Reviewerly/exHarmony huggingface dataset:
GERMAN-BASED INTERNET LINGUISTICS: HISTORY REVIEW AND MODERN TRENDS

This article focuses on a new linguistic discipline called Internet linguistics, reveals historical aspects of its formation, and summarizes research strategies in modern linguistic studies in German-speaking countries. The article provides a brief overview of the main studies on Internet-linguistics by German-speaking researchers from the late 1980s to the present. Research analysis evaluates important terminological aspects: debates around key concepts and objects of study. With the invention of the Internet and rapid progress made in digital media, forms of social interaction were significantly impacted, along with related tendencies of development of the modern German language. The analysis highlights a contribution of German-speaking academic researchers to the debate on the possibility of distinguishing a special language of the Internet and language criticism on the Internet. The article presents the studies addressing the topic focus on linguistic innovations on the Internet and the pragmatic potential of the linguistic phenomena typical for the modern German language. The article briefly describes the research approaches to the problems of new media and new forms of communication in the digital world (SMS text messages, chat rooms, blogs, tweets, etc.), multimodal Internet phenomena (emoji, memes), standards and rules of online behavior (Netiquette). Contemporary studies forced a new viewpoint on some concepts from related disciplines such as “code-switching”, conceptual oralness and conceptual writing, proximity language and distance language. The necessary part of the article deals with the discursive and applied aspects of the Internet linguistics, such as creation of the Internet corpora, launching international projects, etc.
Generalized Net Model of the General Claim Process – Proceeding before an Appeal Court

In the present paper an application ot the apparatus ot Generalized nets is proposed for modeling the proceedings before the appeal court. The advantages of using such model are very different, some of them are expressed in helping to detect and eliminate unnecessary complications in the process itself, leading to its simplification and optimization, presenting a complex process simpler and more understandable, for scientific and teaching needs etc.
INTERACTION BETWEEN MULTISENSORY INFORMATION AND EMOTIONAL ACTIVATION IN VIDEO-FACILITATED PSYCHOTHERAPY

Emotional activation is regarded as central in psychotherapy. We have developed a method called VideoTalk, where patients video themselves at home according to the therapist’s instructions, and the videos are watched together in the therapy session. The aim of the study was to find out whether watching and listening to a video in psychotherapy readily activates the patient's emotions. The video material was analyzed by theory-based content analysis using the Interactive Cognitive Subsystems (ICS) theory. The ICS theory suggests a link between sensory information and emotions and describes two levels of meaning: propositional and implicational. The implicational level gains information from the propositional level and directly from perceptions and is central to the activation of emotions. Five patients participated in our schema therapy-based intervention. Our material included videos of ten therapy sessions, where the patient and the therapist watched a video made by the patient at home in a state of helplessness. Watching the video in the session was done in parts, and between watching periods there were observation phases consisting of discussion about what the patient had seen and heard on the video and how it affected them. Our data included 38 observation phases, and in 35 of these the patient verbalized an emotion after watching the video. The implicational level was involved in almost all observation phases where the patient verbalized emotions. Our findings are in line with the hypothesis that added multisensory information via video enhances input to the implicational level and therefore emotional activation in psychotherapy. This is a possible mechanism by which the use of VideoTalk could facilitate the psychotherapy process.
Learned associations serve as target proxies during difficult but not easy visual search

The target template contains information in memory that is used to guide attention during visual search. The target template is typically thought of as containing features of the actual target object. However, when targets are hard to find, it is advantageous to use other information in the visual environment that is predictive of the target’s location to help guide attention. The purpose of these studies was to test if newly learned associations between face and scene category images lead observers to use scene information as a proxy for the face target. Our results found that scene information was used as a proxy for the target to guide attention but only when the target face was difficult to discriminate from the distractor face; when the faces were easy to distinguish, attention was no longer guided by the scene proxy. The results suggest that attention is flexibly guided by both target features as well as features of objects that are predictive of the target location. The degree to which each contributes to guiding attention depends on the efficiency with which that information can be used to decode the location of the target. The results contribute to the view that attentional guidance is highly flexible in its use of information to rapidly locate the target.
Implementation consensus algorithm and leader-follower of multi-robot system formation

