Reviewerly/exHarmony huggingface dataset:
Evaluating the Classification Performance of Natural Language Processing-Driven Team Communication Analysis Models

In this paper, we evaluate the performance of transformer-based natural language processing models in analyzing team communication captured during a live training event. We use a multi-class confusion matrix technique to identify patterns in the performance of two models which recognize dialogue acts and classify how information flows between team members. The dialogue act recognition model was particularly accurate on utterances related to acknowledgement, commanding, and providing information. For information flow, the model showed good performance on classifying utterances labeled as commands from the bottom and middle of the chain of command, although the error analysis revealed a high number of misclassifications related to providing information down and up the chain of command. Results of the multi-class confusion matrix technique provide insight into performance at a more granular level that may lead to model improvements and a better understanding of how the models can be applied to new datasets.
Robust Hand Gesture Recognition Using a Deformable Dual-Stream Fusion Network Based on CNN-TCN for FMCW Radar

Hand Gesture Recognition (HGR) using Frequency Modulated Continuous Wave (FMCW) radars is difficult because of the inherent variability and ambiguity caused by individual habits and environmental differences. This paper proposes a deformable dual-stream fusion network based on CNN-TCN (DDF-CT) to solve this problem. First, we extract range, Doppler, and angle information from radar signals with the Fast Fourier Transform to produce range-time (RT) and range-angle (RA) maps. Then, we reduce the noise of the feature map. Subsequently, the RAM sequence (RAMS) is generated by temporally organizing the RAMs, which captures a target’s range and velocity characteristics at each time point while preserving the temporal feature information. To improve the accuracy and consistency of gesture recognition, DDF-CT incorporates deformable convolution and inter-frame attention mechanisms, which enhance the extraction of spatial features and the learning of temporal relationships. The experimental results show that our method achieves an accuracy of 98.61%, and even when tested in a novel environment, it still achieves an accuracy of 97.22%. Due to its robust performance, our method is significantly superior to other existing HGR approaches.
Building and Serving the Queerlit Thesaurus as Linked Open Data

This paper describes the creation of the Queer Literature Indexing Thesaurus (QLIT) as well as the digital infrastructure supporting the workflow for editing and publishing it. The purpose of QLIT is to adequately catalogue Swedish fiction with LGBTQI themes. It is continually edited in plain-text RDF and automatically processed for correctness and storage. Finally, it is published online as Linked Open Data and used with external systems. The technical approach relies on scripts and applications developed ad hoc, rather than existing solutions. Code is available on https://github.com/gu-gridh/queerlit-terms.
The Digital Lab as an arena for teaching and outreach activities connected to the Special Collections at the University Library of Bergen

The Digital Lab at the University of Bergen Library was established in 2020 and is set up to be an interdisciplinary hub for researchers, lecturers, and students, both on-site and digitally. The lab provides a space to learn, to discuss, and to apply various digital tools and methods used in research within the Digital Humanities. Each semester the lab sets up different courses, workshops, seminars, and lectures aiming to support and serve the target groups. This paper presents a case study that investigates how the Digital Lab can provide an arena for DH activities connected to the Special Collections at the library. The aim is to explore how these activities can have a cross-disciplinary and collaborative approach, and to investigate the lab’s potential as a community-building arena and its impact on the involvement of the library and the librarian‘s role in DH.
Tracing the Proliferation of Socialist Realism Doctrine in Latvian Periodicals: Case Study of "Literature and Art" and "The Flag"

&#x0D; &#x0D; &#x0D; The paper presents the results of a study on the dissemination of socio-political and aesthetic ideas of Socialist Realism doctrine in the Latvian periodicals Literature and Art (LitArt) and The Flag. Several programmatic, ideologically saturated articles that were published in The Flag in the 1940s were compared to the rest of the corpus to explore the proliferation and persistence of similar ideas in the course of following decades. Authors have employed methodologies commonly used for plagiarism detection: fingerprinting and the comparison of document similarity based on word embeddings and document similarity measures. In particular, three perspectives were used to examine the similarity and reuse of texts: comparison of matching 5-grams processed by the winnowing algorithm and comparison of documents based on the TF-IDF and Doc2Vec embeddings and cosine similarity metrics. To facilitate the analysis, the results were loaded in the open-source version of Neo4j graph database. The findings were further explored and evaluated qualitatively to identify the distribution of direct citations, frequently reused phrases and most similar documents.&#x0D; &#x0D; &#x0D;
Empowering Independence: A Scoping Review on Innovations in Smart Home Technology for People with Motor Disabilities

