Reviewerly/exHarmony huggingface dataset:
Preparation of Population Data Base for Community Unit 11 East Pamulang Village, Pamulang District South Tangerang City

Village and sub-district administration, neighborhood unit functions to record population data and other government administration services, maintain security, support, and inter-living harmony, generate ideas in the implementation of development by developing the aspirations and genuine self-help of the community, and activating self-help cooperation and participation of the community members in their area. So that the purpose of implementing AbdiMas is to increase understanding of simple technology in processing population databases in neighborhood units and Community Unit 11 East Pamulang Village administrators. The method used is Active and Participatory Learning with material discussions, practices, and demonstrations. The results of the training and database input based on all available information provide a very simple understanding of technological knowledge using Microsoft Office Excel and add broad insight to local neighborhood units and Community Unit 11 administrators on the importance of inputting databases to facilitate all the interests and aspirations of the community for the implementation of village government as well as the village. Overall, 90% of the neighborhood and Community Unit 11 administrators understand the use and input of databases using Microsoft Excel, making it easier to formulate policies related to education, health, economy, and social affairs, the government of East Pamulang Village, South Tangerang City.
Expert System for Identification of Skin Disease in Humans using Naive Bayes Classifier Method on Web

Expert System Identification of Skin Disease in Human is an application that adopts expert knowledge, in this case, a dermatologist, to identify skin diseases in humans found in Indonesia. This expert system is constructed using a naive bayes classifier. To make the system interface more dynamic and easy to access by anyone, this system is made based on the web. Through this web application, users can consult with a system like an expert to know the skin disease that occurs to the human and find the correct treatment to solve the problems.
Implementasi Backpropagation ANN dan Algoritma Genetika Terhadap Estimasi Pendapatan Agen Ekspedisi Pengiriman Barang

Income is the amount of income received by a person or resident for their work performance during a certain period, whether daily, weekly, monthly or yearly. This research implements the Backpropagation Artificial Neural Network and Genetic Algorithm methods to estimate the income of JNE Express Sekaran agents. Artificial Neural Network and Genetic Algorithm are modern forecasting methods that produce the smallest and most accurate error values. Based on the research that has been done, the Backpropagation Artificial Neural Network method produces the best architectural model, namely 4-10-1 with 4 as the input layer, 10 as hidden neurons, and 1 as the output layer. With epoch 487 obtains an MSE 0,150807. While the Genetic Algorithm method produces the best fitness value is 10209,02 with a population size of 200, crossover probability 0,8, mutation probability 0,03 in the 498th generation and obtains an MSE value of 0,000098. So, it can be predicted that income for the following month is October 2021 of IDR 25.011.603, November 2021 of IDR 20.920.021, and December 2021 of IDR 50.553.019
AGROMART

AgroMart For several years, farmers in India have had little liberty in choosing markets and purchasers for their produce. All states in the country, except three, agree that marketing and selling of farm produce must be directed through state-owned mandis, retail markets where mediators (middlemen) crush farmers to increase margins. According to research, mediators have become dominating buyers of the agricultural market, resulting in them taking control over the plight of the farmers and gulping all the profits. The farmers work day and night expecting a good yield. They use a lot of financial resources, lending money and buying fertilizers, seeds etc. So, they have the right to enjoy every rupee gained on their crop .In this context, we propose a system which brings farmers close to the retailers cutting the middlemen. The middlemen usually take up to 70% of the profits of farmers leaving them helpless. Our system consists of a mobile or web application which will serve as a platform for farmers, the growers and retailers or customers to sell and buy their farm products. This system aims at giving a profitable price to farmers for their farm products, cutting the middlemen. This allows the retailers or the customers to buy products from the farmers at a lower than the normal price. This system is used by farmers and users. Farmers upload their product with details and buyers view these details and book that product within a time. The System also got support for Krishi Bhavan to know the precautions for the climate change and allowances form the government to farmers.
Design and Implementation of firmware for an embedded system that creates lighting and laser effects

