Reviewerly/exHarmony huggingface dataset:
The Media Spedatua of Human Circulatory System Based on Articulate Storyline to Improve Primary School Science Learning Outcomes

Problems found in pre-research at SDN Cangkiran 02 were the lack of use of technology-based learning media and less than optimal learning outcomes focusing on science learning. The research problem formulation is how to develop feasibility and improve student learning outcomes after using storyline-based Spedatua media that conveys material on the human body's circulatory system. This development research aims to develop learning media to determine the feasibility and effectiveness of articulate storyline-based Spedatua media. The research was conducted using the R&amp;D (Research and Development) research model in Sugiono, which includes 10 stages, namely: (1) potential and problems; (2) data collection; (3) product design; (4) design validation; (5) design revision; (6) product testing (7) product revision; (8) trial use; (9) product revision; (10) producing products. The research aims to determine the results of the development and effectiveness of articulate storyline-based Spedatua media. The research results show that the articulate storyline-based Spedatua media is a learning media that can be used in material on the human body's circulatory system in science learning for class V elementary school. The articulate storyline-based Spedatua media was developed by looking at feasibility assessments by material, media, and language experts according to feasible criteria. The material suitability percentage was 91.6% with very feasible criteria, the media suitability percentage was 89% with very feasible criteria, and the language suitability percentage was 86.6% with very feasible criteria. The t-test results after using the articulate storyline-based Spedatua media showed differences in the average pretest and posttest scores. They have increased student learning outcomes by 33.2%. Meanwhile, the n-gain test results on the pretest - posttest showed that the average difference in learning outcomes was 0.638 in the medium criteria. So, there was an increase in learning outcomes before and after using the articulate storyline-based Spedatua media. Keywords: Spedatua, Learning Media, Science, Articulate Storyline.
Sentiment Analysis on Ibsen’s “A Doll’s House”

This study applied sentiment analysis to Ibsen’s “A Doll’s House” to investigate the potential of deep learning-based sentiment analysis in examining the underlying structure of modern drama and to explore optimal strategies for its practical application. Our exploration results underscore the potential of sentiment analysis as a methodology for analysis in literary studies. We utilized three distinct measures to process sentiment scores: mean sentiment scores, moving average sentiment curves, and cumulative sentiment curves. Each of these measures consistently resonated with the play’s themes and content, thereby underscoring their relevance in literary studies. Specifically, mean sentiment scores proved beneficial in encapsulating the overall sentiment profiles of the characters. Moving average sentiment curves excelled in tracing the dynamic fluctuations of sentiment throughout the narrative. Lastly, cumulative sentiment curves offered a comprehensive perspective of sentiment trends across the play. Despite these encouraging findings, the study also highlights the necessity for more refined and context-specific models and techniques for a more accurate and detailed sentiment analysis in literature.
Comparative Analysis of Support Vector Machine and Convolutional Neural Network for Malaria Parasite Classification and Feature Extraction

Malaria is a dangerous infectious disease because, if it is slow to handle, it can even cause death. Malaria is caused by a parasite called plasmodium, which is transmitted through the bite of a malaria mosquito called Anopheles. Parasites transmitted by mosquitoes attack human blood cells. The inspection method used to identify the type of malaria parasite is microscopic examination, whose accuracy and efficiency depend on human expertise. Examination methods using the Rapid Diagnostic Test (RDT) and Polymerase Chain Reaction (PCR) are not affordable, especially in underprivileged areas. This study compares the performance of classification methods, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN), to identify the type of malaria parasite and its stage and develop a feature extraction algorithm. The method of feature extraction is a decisive step to identifying the type of malaria parasite. The feature extraction process by developing a feature extraction algorithm is called the PEMA and KEHE feature tracking algorithm, or feature tracking with perimeter, eccentricity, metric, area, contrast, energy, homogeneity, and entropy. The classifier uses a convolutional neural network (CNN) to divide the samples into 16 classes. The experiment used 446 images of malaria parasites. The outcome of identification showed that by tracking the PEMA and KEHE features with the SVM classifier, the best accuracy value was 85.08%, compared to CNN with an accuracy value of 61.40%.
Are the Sentiments Portrayed in the Chairperson's Letter to Shareholders Consistent with the Financial Performance?