Individuals with disabilities, particularly those with motor impairments, comprise a significant portion of the global population. smart home technologies have become a viable tool for improving their living conditions and promoting independence. This paper systematically synthesizes recent research on smart home technologies that enhance the living conditions of people with mobility impairments. Among the 27 studies reviewed, individuals with various mobility disabilities benefited from smart home technology, and independent living became more feasible. However, some limitations still exist, including the efficiency and convenience of wearable equipment, system sophistication, and generalization to a wider range of use. The findings of this study may inform the design of next-generation smart home technology for universal users.
The Recognition of an E-Mail Phishing Cyberattack in Business Organizations

information (personal or official) for financial gain. That is one of the oldest cyber threats. There is a wide range of phishing attack techniques, and the most frequent one is performed via electronic mail. Due to major changes in conducting business in the last pandemic, which entail remote work, fast digital transformation and the increase of ICT technologies, the statistics show that phishing attacks are on the rise. Employees who lack developed awareness of phishing attacks, responsibility and knowledge represent a potential danger to the entire organisation. This paper describes the research on e-mail phishing recognition in business organisations in Primorje-Gorski kotar County. The research showed that employees are not aware of phishing attacks to a sufficient extent and that all the habits contributing to the IT security level regarding these attacks are not satisfying. To protect against such attacks, organisations should, in addition to implementing safety technical measures, actively educate employees and periodically implement testing.
Automated Coding of Historical Danish Cause of Death Data Using String Similarity

The study of causes of death has been central to some of the most influential studies of the modern mortality decline in the nineteenth and twentieth centuries. The digitization of individual-level cause of-death data has been game-changing, however, the data presents a major challenge: how do we code the thousands of unique strings for analysis in an efficient way? This paper aims to see how far we can get with automated coding based on string similarity. We do this by applying a Jaro Winkler string similarity algorithm in Python (pyjarowinkler) that codes our cause of death data from the Copenhagen Burial Register 1861-1911 to DK1875, a contemporary coding and classification system from nineteenth century Denmark. We then compare the performance of the algorithm to that of a manual (historian) coder in three different ways: at the level of each unique cause-of-death string, at the level of each cause-of-death group and for the overall cause-of-death pattern for all burials in Copenhagen 1861-1911. Our results show that a minimum-effort algorithm coded approximately half of the causes of death correctly compared to the manually coded dataset. This means that the method applied here is not accurate enough to use for actual data analysis of mortality patterns, as it is not possible to examine individual causes within larger causal groups. However, the results are promising for different uses of the method as a help for the manual coder. A way forward could be to use cut-off points of the Jaro-Winkler scores, coding only those causes where the string similarity match is relatively certain or use the automated method to catch most of the initial cases of a certain disease with a very set phrasing, such as cancer. In both cases, the remainder of the unique cause of death strings could then be coded by a manual coder.
Future of Food Computing: Deepening the Scope by Broadening the Network

In recent years, there has been a growing interest in using food data in various areas of computer science. Food, as a fundamental necessity for human sustenance, undergoes complex processes from farm to fork, involving physical, biological, intellectual and emotional dimensions. The need to ensure the effective and sustainable functioning of global food systems, while addressing human and environmental challenges, necessitates the development of food computing. But the question remains: How should food computing evolve to address fundamental issues in the food systems? While global sharing of data, information andknowledge is a way forward, we propose to increase the effectiveness of this sharing by transcending narrow disciplinary boundaries and engaging a wider range of stakeholders to address practical challengesfacing human society. We argue that food computing requires a multidisciplinary approach that is expansive in both scope and effectiveness. We envision a future where food computing serves as a comprehensive computational infrastructure for all aspects of food, including its production, consumption, and impact on individuals and the environment. The field of Food Computing is highly relevant to Digital Humanities (DH) researchers. By actively engaging in this area, DH researchers can make substantial contributions and shape the trajectory of future food systems, taking into account the intricate interplay of cultural, social and ethical complexities.
Analysis of error propagation: from raw light-field data to depth estimation

