Reviewerly/exHarmony huggingface dataset:
Digital transformation of a Polish accounting firm: tools, impediments, business performance benefits and implications – case study

The main purpose of the article is to present, based on a case study, the tools of voluntary digitalization, including the associated benefits and barriers. For this purpose, an analysis of the legal regulations applicable in Poland and a literature review were conducted. This allowed identification of the research gap in empirical studies on voluntary digitalization. Another research method employed involved a case study of a Polish accounting firm. Subsequent research steps taken by the Authors are outlined in the article. The answers to the research questions posed revealed that the entity surveyed utilizes voluntary digitalization tools, identifies a number of benefits thereof, whereas the greatest barrier entails the task of finding competent contractors. The conclusions drawn provide inspiration for in-depth research on digitalization processes.
Development of an expert system to overpass citizens technological barriers on smart home and living

Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the "Finde-Dein-SmartHome" tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care.
A new Classifier Chain method of BERT Models For Multi-label Classification of Arabic Abusive Language on Social Media

Moving beyond classical single-label classification tasks proves to be crucial nowadays since the majority of real-life problems are inherently multi-label. Various tasks require involving, for one single instance as an input, several classes simultaneously or none of them. Particularly, detecting abusive texts in Arabic social networks intuitively carries several manifestations with regard to the targets’ referred characteristics like gender, race, sexual orientation, etc. Existing multi-label algorithms either invest considerable complexity to handle labels’ correlation or explicitly ignore this information to deal with each label as an independent binary problem. To remedy the abovementioned problems, this paper proposes a new multi-label classification solution based on deep neural networks. The main idea is to exploit the correlation between all the dataset's labels to break it into ordered subsets. Then, follow that order to implement a chain of BERT classifiers. Each classifier is therefore trained on a corresponding subset and passes its output via feature space to the following one. For a new instance, we gather all the classifiers’ results and pick the best one for each class. Experiments showed that our approach significantly outperformed the single classifier's method using BERT as well as other neural network architectures on our Arabic abusive language dataset. 91%,76%, and 82% were achieved respectively for precision, recall, and F1-score. Moreover, we further investigated the performance of our approach on Kaggle multi-label research paper dataset and achieved very promising results.
Task Planning Model of Software Process

Managing software development processes remains a significant challenge despite tool support. To remedy this situation, we propose better use of data collected in management support systems, a lightweight approach to planning inspired by agile approaches, and the assumption that constant change is inevitable. This approach lies at the heart of the described solution. The paper presents a model for planning tasks in an IT project. Its purpose is to support the management of the software development process. It allows planning work in the project, considering changes and learning new requirements, detecting defects by the testing team and by the client, and their repair. It uses a model that classifies project tasks and calculates task statistics. They control stateful and recurring task generators, creating new implementation tasks and defects during the project. Linking the types of tasks created to the roles of project team members allows for planning the composition of the project team. Two experiments demonstrate the model's replication and prediction capabilities using data from real IT software projects.
Estimating Game Dependence with Mobile Traffic Statistics

Game addiction is a disease in which a person becomes engrossed in a game and loses control while they play. WHO and the Ministry of Health, Labor and Welfare specified it new disease among especially young generation. It is needed to predict game addiction in advance and develop a mechanism of encouraging behavioral changes to prevent the disease. Some studies proposed some methods to judge game addiction. Those are subjective methods based on questionnaires and are not possible to confirm with specific figures. Our research focuses on the wireless communication of smartphones, which is the basis for playing many online games, and proposes a method for estimating signs of game addiction. This method automatically notifies a warning message to users when they continue to use the specific game for a long time and can be used to prevent game addiction. We constructed a prototype system and evaluated it. As a result, we confirmed an estimation accuracy of 90%.
Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning

The performance of recognition systems can be significantly affected by various factors, with facial expression poses and lighting changes being the main confounding factors. In order to minimize their impact, we propose an intriguing method in this study that enables the generation of high-quality images specifically tailored to the target domain. Our objective is to utilize disentangled representation to effectively model the decomposition of data variations and generate neutral facial expression images with frontal posture and adaptive illumination. To achieve this, we incorporate 3D priors in the adversarial learning during the training process, simulating the generation of an analytical 3D face deformation as well as rendering operations. Additionally, we employ contrastive learning to control the disentanglement of the generated faces while preserving the essential properties of facial features. This technique enables us to learn an embedding space in which similar data samples are represented closely, while distinct samples are kept far apart from each other. Furthermore, we conduct an analysis of the learned latent space and introduce several other significant properties that enhance the reinforcement of factor disentanglement. These properties include an imitation learning algorithm, which facilitates the acquisition of meaningful patterns and characteristics.
Efficiency and Security Trade-offs of Secure Multi-Party Computation for Machine Learning