This study aims to investigate to what extent the sentiments portrayed in the chairperson's letter to shareholders are consistent with a firm's financial performance. The sample of this study comprises 115 chairpersons' letters to shareholders, representing 13 commercial banks listed in the Amman Stock Exchange (ASE) over a 10-year period (from 2010 through 2019). The chairpersons’ letters to shareholders are analyzed for positive and negative sentiments. Financial performance is measured using return on assets. The data is analyzed using a linear mixed regression model. Communicative disclosure and strategic disclosure are used to explain the relationships found.&#x0D; The findings of this study revealed that the level of financial performance has a significant positive relationship with positive sentiments, while it has a negative and insignificant relationship with negative sentiments. This study concludes that the chairperson's letter to shareholders is both communicative and strategic.
WXGCB: A Clustering Prior Weighting Semi-Supervised Learning Method Based on Space Level Constraint and Mixed Variable Metrics

A clustering prior weighted semi-supervised learning method called WXGCB has been proposed, which combines the characteristics of the cluster-then-label semi-supervised method and space-level constraint semi-supervised method. WXGCB can use mixed variable information, data prior information, and clustering prior information based on different clustering algorithms to adjust the distance matrix, thereby transforming different supervised learning algorithms into semi-supervised learning algorithms for improving their prediction accuracy. Due to the fact that WXGCB does not require internal adjustments to the clustering algorithms and supervised learning algorithms used, this method can flexibly combine different clustering algorithms and supervised learning algorithms to find combinations that can better compensate for each other's shortcomings, and can easily convert various supervised learning algorithms into semi-supervised learning algorithms. To verify the effectiveness of WXGCB, WXGCB transformed two supervised learning algorithms KSNN and DBGLM into semi-supervised mixed variable learning algorithms SMKSNN and SMGLM, and conducted performance comparison experiments with the other two semi-supervised learning algorithms on six benchmark datasets.
Effectiveness of Structured Teaching Program Regarding Neonatal Care among Postnatal Mothers at Koheda Rural Community, Hyderabad Rural, Telangana State

“Effectiveness of structured-teaching program on knowledge regarding neonatal care among postnatal mothers at Koheda rural community, Hyderabad, Telangana State. The objectives of the study: • To assess the knowledge on neonatal care among postnatal mothers by pre-test. • To develop and administer a structured teaching program on neonatal care among postnatal mothers. • To assess the effectiveness of structured teaching programs on knowledge-related neonatal care among postnatal mothers by post-test. • To determine the association between pre-test and post-test knowledge scores with selected demographic variables. The researchers' approach adopted for the study was an evaluative approach and the research design adopted was a pre-experimental “ one group pre-test and post-test” design, in this study independent variable is a structured teaching program on neonatal care and knowledge level of postnatal mothers as dependent variable. In the pre-test, most of the mothers (73.33%) scored average and 26.67% of mothers who obtained below-average and above-average knowledge levels in the pre-test were found to be nil. In the post-test, the knowledge scores improved significantly after the structured teaching program 96.7% of mothers obtained above-average scores whereas 3.33% of mothers obtained average scores and none of the mothers scored below-average scores. The findings show that the knowledge of postnatal mothers was improved with a structured teaching program.
Research on the Cooperation Strategy of Rail-road Transportation Enterprises under the Participation of Blockchain Technology

With the wide application of blockchain technology in the logistics supply chain, the conflict of interest between the road-rail transportation enterprises has become a vital research hotspot. To study the evolution law of the cooperation strategy of rail-road transportation enterprises under different reward and punishment mechanisms. Based on the evolutionary game theory, this paper constructs the three groups' evolutionary game model of blockchain technology platform, road transportation enterprises, and rail transportation enterprises to analyze the stability conditions of each node enterprise and test the effectiveness of the research results. This paper puts forward countermeasures and suggestions for relevant stakeholders.
Signatures of cross-modal alignment in children’s early concepts

Whether supervised or unsupervised, human and machine learning is usually characterized as event-based. However, learning may also proceed by systems alignment in which mappings are inferred between entire systems, such as visual and linguistic systems. Systems alignment is possible because items that share similar visual contexts, such as a car and a truck, will also tend to share similar linguistic contexts. Because of the mirrored similarity relationships across systems, the visual and linguistic systems can be aligned at some later time absent either input. In a series of simulation studies, we considered whether children’s early concepts support systems alignment. We found that children’s early concepts are close to optimal for inferring novel concepts through systems alignment, enabling agents to correctly infer more than 85% of visual–word mappings absent supervision. One possible explanation for why children’s early concepts support systems alignment is that they are distinguished structurally by their dense semantic neighborhoods. Artificial agents using these structural features to select concepts proved highly effective, both in environments mirroring children’s conceptual world and those that exclude the concepts that children commonly acquire. For children, systems alignment and event-based learning likely complement one another. Likewise, artificial systems can benefit from incorporating these developmental principles.
Optimal structure exploration of Bayesian network based on dealing with the route choice model