The paper presents some of the technical aspects which were identified during the process of firmware development for an embedded system that creates lighting and laser effects. The application fields of such a system include multimedia entertainment shows, industrial production and assembly, smart classrooms supported by augmented reality and smart agriculture. The introduction of the paper presents a brief overview of the hardware of the embedded system, which is part of our previous work. With regard to this hardware, relevant technical requirements pertaining to the firmware are identified. Their analysis leads to the creation of the proposed firmware architecture, which is then implemented using open source tools and libraries. Some details of the implementation, which consists of multiple tasks and interrupt handling routines, are discussed along with our reasoning behind the design and implementation choices. Finally, the experimental results and insights obtained during our tests of the prototype firmware are summarized. Areas for future improvement are identified and some ideas for the extension of the firmware capabilities are given.
2nd Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys - Finding Consensus in the Field

The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.
Gender accommodation in teenagers’ spelling of regular verb homophones on social media

The present study investigates the effect of interlocutors’ gender on the production of persistent verb spelling errors in adolescents’ private online communication. Specifically, we examine whether boys adapt to the – more standard – spelling practices of girls, and whether girls produce more errors in conversations with boys. We focus on (presumably) unintentional spelling errors in regular Dutch verb forms, i.e., ‘features’ one would rather avoid since they tend to be highly stigmatized. The results reveal a pattern of unilateral accommodation: they point to downward convergence on the part of the older girls, who converge towards the less standard spelling style of their male interlocutors, but not to upward convergence in boys’ writing. The maintenance pattern in boys’ and the norm relaxation in girls’ mixed gender writing give an interesting clue with respect to the subtle mechanisms of both accommodation and norm perception related to the informal social media context.
Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City

Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.
M-learning in the process of media education and foreign language learning

The purpose of this article is to study the possibilities of English-language mobile applications for media education which are available on the Google Play platform. The main emphasis is on their didactic potential, in particular on their ability to build media literacy skills and improve English language proficiency. The application called Journalism &amp; Mass Communication by Edutainment Ventures — Making Games People Play was chosen as the object of study. This choice is grounded in the attractiveness and successful experience of its use. Other projects focusing on working with English-language texts and expanding vocabulary have also been researched. These ones include Journalism by SQSTECH, Journalism Textbook by Courses Books, Journalism Course by APLUS, Media and Information Literacy by PERCDC Learnhub, Journalism Dictionary Offline by EasyGoing and Journalism Dictionary by Pocket Dictionary. A focus group of students of Oles Honchar Dnipro National University who are majoring in 061 Journalism at various levels of higher education was involved to evaluate the functionality of the applications. In total, there were 17 people in the group, including 12 Bachelor's students, 4 Master's students, and 1 PhD student. The effectiveness or ineffectiveness of the application options was determined by analysing the results of a survey conducted among focus group participants. A general analysis of applications for media literacy and media education shows that mobile learning is a relevant and effective tool in the educational process. However, it cannot completely replace traditional education. The diversity of functionality, approaches to content, and audience engagement underscores the importance of creating mobile applications that cater for specific educational programs. Mobile learning has great potential to improve the effectiveness of extracurricular education. The use of mobile applications in media education and learning English has significant didactic potential. The results of the study confirm the feasibility of an integrated approach to learning, which combines the study of a subject with learning a foreign language. Since the study of the English language is gaining relevance in Ukraine, the combination of these two directions is considered to be well grounded.
E-Learning Readiness in Higher Education Institutions in Nigeria during COVID-19 Pandemic

