Reviewerly/exHarmony huggingface dataset:
A Study of Contrastive Learning Algorithms for Sentence Representation Based on Simple Data Augmentation

In the era of deep learning, representational text-matching algorithms based on BERT and its variant models have become mainstream and are limited by the sentence vectors generated by the BERT model, and the SimCSE algorithm proposed in 2021 has improved the sentence vector quality to a certain extent. In this paper, to address the problem that the SimCSE algorithm has—that the greater the difference in sentence length, the smaller the probability that the sentence pairs are similar—an EdaCSE algorithm is proposed to perturb the sentence length using a simple data enhancement method without affecting the semantics of the sentences. The perturbation is applied to the sentence length by adding meaningless English punctuation marks to the original sentence so that the model no longer tends to recognise sentences of similar length as similar sentences. Based on the BERT series of models, experiments were conducted on five different datasets, and the experiments proved that the EdaCSE method improves an average of 1.67, 0.84, and 1.08 on the five datasets.
Integration of Electronic Resources in Library Environment of Higher Education Institutions: Advantages and Challenges

The purpose of this research is to study the integration of electronic resources in the library environment of higher education institutions, to identify the benefits and challenges associated with this process. The study considers the main areas of development of electronic resources, such as access to digital libraries, online catalogues, and databases, providing quality information support for research and teaching activities of students and faculty, as well as improving the digital user service system. The research methodology involves a multi-level approach to analysing the integration of electronic resources in the library environment of higher education institutions. Firstly, a thorough review of the scientific literature is conducted, covering the issues of introducing digital technologies and electronic resources in the educational process and library work. Subsequently, practical examples of successful library modernisation in higher education institutions libraries are analysed in order to identify key success factors, advantages, and challenges on the way to the development of digital tools and services. The research results will help identify trends and prospects for the development of digital libraries. A particularly important element of the methodology is conducting surveys and interviews with professionals in library science, faculty members, and staff of higher education institutions. Engaging various groups of employees will enable a detailed understanding of the needs, attitudes, and beliefs regarding the integration of electronic resources, as well as their impact on the learning and cognition process. The application of these methods in a comprehensive manner will contribute to the creation of a generalised understanding of the problem and the development of effective recommendations for the integration of electronic resources into the library environment of higher education institutions. Scientific novelty. The study focuses on the changes that are taking place in the libraries of higher education institutions as a result of digital transformation and highlights the importance of adapting to new technologies and user needs. Conclusions. The research findings indicate that integrating electronic resources into the library environment of higher education institutions provides a range of benefits, such as increased accessibility and diversity of information resources, improved service, and user convenience. On the other hand, there are challenges that require attention, such as ensuring information security, facilitating library staff adaptation to new technologies, and developing strategies for the successful implementation of digital innovations.&#x0D; Key words: digital resources; library environment; higher education institutions; technology integration; information literacy.
Readers Theater in Desktop VR: A Pilot Study with Grade Nine Students

Appropriate techniques for promoting reading fluency are difficult to implement in the classroom. There is little time to provide students with individualized feedback on reading aloud or to motivate them to do so. In this context, Virtual Reality (VR) can be beneficial for learning because it allows for individualized feedback and for increasing learner engagement. Studies that analyze established methods of language learning in VR at school are thus far lacking. Therefore, this pilot study is one of the first to analyze student acceptance of reading fluency training in desktop VR at a secondary school. The interview guide was developed in accordance with the Technology Acceptance Model. The desktop VR environment is web-based and provides individual and collaborative opportunities for training reading fluency, giving, and receiving feedback, and deepening content understanding of reading texts. To analyze the acceptance of the desktop VR environment, five guided interviews were conducted. The results reveal that despite various technical challenges within the VR environment, students not only accepted but also appreciated the reading fluency training in VR. The integration of established concepts of reading fluency training in foreign language classrooms has great potential as an additional value in addressing the challenges of face-to-face instruction.
The Effectiveness of Repetition Drill Technique To Improve Electrical Engineering Student’s Speaking Skill

