Reviewerly/exHarmony huggingface dataset:
Spectator Awareness and Support of Safety and Security Practices and Technologies during COVID-19

Since 2020, a global pandemic has challenged the sports industry in unprecedented ways; the industry came to a standstill at one point, eventually returning with limited capacities and public health safety and security precautions. This exploratory study examined spectators' awareness and level of support for general safety and security measures and their willingness to embrace COVID-19-specific policies as they returned to live events in the U.S. in 2021. A total of 609 participants who attended a professional or collegiate sporting event within the previous three years completed the survey. Results indicated that spectators considered security measures when deciding to attend events and preferred visible measures (e.g., law enforcement presence, screening technologies, and security cameras). Spectators were willing to encounter COVID-related measures (e.g., temperature checks, digital health passes, and touchless screening) to return to live events. The most important decision-making factor in future event attendance was enhanced venue/event cleaning/hygiene protocols.
VQEG Column: VQEG Meeting Dec. 2021 (Virtual/Online)

Welcome to a new column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG).
Embedding a Microblog Context in Ephemeral Queries for Document Retrieval

With the proliferation of information globally, the search engine had become an indispensable tool that helps the user to search for information in a simple, easy and quick way. These search engines employ sophisticated document ranking algorithms based on query context, link structure and user behavior characterization. However, all these features keep changing in the real scenario. Ideally, ranking algorithms must be robust enough to time-sensitive queries. Microblog content is typically short-lived as it is often intended to provide quick updates or share brief information in a concise manner. The technique first determines if a query is currently in high demand, then it automatically appends a time-sensitive context to the query by mining those microblogs whose torrent matches with query-in-demand. The extracted contextual terms are further used in re-ranking the search results. The experimental results reveal the existence of a strong correlation between ephemeral search queries and microblog volumes. These volumes are analyzed to identify the temporal proximity of their torrents. It is observed that approximately 70% of search torrents occurred one day before or after blog torrents for lower threshold values. When the threshold is increased, the match ratio of torrent is raised to ∼90%. In addition, the performance of the proposed model is analyzed for different combining principles namely, aggregate relevance (AR) and disjunctive relevance (DR). It is found that the DR variant of the proposed model outperforms the AR variant of the proposed model in terms of relevance and interest scores. Further, the proposed model’s performance is compared with three categories of retrieval models: log-logistic model, sequential dependence model (SDM) and embedding based query expansion model (EQE1). The experimental results reveal the effectiveness of the proposed technique in terms of result relevancy and user satisfaction. There is a significant improvement of ∼25% in the result relevance score and ∼35% in the user satisfaction score compared to underlying retrieval models. The work can be expanded in many directions in the future as various researchers can combine these strategies to build a recommendation system, auto query reformulation system, Chatbot, and NLP professional toolkit.
Study of two heterogeneous servers with service feedback, vacation and a particular service interruption

Due to the huge impact on real-life application, queueing models with different types of servers have been studied widely. This paper-based on M/M/2 queues presents the study of two heterogeneous servers with particular service interruption, vacation and maintenance. Feedback facility is provided to the customers which are served by a particular server and this server will be interrupted by reactive maintenance after getting a threshold number of negative feedback. Another server will work simultaneously and no feedback will be recorded for this server. To derive the steady-state probabilities, a detailed computational procedure is followed using the matrix geometric method, and then performance measures are calculated. The economic analysis of the model is done. This model is compared with the M/M/1 model in which the server is interrupted by reactive maintenance after getting threshold number of negative feedback.
Intelligent VR technology empowers the construction of intelligent learning environment for preschool education of kindergartens with emblematic characteristics

Abstract Due to the current kindergarten preschool education being increasingly emphasized, this paper combines intelligent technology to explore, firstly, analyzes the current intelligent learning environment model and designs the intelligent learning environment based on human-computer interaction. Secondly, the emblem elements are integrated into the physical learning environment, combined with intelligent VR technology, gesture interaction recognition based on feature vectors and pose interaction recognition based on vector machines to realize the construction of the smart learning environment. Finally, the interaction performance in the smart learning environment is analyzed based on simulation experiments, and the changes in learning effects are explored. The application yields that young children’s independent ability and social interaction ability increased by 0.5 and 0.48, respectively, and the mathematical thinking ability, artistic creation ability and Hui culture cognitive ability increased by 0.35, 0.39 and 0.32, respectively.
Development of a Safety Helmet Wear Detection System for Photovoltaic Power Plants using Enhanced YOLOv5 Algorithm