Purpose: The emergence of coronavirus disease 2019 (COVID-19) pandemic necessitated the closure of schools and higher institutions worldwide. Most schools resorted to online learning without considering its attendant effects. Most institutions in the developing countries that were stocked with traditional face-to-face learning method migrated to online learning unprepared. The study, therefore, evaluates lecturers’ readiness for e-learning in higher education institutions in Nigeria amid COVID-19 pandemic.&#x0D; Design/Methodology/Approach: To achieve this, a structured questionnaire was administered to 324 lecturers in selected federal, state, and private universities in Nigeria. The study adopts a quantitative research method. The quantitative data was analyzed using descriptive statistics.&#x0D; Findings: Findings reveal that Nigerian universities were not ready for e-learning prior to COVID-19 pandemic as the needed critical components to take-off e-learning were not available. Study indicates that resource utilization, resource availability, and e-learning facilitations are the critical factors that affected e-learning readiness in Nigeria universities amid COVID-19. Other challenges include internet unreliability, unavailability of e-learning pedagogy, and lack of prerequisite skills for the instructors and the learners.&#x0D; Research Limitations/Implications: A major limitation of the study is the failure to evaluate the challenges of e-learning readiness from the students’ perspective. This limitation creates opportunities for further research into the subject matter. Despite this limitation, the findings from the study have potential implications for university management stakeholders, education policy maker, and e-learning implementation in Nigeria HEIs.&#x0D; Originality/Value: Given the fact that e-learning is still a new method of teaching in HEIs in Nigeria, switching to this method of teaching unprepared poses a major challenge for both the learners and instructors. Therefore, it is crucial to evaluate facilitators and beneficiaries’ readiness to embark on this new method of learning.
Exploring the Effectiveness of Virtual Reality Role-Playing in Debating Repatriation of Artworks in Active Learning Art History Classes

This paper addresses the pressing issue of repatriation debates for artworks, heightened by notable instances like the return of the Benin Bronzes and discussions around the Elgin Marbles. Given the challenges in conducting effective classroom debates on such sensitive topics due to the pandemic, generational shifts in learning preferences, and increased student anxiety, this study identifies a gap in the integration of active learning strategies in a virtual setting. The primary aim of this research is to examine the efficacy of virtual reality role-playing games (VR-RPGs) in enhancing student engagement, immersion, presence, and learning outcomes within a virtual learning environment (VLE). This is set against the backdrop of the potential advantages of VR-RPGs, which allow students to adopt different personas, thus potentially reducing direct confrontation and facilitating a more immersive learning experience. The research contributes to the pedagogical field by assessing the applicability of avatar-based VR-RPGs in VLEs and examining their correlation with improved understanding of cultural repatriation ethics, diverse perspectives, and alleviating student anxiety in groupwork. Our results indicate a notable positive relationship between VR-RPG activities and the aforementioned learning outcomes, signifying the importance of such approaches in contemporary art history education.
Digital Video Summarization: A Survey

Video summarization has arisen as a method that can help with efficient storage, rapid browsing, indexing, fast retrieval, and quick sharing of the material. The amount of video data created has grown exponentially over time. Huge amounts of video are produced continuously by a large number of cameras. Processing these massive amounts of video requires a lot of time, labor, and hardware storage. In this situation, a video summary is crucial. The architecture of video summarization demonstrates how a lengthy film may be broken down into shorter, story-like segments. Numerous sorts of studies have been conducted in the past and continue now. As a result, several approaches and methods—from traditional computer vision to more modern deep learning approaches—have been offered by academics. However, several issues make video summarization difficult, including computational hardware, complexity, and a lack of datasets. Many researchers have recently concentrated their research efforts on developing efficient methods for extracting relevant information from videos. Given that data is gathered constantly, seven days a week, this study area is crucial for the advancement of video surveillance systems that need a lot of storage capacity and intricate data processing. To make data analysis easier, make it easier to store information, and make it easier to access the video at any time, a summary of video data is necessary for these systems. In this paper, methods for creating static or dynamic summaries from videos are presented. The authors provide many approaches for each literary form. The authors have spoken about some features that are utilized to create video summaries.
End-to-end physics-informed deep neural network optimization of sub-Nyquist lenses