The purpose of this study is to investigate how effective the repetition drill technique is in improving the speaking ability of the 2022–2023 Second Semester Electrical Engineering students at Cilacap State Polytechnic. The study was conducted using a classroom action research technique, and 23 students participated as study participants. A pretest was administered to find out what obstacles the students faced, and the average score was 39.67. Data were collected using an oral exam over three study cycles. The average score of the students in the first cycle, conducted on March 21, 2023, was 46.73. Next, the average score of the students for the second test conducted on March 22, 2023, was 67.93. On March 24, 2023, the average score for Cycle 3 students would be 82.06. The student's average score improves on rounds 1–3. The student's speaking ability improves gradually. This is confirmed by the post-test results obtained in the categories of 'good' and 'excellent' after 3 cycles of review exercises in English practice class. Students' pronunciation and language skills improves significantly. In summary, this study proved that repetition drill techniques can be used very effectively in English practice courses.
Virtual-Agent-Based Language Learning: A Scoping Review of Journal Publications from 2012 to 2022

Researchers have pointed out the importance of virtual agents in technology-supported language learning; however, how to effectively combine the two remains a challenge for educators and educational technologists. To this end, this study reviewed publications in the field of virtual-agent-based language learning research from 2012 to 2022 in the Web of Science SSCI Core Collection database and explored the dimensions of publication trends, country and regional distribution, participants, research methodology, research platforms, role of virtual agents, language proficiency, research hot topics, theoretical foundations, and hot issues and trends in the field of virtual-agent-based language learning research. Cluster and co-occurrence analysis using VOSviewer software was used to analyze the links among country and region distribution, keywords, and terms. It was found that (1) the top four regions in terms of the number of citations for authors were, in descending order, Iran, Japan, South Korea, and Brazil; (2) the learner characteristics that scholars were most concerned about were learning effectiveness, memory performance, social presence, learning experience, and motivation; and (3) the results of co-occurrence analyses classified virtual-agent-based language learning research into eight clusters, namely, anthropomorphic virtual agents, the effects produced by virtual agents, the social interaction of virtual agents, animated virtual agents and language achievement, the gestures of virtual agents, the effects of virtual agents on learner characteristics, computer-assisted learning, and the design of virtual agents. The lack of the systematic application of virtual agents in language learning prevented previous studies from revealing the language learning process in virtual-agent-based learning environments. Therefore, this study made appropriate recommendations for future investigations on how virtual agents can improve language learning for researchers, teachers, and decision makers.
From Optimal Control to Mean Field Optimal Transport via Stochastic Neural Networks

In this paper, we derive a unified perspective for Optimal Transport (OT) and Mean Field Control (MFC) theories to analyse the learning process for Neural Network algorithms in a high-dimensional framework. We consider a Mean Field Neural Network in the context of MFC theory referring to the mean field formulation of OT theory that may allow the development of efficient algorithms in a high-dimensional framework while providing a powerful tool in the context of explainable Artificial Intelligence.
Review of age and gender detection methods based on handwriting analysis

Abstract Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation.
Abstracting road traffic via topological braids: Applications to traffic flow analysis and distributed control

Despite the structure of road environments, imposed via geometry and rules, traffic flows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euclidean spaces are often limited by their high sample complexity and the sparseness of available datasets. Our key insight is that the structure of traffic behavior could be effectively captured by lower-dimensional abstractions that emphasize critical interaction relationships. In this article, we abstract the space of behavior in traffic scenes into a discrete set of interaction modes, described in interpretable, symbolic form using topological braids. First, through a case study across real-world datasets, we show that braids can describe a wide range of complex behavior and uncover insights about the interactivity of vehicles. For instance, we find that high vehicle density does not always map to rich mixing patterns among them. Further, we show that our representation can effectively guide decision-making in traffic scenes. We describe a mechanism that probabilistically maps vehicles’ past behavior to modes of future interaction. We integrate this mechanism into a control algorithm that treats navigation as minimization of uncertainty over interaction modes, and investigate its performance on the task of traversing uncontrolled intersections in simulation. We show that our algorithm enables agents to coordinate significantly safer traversals for similar efficiency compared to baselines explicitly reasoning in the space of trajectories across a series of challenging scenarios.
Improved conversational recommender system based on dialog context