In photovoltaic power plants, high voltage areas are common, and it is a safety requirement for workers to put on safety helmets when accessing these areas. However, manually detecting compliance with this safety measure is labor-intensive and costly. This paper proposes an improved YOLOv5 safety helmet monitoring system that employs real-time detection. The proposed system leverages the Focal_EIoU_Loss loss, which improves the base YOLOv5 by enabling it to learn high-quality samples and mitigating false detection probability. Additionally, the C2F module is introduced to enhance the network's feature extraction and reduce the likelihood of missing detections. Experimental results show that the improved YOLOv5 model outperforms the base model by .4% in the mAP@.5 index and 1.1% in mAP@.5:.95:.05 index.
Satellite Internet-Based High-Precision Space-Ground Coordinated Ultra-Short-Term Photovoltaic Power Prediction

The high-precision ultra-short-term photovoltaic power prediction is the key guarantee for efficient solar energy utilization. However, various factors significantly challenge the accurate photovoltaic (PV) ultra-short-term power prediction, including the remote location of PV power plant, high communication cost and significant delay. In this work, a satellite internet-based ultra-short-term power prediction (SUPP) framework is proposed that data from space and ground. The core algorithm of SUPP is established with multilayer heterogeneous neural network consisting of convolutional neural network (CNN), gated recurrent unit (GRU) and attention mechanism to utilize the technical advantages of high bandwidth, wide coverage and low latency of satellite internet. The proposed SUPP framework can realize real-time transmission and fusion of local cloud map information collected by ground-to-air cameras and wide-area meteorological data collected by remote sensing satellites. Simulation results show that the proposed SUPP framework can provide a reliable decision basis for system-level scheduling and control by accurately predicting the trend and value of PV power generation with the efficient fusion of multivariate data.
Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User Feedback

Visual data stories can effectively convey insights from data, yet their creation often necessitates intricate data exploration, insight discovery, narrative organization, and customization to meet the communication objectives of the storyteller. Existing automated data storytelling techniques, however, tend to overlook the importance of user customization during the data story authoring process, limiting the system's ability to create tailored narratives that reflect the user's intentions. We present a novel data story generation workflow that leverages adaptive machine-guided elicitation of user feedback to customize the story. Our approach employs an adaptive plug-in module for existing story generation systems, which incorporates user feedback through interactive questioning based on the conversation history and dataset. This adaptability refines the system's understanding of the user's intentions, ensuring the final narrative aligns with their goals. We demonstrate the feasibility of our approach through the implementation of an interactive prototype: Socrates. Through a quantitative user study with 18 participants that compares our method to a state-of-the-art data story generation algorithm, we show that Socrates produces more relevant stories with a larger overlap of insights compared to human-generated stories. We also demonstrate the usability of Socrates via interviews with three data analysts and highlight areas of future work.
Balancing Feature Alignment and Uniformity for Few-Shot Classification

In Few-Shot Learning (FSL), the objective is to correctly recognize new samples from novel classes with only a few available samples per class. Existing methods in FSL primarily focus on learning transferable knowledge from base classes by maximizing the information between feature representations and their corresponding labels. However, this approach may suffer from the “supervision collapse" issue, which arises due to a bias towards the base classes. In this paper, we propose a solution to address this issue by preserving the intrinsic structure of the data and enabling the learning of a generalized model for the novel classes. Following the InfoMax principle, our approach maximizes two types of mutual information (MI): between the samples and their feature representations, and between the feature representations and their class labels. This allows us to strike a balance between discrimination (capturing class-specific information) and generalization (capturing common characteristics across different classes) in the feature representations. To achieve this, we adopt a unified framework that perturbs the feature embedding space using two low-bias estimators. The first estimator maximizes the MI between a pair of intra-class samples, while the second estimator maximizes the MI between a sample and its augmented views. This framework effectively combines knowledge distillation between class-wise pairs and enlarges the diversity in feature representations. By conducting extensive experiments on popular FSL benchmarks, our proposed approach achieves comparable performances with state-of-the-art competitors. For example, we achieved an accuracy of 69.53% on the miniImageNet dataset and 77.06% on the CIFAR-FS dataset for the 5-way 1-shot task.
Polyplane: Art, Visualization, and Outreach

"Polyplane: Art, Visualization, and Outreach." Math Horizons, 31(2), p. 34 Additional informationNotes on contributorsAlex KontorovichAlex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, and Glen Whitney currently serves as the Problem Warden for the Prison Math Project.
Error-diffusion-kernel parameters for binary pattern in 1-bit fringe projection profilometry