Robot technology has recently been applied to many applications to help human activities. Mobile Robot is one of the most flexible robot technology. This research uses a mobile robot designed using an omnidirectional wheel for the movement mechanism. Coordination and control of multi-robots can be assigned to perform any task from a different kind of field. Therefore, this paper aims to develop a multi-robot system to form a formation to do the task. The multi-robot system consists of three units Mobile Robot. The formation system will be built based on a coordinate point determined by a consensus point. The leader-follower topology is used to determine the orientation of the robot. ROS (Robot Operating System) is used as middleware to create a multi-robot system. The Open Base package in Gazebo Simulator is also used to simulate the movement of the multi-robot. From three test scenarios, this research results show that all the robots can do and follow the tasks simulated in the Gazebo with an average accuracy of 88.14%. Furthermore, no feedback from the robot to the Gazebo Simulator affects the robot's accuracy average below 90%.
Exploring EFL Students' Strategies in Writing Research Background at One of the Universities in Cirebon

Learning strategies assist students in revealing their individual learning style, recognizing their identity as “learners”, and being conscious of their learning obstacles. The current study aimed to investigate the strategies in writing research background encountered by English Department students at one of Cirebon’s institutions throughout the academic year 2021/2022. This study employed a qualitative technique with a case study design, with thirty students participating as respondents. The data was gathered via a questionnaire and an interview. There are two research instruments in this research, especially close-ended questionnaire and semi-structured interview.The instruments were also distributed in Bahasa Indonesia. According to the findings, students adopted strategies such as reviewing the references of journal articles. This causes students to use strategies to reduce these difficulties, including reading reference journals or articles. The score using this strategy is 95%. Furthermore, respondents indicated that they were requesting revisions from their supervisor.This is the experience of lecturers were better because they have done research many times. As a result, they were changing the sentence structure check.In addition, it creates sentences that are difficult to understand between sentences and paragraphs that are out of sync. There was a need for a revision of the review to produce a coherent reading and not confused the readers.It is suggested that the students choose the appropriate strategy, so that the strategy can be implemented properly.
Show me the data! Data sharing practices demonstrated in published research at the University of Massachusetts Chan Medical School

Objective: In the interest of making data findable, accessible, interoperable, and reusable (FAIR), the National Institutes of Health (NIH) will institute a new Data Management and Sharing Policy in January 2023. This policy will require researchers applying for NIH funding to submit a Data Management and Sharing Plan. As 63% of grant dollars received by University of Massachusetts Chan Medical School (UMass Chan) researchers comes from the NIH, we explored whether UMass Chan researchers are currently sharing data associated with their published research and how they shared their data.&amp;nbsp;Methods: PubMed was searched for articles published in 2019 with a UMass Chan researcher as either the first or last author. These articles were examined for evidence of original or reused data, the type of data, whether the article stated that data was available, and where and how to find that data.&amp;nbsp;Results: Of the 361 articles with original data, 26% had a data availability statement. However, most articles (71%) did not mention where data could be accessed. The data storage location of the estimated 1551 original datasets was similarly not mentioned for 74% the datasets with the next largest category being available upon request (8.6%). Genomic data repositories such as the Gene Expression Omnibus were among the top repositories used by authors. Similar areas for improvement were noted for permanent identifier use (46% had a permanent identifier), using non-proprietary file formats (most popular format was Excel), and citing reused data. Authors who published open access were more likely to share their data.&amp;nbsp;Conclusions: While some researchers at UMass Chan have embraced data sharing, particularly genomic data sharing, we expect there will be more data shared in the coming years with the implementation of the new NIH Data Management and Sharing Policy.
There's no "I" in Research Data Management: Reshaping RDM Services Toward a Collaborative Multi-Stakeholder Model