In micro-lens-array-based light-field imaging, the micro-lens centers serve as the origins of local micro-lens coordinate systems. Each micro-lens receives angular/depth information coded according to its center location. Therefore, the errors in positioning the micro-lens centers will lead to errors in depth estimation. This paper proposes a method that resolves error propagation from raw light-field data to depth estimation based on analyzing large amounts of simulated images with various aperture sizes, noise levels, and object distance values. The simulation employs backward ray tracing and Monte Carlo sampling to improve computational efficiency. The errors are counted and accumulated stepwise from center positioning and generation of sub-aperture images to depth estimation. The disparity errors calculated during depth estimation are shown to be more apparent either with more significant center positioning errors or with a greater defocusing distance. An experiment using an industrial light-field camera is conducted, confirming that disparity errors at considerable object distances can be reduced significantly when the micro-lens centers are positioned with higher accuracy.
Model Blindness II: Investigating a Model-Based Recommender System’s Impact on Decision Making

Adoption and use of misspecified models can lead to impoverished decision-making—a phenomenon we term model blindness. A series of two experiments investigated the consequences of model blindness on human decision-making and performance and how those consequences can be mitigated via an explainable AI (XAI) intervention. The experiments implemented a simulated route recommender system as a Decision Support System (DSS) with a true data-generating model. In Experiment 1, the true model generating the recommended routes was misspecified at two different levels to impose model blindness on users. In Experiment 2, the same route-recommender system was augmented with a mitigation technique to overcome the impact of model-misspecifications on decision-making. Overall, the results of both experiments provided little support for performance degradation. The participants' decision strategies revealed that they could understand model limitations from feedback and explanations and could adapt their strategy to account for those misspecifications.
Data from Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data

&lt;div&gt;Abstract&lt;p&gt;Cancer is the second leading cause of death in the United States, and breast cancer is the fourth leading cause of cancer-related death, with 42,275 women dying of breast cancer in the United States in 2020. Screening is a key strategy for reducing mortality from breast cancer and is recommended by various national guidelines. This study applies machine learning classification methods to the task of predicting which patients will fail to complete a mammogram screening after having one ordered, as well as understanding the underlying features that influence predictions. The results show that a small group of patients can be identified that are very unlikely to complete mammogram screening, enabling care managers to focus resources.&lt;/p&gt;Significance:&lt;p&gt;The motivation behind this study is to create an automated system that can identify a small group of individuals that are at elevated risk for not following through completing a mammogram screening. This will enable interventions to boost screening to be focused on patients least likely to complete screening.&lt;/p&gt;&lt;/div&gt;
TABLE 4 from Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data

&lt;p&gt;Cross-validated model training results on test fold showing logistic regression as the best performer by AUC, and SVM by F1 score&lt;/p&gt;
A quadrotor unmanned aerial vehicle trajectory generation algorithm based on RRT-JPS and corridors

This paper proposes a novel algorithm about generating trajectory planning for quadrotor unmanned aerial vehicle (UAV) using fusion algorithm, In the path search part, this paper uses the fusion algorithm based on rapidly-exploring random tree (RRT) and jump point search (JPS), and uses the algorithm based on flight corridor in the trajectory generation stage. Firstly, we use grid map to store the environment information, which is the best mapping way for JPS. Based on JPS algorithm, we adopt RRT-JPS algorithm by combine JPS with the improved RRT algorithm, to enhance the smoothness and speed of path search. Then, by expanding the path block selected by RRT-JPS algorithm, a safe flight corridor can be generated. Finally, by adding time and energy consumption to the cost function in the trajectory optimization algorithm, optimization result can be faster under the premise of ensuring smoothness. The simulation and experimental results show the application of the method is smoother and fewer resource consumption.
The Effect of 7th Grade “Cell and Divisions” Unit Teaching with Augmented Reality Technology on Students' Academic Achievement

This study was carried out to determine the effect of the 7th grade 'Cell and Divisions' unit of the science course with augmented reality technology on the academic achievement of the students. The study group of this research, which was prepared in the "pretest-posttest control group" quasi-experimental model, consists of 79 students in the 7th grade attending a public school in the Antakya district of Hatay province in the 2017-2018 academic year. There are 40 students in the experimental group and 39 students in the control group. While the "Cell and Divisions" unit was taught to the experimental group students by using active learning, technology-centered and internet interactive learning methods with augmented reality applications, the control group students received regular education in line with the program. The application was completed by the researcher within 16 lesson hours. The "Cell and Divisions Achievement Test" (CDAT), which was used as a data collection tool and for which validity and reliability studies were conducted, was administered to both groups before and after the application. In the analysis of the collected data, dependent and independent t-tests from statistical procedures were tried to be analyzed using SPSS 21 package program. At the end of the evaluation of the data; it was concluded that the academic achievement of the students in the experimental group was significantly higher than the students in the control group and the effect size was calculated intermediate level effect. It is thought that the use of augmented reality applications in science courses may have positive effects on students' achievement.
Coating Silver Tree‐like Fractal Structure with Silica Layer for Inhibiting Chemical Reactions of Analytes in Surface‐Enhanced Raman Scattering

