Reviewerly/exHarmony huggingface dataset:
Role Mechanism of Enterprise Management Efficiency Improvement Based on Improved Drosophila Algorithm

INTRODUCTION: With today's intelligent technology in full swing, many companies and enterprises need to catch up to the reality of internal management. To make the company better adapt to society and realize its sustainable development, it is essential to optimize the internal management means and improve the management efficiency of the enterprise.&#x0D; OBJECTIVES: To find the optimal staff allocation scheme and the best decision path by utilizing the improved fruit fly algorithm and establishing the enterprise's regular task and staff allocation model.&#x0D; METHODS: We analyze the standard swarm intelligence algorithms, and then we compare the differences between the basic fruit fly algorithm, the optimized fruit fly algorithm, and the above swarm intelligence algorithms. The fruit fly algorithm is utilized, and the algorithm is optimized to fit the actual enterprise management model. At the same time, the influence of Levy's flight on the Drosophila algorithm in enterprise management efficiency improvement is studied. Finally, it points out the application fields, the optimized Drosophila algorithm's current situation, and the existing problems and shortcomings.&#x0D; RESULTS: By comparing the performance of the Drosophila algorithm with that of the other three optimization Drosophila algorithms, the influence factors of Levy flight on the enterprise's internal management allocation are obtained, making the algorithm a good fit for the model. The results of the optimal allocation scheme under known conditions and the best decision were obtained.&#x0D; CONCLUSION: The experimental results show that the optimized fruit fly algorithm can solve the multi-parameter allocation problem of enterprise management, which has high reference value and significance for general enterprise internal management.
Lightweight real-time hand segmentation leveraging MediaPipe landmark detection

Abstract Real-time hand segmentation is a key process in applications that require human–computer interaction, such as gesture recognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variability in skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complex problem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time hand segmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctly interpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipe Hands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe using morphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Different experiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color space chosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands dataset running at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed segmentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immersion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/lightweight-hand-segmentation .
Seven Hypertexts

What is Hypertext? It has been studied and explored for over 50 years but a complete definition seems ever more elusive. The term is invoked in multiple communities, and applied in radically different domains, but if we cannot reconcile the different perspectives then we will be unable to learn from our shared history, or from each other in the future. In this paper we argue that the longevity and variety of hypertext work makes a simple definition impractical. Instead we suggest different contexts in which hypertext work has been conducted, and then attempt to draw out the relationships and commonalities between them. We describe seven contexts drawn from the literature: Hypertext as a Tool for Thought, as Knowledge Representation, as Social Fabric, as Literature, as Games, as Infrastructure, and as Interface. We argue that these are connected by a common requirement for non-regularity, driven by post-structuralist philosophy, and enshrining existentialist values in our technology. It is the application of these ideas to different problems that gives rise to current Hypertext, as we see the same technical features, and engineering and creative challenges, manifest in otherwise quite different digital domains.
Why do we Hate Migrants?

Abstract: AI-based NLP literature has explored antipathy toward the marginalized section of society, such as migrants, and their social acceptance. Broadly, extant literature has conceptualized this as an online hate speech detection task and employed predictive ML models. However, a crucial omission in this literature is the genesis (or causality) of online hate, i.e., why do we hate migrants? Drawing insights from social science literature, we have identified three antecedents of online hate: Cultural, Economic, and Security concerns. Subsequently, we probe -which of these concerns triggers higher toxicity on online platforms? Initially, we consider OLS-based regression analysis and SHAP framework to identify the predictors of toxicity, and subsequently, we use Double Machine Learning (DML)-based casual analysis to investigate whether good predictors of toxicity are also causally significant. We find that the causal effect of Cultural concerns on toxicity is higher than Security and Economic concerns.
Legal Information Retrieval meets Artificial Intelligence (LIRAI)

