Reviewerly/exHarmony huggingface dataset:
Topic modelling and text classification models for applications within EFSA

This report presents an overview of topic modelling and classification models in relation to four case studies in the EFSA project OC/EFSA/AMU/2020/02. As adequate document embeddings have a positive influence on the effectiveness of topic modelling as well as text classification, an extensive number of different possibilities for word and document embeddings are discussed. It was found that a multitude of increasingly more complex embeddings are readily available for off-the-shelf use. But as they are trained on large but mostly general text corpora, their utility for domain specific text varies. Fine tuning or creating document embeddings from scratch is only feasible in the presence of enough data and has an associated computational cost. For some domains (like scientific articles), pretrained embeddings are available. For topic modelling, we discuss standard techniques like non-negative matrix factorization and latent Dirichlet allocation as well as more recent methods based on clustering of document embeddings like Top2Vec and BERTopic. For text classification, we consider hierarchical text classification approaches combined with established techniques for text classification via document embeddings. We propose a selection of techniques for each of the case studies justifying their choice and present a plan for evaluation. Finally, we discuss our findings after having implemented and validated the selected techniques.
DAScheduler: Dependency-Aware Scheduling Algorithm for Containerized Dependent Jobs

Abstract Containers have emerged recently as a cloud technology for improving and managing cloud resources. They improve resource sharing by allowing instances to run on top of the host’s operating system. Container-based virtualization runs and manages hosted instances via the host kernel. Resource sharing can cause resource contention. In addition, dependent jobs, which may be deployed across multiple hosts, require frequent communication, resulting in a high volume of network traffic and network contention. The majority of existing research focuses on load balancing, with no consideration for the fact that network contention also plays a significant role in container performance. In this research, we propose a Dependency-aware Scheduling algorithm (DAScheduler) that deploys jobs into containers while accounting for both load balancing and job dependencies. The experimental results show that DAScheduler reduces network traffic by more than half and balances the loads. In comparison to one of the existing state-of-the-art techniques, DAScheduler improves overall cloud performance.
Adding a Web-Based Virtual Reality Classroom Experience to a Hybrid, Blended Course Modality

The blended classroom is a unique space for face-to-face (F2F) interaction and online learning. The blended classroom has three distinct interaction types: in-person synchronous, virtual synchronous, and virtual asynchronous; each of these modalities lends itself to different forms of extended reality. This case study looks at using a virtual reality (VR) classroom for an online synchronous weekly meetings for three upper-division or advanced (junior and senior level) higher education design classes at a university. The use of social web VR for a classroom can offer a collaborative, real-time environment that bridges the gap between virtual video conferences and gaming platforms. This paper examines how to use social web VR in a virtual classroom. Mixed methods were used to collect usability data at the end of the semester survey. The system usability scale (SUS) and several qualitative questions gathered student feedback. Overall, the students enjoyed using the VR classroom, but audio issues seemed to be the most significant pain point. While the overall response was positive, this study will address several areas for improvement from both the student and instructor perspectives. Social, web-based VR offers promising potential. Designing a human-centered virtual environment and considering all participants’ total user experience is critical to a successful learning tool.
Person Re-Identification Method Based on Dual Descriptor Feature Enhancement

Person re-identification is a technology used to identify individuals across different cameras. Existing methods involve extracting features from an input image and using a single feature for matching. However, these features often provide a biased description of the person. To address this limitation, this paper introduces a new method called the Dual Descriptor Feature Enhancement (DDFE) network, which aims to emulate the multi-perspective observation abilities of humans. The DDFE network uses two independent sub-networks to extract descriptors from the same person image. These descriptors are subsequently combined to create a comprehensive multi-view representation, resulting in a significant improvement in recognition performance. To further enhance the discriminative capability of the DDFE network, a carefully designed training strategy is employed. Firstly, the CurricularFace loss is introduced to enhance the recognition accuracy of each sub-network. Secondly, the DropPath operation is incorporated to introduce randomness during sub-network training, promoting difference between the descriptors. Additionally, an Integration Training Module (ITM) is devised to enhance the discriminability of the integrated features. Extensive experiments are conducted on the Market1501 and MSMT17 datasets. On the Market1501 dataset, the DDFE network achieves an mAP of 91.6% and a Rank1 of 96.1%; on the MSMT17 dataset, the network achieves an mAP of 69.9% and a Rank1 of 87.5%. These outcomes outperform most SOTA methods, highlighting the significant advancement and effectiveness of the DDFE network.
Modeling users’ preference changes in recommender systems via time-dependent Markov random fields

Recommender Systems are helpful to many by filtering the information according to an individual’s preferences. However, the choice of a person may change with time. Keeping track of these changes or predicting the next sequence of items is difficult for any recommender system. Most time-aware recommender systems exploit users’ rating distribution to predict the next possible set of items. But due to the sparsity (users’ preferences) in real-world datasets, these models suffer from low accuracy. To overcome such issues, this study proposes a collaborative filtering model which uses a preference estimation technique that uses user and item feature information along with the ratings. Further, we have used preference relations (instead of ratings) to improve the ranking quality of the recommender system. We have used the ML-1M, NetFlix, and Last.fm datasets for evaluation, the benchmark datasets for recommender systems testing. All these three datasets have sparsity levels above 92%. Experimental results indicate significant improvement (5 to 7%) over the state-of-the-art related baselines.
Development of flash-based learning media to improve critical thinking ability of grade VII middle school students