In fringe projection profilometry, 1-bit processing of 8-bit raster patterns is a common method to suppress nonlinear errors in commercial projectors and realize high-speed projection in industrial projectors. In the process of generating 1-bit fringes from sinusoidal fringes, the generation of high-order harmonics is inevitable; choosing to introduce fewer high-order harmonics of the algorithm is conducive to defocus to obtain a better sinusoidal pattern. This paper proposes a method to expand the error-diffusion kernel of the conventional Floyd–Steinberg diffusion dithering algorithm from 2×3 to 3×5, which can reduce the grayscale change of surrounding pixels and generate 1-bit fringes with fewer high-order harmonics. Meanwhile, this paper optimizes the parameters of the 3×5 error-diffusion kernel and proposes the optimal parameters for this kind of diffusion kernel. The simulation results show that the fringes generated by the proposed 3×5 error-diffusion-kernel algorithms are closer to sinusoidal fringes after Gaussian low-pass filtering. The experimental results show that the accuracy of the 3×5 diffusion kernel algorithms is higher.
Digital Transformation for the Future of Education: Community Service in Website Design Company Profile Tutoring Institution Scholastica Education Center (SEC)

This community service project explores the growing and crucial role of tutoring institutions (Bimbel) in providing important academic support to students in need. Traditionally, tutoring has been conducted in physical settings, involving face-to-face interactions between students and teachers. However, with the rapid advancement of technology and the soaring popularity of the internet, online tutoring has witnessed a tremendous surge in demand, becoming easily accessible and highly sought after. The Scholastica Education Center (SEC) located in Larantuka, East Flores, NTT, recognizes the importance of following technological advances and using them to increase access to education for children throughout Indonesia. Through these community service initiatives, SEC is wholeheartedly dedicated to making the most impactful contribution to the education sector, catering to the diverse needs and academic levels of its students. An integral aspect of promoting online learning programs is the role of official websites. As a result, Servants designed and launched a company profile website for the SEC, leveraging cutting-edge web technologies such as HTML, CSS, and Javascript. The implementation of this website aims to increase SEC's reach to its target audience, effectively disseminate information about their online tutoring services and programs, and provide an interactive and enriching learning experience for all students. The overall goal of this community service project is to generate a positive and transformative impact by improving the overall quality of education in Indonesia. The seamless integration of technology and the ease of access to learning through online tutoring offered by leading institutions such as the Scholastica Education Center actively contribute to the educational progress and holistic success of students.
Pengembangan E-LKPD Bermuatan Literasi Sains pada Materi Sel Kelas XI SMA/MA

This study aims to analyze the motion of measles dance in pelandok laki studio, Tanjung Pandan sub-district, Belitung Regency. This research uses qualitative research with descriptive methods. The object of this research is Measles Dance in Sanggar Pelandok Laki, Tanjung Pandan District, Belitung Regency. The main instrument of this research is the researcher himself. Based on the results of the study it can be concluded that Measles dance seen from the aspects of space, time and energy aspects of Measles dance has several elements contained in Measles dance movements. Measles dance observed from the perspective of the element of motion space is 6 straight lines. So the dominant in the motion of Measles Dance is a straight motion that has the impression of calm and balanced with full of enthusiasm. The volume in the measles dance is 6 medium volumes, so the dominant volume is medium volume, which means it describes the joy or cheerfulness of teenagers in entertaining the community after the rice harvest. The dominant level is the medium level. The direction of facing in the measles dance is 4 facing the front 2 facing the right side left which describes intimacy. While the focus of view in Measles Dance movements is dominant to the hand or shawl.
Analysis of abnormal data in sensor networks based on improved LSTM in the Internet of Things environment

Summary The proposed method addresses the challenge of online detection of high‐dimensional data in the IoT environment by introducing an anomaly data analysis technique based on improved LSTM. The method involves normalizing both normal and abnormal data using the correlation between multidimensional data and transforming them into gray image representations for input. Additionally, an enhanced abnormal data detection approach is presented through the construction of two parallel network models: a “two‐layer model” and a “single‐layer model.” This approach aims to improve stability in modeling normal data and enhance the detection capability for abnormal data. The proposed method was evaluated on the Human Activity Recognition (HAR) dataset, which consists of 561 dimensions. The experimental results showcased the effectiveness of this method, achieving a detection rate of 94.12% and a recall rate of 95.21%. These rates surpassed the performance of existing techniques in the field of abnormal data detection. Consequently, this method has demonstrated significant advancements and offers improved system performance when compared to current methods.
Validity of Android Application-Based Interactive Media to Improve Literacy