Abstract Conversational recommender system (CRS) needs to be seamlessly integrated between the two modules of recommendation and dialog, aiming to recommend high-quality items to users through multiple rounds of interactive dialogs. Items can typically refer to goods, movies, news, etc. Through this form of interactive dialog, users can express their preferences in real time, and the system can fully understand the user’s thoughts and recommend corresponding items. Although mainstream dialog recommendation systems have improved the performance to some extent, there are still some key issues, such as insufficient consideration of the entity’s order in the dialog, the different contributions of items in the dialog history, and the low diversity of generated responses. To address these shortcomings, we propose an improved dialog context model based on time-series features. Firstly, we augment the semantic representation of words and items using two external knowledge graphs and align the semantic space using mutual information maximization techniques. Secondly, we add a retrieval model to the dialog recommendation system to provide auxiliary information for generating replies. We then utilize a deep timing network to serialize the dialog content and more accurately learn the feature relationship between users and items for recommendation. In this paper, the dialog recommendation system is divided into two components, and different evaluation indicators are used to evaluate the performance of the dialog component and the recommendation component. Experimental results on widely used benchmarks show that the proposed method is effective.
Aspect term extraction via adaptive fusion of sequential and hierarchical representation

In aspect-based sentiment analysis, a fundamental task is extracting aspect terms from opinionated sentences. Aspect term extraction (ATE) has been found to play a critical role among several scenarios, such as service quality improvement and recommendation systems. While deep learning-based methods have achieved great progress in ATE, they mainly consider sequential semantic information and generally ignore the utilisation of syntactic relations of the whole sentence on overall meanings. Furthermore, performances of these methods may also be diminished by poor handling of relation and text noises. To address these issues, we propose a fused sequential and hierarchical representation (FSHR) model, wherein both sequential and hierarchical representations are generated, which facilitates not only the capture of linear semantic information for predicting meaning-related aspect terms but also the utilisation of syntactic relations over the entire sentence to better identify structure-related aspect terms. Moreover, to refine the aspect representation, we incorporate relation-gate mechanism which selectively activates meaningful syntactic dependency paths and design the multi-way aspect attention which prompts the model to focus on relevant text segments about particular aspects. Eventually, sequential and hierarchical representations are adaptively fused for aspect prediction. Experiment results on four datasets demonstrate that FSHR outperforms competitive baselines, and further extensive analyses reveal the effectiveness of our model.
The impact of pedagogical and ICT training in teachers’ approaches to online teaching and use of digital tools

This study examines the relationship between teachers’ pedagogical and ICT training, their approaches to online teaching and the use of digital tools. The participants were 265, teachers from the large multi-disciplinary university in Finland. The data was collected in May–June 2020 during the early stages of COVID-19 pandemic. The results showed that teachers used digital tools more for delivering information and less for activating students. The study revealed that pedagogical training enhanced teachers’ learning-focused approach to online teaching and diverse use of digital tools. Contrary to expectations, teachers’ ICT training was not related to the use of digital tools. Therefore, it is important to provide pedagogical training to support teachers in implementing teaching in diverse teaching-learning environments and enhance building an online teaching, in which digital tools are used to promote interaction in online environment.
Evaluation of an immersive virtual reality wayfinding task

A recommendation of: Eudave L., Martínez M., Valencia M., Roth D. Evaluation of spatial learning and wayfinding in a complex maze using immersive virtual reality. A registered report https://osf.io/c2zvr
German Human Genome-Phenome Archive in an International Context: Toward a Federated Infrastructure for Managing and Analyzing Genomics and Health Data