Objective: This article examines a reshaped service model for research data management (RDM) founded on centralized and cohesive collaboration between multiple stakeholders at a large research university in Canada. This initiative, along with a newly formed team dedicated to RDM service provision, is a joint effort by the institution’s Vice-Principal Research and Innovation (VPRI), Library, IT Services, and Research Ethics units.Methods: This article presents a single case study methodology. The authors reflect on services such as “query the panel” sessions where researchers across all disciplines bring their questions to representatives from the Library, IT, Research Ethics, and VPRI. This case study also highlights the use of Jira’s service desk software as a user management system. The authors also present descriptive statistics representing engagement with this new unit and our services.Results: Support for RDM requires expertise from multiple domains. With a collaborative approach as a guiding principle and a focus on establishing a small, but agile team comprised of a librarian along with stakeholders from IT and VPRI, it is possible to leverage resources and support for RDM from a broad range of units across an institution.&amp;nbsp;Conclusions: At many institutions, RDM services are siloed within the library or an adjacent campus unit. New digital technologies have profoundly transformed academic research across all disciplines, necessitating the evolution of corresponding research data-related services. The authors will conclude by outlining specific lessons learned in reshaping digital research infrastructure-related services at their institution.
The Open Science of Deep Learning: Three Case Studies

Objective: An area of research in which open science may have particularly high impact is in deep learning (DL), where researchers have developed many algorithms to solve challenging problems, but others may have difficulty in replicating results and applying these algorithms. In response, some researchers have begun to open up DL research by making their resources available (e.g., code, datasets and/or pre-trained models) to the research community. This article describes three case studies in DL where openly available resources are used and we investigate the impact on the projects, the outcomes, and make recommendations for what to focus on when making DL resources available.Methods: Each case study represents a single project using openly available DL resources for a research project. The process and progress of each case study is recorded along with aspects such as approaches taken, documentation of openly available resources, and researchers' experience with the openly available resources. The case studies are in multiple-document text summarization, optical character recognition (OCR) of thousands of text documents, and identifying unique language descriptors for sensory science.Results: Each case study was a success but had its own hurdles. Some takeaways are well-structured and clear documentation, code examples and demos, and pre-trained models were at the core to the success of these case studies.Conclusions: Openly available DL resources were the core of the success of our case studies. The authors encourage DL researchers to continue to make their data, code, and pre-trained models openly available where appropriate.
Developing a Data Fellowship Program and Peer-to-Peer Support Model

Objectives: To support the library’s research data services program and provide opportunities for student employees to gain skills in supporting data science research and teaching, the author led the creation of a data fellowship program at their institution. The author describes the development of the program’s peer-to-peer support model and how that model was integrated into the library’s broader research data services program.Methods: There were two aspects to the fellowship’s peer-to-peer support model. First, the data fellows provided direct support to their peers—through instruction, reference support, and outreach—who were engaged in data-related research projects and class assignments. Second, the fellows engaged in professional development and learning opportunities led by library colleagues.&amp;nbsp;Results: During the first year of the program, the data fellows provided virtual data reference support, developed two CANVAS modules, conducted an email outreach campaign, and authored several blog posts. Furthermore, the data fellows received data reference training and engaged in discussions with library experts on pertinent data-related topics. These discussions provide the fellows with knowledge and skills that can be utilized both during and after the fellowship.Conclusions: The first year of the STEM Data Fellowship program proved to be successful in establishing the foundation for a robust peer-to-peer learning and support model for research data services at the author’s institution. The author plans to further enhance and grow the program in future years by providing more opportunities for established and new data fellows to contribute to the library’s ongoing RDS efforts.
Developing a Discipline-Specific Corpus and High-Frequency Word List for Science and Engineering Students in Graduate School