The Legal Information Retrieval meets Artificial Intelligence (LIRAI) workshop series aims to provide a venue hosting discussion of novel ideas, evaluations, and success stories concerning the application of Artificial Intelligence (AI) and Information Retrieval (IR) to the legal domain. All around the world, lawmakers, legal professionals, and citizens must cope with the sheer amount of legal knowledge present in legal documents. These documents can be norms, regulations, directives, legal cases, and other relevant material for legal practitioners, such as legal commentary. The continuous evolution of legal documents is a challenging setting, with implicit relationships playing an important role beyond explicit references. Recently, the adoption of shared machine-readable formats and FAIR principles, as well as methods and practices from the Semantic Web, have certainly improved the accessibility of legal knowledge and its interoperability. Still, retrieving legal knowledge and making sense of it are not solved problems. The legal community often has special requirements for retrieval systems (e.g., high recall, explainability). Artificial Intelligence (AI) is positioned as a lever to enhance our ability to find, understand, and correlate legal information, and to comprehend its relationship to reality, in terms of compliance evaluation and risk/benefit analysis. We call contributions on these topics in the form of papers, which will be collected in an open-access proceedings published on CEURWS.org and thus indexed by Scopus, DBLP, Google Scholar, and other citation databases.
Sustainable Approach Development for Education of Electrical Engineers in Long-Term Online Education Conditions

The prolongation of the online education period is a significant challenge nowadays in Ukraine. Due to the ongoing war in Ukraine, educational institutions have been destroyed, leading to a significant movement of students both within the country and abroad. Consequently, the quality of educational services has suffered, particularly in technical specialties that require hands-on and laboratory work. The objective of this article is to explore solutions for training Ukrainian students in technical fields under the circumstances of war, with a focus on ensuring their safety and improving the overall quality of education. This article analyzes the existing methods of conducting laboratory work in remote settings. It identifies that distance learning at foreign universities, without the necessity of physical relocation, holds the most promising potential. Moreover, it recommends leveraging the “My Digital Electrical Engineering” (MDEE) laboratory, developed at RTU, for conducting remote laboratory work. A comprehensive examination of the MDEE laboratory is presented, along with a step-by-step algorithm for integrating it into the educational process at KhNAHU. The findings of this study can be utilized to enhance distance learning practices in Ukrainian technical universities. This, in turn, will enable students to continue their education during the war while ensuring their safety and facilitating the acquisition of knowledge at a high standard.
Siamese Multi-Scale Adaptive Search Network for Remote Sensing Single-Object Tracking

With the development of remote sensing earth observation technology, object tracking has gained attention for its broad application prospects in computer vision. However, object tracking is challenging owing to the background clutter, occlusion, and scale variation that often appear in remote sensing videos. Many existing trackers cannot accurately track the object for remote sensing videos with complex backgrounds. Several tracking methods can handle just one situation, such as occlusion. In this article, we propose a Siamese multi-scale adaptive search (SiamMAS) network framework to achieve object tracking for remote sensing videos. First, a multi-scale cross correlation is presented to obtain a more discriminative model and comprehensive feature representation, improving the performance of the model to handle complex backgrounds in remote sensing videos. Second, an adaptive search module is employed that augments the Kalman filter with a partition search strategy for object motion estimation. The Kalman filter is adopted to re-detect the object when the network cannot track the object in the current frame. Moreover, the partition search strategy can help the Kalman filter accomplish a more accurate region-proposal selection. Finally, extensive experiments on remote sensing videos taken from Jilin-1 commercial remote sensing satellites show that the proposed tracking algorithm achieves strong tracking performance with 0.913 precision while running at 37.528 frames per second (FPS), demonstrating its effectiveness and efficiency.
Quantum algorithms for optimal effective theory of many-body systems

A common situation in quantum many-body physics is that the underlying theories are known but too complicated to solve efficiently. In such cases one usually builds simpler effective theories as low-energy or large-scale alternatives to the original theories. Here the central tasks are finding the optimal effective theories and proving their equivalence to the original theories. Recently quantum computing has shown the potential of solving quantum many-body systems by exploiting its inherent parallelism. It is thus an interesting topic to discuss the emergence of effective theories and design efficient tools for finding them based on the results from quantum computing. As the first step towards this direction, in this paper, we propose two approaches that apply quantum computing to find the optimal effective theory of a quantum many-body system given its full Hamiltonian. The first algorithm searches the space of effective Hamiltonians by quantum phase estimation and amplitude amplification. The second algorithm is based on a variational approach that is promising for near-future applications.
Satisfaction with the university lecturers’ work in the human resource tertiary education’s management system

