Reviewerly/exHarmony huggingface dataset:
Perceptions of Peer Coaching Technique by the Alternative Learning System Community Implementers

Peer coaching is a process where peers collaborate to improve their skills, knowledge, and abilities in a particular area. The peer coaching method begins with the individual defining the problem or goal that they wish to work on. The Alternative Learning System (ALS) is a program of the Philippine Department of Education that offers non-formal education to out-of-school youths, adults, and other marginalized communities. The respondents of this study were the two hundred (200) community implementers of the Second Congressional District of the Division of Misamis Oriental from the municipalities of Opol to Lugait during the School Year 2022-2023. This study is adapted and modified from the study of Gutierez et al. (2018) on Peer Coaching in a Research-Based Teachers Professional Learning Method for Lifelong Learning: A Perspective and employed a quantitative research design, with data collected from the two hundred (200) ALS community implementers in the second congressional district of Misamis Oriental. Frequency and percentage, mean and standard deviation, regression analysis, and multiple correlation analysis. Findings indicate that the teacher respondents level of perception towards the peer coaching technique is highly effective in terms of teachers self-efficacy, understanding of the teaching and learning process, decision-making, and reflective practice. The teacher respondents level of content knowledge on the learning strand in terms of scientific and critical thinking skills, life and career skills, and understanding the self and society showed significant effects towards the peer coaching technique. Furthermore, the weak but significant relationship between the designation of the teacher-respondent and the length of service as an ALS implementer implies that more experienced teachers and those in leadership roles may be more effective in adopting peer coaching strategies. Teachers should provide a comfortable working environment, give enough training and seminars, encourage ALS Implementers to attend conferences and seminars and engage in training and development programs to assist workers in enhancing their skills and competence.
SELF-READINESS TO CHANGE AMONG TEACHERS IN CAGAYAN DE ORO CITY: BASIS FOR PERSONALITY DEVELOPMENT PLAN

This study was primarily undertaken to determine the level of assessment on self- readiness to change among public elementary school teachers in Cagayan de Oro City, School Year 2022-2023. The research problems include the profiling of the respondents the level of assessment on self- readiness to change the relationship between teachers self-readiness to change and respondents profile and personality development plan on self-readiness to change based on the findings of the study. The respondents of the study were the 280 public elementary school teachers in the schools where the study was conducted. Descriptive survey method of research was used in this study. The questionnaire used in the collection of pertinent data was adapted from Robert Kriegel &amp; David Brandt on Change Readiness Questionnaire. The data were analyzed using frequency, mean, percentage, and standard deviation to describe the variables in the study. Moreover, Pearson Product Moment Correlation (r) was used to determine the significant relationship between teachers readiness to change and their profile. Findings revealed that the level of self-readiness to change among teachers on the following traits: resourcefulness, adaptability, optimism, confidence, adventurousness, tolerance for ambiguity, and passion is ready described as agree. There is a significant relationship between teachers self- readiness to change and their profile. Thus, self-readiness to change can help the person to sustain the desired change or goal and match personality development plan to individual need. Based on these findings, it was recommended that teachers should be more adaptive to change to keep up in an ever-changing world.
INKLUZIVNA DIMENZIJA CJELOŽIVOTNOG UČENJA

The article analyses the relationship between lifelong learning and inclusive policies for persons with disabilities. The first part analyses the concept of inclusiveness in lifelong learning and the application of this concept in educational policies. The emphasis is on the educational obstacles faced by persons with disabilities and the opportunities that the concept of lifelong learning provides for their inclusion in various forms of education and learning. In doing so, dispositional, situational and institutional obstacles for persons with disabilities and different dimensions of inclusivity are distinguished. The second part of the article analyses the validity of this conceptual framework for describing the educational inclusion of persons with disabilities in Croatia. The difficulties in ensuring the coherence of the policy towards persons with disabilities and the importance of the time dimension in the implementation of these policies are described. Special attention is paid to the final results of inclusive policies. This refers to the lower inclusion of persons with disabilities, their earlier school leaving, and lower overall educational achievements compared to the general population. The analysis is based on data from research on the education of persons with disabilities on obstacles to the inclusion of persons with disabilities in the process of lifelong learning, which were conducted as part of the Academic Network of European Disability Experts and within the thematic network Lifelong education available to all.
Geochemical study to determine what elements are most influential in soil fertility in the southern part of Lampung region