With increasing numbers of human omics data, there is an urgent need for adequate resources for data sharing while also standardizing and harmonizing data processing. As part of the National Research Data Infrastructure (NFDI), the German Human Genome-Phenome Archive (GHGA) strives to connect the data from German researchers and their institutions to the international landscape of genome research. To achieve this, GHGA partners up with international activities such as the federated European Genome-Phenome Archive (EGA) [1] and the recently funded European Genomic Data Infrastructure (GDI) project to enable participation in international studies while ensuring at the same time the proper protection of the sensitive patient data included in GHGA.
Exchanging Research Data with SampleDB

Exchanging research data between systems is a vital task in research data management, especially in the context of collaborations. Therefore mapping data into standardized data descriptions and offering interfaces for efficient data exchange are required. The metadata database and electronic lab notebook SampleDB supports various ways of exporting data to other services, such as Dataverse, SciCat, and JupyterHub, as well as file exports in open formats. The concept of SampleDB federations allows data exchange in loosely coupled federations of SampleDB instances.
Knowledge Graph Based RDM Solutions

Based on our experience within the NFDI4Culture and NFDI-MatWerk projects we propose generalized knowledge graph based research data management solutions, which are applicable to other consortia. Our solution covers the construction of a common NFDI core ontology adapted to specific domains via domain extensions as a basis for a knowledge graph (KG) providing information about a consortium and its related research data and software resources. This KG serves as a backend for the web portal that enables interactive access and management of this data. Already implemented for NFDI4Culture and to be adapted by NFDI-MatWerk, this solution might serve as an example solution also for other consortia. We are synchronizing our efforts with ongoing work to implement knowledge graph based research data management in NFDI4DataScience.
Two-Step Approach in Metadata Management for Data Publications at Research Centers

Data repositories like Zenodo have a limited list of metadata to search for. Metadata catalogue are designed to provide a community-specific parameters search, but their deployment has just started. These catalogues require metadata standards for interoperability, which in turn are often in development for many communities. To support publications with a metadata standard in the future, a two-step concept is presented in this article. It discusses how the electronic documentation should be constructed, in order to convert this later into a standardized schema for publication. We will present examples from the laser-plasma community for both steps, firstly how we deal with the complex challenges of metadata management and secondly for methods for developing metadata schemas.
We are Still Here, too! Research Data Management at Universities of Applied Sciences

The objective of the German non-profit association NFDI (German short form for ”National Research Data Infrastructure”) is to make the data stock of the entire German science system accessible to the public. To do so, it should involve all stakeholders. However, currently the Universities of Applied Sciences (UAS) are underrepresented in the NFDI, and there is a danger of neglecting their needs. Therefore, we present the project ”Research Data Management at Universities of Applied Sciences in the State of Rhineland-Palatinate” (FDM@HAW.rlp), which is funded by the German Federal Ministry of Education and Research (BMBF) and financed within the Recovery and Resilience Facility of the European Union. In the project, seven public UAS in Rhineland-Palatinate and the Catholic University of Applied Sciences (CUAS) Mainz follow a common goal: They intend to establish an institutional RDM within a period of three years by building up competencies at the UAS, setting up services for researchers and finding solutions for a common technical infrastructure.
Swiss-AL: Platform for Language Data in Applied Sciences

Open Science is transforming the way researchers collect, process, analyze, and store empirical research data, particularly in the social sciences and humanities, where language data is crucial. This transformation process especially concerns developers and providers of large language corpora and manifests itself in at least three challenges when providing these corpora as Open Research Data (ORD). Challenges concern heterogeneous practices that researchers apply when working with language data, research data lifecycle, and legal and ethical aspect. In this paper, we present Swiss-AL, a language data platform developed in Switzerland that is being transformed into an Open Research Data Resource for Applied Sciences within the Swiss Open Science Strategy. The paper gives an overview over the data contained in Swiss-AL and the infrastructure that is used to process and analyze the data. Furthermore, it presents approaches to the three abovementioned challenges to language ORD.