In the conditions of tertiary education’s modernization, the effective management’s importance of the personnel potential’s development of universities increases. One of the significant factors determining the success of management mechanisms in the tertiary education system is the satisfaction with the work of lecturers. The purpose of the study is to assess the level of satisfaction with the work of lecturers, to reveal the significance of its parameters in the management system of the tertiary education’s personnel potential. Based on empirical research data, the article analyzes the most important parameters of work at the university for lecturers and the realization level of the opportunities that work at the university provides. The segmentation author’s version of lecturers according to the level of job satisfaction is proposed, the use of which allows optimizing the process of making managerial decisions based on considering the characteristics of employees’ various segments. The work directions are highlighted on improving the management efficiency of the tertiary school’s personnel potential.
Intuitive Semantic Graph Tool for Enhanced Archive Exploration

The paper introduces a new method for visualizing and navigating information in a cultural heritage archive in a simple and intuitive way. The proposed approach employs pre-trained language models to cluster data and create semantic graphs. The creation of multilayer maps enables deep exploration of archives with large datasets, while the ability to handle multilingual datasets makes it suitable for archives with documents in various languages. These features combine to provide a user-friendly tool that can be adapted to different contexts and provides an overview of archive contents, to allow even non expert users to successfully query the archive.
Attitudes towards Digital Educational Technologies Scale for University Students: Development and Validation

Numerous studies of the digitalization of higher education show that university students’ attitudes toward digital educational technologies (DETs) are one of the important psychological factors that can hinder or facilitate the optimal implementation of digital technologies in education. International researchers have developed many tools for diagnosing the attitudes of university students toward various aspects of the digitalization of education; however, until recently, similar scales in Russian have not been developed, which determined the purpose of this present research. The proposed version of the Attitudes towards DETs Scale for University Students (ATDETS-US) includes the cognitive, emotional, and behavioral subscales corresponding components of the attitude according to the ACB Model. The validation sample included 317 (160 females and 157 males) bachelor and master students from different Russian universities. Psychometric testing using Cronbach’s Alpha and McDonald’s Omega coefficients, hierarchical factor analysis, and CFA confirm the high internal consistency, reliability of the ATDETS-US and its subscales, and the good fit of the model. ATDETS-US will be used for obtaining reliable data on the attitudes towards DETs in university students, which should be taken into account when designing programs for their psychological support in the educational process and developing their digital competence.
Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System

We present the results of a study where we provided students with textual explanations for learning content recommendations along with adaptive navigational support, in the context of a personalized system for practicing Java programming. We evaluated how varying the modality of access (no access vs. on-mouseover vs. on-click) can influence how students interact with the learning platform and work with both recommended and non-recommended content. We found that the persistence of students when solving recommended coding problems is correlated with their learning gain and that specific student-engagement metrics can be supported by the design of adequate navigational support and access to recommendations' explanations.
The Positive Impact of Digital Literacy on Science Process Skills

Students' science process skills are important to improve, one of which is by using technology so that students' digital literacy skills are also needed in using technology. The purpose of this study was to analyze the effect of digital literacy on science process skills. The type of research used is a mix method with a sequential explanatory model. The population in sthis study were students in class X MIPA at senior high school. The sampling technique used was simple random sampling and a sample of 50 students was obtained and the instruments included questionnaires, observations and teacher interviews. Quantitative data were obtained from questionnaires and observations while qualitative data were obtained from interviews. After the research is done, the data will be analyzed. Quantitative data analysis used statistical tests, namely descriptive statistics and parametric inferential statistics. Descriptive statistical analysis is presented in tabular form in the form of frequency, mean, median, max value and min value while inferential parametric analysis is in the form of a t test with prerequisite tests: normality test and linearity test. While the qualitative data uses Miles &amp; Huberman. The research results obtained were that digital literacy had an effect on science process skills with a percentage of 93.5%.
The Concept of a Subsystem to Support the Management of the Protection of Intangible Assets of Companies from a Behavioural Perspective