Abstract Lampung is one of the best provinces in Indonesia in the agricultural sector. Research on agriculture in Lampung continues to develop over time. To expand land, research on agricultural land is needed. Agricultural soil contains chemical elements that support soil fertility. The aim of this research is to find out which chemical elements have the best correlation with soil fertility. The samples used were agricultural soil samples from the South Lampung and Pesawaran areas. The method used is a geochemical method with pH measurements and X-Ray Fluorescence testing. The results obtained were that the Mg element had a very strong correlation with soil fertility so that in the future the Mg element could be used as an indicator of soil fertility levels.
WTO, Women’s Empowerment and Financial Inclusion

Promoting financial inclusion for women is a goal supported by many multilateral organizations and policymakers globally, and the WTO is no exception. This article argues that financial inclusion is crucial to empower women economically and socially. It emphasizes historical barriers that women have faced in accessing financial services. In addition, it underlines the importance of incorporating women's financial inclusion into the World Trade Organization (WTO) and advocates for leveraging existing structures within the WTO, such as the Council for Trade in Services and the Committee on Trade in Financial Services, to advance this issue. The objective is to facilitate the exchange of experiences and best practices among Members in a structured manner, addressing the regulatory framework of financial institutions and learning from each other, rather than creating new rights or obligations or launching joint initiatives. Financial Inclusion, World Trade Organization, Women’s Empowerment, Gender, Trade in Services, Financial Services, WTO Ministerial Conference, Committee on Trade in Financial Services, Remittances, and Gender Equality
Semiautomatic assessment of immunofluorescence microscopy on blood smears in inherited platelet disorders using artificial intelligence: a proof of concept.

Introduction Inherited platelet disorders (IPD) are a group of rare diseases caused by mutations in more than 70 genes. Despite advances in molecular diagnosis, platelet phenotyping still represents a cornerstone in the diagnostic workup of IPD. Immunofluorescence microscopy on the blood smear, combined with light microscopy, has been established as a screening tool for IPD and validated as sensitive and specific for 9 of the most frequent IPD [1]. A major drawback of the method lies in the subjective nature of morphological evaluation. Recent advances in computer vision and artificial intelligence tools offer improved performance and increasing accessibility of these technologies even for diagnostic laboratories.
Do Democracy and Government Capacity Positively Contribute to Technological Innovation?

Purpose: In this study, we analyze the f ollowing two points based on these existing studies. F irst, we examine the c orrelation between democracy and technological innovation, using cross-national data.We examine whether more democratized countries have an advantage over less democratic c ountries in t erms o f technological i nnovation, and whether d emocratic countries have an advantage over non-democratic countries in terms of innovation performance. we examine how government capabilities affect technological innovation by dividing them into democratic and non-democratic countries. We aim to analyze whether government capacity is important for technological innovation not only in democratic countries but also in non-democratic countries.&#x0D; Research design, data, and methodology: This paper analyzed how political systems, especially democracy and government capacity, affect technological innovation for the period 1997-2017 in 101 countries using data extracted from the World Bank’s World Development Indicators. The relationship between political systems and technological innovation was examined using the Negative Binomial Regression Model (NBRM), which is commonly used to analyze count data with the characteristic of over-dispersion.&#x0D; Results: The findings indicate that as democratization progresses, there is a corresponding increase in innovation performance. Additionally, democratic nations demonstrate higher levels of innovation compared to non-democratic countries. Furthermore, the research reveals that countries with robust government capacity, as measured by factors such as the effectiveness of public officials, provision of public services, regulation and support of the private sector, and corruption control, tend to achieve superior innovation performance.&#x0D; Implications: These f indings support t he a rgument put forth b y Acemoglu a nd R obinson, s uggesting that i n order to a ttain s ustained economic development and innovation, countries require a certain level of government capacity associated with the operation of exploitative economic institutions. The escalating power struggle between the United States and China has now evolved into a technological competition between market democracies and s tate capitalism. Consequently, there i s a f ervent debate regarding which political systems and forms of capitalism have a positive influence on innovation. This study is expected to offer valuable insights and serve as a crucial reference point for this ongoing discourse.
Real-Time Sign Language Recognition and Translation Using Deep Learning Techniques