In this paper, an approach for optimizing sub-Nyquist lenses using an end-to-end physics-informed deep neural network is presented. The simulation and optimization of these sub-Nyquist lenses is investigated for image quality, classification performance, or both. This approach integrates a diffractive optical model with a deep learning classifier, forming a unified optimization framework that facilitates simultaneous simulation and optimization. Lenses in this work span numerical apertures from approximately 0.1 to 1.0, and a total of 707 models are trained using the PyTorch-Lightning deep learning framework. Results demonstrate that the optimized lenses produce better image quality in terms of mean squared error (MSE) compared to analytical lenses by reducing the impact of diffraction order aliasing. When combined with the classifier, the optimized lenses show improved classification performance and reduced variability across the focal range. Additionally, the absence of correlation between the MSE measurement of image quality and classification performance suggests that images that appear good according to the MSE metric may not necessarily be beneficial for the classifier.
Likelihood function modelling of radar targets under high‐speed observation

Abstract The likelihood ratio function of the target plays a crucial role in applications such as tracking before detection (TBD). In the framework of random finite sets, the performance of radar target TBD based on particle filters is limited by the performance of the likelihood ratio function. Traditional likelihood ratio functions only consider the likelihood ratio between the target and background noise, thus requiring a high signal‐to‐noise (SNR) ratio for the input signal. In scenarios where the observation time interval is extremely short, there exists a strong autocorrelation between the complex amplitudes of the radar target at different time instants. Based on this characteristic, an autoregressive model is utilized to establish the autocorrelation of the target complex amplitudes and update the target's complex amplitudes using a Kalman filter. The likelihood ratio is derived from the model and it is compared with the traditional likelihood ratio under pulse‐Doppler radar. This comparison aims to demonstrate the effectiveness of the proposed likelihood ratio. Due to the fact that this likelihood ratio only requires the extraction of the complex amplitudes of the target scattering centre, without the need to extract the energy around the scattering centre like traditional point spread function methods, the algorithm complexity is greatly reduced, enabling real‐time operation of the proposed method.
Festival of Touch: a personal impression

After a postponement of two years because of covid, the Festival of Touch, was held in Marseille, France, July 4 to 7, 2023. This event was a joint meeting of two organizations: the International Association for the Study of Affective Touch/IASAT and Research in Touch. It aimed to bring together researchers working in the sense of touch from across the world.
Exploratory Spatial Data Analysis Using Geoda for Regional Apparatus in Malang Regency

The Malang Regency Communication and Information Office (Diskominfo) since 2019 has published the Malang Regency One Data book. Some KMSD data contain location information which is referred to as spatial data. The Malang Regency Communication and Information Office (Diskominfo) since 2019 has published the Malang Regency One Data book. Some KMSD data contain location information which is referred to as spatial data. However, the problem faced by Diskominfo is the limited Human Resources both Diskominfo and data producers (OPD) in exploring sectoral data involving spatial data and presenting it in a map. The purpose of this activity is to provide training in exploratory spatial data analysis for OPD to improve sectoral data processing capabilities, especially those containing spatial information. The training was conducted through the provision of materials and discussions on exploratory spatial data analysis and its application using the Geoda software.
Autonomous Vehicles and the Linkages to Smart Mobility: Impact and Implications on Digital Globalisation

Abstract Human civilisation is undergoing a transformation, perceived as the institutionalisation of digital globalisation. The 20th century witnessed greater strides in the form of the internet revolution, automobile technologies and smart mobility, related transformation, dissemination and integration of related technologies as well, leading to the emergence of digital globalisation. The integration of cyber-physical systems has accelerated the rate of urbanisation, and the currently prevailing enhanced standards of living have increased the number of automobiles in cities that have been integrated into the digitally globalised ecosystems, leading to the emergence of autonomous vehicles (AVs). The present study analyses the emerging threats and vulnerabilities of AVs and smart mobility, which have created more security concerns for AVs across nation-states, leading as well to the emergence of digital globalisation. The article theorises the aspects of human security linkages and utilises a capacity matrix (qualitative approach) to examine the development, institutionalisation and integration of AVs into digital globalisation and the related challenges emerging along with it.