Reviewerly/exHarmony huggingface dataset:
Application of ChatGPT in information literacy instructional design

Recent developments in ChatGPT have initiated a prompt response in the academic libraries’ community. This includes discussions on possible applications of AI text generators (Fernandez, 2023) and the implications of ChatGPT for academic libraries (Cox and Tzoc, 2023). In various segments of library services, ranging from reference services, cataloguing and metadata creation to content creation, ChatGPT can be used for natural language processing activities.In this paper, our aim is to investigate a specific application of ChatGPT in relation to Open Educational Resources (OER): instructional design for information literacy courses in academic libraries. The starting point for our research is using openly-licensed informational resources, i.e. ‘content infrastructure’ as facilitators in creating educational resources. It is widely recognised that large language models (LLMs), which use deep learning techniques to generate text based on prompts, greatly contribute to the speed of creating informational resources which comprise the content infrastructure (Wiley, 2023). However, ensuring the quality of the generated output and the requirement for review and improvements is an issue.Our analysis of the model will be conducted in three stages: 1. developing a strategy for producing information literacy syllabus using ChatGPT; 2. training the model according to predefined standards; 3. conducting the review process. In the first stage, the following guidelines will be applied: assign the model the role of a subject-matter expert (SME); provide context; instruct the model to use specific standards, i.e. “Information Literacy Competency Standards for Higher Education” (ALA/ACRL, 2000) and direct the model to use a specific output format. The second stage, or model training, will consist of a series of structured prompts, including feedback and reflection prompts. In the third stage, two external reviewers will be included in the process, with the following profiles: 1. an academic library information literacy programme coordinator; 2. English language specialist (assistant professor, PhD level). The purpose of the review is twofold: to check for pedagogical accuracy and psychometric validity, as well as usability in the actual academic environment.The results will provide some insight into the benefits of strategic and well-planned use of ChatGPT technology in creating content, while also considering responsible and ethical use of the new technology in the academic library sector.
A Multistage High Capacity Reversible Data Hiding Technique Without Overhead Communication

Reversible Data Hiding(RDH) has been extensively investigated, recently, due to its numerous applications in the field of defence, medical, law enforcement and image authentication. However, most of RDH techniques suffer from low secret data hiding capacity and communication overhead. For this, multistage high-capacity reversible data hiding technique without overhead is proposed in this manuscript. Proposed reversible data hiding approach exploits histogram peaks for embedding the secret data along with overhead bits both in plain and encrypted domain. First, marked image is obtained by embedding secret data in the plain domain which is further processed using affine cipher maintaining correlation among the pixels. In second stage, overhead bits are embedded in the encrypted marked image. High embedding capacity is achieved through exploiting histogram peak for embedding multiple bits of secret data. Proposed approach is experimentally validated on different datasets and results are compared with the state-of-the-art techniques over different images.
A Data Mining-Based Approach to Managing Intercultural Teaching Activities in Online Classrooms

Driven by globalization and technological advances, online education is coming into its own form, opening a window for students to learn across cultural boundaries. In such a context, the intercultural teaching activities in online classrooms are particularly important, as they provide students with a good opportunity to gain a deeper understanding of different cultures and merge into different backgrounds. However, most of the currently available methods of intercultural teaching activity management focus on conventional education modes or strategies, and there isn’t a deep enough analysis about the features of network environment. Aiming at these matters, this study gave an in-depth discussion on the current status of the management of intercultural teaching in online classrooms, and introduced the technology of data mining to propose a more comprehensive and systematic solution for educational issues caused by cultural differences.
Global Patent Technology Portfolio Study of Agricultural UAV Innovations