Sign Language Recognition (SLR) recognizes hand gestures and produces the corresponding text or speech. Despite advances in deep learning, the SLR still faces challenges in terms of accuracy and visual quality. Sign Language Translation (SLT) aims to translate sign language images or videos into spoken language, which is hampered by limited language comprehension datasets. This paper presents an innovative approach for sign language recognition and conversion to text using a custom dataset containing 15 different classes, each class containing 70-75 different images. The proposed solution uses the YOLOv5 architecture, a state-of-the-art Convolutional Neural Network (CNN) to achieve robust and accurate sign language recognition. With careful training and optimization, the model achieves impressive mAP values (average accuracy) of 92% to 99% for each of the 15 classes. An extensive dataset combined with the YOLOv5 model provides effective real-time sign language interpretation, showing the potential to improve accessibility and communication for the hearing impaired. This application lays the groundwork for further advances in sign language recognition systems with implications for inclusive technology applications.
A multi-view heterogeneous knowledge learning method for personalized POI recommendation

Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method.
PRECEDENT NAMES FROM DIFFERENT SOURCES IN ENGLISH LANGUAGE PRINT MEDIA

The article, based on precedent names identified in the English-language print media, establishes their source areas (politics, cinema, literature, music, religion), the frequency and relevance of individual precedent names and their source, use in texts and, mainly, in article headings to attract the reader's attention and increase the impact on his opinion.
Semiquantum proxy blind signature based on quantum teleportation

<p indent="0mm">In this paper, we propose a novel semiquantum proxy blind signature scheme using quantum teleportation based on <italic>χ</italic> states, where the original message owner, proxy signer, and third party are quantum participants with complete quantum capabilities, while the original signer and signature verifier are semiquantum participants with limited quantum capabilities. Our protocol not only has complete blindness, unforgeability, and nonrepudiation, but it also resists the attack behavior of an eavesdropper. Because of the limited quantum capabilities of the original signer and signature verifier, our protocol does not require them to prepare and measure the quantum superposition and quantum entangled states when used in practical applications. Hence, our protocol can reduce the burden of quantum state preparation and measurement for the original signer and signature verifier.
Development of a smart home suitability indicator and indicative self-assessment platform for the disabled facilities grants (DFGs)

Purpose Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays. Design/methodology/approach The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system. Findings The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold. Originality/value These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.
Deep survival analysis model for incident clearance time prediction

Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers "duration dependence" of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.
Knowledge graph‐driven data processing for business intelligence

Abstract With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains. This article is categorized under: Algorithmic Development &gt; Text Mining Application Areas &gt; Business and Industry
Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Drone or unmanned aerial vehicle (UAV) technology has undergone significant changes. The technology allows a UAV to carry out a wide range of tasks with an increasing level of sophistication, since drones can cover a large area with cameras. Meanwhile, the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications. The primary target in UAV-based detection applications is humans, yet aerial recordings are not included in the massive datasets used to train object detectors, which makes it necessary to gather the model data from such platforms. You only look once (YOLO) version 4, RetinaNet, faster region-based convolutional neural network (R–CNN), and cascade R–CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes. Here, we used the search and rescue (SAR) dataset to train the you only look once version 5 (YOLOv5) algorithm to validate its speed, accuracy, and low false detection rate. In comparison to YOLOv4 and R–CNN, the highest mean average accuracy of 96.9% is obtained by YOLOv5. For comparison, experimental findings utilizing the SAR and the human rescue imaging database on land (HERIDAL) datasets are presented. The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.