Reviewerly/exHarmony huggingface dataset:
Development of Augmented Reality Based Applications for Brain Memory Training

This paper presents the development of augmented reality (AR) based brain memory training to improve the memorizing capability of the student. The AR visual memory training application is built on top of the mobile phone by utilizing the Unity and Vuforia platform. The developed visual memory test is a flipping card test that can measure a person’s memory capability to retain visual images and spatial perception in the mind. In this study, it is aimed to prove that AR technology is suitable to be employed in the education field. The results are justified based on the visual memory test score and the engaging level of the user computed from the electroencephalogram (EEG) signal. The results are assessed by comparing with the physical mode and computer-based mode. As result, it is shown that the student performed better in the AR-based visual memory test compared to physical and computer-based modes. Besides, the EEG signals also show that students are more engaged and attentive while using AR technology.
Research on the implementation of Kubernetes automatic scaling

Elastic scaling is one of the important features of cloud native. In the actual production environment, because the business requirements and application resource load are in a dynamic change, the deployment of business and allocation of resources can be adjusted in real time through elastic scaling to ensure the overall quality of service of the system. As the current mainstream container orchestration technology, Kubernetes' built-in elastic scaling strategy HPA (Horizontal Pod Autoscaler) obtains the corresponding indicators by monitoring the component metrics and calculates the expected value of the replica by comparing it with the user-defined threshold, thus realizing the elastic scaling function. Although this scaling strategy can solve the problem of dynamic scaling, there are problems of response delay and scaling jitter, which makes the quality of service of the system unable to be guaranteed in a specific scaling period. In view of the above problems, the predictive elastic scaling strategy is improved, and the fuzzy AR (p) time series model is built to predict the specified load indicators, so as to achieve predictive elastic scaling. The experimental results show that this strategy can solve the response delay problem well and reduce the unnecessary jitter scaling.
PELATIHAN PENINGKATAN BAHASA INGGRIS GUIDE UNTUK PEMUDA DESA BONDER KECAMATAN PRAYA BARAT LOMBOK TENGAH

AbstractThis community service activity aims to improve English communication skills for tour guides in Bonder Village, West Praya District, Central Lombok Regency. In carrying out this community service activity, the service team uses the interview method and provides questionnaires related to the problems and constraints of the tour guides in communicating using English. The implementation of the service is carried out by giving a pre-test by observing the tour guides in carrying out their duties to accompany and explain the objects of each tourist village to the trainer. From this activity, the tour guides have high motivation to take part in these activities so that training can provide benefits in an effort to improve the English language skills of local guides, so that it is hoped that service to guests who visit tourist areas in Bonder village, Praya Barat District, Central Lombok, in particular can be given optimally, especially foreign guests who come
Ursa Major

Research Article| May 01 2023 Ursa Major Leigh Lucas Leigh Lucas Leigh Lucas (she/her) is a poet and writer in San Francisco. She holds an MFA in creative writing from Warren Wilson and a BA in creative writing from Stanford, where she won an Urmy/Hardy Poetry Prize. She won the Association of Writers and Writing Programs’s 2020 Kurt Brown Prize for emerging poet and was a 2021 Pushcart Prize nominee. She has been awarded residencies at Tin House Summer Workshop, Sewanee Writers’ Conference, Community of Writers in Olympic Valley, and Kenyon Review Writers Workshop. Her poems appear in and are forthcoming from Smartish Pace, Frontier Poetry, and Poet Lore. Search for other works by this author on: This Site Google the minnesota review (2023) 2023 (100): 22. https://doi.org/10.1215/00265667-10320828 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Leigh Lucas; Ursa Major. the minnesota review 1 May 2023; 2023 (100): 22. doi: https://doi.org/10.1215/00265667-10320828 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. © 2023 Leigh Lucas2023 Article PDF first page preview Close Modal Issue Section: Creative You do not currently have access to this content.
Supplemental Material: Iron, boron, and sulfur isotope constraints on the ore-forming process of subseafloor replacement-style volcanogenic massive sulfide systems

&lt;p&gt;Table S1: Locations of samples collected for Fe, S, and B isotope, and EPMA analyses. Table S2: The Fe isotope compositions of whole-rock and minerals (pyrite, chalcopyrite, and chlorite) from the Ashele deposit. Table S3: The S isotope compositions of pyrite and chalcopyrite from the Ashele deposit. Table S4: The B isotope compositions of tourmaline from the Ashele deposit. Table S5: EPMA data of chlorite and tourmaline from the Ashele deposit.&lt;/p&gt;
NLP based Video Summarisation using Machine Learning