Route selection in residents' travel is a key link in travel decision-making in transportation systems. In existing studies, discrete choice models are often used to describe residents' travel path choices. However, with today's highly developed big data, the traditional discrete choice model is too simple to represent residents' travel path choices, ignoring the connection between various factors. Therefore, a big data-driven approach is proposed to solve this problem in this paper. Driven by research data and with the introduction of the Bayesian network model, the paper adopts a hill-climbing search strategy to improve the search efficiency, uses a variety of scoring functions to train data, which optimizes the Bayesian network structure. The experimental results show that the model fully considers the causation between the independent variables, which is more logical and makes the construction of the resident travel route selection model more accurate and efficient.
IMPLEMENTATION OF INNOVATIVE TECHNOLOGIES IN THE TEACHING OF SOCIO-ECONOMIC DISCIPLINES AS A RESPONSE TO THE CHALLENGES OF TODAY’S TURBULENCE

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Intent-Based AI-Enhanced Service Orchestration for Application Deployment and Execution in the Cloud Continuum

Given the complexity of contemporary applications, the varying goals and intents of their owners, and the availability of resources with fundamentally different characteristics and capabilities, the optimal deployment and execution of applications and their internal components is a rather challenging subject in the Cloud Continuum era. This includes the selection and the configuration of the resources to adequately cover the set technological and business requirements and constraints from the side of both application owners and resource providers. The aforementioned process is often and to a great extent, done manually and hence not optimally, with direct impact to the execution of an application and the usage or the available resources. In this work, we present the approach followed for the design and development of a Service Orchestrator equipped with AI techniques and the underlying multi-layered abstraction model enabling its functionality. These components were incorporated in a platform for infrastructure-agnostic deployment of data-intensive applications and tested in real-life scenarios.
THE USE OF GOOGLE JAMBOARD TO IMPROVE STUDENTS’ READING COMPREHENSION

The development of information technology makes technology used in various fields, one of which is education. At first, traditional learning was synonymous with the use of whiteboards, chalk, and markers to facilitate the delivery of material. However, the presence of technology makes learning more interesting for students. Google Jamboard is one of the technologies known as digital whiteboards that can be used to teach English skills. This study aims to determine the use of Google Jamboard as a tool to improve reading comprehension in EFL students. This research is qualitative research using the results of interviews with English teachers who use Google Jamboard as a teaching tool and EFL students. The results of the study show that Google Jamboard can assist teachers in teaching reading comprehension activities to students and is able to provide interactive learning for students.
Transformative Advances in English Learning: Harnessing Neural Network-Based Speech Recognition for Proficient Communication

The popularity of neural networks in oral English learning has revolutionized the learning experience, with speech recognition as a key technology. By leveraging neural networks, speech recognition facilitates convenient and intuitive oral English practice, enhancing comprehension of human speech. Learners utilize oral practice applications to improve pronunciation and intonation, benefiting from neural networks’ capabilities. These applications identify errors, providing tailored feedback for skill refinement. Neural networks also extend beyond speech recognition, encompassing vocabulary, grammar, and listening activities. Comparative evaluations demonstrate neural networks’ superiority, achieving impressive performance rates up to 96.76%, surpassing traditional circulating neural networks’ 76.67%. These results underscore their effectiveness and motivate further advancements. As neural network and speech recognition technologies progress, their transformative impact on oral English learning becomes evident, facilitating proficient communication. Learners benefit from efficient methods to enhance speaking skills, unlocking new possibilities and empowering language learners worldwide.
THE CONTENT OF TRAINING IN THE CONSTRUCTION OF CLASSICAL DECOMPOSITIONS OF QUANTUM GATES

The authors of the paper select and subsequently structurise the content of training in the construction of classical decompositions of single-qubit quantum gates, used in teaching the “Quantum Computing and Programming in the QCL Language” section of the training course “Applied Programming in High-Level Languages” to students of the Institute of Information Technologies and Technology Education (3rd year, Herzen State Pedagogical University of Russia).
HUMAN vs. ROBOT: THE APPLICATION OF SMART TECHNOLOGY IN THE HOTEL INDUSTRY