Machine learning methods require massive data collection to produce accurate predictions, raising privacy concerns. Secure Multi-Party Computation (SMPC) is one of the possible techniques to preserve data privacy during the computation process. Despite recent advances, the efficiency and scalability of SMPC in combination with machine learning remains an uncertain area. Moreover, various implementations of protocols exist that were optimized for the particular use cases, making the decision towards a suitable solution more complicated. The question of which protocol should be used under what circumstances naturally arises. In this case, when is defined as a security requirement and participant number. This work focuses on exploring the feasibility of using SMPC and machine learning in different settings. The work presents a comparison of selected multi-party computation protocols with different numbers of participants, malicious, and semihonest security models. Each relevant group of settings was constructed for the given input parameters, and the same inference scenario is performed within those settings. The inference scenario involves using a model trained on private statistical data inside a simulated multi-party computation session with a given number of participants and a security model. The results revealed a noticeable increase in computation time when moving from two to three participants, and the addition of a fourth participant led to a less substantial increase in computation time. Furthermore, as the number of participants continued to increase, the increase pattern became less aggressive. The study also noted that transitioning from a semi-honest to a malicious setting resulted in a significant increase in computation time. The results achieved help to outline the trade-offs between the protocol, the security model, and the number of participants, and help to choose the most efficient approach for future work.
Agent-based population learning algorithm for over-sampling in the classification of imbalanced data streams

In this paper, the problem of imbalanced data stream classification is considered. To eliminate the problem of imbalanced data, over- and under-sampling procedures are implemented within a dedicated framework called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). To increase the quality of synthetic instance generation (carried out under the umbrella of an over-sampling procedure), an agent-based population algorithm is proposed. The problem is defined, and the agent-based algorithm is presented. A discussion of selected computational results is also included.
Practice-based Collection of Bad Smells in Machine Learning Projects

In this study, we consider projects for developing service systems using machine learning (ML) techniques. As ML techniques have been introduced in various domains, there is reusable knowledge on ML projects that can be employed for conducting such projects without facing major failures. The usage of such knowledge during a project has not yet been clearly described in the form of reusable knowledge such as best practices or patterns. Thus, in this study, we propose a method for collecting the ominous signs in ML projects as "bad smells" and incorporating them as a part of such reusable knowledge. We confirmed the effectiveness of the proposed method through an evaluation.
Russia in the 20th century: The results of the 10th International Scientific Forum “Dialogue of Arts and Art Paradigms” of the International Center of Complex Artistic Research of the Saratov State Conservatory

The book review is devoted to the materials of the 10th International Scientific Forum “Dialogue of Arts and Art Paradigms” (“SCIENCEFORUM PAN-ART 9”), held on December 30, 2022 by the International Center of Complex Artistic Research of the Saratov State Conservatory (volumes 46-56 of the almanac). The theme of the forum is “A grand experiment of the 20th century: Pro et contra (To the 100th anniversary of the formation of the USSR and the 30th anniversary of its collapse)” (volumes 46-53), the study of artistic culture in all its manifestations (volumes 54-55). Volume 56 is dedicated to the 80th anniversary of the birth of the forum’s author A. I. Demchenko. The materials of the forum are addressed to specialists in various fields – literature, fine arts, architecture, musical art, theater, cinema, folklore.
A study on the relevance of semantic features extracted using BERT-based language models for enhancing the performance of software defect classifiers

In the context of new research in the software defect prediction (SDP) task using pre-trained language models, the present study aims to analyze the relevance of semantic features extracted using BERT-based language models for the detection of defective source codes. RoBERTa and CodeBERT-MLM language models are used to generate source code embeddings that capture semantic and contextual features which help in solving language understanding tasks such as SDP. The learned representations are then fed to a neural network-based SDP classifier in order to decide which of the learned code embeddings are more informative in discriminating between faulty and non-faulty software entities. Extensive experiments are conducted in a cross-version SDP scenario for Apache Calcite, an open-source framework for data management. The evaluation results of the defect classifiers show a statistically significant improvement when the code representations are learned by the pre-trained models compared with the semantic representations provided by other natural language-based models, doc2vec and LSI.
Brute force concept drift detection

We present a brute-force approach to detect concept drift behind time sequence data. This approach, named Select-Starţ searches for start points of concept drift to minimize error. In other words, Select-Start searches for the start points of new concepts from the input sequence. Unlike many related works, Select-Start does not require a pre-specified error threshold to detect drift. This paper compares Select-Start with previous representative methods and clarifies its characteristics. The experimental results show that Select-Start is accurate for concept drift problems where the threshold changes slowly. However, existing methods are better at analyzing concept drift problems where the model behind data changes rapidly.
An Insight into NeuroEvolution and Genetic Algorithms for Text Classification