With the rapid development of technologies, unmanned aerial vehicles (UAVs), commonly called drones, will become affordable with a wide variety of applications. UAV technologies in agriculture are critically important due to climate changes and the continuous growth of global populations that need huge food production. Demand for food and produce is increasing, while agricultural labor forces continue to decline. According to a report presented by The World Bank, the percentage of the global workforce related to agriculture has already fallen from 40% in 2000 to 27% in 2019. To plant efficiently and manage agricultural labor issues, many countries are investing in agricultural UAV technologies and applications. By analyzing the patent portfolio of UAV-related technologies for agricultural applications, this technology mining research helps to discover the critical trends and potentials of agricultural UAV development. This study demonstrates the crucial necessity of transdisciplinary engineering practices that are closely related to precision, automation, and even intelligent agricultural activities and productions. We identify the transdisciplinary domain knowledge ontology schema, i.e., the integrated agriculture, UAV-related technologies and their sub-technologies. These domain patents are searched and found using intelligent patent text analysis techniques. Finally, a strategic agricultural UAV R&amp;D plan is presented to promote precision farming with high efficiencies and profitability (e.g., Taiwan farmers focus on organic farming, quality excellence of produce, and happy agriculture) using innovative UAV technologies.
Hybrid Lattice Boltzmann Model for Nonlinear Diffusion and Image Denoising

In the present paper, a novel approach for image denoising based on the numerical solution to the nonlinear diffusion equation is proposed. The Perona–Malik-type equation is solved by employing a hybrid lattice Boltzmann model with five discrete velocities. In this method, the regions with large values of the diffusion coefficient are modeled with the lattice Boltzmann scheme for which hyper-viscous defects are reduced, while other regions are modeled with the conventional lattice Boltzmann model. The new method allows us to solve Perona–Malik-type equations with relatively large time steps and good accuracy. In numerical experiments, the removal of salt and pepper, speckle and Gaussian noise is considered. For salt and pepper noise, the novel scheme yields better peak signal-to-noise ratios in image denoising problems compared to the standard lattice Boltzmann approach. For certain non-small values of time steps, the novel model shows better results for speckle and Gaussian noise on average.
Analyzing teacher–student interactions through graph theory applied to hyperscanning fNIRS data

Teacher–student relationships have been found consistently important for student school effectiveness in mathematics in the last three decades. Although this observation is generally made from the teacher's perspective, neuroscience can provide new insights by establishing the neurobiological underpinning of social interactions. This paper further develops this line of research by utilizing graph theory to represent interactions between teachers and students at the neural level. Through hyperscanning with functional near-infrared spectroscopy (fNIRS), we collected data from the prefrontal cortex and the temporoparietal junction of 24 dyads composed of a teacher and a student. Each dyad used a board game to perform a programming logic class that consisted of three steps: independent activities (control), presentation of concepts, and interactive exercises. Graph theory provides results regarding the strength of teacher–student interaction and the main channels involved in these interactions. We combined graph modularity and bootstrap to measure pair coactivation, thus establishing the strength of teacher–student interaction. Also, graph centrality detects the main brain channels involved during this interaction. In general, the teacher's most relevant nodes rely on the regions related to language and number processing, spatial cognition, and attention. Also, the students' most relevant nodes rely on the regions related to task management.
REVIEW AND ANALYSIS OF APPROACHES AND PRACTICAL APPLICATIONS OF HUMAN EMOTION RECOGNITION

Human emotions are complex and multifaceted, making them difficult to quantify and analyze. However, as technology advances, researchers are exploring the artificial intelligence used to better understand and classify human emotions. In particular, neural networks are becoming increasingly popular for emotion recognition and analysis because of their ability to learn and adapt from large datasets. Objective. This study aims to review and analyze different approaches and practical applications of recognizing human emotions using neural networks. In particular, the study focuses on examining neural networks different types used for emotion recognition, data collection methods, as well as emotion recognition practical applications in various fields. The study also aims to identify limitations and issues associated with emotion recognition using neural networks. Methods. This study used a comprehensive review of relevant literature, including scholarly articles, conference proceedings, and books, to gather information on approaches and practical applications of human emotion recognition using neural networks. The review focused on recent research. The information collected was analyzed to identify the neural networks different types used for emotion recognition and data collection methods. Results. The literature review revealed several approaches to emotion recognition using neural networks, including convolutional neural networks, recurrent neural networks, and hybrid neural networks. Practical applications of emotion recognition using neural networks are found in a variety of fields, including marketing, health care, and education. The review also identified limitations and challenges associated with emotion recognition using neural networks, including dataset bias and the need for more diverse and representative datasets. Conclusion. A review and analysis of the approaches and practical applications of human emotion recognition technology using neural networks highlight the potential benefits and challenges associated with this technology. The results of this study can be used to guide future research on emotion recognition using neural networks to improve the accuracy and applicability of emotion recognition in various fields.
Managing the Purchase-Sale Process of Digital Currencies Under Fuzzy Conditions