Surface-Enhanced Raman Scattering In article number 2300085 by Hiroya Watanabe, Nobuyuki Takeyasu, and colleagues, silver tree-like fractal structures were self-grown, and covered with thin silica layers. Surface-enhanced Raman scattering was measured with the silica-coated silver tree-like fractal structures. The unwanted peaks, which were observed with non-coated silver tree-like structure, disappeared although the sensitivity reduced with the thin silica layer.
Pedagogical practice as a factor of selection of future teachers

Studies of teachers’ professional development indicate that in different regions of the country, the proportion of those who do not want to work at school and have little motivation for teaching is about a third. It is not known what stages of development negative professional identity goes through and under the influence of what factors it develops. The influence of the first active experience of performing a professional role in the course of pedagogical practice was tested on the principle of ‘before-after’ on a panel from a real group of students with dual pedagogical specialization. Practice created quasi-experimental conditions. Before and after, students filled out a questionnaire determining the relationship of the desire to get a job at school with a number of socialization features, with the student’s idea of his compliance with the role of a teacher, with an assessment of the attractiveness of individual teaching functions, the material and social status of teachers in the aspect of the student’s personal claims. The measure of the impact of practice was defined as the delta of changes in numerical variables and as the presence of changes in nominal variables. About a third of the students had previously formed an internal positive motivation for the teaching profession. The share of school-oriented students is slightly less than a third (28%). It is unchanged before and after practice. Quantitative shifts are very small, which indicates either the stability of orientations, or the adaptation shock already experienced during the introductory practice and requires monitoring from the first year.
Secure Authentication Process with Intruder Detection and Reporting Mechanism

Abstract—The implementation of robust authentication mechanisms is essential for ensuring the security of sensitive systems and preventing unauthorized access. This research paper presents a novel approach to enhance the authentication process by incorporating an intruder detection and reporting mechanism. The proposed system captures the image of an individual attempting unauthorized access after multiple failed login attempts and sends it to the administrator using the Twilio platform. Additionally, the system tracks the IP address of the intruder and provides geolocation information to aid in identifying the potential threat. The integration of these security measures enhances the overall security posture and aids in timely response and mitigation against potential threats. Keywords—Authentication mechanisms, Security, Unauthorized access, Intruder detection, Reporting mechanism, Image capture, Twilio platform, IP address tracking, Geolocation information, Security measures, Timely response, Mitigation, and Potential threats
A Study on on the People's Terrorism Response Behavior

This study attempted to verify the impact of the public's perception of terrorist risk on terrorist response behavior using the research model of Lee and Lemyre (2009).As the importance of civic participation in counterterrorism policies is already recognized in developed countries and policy research is being conducted, Korea also needs to verify the impact of terrorism risk awareness on terrorist response actions in the event of a risk.Because people's behavior is predictable through this inquiry and predicted behavior can be controlled in advance, policy measures are proposed to avoid confusion and induce efficient response behavior in the event of a risk. Ultimately, it aims to understand and manage the public's response to terrorist risks that harm the people in the future and lay the foundation for designing effective social measures.
Experiences of digital education from the students’ perspective

According to some literature about communication in a target language, digital competency and creativity are highly important, and achieving this is exceedingly feasible with the methods employed in experiential education.The aim of the present study was to identify the advantages and disadvantages of the online and offline digital education of students at the University of Public Service (hereinafter: UPS) in Budapest, Hungary, utilizing planned experience-based research. The relevance of the research covers two independent and separate quantitative research themes: 1. digital education during the first period of the coronavirus pandemic; and 2. online quantitative exploratory research. The results of the research support the planned experience-based research and confirm it is suitable for measuring the “flow state” of students, where flow is the optimal state where someone is fully engaged in a task at hand with little distraction. It was revealed that students can reach a high flow state when participating in a digital learning environment, which facilitates high motivation to participate in the lessons and has positive effects on the development of the students’ communication and digital competences.