The development of technology is certainly very helpful for developing the world of education. But, many learning activities are carried out using conventional methods, namely lectures with books and blackboards which result in boring learning and poor critical thinking skills on the initial test. The purpose of this study was to determine the level of validity, practicality, and effectiveness of interactive media used to improve critical thinking skills. Research using the ADDIE model shows that the developed interactive media has very valid criteria based on the media validator's assessment of 97.67 and the material validator's assessment of 57 categories. The media developed with very practical criteria based on student practicality questionnaires of 85.06% and teacher practicality questionnaires of 96% categories. In addition, the developed media obtained an effectiveness score of 90.63% based on the posttest of critical thinking skills. From the above findings, it can be concluded that interactive media can be used validly, practically, and effectively to improve critical thinking skills.
VALIDASI E-MODUL BERBASIS ANDROID PADA MATERI PENYAJIAN DATA DENGAN BERBANTUAN SIGIL SOFTWARE

The purpose of this research is to produce an Android-based e-Module using Sigilsoftware that is valid and practical for Class VIIA Data Presentation at SMP N 24 Padang that is valid and practical so that it can be used as a source of learning mathematics, especially data presentation material. The type of research used is development research. This research uses the plomp model. The stages used in this development model are only stages 1 to stage 2, namely the initial investigation stage, the prototyping phase which concurrently tests the validity and practicality. The research instrument used is a validation questionnaires, a one-on-one test questionnaire that is useful for seeing the practicality of e-Modules by teachers, small group test questionnaires that are useful for seeing the practicality of e-Modules by students and interview guidelines. Based on the validator's assessment, the results of the validity of the Android-based e-Modul using the Sigilsoftware were 90.33% with a very valid category. The final value of practicality with the teacher obtained a final score of 80% in the practical category. The final value of practicality with students was obtained 88.61% with a very practical category. Based on the results of the study, it can be concluded that the Android-based e-Module using Sigilsoftware in Data Presentation in class VIIA SMP N 24 Padang is valid and practical for use by teachers and students.
Integrating Electronic Customer Relationship Management and Artificial Intelligence

This chapter aims to provide a theoretical foundation that integrates electronic customer relationship management (e-CRM) and artificial intelligence (AI) in marketing, specifically, marketing intelligence (MI) concepts, values, tools, and practices in the service industry. This chapter employs a critical review of relevant literature on e-CRM and marketing intelligence. Based on the reviewed sources, AI is transforming CRM systems, as evidenced by the latest software. Traditionally, these systems required significant human involvement. However, with the integration of AI, CRM systems are becoming self-updating and auto-correcting, ensuring that relationship management is effectively managed with minimal manual intervention. The outcomes of the chapter are to develop a better understanding that bridges the gap in integrating e-CRM and MI. Additionally, this chapter provides knowledge-based evidence to empower an integrated approach of e-CRM and MI practices in the service industry. It reveals best practices for executives, marketers, and policymakers e-CRM and MI integration
Security Systems Using Machine Vision

Surveillance systems are image acquisition systems that use commercial security cameras fixed at specific locations in stores, houses, or warehouses to enhance safety and protect staff, assets, and buildings. Although these systems are good security options, a temporal secure area or nearby mobile needs an alternative personal monitoring solution that can be constructed by using the smartphone multimedia resources to access a PC's machine vision software, providing a tool for surveillance. In this chapter, an application is developed using only a smartphone camera and a pattern matching algorithm to detect presence of people in a secure area; when presence occurs, a signal is sent to start a recording and/or activate an alarm to warn the users. A virtual instrument algorithm application was able to detect objects in a room, recognizing who are residents and who are guests. The functionality of this project with encouraging results obtained provides a flexible security instrument.
Проблемы и перспективы организации долговременного сохранения цифрового культурного наследия

Long-term digital preservation aims to ensure that the digital content of traditional socio-cultural insti-tutions (libraries, museums, archives, etc.) remains accessible to user communities for a long period of time and for future generations. The article considers the problems and prospects of organizing long-term digital storage of electronic information resources. It is proposed to use for solving the problems of long-term digital preservation approaches focused on the reference model of the open archival information system (OAIS), de-scribed in the ISO 14721:2012 standard. To describe events/actions with a document during archival storage, it is proposed to use the PREMIS metadata model.
Nature and conditions of gold mineralization at the Tau deposit, Mupane mine, Tati Greenstone Belt, Botswana: Evidence from fluid inclusion and arsenopyrite geothermometer