More people are capturing their daily lives with video data, because to the wide availability of recording equipment. However, the sheer volume of video content makes it more challenging to manage, especially for lengthy movies like security or CCTV footage. For a richer and more succinct condensing of the film will result from automatically detecting the key sections and frames of larger videos and providing them with captions. Users still need to spend time searching or navigating through a summarized video. To extract a shortened version of the footage's information into text form, automatic video summarizing has been proposed. With simply a text summary, the suggested system provides a quick semantic understanding of a lengthy film using LSTM model and the summary can be taken in 3 major different languages (English, Hindi, &amp; Marathi).
Supplementary material to "Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model"

S1.The Federal Equivalency Method (FEM) in situ sites.Fig. S1b shows the diel variation of the nine types of instruments used at FEM in-situ sites, which provide hourly measurements of PM2.5 across the US.The number and location of instrument types are in Fig. 1.The majority (90.2%) of these instruments belongs to the first four kinds, which are shown in Fig. S1a.They exhibit generally consistent average diel profile of measured PM2.5 masses, which we target as the typical variation to investigate.The other five types, shown as colored or dashed curves in Fig. S1b, are more deviated from the typical profiles.Specifically, the Teledyne Model 602 measures PM2.5 concentrations variability which largely deviates from the typical pattern in Fig. S1a.The GRIMM Model 180 measures a pronounced morning peak.The Met One BAM-1022 shows a morning minimum of PM2.5.The TEOM 1400 measures notably lower concentrations from midnight to early morning.Considering that these five types of instruments with deviated PM2.5 diel patterns only account for less than 10% in all types, we exclude them from our analysis.Our analysis focuses on investigating the typical diel cycles in Fig. S1a.
Limitations of ICT Support in Post-Pandemic E-Learning among Students of Islamic Studies, UiTM Shah Alam

ICT is an important factor in supporting e-learning, especially in the post-pandemic period. However, ICT has also been found to have limitations in supporting e-learning. This study focuses on identifying the limitations of ICT support in pursuing e-learning and methods to address it at the higher education level. The final year student of Islamic studies at the Academy of Contemporary Islamic Studies, Universiti Teknologi MARA Shah Alam was involved as a respondent (n=87) after screening. The online questionnaire is used as an instrument of data collection as e-learning is still applied. The data is analyzed using IBM SPSS Statistics computer software. Friedman’s test results found a significant difference in the variable “Unsatisfactory internet access and coverage” (mean rank: 2.75) with χ2(3)=12.507, ρ=.006, and on the variable “Working in part-time jobs to accommodate the needs” (mean rank: 2.83) with χ2(3)=24.788, ρ=.000. This study suggests unsatisfactory internet access and coverage as a major factor in ICT limitations that is common in pursuing online learning among higher education students in UiTM Shah Alam. The students also had to work part-time jobs to accommodate this need. A qualitative approach, especially ethnography is recommended to be adapted to deepen the real situation based on the current reality of higher education students for future studies.
Towards an Intelligent Assistive System Based on Augmented Reality and Serious Games

Age-related cognitive impairment is generally characterized by gradual memory loss and decision-making difficulties. The aim of this study is to investigate multi-level support and suggest relevant helping means for the elderly with mild cognitive impairment as well as their caregivers as the primary end-users. This work reports preliminary results on an intelligent assistive system achieved through the integration of the Internet of Things, augmented reality, and adaptive fuzzy decision-making methods. The proposed system operates in different modes, including automated and semi-automated modes. The former helps the user complete their daily life activities by showing augmented reality messages or making automatic changes, while the latter allows manual changes after the real-time assessment of the user’s cognitive state based on the augmented reality serious game score. We have also evaluated the accuracy of the serious game score with 37 elderly participants and compared it with users’ paper-based cognitive test results. We further noted that there is an acceptable correlation between the paper-based test and users’ serious game scores. Moreover, we observed that the system response in the semi-automated mode causes less data loss compared with the automated mode as the number of active devices decreases.
Smart Schools: Inclusion and Participation of Vulnerable Students During at Distance Learning

Students in many countries during COVID-19 pandemic, moved through a deep change in their school experience, moving from face-to-face teaching in schools to distance learning. The distance learning has represented an opportunity to develop strategies and resources to guarantee continuity in the learning experience. However, the recent literature has highlighted also several factors that negatively affected learning outcome of students, underlined in particular more negative effects on students in conditions of fragility. This work has the objective to analyse problems emerged during DL and reflect on possible solutions, by considering the analysis of data obtained from recent national and international literature. The article proposes a critical reflection to rethink digital learning environments in the post-pandemic era in order to support learning path of all students and promote inclusion and participation also of those in condition of vulnerability.
Anchor-free Proposal Generation Network for Efficient Object Detection