Natural Language Processing (NLP) systems have, over the past decade, shifted from using rule-based techniques to using machine learning-based algorithms. This has led to the development of different architectures and models for different tasks. Some of these architectures include models like the transformers, the CNN and the RNN, which have now become ubiquitous in NLP. However, designing these neural network architectures usually requires in-depth analysis and knowledge of multiple domain areas involved with the problem at hand. In our work, we evaluate an alternative solution to this problem in the domain of text classification. Here, we suggest using the Genetic Algorithm with gradient descent (GAGD) and NeuroEvolution of Augmenting Topologies (NEAT) to search for an optimal neural architecture for the Reuters-21578 and 20 Newsgroups datasets. We evaluate and compare the results of the two algorithms against the current state-of-the-art architectures and provide insight into their performance.
Blockchain for Supply Chain Management: A Literature Review and Open Challenges

In the era of digital transformation, supply chain management faces major challenges induced by the lack of transparency and the evolving industry. In this context, blockchain technology has emerged as a possible answer to the future problems of the supply chain. In this paper, we present a systematic literature review on blockchain in the context of supply chains. The goal of our work is to present the factors and capabilities of blockchain technology that contribute to improving supply chain resilience. We also show which supply chain management factors limit the use of blockchain technology. Based on this, we identify various areas and applications of blockchain technology to support supply chains and highlight current work in the field. From the reviewed literature, we deduce a number of open challenges regarding the application of blockchains in the context of supply chains, e.g. the need (a) to improve the implementation process, (b) to make blockchain more cost-effective, (c) to educate potential users regarding blockchain security aspects, and (d) to further digitize supply chains as part of the digital transformation process.
Phrase2Onto: A Tool to Support Ontology Extension

Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology.
Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling

Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence.
An algorithm for estimating the number of objects in a selected area of space based on ordinary photos

There are many areas of activity where the problem of counting objects in three-dimensional space appears. It is often the case that the primary source of information in such a situation are photos or digital videos. In mathematical language, a two-dimensional photo can somewhat be treated as a projection of a selected space area. The significant issue in such a situation is that the counted objects in the image may overlap many times, which may cause some objects to obscure others. This approach addresses this issue by using appropriate statistical estimations of the amount of obscured objects based on computer image analysis. The proposed algorithm has already been initially used to count the number of birds in digital photos and films.
The 10 most popular Concept Drift Algorithms: An overview and optimization potentials

In a dynamic world, data streams are continuously generated, which poses immense challenges for machine learning (ML) algorithms to adapt to changing statistical properties that are subject to a non-stationary context. The underlying scenario is defined as concept drift (CD), where changes in the relationship between response and prediction variables (real CD) or a change in input data (virtual CD) are accompanied by a significant degradation in the predictive performance of the models, causing ML models to reach unacceptable levels of system accuracy. In this paper, the state of the art for CD algorithms is analyzed and compared. For this purpose, a systematic literature review was performed. Then, the 10 most popular CD algorithms were extracted from the literature using a newly-developed metric. Subsequently, the algorithms were analyzed and compared with respect to their functionality and limitations. Based on these, the optimization potentials were systematically derived. This work presents a summarized overview of CD algorithms and provides the basis for algorithm optimization in this domain.
Crafting Attributes of a Geological Now

Taking an Earth Science framework as its point of departure, this paper delves into the sociocultural ramifications of two distinct geological interpretations—epoch and event—of the contested but widely acknowledged term “Anthropocene.” While the term has gained considerable traction for encapsulating planetary-scale anthropogenic changes (PSAC), its meanings are varied, spanning scientific, philosophical, and cultural discourses. Earth scientists often examine PSAC through the lens of their discipline, yet the complex sociocultural implications attached to such geoscientific concepts call for an interdisciplinary perspective. Introduced by Crutzen and Stoermer in 2000, the term “Anthropocene” is designed to account for the transformative impact of modern societies on Earth System dynamics. Presently, two opposing viewpoints exist within geological discourse to describe PSAC: the “Anthropocene-as-an-event” concept, which underscores the accretion of human-induced changes, and the “Anthropocene-as-an-epoch” concept, focusing on a substantial alteration in Earth System dynamics. Utilising Renn’s theory of “The Evolution of Knowledge,” the paper posits these concepts as scientific borderline problems, catalysts for epistemic inquiries, linked to specific “economies of knowledge.” The ensuing analysis accentuates the multidimensionality of articulating PSAC from an Earth Science viewpoint, asserting that it not only mandates understanding the geological dimensions but also calls for integrating sociocultural and philosophical contexts. The paper concludes that geological terminology should consider associated cultural contexts of Earth Science to establish leadership for vital epistemic concepts.
Digital utopia and dystopia of schools after the COVID-19 pandemic

This paper aims to capture the digital imaginaries of Hungarian schools through the lens of digital utopianism as a theoretical framework. Employing a qualitative research approach and semi-structured interviews, this study contributes to the body of literature concerning organizational and policy-level educational management. It investigates utopian and dystopian visions of digitalized schools within the Hungarian education system, featuring participants comprising school leaders, teachers, and administrative staff drawn from five institutions, offering either general or vocational education, representing diverse ownership structures, including state and religious ownership. The study highlights prominent themes of the imaginaries, such as funding and infrastructure, equity, misuse, and social and pedagogical relations and suggests further research directions and methodologies applicable in this field.