Abstract The gold mineralization at the Tau deposit in northeastern Botswana can be distinguished into two stages. The first stage of mineralization consists of invisible gold associated with early, sulfur‐rich arsenopyrite (Apy1). The second stage is characterized by native gold associated with late, sulfur‐poor arsenopyrite (Apy2). This study aims to constrain the nature of the ore‐forming fluid at the Tau deposit on the basis of fluid inclusion microthermometry and arsenopyrite geothermometry. Quartz crystals closely associated with gold‐bearing sulfides (Apy1) from the host rock host three types of fluid inclusions occurring in the same assemblages: Type I, two‐phase aqueous fluid inclusions, Type II three‐phase aqueous‐carbonic fluid inclusions and Type III one‐ or two‐phase CO 2 ‐rich fluid inclusions. Microthermometric and Raman spectroscopic studies revealed the dominance of coexisting H 2 O+NaCl (Type I) and CO 2 N 2 CH 4 (Type III) fluid inclusions. The coexisting Type I and Type III fluid inclusions could represent products of the immiscibility of a homogenous H 2 OCO 2 NaClN 2 CH 4 primary ore fluid. The salinity of Type I (calculated from melting temperature of ice) and Type II (calculated from dissolution temperature of clathrate) fluid inclusions vary from 3.4 to 9.2 wt% NaCl equiv. and 5.8 to 9.8 wt% NaCl equiv., respectively. The overall fluid inclusion salinity range (&lt;10 wt% NaCl equiv.) and gas compositions of fluids suggest that the primary ore‐forming fluid at the Tau deposit was of low salinity and H 2 OCO 2 N 2 CH 4 NaCl composition. Integration of fluid inclusion microthermometry and arsenopyrite geothermometer results suggest that the first stage and second stages of gold mineralization and the associated alteration at the Tau deposit occurred at pressure and temperature conditions of 75–145 MPa and 290–370°C and 85–160 MPa and 330–370°C, respectively. These results suggest that there was an increase in temperature from the first stage to the second stage of mineralization, resulting in the recrystallization of invisible gold‐bearing arsenopyrite in the first stage to form native gold‐bearing arsenopyrite in the second stage. Fluid composition and the estimated physico‐chemical conditions at the Tau deposit confirm that gold was transported as reduced bisulfide complexes and initial gold deposition was likely caused by a reduction in sulfur contents as a result of sulfidation reactions and pressure‐induced phase separation (fluid immiscibility).
Logic

The terms logic and mathematics are often paired in the historiography of the latter. But, as discussed in this book, logic is not a monolithic process, since it manifests itself in various ways, spanning a cognitive continuum that starts from the highly poetic to the highly rational. Starting with philosophers such as Heraclitus, Parmenides, and Aristotle, rational logic came to be viewed as equivalent to reasoning, a view that became the core of Euclidean geometrical method, which started from a small set of self-evident axioms upon which it built a comprehensive system based on proofs using these axioms. But to construct the system, Euclid showed that proof involved more than one form of logic, as discussed in previous chapters.
Development of a Scale to Measure Children's Educational Mobile Application Usage for Foreign Language

The widespread use of mobile applications in education has led to the need for reliable and valid measurement tools to assess their impact on language learning, particularly among young learners. This study investigated the developmental process and psychometric properties of the Children's Educational Mobile Application Usage for Foreign Languages Scale. The item pool was created by examining the studies obtained from the literature review. To evaluate the appearance and content validity of the scale, the expert assessment of the scale, which the researcher developed, was applied to 19 experts. Consequently, the experts' opinions were considered to update the Children's Educational Mobile Application Usage for Foreign Languages Scale. Pilot studies were conducted with 37 students in the 4th grade. Data were collected from 309 4th-grade students to conduct reliability and validity studies. Confirmatory Factor Analysis was used to determine the scale's construct validity. The Chi-square/degrees of freedom ratio is 2.54, and the Comparative Fit Index value is calculated as .954. The Confirmatory Factor Analysis confirmed the theoretical structure consisting of 16 items with three factors, and the compliance indices showed perfect compliance. Cronbach Alpha (α) coefficient was used to determine the scale's reliability. The Cronbach Alpha value is calculated as 0.945. As a result, the Children's Educational Mobile Application Usage for Foreign Languages Scale was valid and reliable. The study highlights the novelty of the scale, being one of the first psychometric scales in the literature explicitly targeting children's educational mobile application usage for foreign language learning. It can aid institutions in developing or updating mobile applications and investigating the relationship between children's educational mobile application use and other variables. While this study presents a valuable contribution to the field, it also acknowledges limitations, such as the sample size and reliance on personal statements for data collection. Nonetheless, Children’s Educational Mobile Application Usage for Foreign Languages opens new avenues for research and has the potential to enhance language education by harnessing innovative technology-driven approaches.
Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty

Abstract Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE; MacrosystemsEDDIE.org ) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts.
Towards Controlling Mouse through Hand Gestures: A Novel and Efficient Approach

We tend to notice a lot of development in the computing world in today’s modern world. Computer science is a technique that combines today's technologies. This paper is also backed by a small portion of AI. This paper shows how to use our computer's window exploitation camera to create a hand gesture-based virtual mouse that allows you to control the entire system by merely moving your fingertips. Finger detecting methods for quick camera access and simple computer software make it even more accessible. A motion tracking mouse is implemented using the system. This technology eliminates the need for a physical mouse, saving time and effort.