Japanese graduate school students in the field of science and engineering need to read academic research in their second language (L2), and such tasks can be challenging. Studies showed a strong (0.78) correlation between vocabulary size and reading comprehension (McLean et al., 2020), and providing high-frequency word lists could enhance comprehension. In this work-in-progress, 1.35 million tokens of professor-recommended reading materials were used to investigate a method to create a vocabulary list that would benefit science majors in graduate school; the procedures to create a corpus and a high-frequency word list efficiently; and the steps required to create a cleaner corpus. This paper outlines a systematic literature-informed method that includes input from professors in the field; the combined use of tailored script in MATLAB and AntCont (Anthony, 2022) generated corpus and high-frequency words efficiently; and repeated comparison of original PDFs and the matching text files, then adding MATLAB script to deal with specific issues created by a cleaner text. This proposed method can be applied in other contexts to enhance the generation of high-frequency word lists
Public Perceptions of The Government in Handling Covid-19 Pandemic with Covid-Score-10 Approach and Dematel Analytical Network Process (DANP)

COVID-19 puts intense pressure on governments to take unprecedented and rapid action around the world in dealing with the COVID-19 pandemic. This research takes steps to determine the public's assessment of government programs to resolve the COVID-19 pandemic. The most likely solution is to use the combined method of the Covid Score and the Analytical Network Process (ANP) in determining how to increase public support for government programs to resolve this pandemic. The results of the data processing provide a significant picture of support for the government by 34% in handling the pandemic. To increase wider community support, it is proposed to apply more stringent rules as shown in the ANP value of 16.2. Research contribution to implementing COVID SCORE 10 with Analytical Network Process (ANP) review for implementation in Covid-19 cases in the sample area
Designing Standard Operation Procedure to Produce Environmentally Friendly Liquid Detergent

Washing clothes is an activity that is often done by people in the world. However, detergents sold in the market are harmful to human skin and aquatic ecosystems in rivers. Therefore, this research focuses on environmentally friendly detergents that can minimize the impact on the environment and humans. This study aims to develop standard operating procedures (SOP) in the production of environmentally friendly detergents. This was conducted by developing the initial SOP design, simulating the initial SOP, and identifying problems. Then, followed by improving, designing, and simulating the final SOP. The results of this study acquire SOP that generates products with better consistency and quality.
Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

Abstract The current research aimed to establish a method for measuring the percentage of diaminobenzidine (DAB) staining on immunohistochemical slides with high accuracy and efficiency. The R programming language was utilized in this endeavor. A total of 50 slides were collected from various types of tissue, and were stained using an anti-cytokeratin antibody and the DAB detection method. These slides were then scanned using a high-resolution scanner, and the resulting images were analyzed using R, a custom script was specifically developed to segment the tissue and DAB-positive areas, and calculate the percentage of DAB staining on the slide. The results were then compared to manual measurements of DAB staining performed by a trained technician. The R-based method was found to be highly accurate, with a mean absolute error of only 0.76 % compared to manual measurements, this study provides evidence that the use of R for DAB quantification is a fast and reliable alternative to manual methods, enabling the analysis of large numbers of slides in a short period of time. It offers a valuable tool for researchers and technicians in the field of histopathology, enabling them to quickly and accurately analyze DAB staining on immunohistochemical slides, which is essential for the diagnosis and treatment of various diseases.
ISP Distillation

Nowadays, many of the images captured are ‘observed’ by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition or semantic segmentation, assume images are transformed into some canonical image space by the camera Image Signal Processor (ISP). However, the camera ISP is optimized for producing visually pleasing images for human observers and not for machines. Therefore, one may spare the ISP compute time and apply vision models directly to RAW images. Yet, it has been shown that training such models directly on RAW images results in a performance drop. To mitigate this drop, we use a RAW and RGB image pairs dataset, which can be easily acquired with no human labeling. We then train a model that is applied directly to the RAW data by using knowledge distillation such that the model predictions for RAW images will be aligned with the predictions of an off-the-shelf pre-trained model for processed RGB images. Our experiments show that our performance on RAW images for object classification and semantic segmentation is significantly better than models trained on labeled RAW images. It also reasonably matches the predictions of a pre-trained model on processed RGB images, while saving the ISP compute overhead.