The article discusses a model for managing the process of trading digital cryptocurrencies (DCC) in a fuzzy environment. It is demonstrated that the controllability of such a process can be described from a game theory perspective. This approach enables the development of constructive strategies for maintaining stability in relationships between players in the DCC market, particularly in cases where one of the players provides fuzzy information about the DCC volume of the counterparty player. Consequently, solving such problems will lead to a more accurate predictive evaluation of transaction effectiveness. The proposed model is novel in that it employs a constructive approach to solve bilinear multi-stage quality games with several terminal surfaces, specifically for the case of fuzzy information. A computational experiment is conducted, taking into account various ratios of parameters that describe the process of DCC trading operations. The results of this study provide interested parties, particularly players in the DCC market, with the means to maintain stability in both traditional currency and DCC markets. The model may also be useful for software products designed for trading platforms where DCC transactions are conducted.
Fluxion: A Scalable Graph-Based Resource Model for HPC Scheduling Challenges

The current era of exascale supercomputing and the emergence of a computing continuum present several significant resource management challenges. These include, but are not limited to, management of complex and high-throughput scientific workflows, diverse resources such as power and network bandwidth, extremely heterogenous and dynamic systems, elasticity in user jobs, and disaggregated and converged environments. The resource models that underpin today’s job scheduling frameworks reflect the node- (or core-) centric system architectures prevalent when the frameworks were designed. Consequently, they are not suited to capturing resource relationships or dynamism. The inability to express relationships or dynamism limits their applicability to the emerging multifaceted challenges in high-performance computing (HPC) and Cloud, as well as other converged environments. In this paper, we propose a scalable graph-based resource model to overcome these challenges, which allows for representation of complex, changing resource relationships and multiple containment hierarchies. We implement this model, Fluxion, in a production-quality scheduling framework, and evaluate its performance. Additionally, we present emerging and advanced scheduling use cases that are enabled by our model.
msr-genie: Navigating Model Specific Registers Across Processor Generations

Performance tuning of High-Performance Computing (HPC) applications depends on sophisticated tuning of parameters on diverse architectures. These parameters are made available by vendors through low-level dials such as model-specific registers (MSRs). While the MSRs themselves provide a powerful mechanism for users to monitor and control processor features, accessing them is laborious due to lack of standard interfaces and clear documentation. As a result, the burden of determining which MSRs to consider and how to fine-tune them for an application lies with the end user. In this paper, we present msr-genie, an efficient and extensible query tool that reduces this user-level burden. The tool allows them to query bi-directionally across MSR lists as well as processor families and models, and provides them with guidance on appropriate bitmasks. The msr-genie tool is open-source and easily extensible, and we demonstrate its effectiveness with over thirty Intel processor models and over two-thousand unique MSRs.
Measuring Recidivism Rate: Its Predictive Variables and Antecedents

This study examined the recidivism rate at the Bureau of Jail Management and Penology – National Capital Region (BJMP-NCR), Philippines, from 2020 to 2022 based on predictive variables and antecedents of recidivism. The results revealed that young individuals; males; married; Person Deprived of Liberty (PDL) with drug cases, leading to death or intending to cause death, and acts involving fraud and deception; and those having shorter stays in prison tend to recidivate more. Likewise, the pattern of recidivism in the Metro Manila District Jail (MMDJ) from 2020 to 2022 was affected by the existence of the COVID-19 pandemic. In conclusion, both predictive variables and antecedents of recidivism show no significant association with the pattern of recidivism.
PENGARUH TEKNIK PERMAINAN MENYUSUN KATA PADA MATA PELAJARAN BAHASA INDONESIA TERHADAP KEMAMPUAN MEMBACA SISWA KELAS I SDN 24 GELUMBANG