Literacy is a major component of school success, so students with deficits in reading skills generally experience a disadvantage in educational attainment. Reading is an activity to find meaning and writing that involves physical and mental activities. This is also in line with the opinion of) that Reading is one's mental process towards a text. In its narrow sense, reading means analyzing a text composed of letters and trying to interpret it as a whole. Learning to read according to is one of the most important skills children will acquire in the early years of school and difficulties in acquiring this skill can have adverse educational outcomes. there are still grade 1 Students whose reading skills are low, so there needs to be an effort made by the teacher in improving reading skills, one of which is using learning media. This study aims to create an interactive media product that is valid for use in early reading learning activities for grade 1 elementary school students. It is hoped that this developed product can help students learn to read. interactive media development using the Lee &amp; Owen development model. products developed in validity by media, material, and language experts. the results of the product development validation test fall into the valid criteria and can be used to learn to read beginning. from the results of the interactive media validation carried out, it obtained a validity value of 81.66% in the "very valid" category. Interactive media products were developed based on the needs of 1st grade students. Validity of Android Application-Based Interactive Media to Improve Literacy in Class 1 Elementary Schools.
Music as an Element of the Screen Language of the Director. Part 2. Ennio Morricone's compositional practices in screen directing models

The purpose of the article is to determine the peculiarities of the use of music in the screen plane through the study of artistic director-composer tandems. Research methodology. In the development of the topic, the methods of scientific analysis, comparison, and generalisation were comprehensively applied. Analytical and systematic methods in their unity were involved to consider the art-historical aspect of the problem. The scientific novelty of the research lies in the fact that the director's creativity is investigated in the context of composer practices of creating music for audiovisual art; the appropriateness of using the systematic method in studying the features of composers' creativity in the context of audiovisual art and production has been proven. Conclusions. The materials presented in the article expand the arsenal of knowledge regarding the specifics of the co-creation of the director and composer in the screen arts and enable their application in educational courses, the creation of educational and methodological literature on the theory and history of music, film and television, directing, composition.&#x0D; Keywords: musical art, composer's practices, screen arts, directing creativity, musical image, symphonic music, composer's technologies.
Assessing the needs for Javanese learning for speakers of other languages

This article presents a report on the needs analysis of Javanese learning for speakers of other languages. Applying the descriptive-analytical method, this study used data gathered from the survey of 57 participants from 27 different countries. The present study revealed that the most difficult aspect of studying Javanese is on mastering its vocabulary (54.8%). This condition limited the learners’ confidence to engage in any conversation with the locals (74.2%) and made them less progressive in some other related competencies such as reading and writing. Based on the participants’ responses to the survey, the approach recommended to facilitate the program of Javanese for speakers of other languages is the communicative approach. The design of the program targeted proficiency in communicating using Javanese. Vocabulary learning is needed in real situations and contexts. Therefore, the learners could improve their capacity in Javanese and engage with the local people better. Through its comprehensive examination of the challenges, opportunities, and potential solutions, this research not only contributes to the field of language education but also reinforces the value of embracing linguistic diversity as a cornerstone of our shared human experience.
Analisis Perbandingan Algoritme Penghapusan Noise pada Citra X-Ray Paru - Paru

Pulmonary X-ray is a medical diagnostic method used to produce internal lung images. However, the X-ray process is often interrupted when capturing images, resulting in noisy image results. This condition diminishes the clarity of information contained in the lung X-ray images. Therefore, noise removal or denoising is essential. Denoising is a fundamental image processing technique aimed at improving image quality for optimal information transmission. This study applies denoising methods to 20 datasets of pulmonary X-ray images using Median, Mean, Gaussian, Bilateral, and Wiener filters, with Python and the OpenCV Library. Error measurement for noise filtering is conducted using Peak Signal-to-Noise Ratio and Mean Square Error methods. The research results show that the median filter stands out as an excellent denoising method, outperforming others with a Peak Signal-to-Noise Ratio of 37.6444 and a Mean Square Error of 11.3339 for Salt and Pepper Noise. Keywords: Denoising; Filtering; MSE; PSNR; X-Ray.
Implementasi Dempster Shafer Untuk Deteksi Dini Gizi Buruk Pada Balita

Malnutrition that occurs in toddlers is often caused by an unbalanced intake of nutritious food or caused by certain diseases. Malnutrition is also a cause of digestive disorders in toddlers. Parents who do not understand the problem of malnutrition, make parents not aware of the problems that occur in their children. So, to get accurate and fast information, we need a system that can solve this problem. The dempster shafer method was used in this study to perform early detection of malnutrition in children under five. In this expert system, 3 types of malnutrition can be detected with 23 related symptoms. From the expert accuracy tests that have been carried out, it can be concluded, based on 20 data on early detection of malnutrition in toddlers who have been tested, gives a match rate of 85%. The value of 85% is obtained from the calculation of 17 matching data compared to 20 test data.