The human factor is the biggest challenge for enterprises in providing the expected level of security, whereas the lack of educated personnel is one of the key problems in building an effective system for protection against data and information threats. A human being is a non-programmable element of the system and it is difficult to predict his or her behavior in information management processes and in the face of a specific event. Humans cannot be programmed like some security applications or hardware solutions with predictable performance. Human actions very often have a stochastic effect on the operation of the system. They can be ill-considered, haphazard, affected by emotions, and taken without due attention and adequate knowledge and experience (Pham et al., 2019). All these imperfections are exploited by those whose goal is to destroy or obtain information. According to data published by several information security companies, attacks carried out by purpose-built bots and web applications that exploit a technical factor (e.g., system vulnerabilities) are becoming increasingly rare, and are being replaced by attacks in which human interaction is a key factor. The curiosity and trust, leading well-meaning individuals to click, install, open, and send information, are being exploited by cybercriminals who are increasingly adept at using social engineering techniques.&#x0D; The aim of the present paper is to discuss the theoretical basis of information security issues from the behavioral perspective and to present the concept of a subsystem that implements measures to minimize the impact of the human factor on the emergence of threats to the intangible resources of a business entity. The concept is to create an information and organizational space to support the operation of the traditional information security management system in small and medium-sized enterprises. The concept is presented using the object-oriented approach which focuses on the functional elements of the system, and the subject-oriented approach, which takes into account the relationships between the various individuals who affect the security of the information system. The author's models of each approach were presented along with a description of how they work.
Awareness Level of Information Security Managers in Small Enterprises in Poland

The security of information assets largely depends on the real level of knowledge of those managing the information system. Particularly important is the knowledge acquired on an ongoing basis through reading and analysing: new reports published by specialised intelligence services worldwide, thematic portals, industry and scientific publications. Today's rapid pace of development of information, communication systems means that knowledge of digital information security acquired a year or more ago becomes partly outdated. Although there are specific, unchangeable canons of behaviour, proceedings in the processes of information protection - they are only a foundation for building an effective system of protecting intangible resources. Special attention should be paid to the fact that information protection is not an individual activity - it is a process running parallel to all processes operating in enterprises. The global pandemic situation has had an irreversible impact on the perception of information asset security. The situation in which most business entities were forced to work remotely necessitated a new approach to information security among its managers. The relatively stable environment of business entities until the pandemic suddenly forced the use of new techniques and technologies enabling work from private, home networks. Have such conditions changed the attitudes of security managers to the need for continuous education, to expand their knowledge in this area? Did those responsible for the security of intangible assets gain more experience with information security during the pandemic? The aim of this article is to present research on the knowledge and awareness of information security managers conducted before the pandemic in 2018 and 2019 among small businesses in Poland on the real perceptions of those responsible for information security and to compare them with the same research conducted in 2022 after the pandemic.
Autonomy and Knowledge Processes - a Systematic Literary Review

Knowledge processes, such as knowledge creation, sharing and application, enable knowledge production that increases innovation and superior organizational performance. Knowledge processes are ultimately and fundamentally conducted – or not engaged in – by the individuals. Hence, knowledge production is disrupted if individuals are prevented from using their knowledge production potential. One potential factor that causes disruption in knowledge production is the lack of autonomy. As autonomy means self-governance and independency, it enables independent and unique knowledge processing by the individual, and thereby supports both individual and collective level knowledge production. Despite the importance of the role of autonomy in knowledge production, there is no research-based knowledge of which knowledge process have been studied from the perspective of autonomy, and what has been left unexplored. This research clarifies the research literature by finding out which knowledge processes have been studied from the point of view of autonomy. Also, what has been found concerning the relationship between autonomy and knowledge processes is considered. The research was conducted as a systematic literature review supplemented by qualitative content analysis in which the articles up the year 2022 from databases Scopus and Web of Science were reviewed. The most notable finding of this study is the lack of research between autonomy and knowledge identification and the paucity of research between autonomy and knowledge internalization. This is a significant finding since knowledge identification affect what knowledge is processed and produced in the organization. Internalizations in turn affects knowledge-based orientation and knowledge production in the organizational environment. This paper contributes to the organizational research literature by summing up the existing research on the interface of autonomy and knowledge processes and provides a refined understanding on the central role of autonomy in the effective implementation of knowledge processes, as well as avenues for future research.