks; (iii) in-context learning and chaining thoughts offered only marginal improvements; (iv) retrieval-based techniques demonstrated substantial benefits for short question-answering, while strategies for extending context window length had limited impact on long context understanding. As such, LooGLE not only provides a systematic and comprehensive evaluation schema on long-context LLMs, but also sheds light on future development of enhanced models towards "true long-context understanding".
Personalized Online Federated Learning with Multiple Kernels

Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear function approximation. Federated learning enables a group of learners (called clients) to train an MKL model on the data distributed among clients to perform online non-linear function approximation. There are some challenges in online federated MKL that need to be addressed: i) Communication efficiency especially when a large number of kernels are considered ii) Heterogeneous data distribution among clients. The present paper develops an algorithmic framework to enable clients to communicate with the server to send their updates with affordable communication cost while clients employ a large dictionary of kernels. Utilizing random feature (RF) approximation, the present paper proposes scalable online federated MKL algorithm. We prove that using the proposed online federated MKL algorithm, each client enjoys sub-linear regret with respect to the RF approximation of its best kernel in hindsight, which indicates that the proposed algorithm can effectively deal with heterogeneity of the data distributed among clients. Experimental results on real datasets showcase the advantages of the proposed algorithm compared with other online federated kernel learning ones.