The current use of robotics, artificial intelligence, and service automation (RAISA) illustrated that innovation developed very quickly and had an enormous impact on how consumers use tourism services. This study aimed to determine how tourism and hospitality employees dealt with the pandemic, particularly when implementing the protocol called physical distance in the new normal era. This study also intended to discover our hospitality industry's readiness to confront a new normal era by applying technology to serve guests. This study conducted semi-structured interviews with 15 stakeholders. The findings indicated that the government and hotel management exploit this occurrence to enhance service quality, explicitly emphasizing hotel consumer comfort and safety. It was posited that the issues associated with the interaction between humans and robots extend beyond budgetary considerations, readiness, and maintenance. A crucial aspect to consider was robots' potential replacement of service uniqueness, particularly in developing countries. Integrating robotics and artificial intelligence (AI) inside the hotel industry may need to reevaluate the hospitality and tourism education curriculum.
Aplikasi Matriks Leslie untuk Memproyeksi Jumlah Penduduk Perempuan dan Laju Pertumbuhan Penduduk di Kabupaten Banyumas Tahun 2027

BPS knows the population and the rate of population growth, namely using a population census that is carried out once every 10 years, because this timeframe is the right time to see the birth rate and death rate for an area. So it takes a long time to know the amount of population growth and population growth rate. Based on BPS data for Banyumas Regency for 2021, the population is 1,789,630 people, the population growth rate is 95%. And in 2022 the population will be 1,806,013 people, the population growth rate is 93%. So for 1 year from 2021 to 2022 there will be an increase of 16,383 people, based on data from BPS the number of Banyumas districts has increased. The Leslie Matrix is ??one of the mathematical tools used to model the growth and changes in populations of living things, so that it can assist BPS in predicting population numbers and population growth rates at a time. From Leslie's matrix calculations, the prediction for the female population in Banyumas Regency in 2027 is 985,304 people. And the result of calculating the dominant eigenvalue is ?=1.23 which shows that population growth is increasing.
A novel occluded face detection approach using Enhanced ORB and optimized GAN

Objectives:This research presents an approach to detect occluded face images. In order to achieve this, the research presents a novel technique that involves feature extraction and occluded face recognition. Methods: Feature extraction is performed by the enhanced ORB algorithm, which is proposed by the modification of the Oriented Fast and Rotated Brief (ORB) algorithm, by adding a phase for contrast adjustment, together with CNN features. For occluded face recognition, a Generative Adversarial Network (GAN) optimized by the proposed SR-SSA is designed. SR-SSA is proposed by the integration of Search and Rescue Optimization (SAR) in the Sparrow Search Algorithm (SSA). Results: The experimental results demonstrate that the SR-SSA-based GAN algorithm outperforms existing methods in terms of accuracy of 0.956, FAR of 0.045 and FRR of 0.021.
Surfacing Structural Barriers to Community-Collaborative Approaches in Human-Computer Interaction

extended-abstract Share on Surfacing Structural Barriers to Community-Collaborative Approaches in Human-Computer Interaction Authors: Calvin Alan Liang University of Washington, United States University of Washington, United States 0000-0002-3795-3441View Profile , Emily Tseng Cornell Tech, United States Cornell Tech, United States 0000-0003-1087-1101View Profile , Akeiylah Dewitt University of Washington, United States University of Washington, United States 0000-0002-4534-7273View Profile , Yasmine Kotturi Carnegie Mellon University, United States Carnegie Mellon University, United States 0000-0001-6201-7922View Profile , Sucheta Ghoshal University of Washington, United States University of Washington, United States 0000-0002-9990-7221View Profile , Angela D. R. Smith University of Texas at Austin, United States University of Texas at Austin, United States 0000-0001-5546-5452View Profile , Marisol Wong-Villacres Escuela Superior Politécnica del Litoral, Ecuador Escuela Superior Politécnica del Litoral, Ecuador 0000-0002-8071-6716View Profile , Lauren Wilcox Google, United States Google, United States 0000-0001-6598-1733View Profile , Sheena Erete University of Maryland College Park, United States University of Maryland College Park, United States 0000-0003-2819-1083View Profile Authors Info & Claims CSCW '23 Companion: Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social ComputingOctober 2023Pages 542–546https://doi.org/10.1145/3584931.3611294Published:14 October 2023Publication History 0citation89DownloadsMetricsTotal Citations0Total Downloads89Last 12 Months89Last 6 weeks50 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access