&#x0D; The background of one of the subjects that must be given in elementary schools is Indonesian.The scope of learning Indonesian in elementary schools includes 4 (four) components of language skills, namely listening / listening, reading, writing, and speaking. The above problem makes the author feel the need to conduct a study entitled "The Effect of Word Arranging Game Techniques on Indonesian Subjects on the Reading Ability of Grade I Students of SDN 24 Gelumbang"This research is to determine whether or not there is an influence of word composing game techniques on the reading ability of grade I students of SDN 24 Gelumbang. Experimental research methods are research methods used to look for the effect of certain treatments (treatments) under controlled conditions. The subjects of this study were grade I students of SD Negeri 24 Gelumbang totaling 24 students. The data collection method uses observation, tests, and documentation. The test instrument is in the form of multiple-choice questions totaling 20 questions to measure the results of students' reading skills. Data analysis technique using Independent Sample T-test. The results showed that the influence of word composing game techniques can improve the learning outcomes of elementary school students in Indonesian subjects which can be seen from the learning results of the hypothesis test (Independent Sample T-Test) in the form of the significance value of this study, which is 0.000&lt; 0.05 which Ho rejected Ha accepted. So it can be concluded that "There is an influence of word composing games in Indonesian subjects on the reading ability of grade I students of SDN 24 Gelumbang".
Improving Anomalous Sound Detection by Distance Matrix-Based Visualization of Measurement Flaws

Although recent DNN-based methods have improved the performance of anomalous sound detection systems, it is still difficult to deploy a system in a real environment without performance degradation. This is often due to measurement flaws such as sensor variability, poor setup, or environmental noise. Since such adverse effects are difficult to model by machine learning, a practical approach to this issue is for humans to identify such flaws and correct them. To this end, we propose a method to visualize measurement flaws as a heatmap based on the distance matrix of the samples in the dataset. This method is designed to find unexpected flaws in the measurement process. Using this method, we were able to identify measurement flaws of anomalous sound detection systems in real production lines. The robustness of anomalous sound detection can be improved by correcting the flaws found by our method.
Analysis of Personal Hygiene, Premises Sanitation and Equipment Sanitation on Coliform Bacteria Content in Bubble Drinks

Abstract. Ice bubble (bubble drink) is a popular ready-to-eat drink and is in great demand by the public because of the variety of flavors with a mixture of bubble topping (boba) in it. The personal hygiene of drink handlers, the cleanliness of sales locations, and the equipment used can all cause microbiological damage to bubble ice, which can lead to coliform bacteria contamination of bubble ice drinks. Coliform bacteria can cause various diseases, one of which is diarrhea. This study aims to determine the relationship between personal hygiene, place sanitation, and equipment sanitation and the content of coliform bacteria in bubble ice drinks in Pontianak City in 2022. According to the findings, coliform bacteria were present in 27 out of the 30 samples that had undergone coliform bacteria testing. This study obtained results of poor personal hygiene for as many as 26 people (86.7%), poor sanitation of selling places for as many as 27 people (90%), and poor sanitation of equipment for as many as 27 people (90%). This study also shows that there is a relationship between personal hygiene and coliform bacteria content with a value of p = 0.039, a relationship between place sanitation and coliform bacteria content with a p value = 0.02, and a relationship between equipment sanitation and coliform bacteria content in ice bubble drinks. with a p value of 0.039. Therefore, it is necessary to pay attention to the cleanliness of tools, the environment, and individual traders so that bubble ice is safe for consumption
Ship detection and identification in SDGSAT-1 glimmer images based on the glimmer YOLO model

Remote sensing technology has been widely used for marine monitoring. However, due to the limitations of sensor technologies and data sources, effective monitoring of marine ships at night remains challenging. To address these challenges, our study developed SDGST, a high-resolution glimmer marine ship dataset from SDGSAT-1 satellite and proposed a ship detection and identification method based on the YOLOv5s model, the Glimmer YOLO model. Considering the characteristics of glimmer images, our model has made several effective improvements to the original YOLOv5s model. In particular, the improved model incorporates a new layer for detecting small targets and integrates the CA (Coordinate Attention) mechanism. To enhance the original feature fusion strategy, we introduced BiFPN (Bi-directional Feature Pyramid Network). We also adopted the EIOU Loss function and replaced the initially defined anchors with clustering results, thus improving detection performance. The mean Average Precision (mAP%) reaches 96.7%, which is a 5.1% improvement over the YOLOv5s model. Notably, it significantly improves the detection of small ships. This model demonstrates superior performance in ship detection under glimmer conditions compared to the original YOLOv5s model and other popular target detection models, and may serve as a valuable reference for achieving high-precision nighttime marine monitoring.