Deep learning object detection methods are usually based on anchor-free or anchor-based scheme for extracting object proposals and one-stage or two-stage structure for producing final predictions.As each scheme or structure has its own strength and weakness, combining their strength in a unified framework is an interesting research topic.However, this topic has not attracted much attention in recent years.This paper presents a two-stage object detection method that utilizes an anchor-free scheme for generating object proposals in the initial stage.For proposal generation, this paper employs an efficient anchor-free network for predicting object corners and assigns object proposals based on detected corners.For object prediction, an efficient detection network is designed to enhance both detection accuracy and speed.The detection network includes a lightweight binary classification subnetwork for removing most false positive object candidates and a light-head detection subnetwork for generating final predictions.Experimental results on the MS-COCO dataset demonstrate that the proposed method outperforms both anchor-free and twostage object detection baselines in terms of detection performance.
Towards Robust and Bias-free Federated Learning

Federated learning (FL) is an exciting machine learning approach where multiple devices collaboratively train a model without sharing their raw data. The FL system is vulnerable to the action of Byzantine clients sending arbitrary model updates, and the trained model may exhibit prediction bias towards specific groups. However, FL mechanisms tackling robustness and bias mitigation have contradicting objectives, motivating the question of building a FL system that comprehensively combines both objectives. In this paper, we first survey state-of-the-art approaches to robustness to Byzantine behavior and bias mitigation and analyze their respective objectives. Then, we conduct an empirical evaluation to illustrate the interplay between state-of-the-art FL robustness mechanisms and FL bias mitigation mechanisms. Specifically, we show that classical robust FL methods may inadvertently filter out benign FL clients that have statistically rare data, particularly for minority groups. Finally, we derive research directions for building more robust and bias-free FL systems.
Safe Security System Using Face Recognition Based on IoT

Face recognition is widely used in various applications, especially in the field of surveillance and security systems. This study aims to design and build a safe security system using face recognition via camera based on internet of things. This system uses the Raspberry Pi 3B and the OpenCV library as face recognition data processing which produces output on the Selenoid to open and close the safe, LCD 16x2 to display system status, IoT-based email delivery when smugglers occur. This study performs face recognition through the face detection stage using the Viola Jones method, feature extraction using the PCA (Principal Component Analysis) method and face recognition, then matched with the existing profile data in the directory. The results of this study indicate that the safe is open when a face is detected and will send a face capture to the e-mail address of the owner’s safe if the detected face is not recognized. Tests carried out on the safe security system using face recognition based on IoT build reach validity 90,25%.
Implementation of Naïve Bayes in the Analysis of Public Sentiment on Twitter for Shopee's Flash Sale

Rumors spread among the public that there was fraud occurring during the ongoing flash sale Shopee process. In order to answer the problems found, this study aims to find out how the public feels about the flash sale activity. Data was taken using the Twitter API, and the tweets used started from January 2021–June 2021. As a result, there were 3,467 Twitter data points. After passing through the text preprocessing stage, the data becomes clean data, which amounts to 457. The research results obtained using the Nave Bayes Classifier method show the results of public opinion on Twitter with positive sentiment categories of 19.4%, negative sentiment categories of 43.1%, and neutral sentiment categories of 37.4%, with an accuracy rate of 98%.&#x0D;
The Alcatraz East Crime Museum: Pigeon Forge, Tennessee, USA

American National Museum of Crime and Punishment (Crime Museum) opened in 2008 in Washington, DC. As described in its advertising,; it was the first museum in America to bring together the curiosity and passion for true crime, forensics, and the history of law enforcement in an interactive environment. In 2016 the Crime Museum collection moved to a new home in Pigeon Forge, Tennessee.
Relationship between Role Recognition, Professionalism, and Job-Satisfaction of Child Welfare Teachers

본 연구는 아동복지교사의 자신의 역할 인식 수준을 확인하고, 역할 인식에 따른 전문성과 직무만족도 차이를 확인하고자 한다. 이를 확인하기 위해서 전국 아동복지교사를 대상으로 역할 인식, 전문성, 직무만족도 설문지를 온라인으로 조사하였다. 아동복지교사 825명이 온라인 설문에 응답하여 그 자료를 기술 분석, ANOVA, 회귀분석을 실시하였다. 주요 결과는 다음과 같다. 아동복지교사는 본인 스스로 교육자이며 아동복지사로 인식하는 비율이 87.6%로 나타났다. 아동복지교사의 역할을 교육자와 아동복지사, 교육자로 인식한 집단이 아동복지사만으로 인식한 집단에 비해 전문성, 직무만족도를 더 높게 인식하였다. 또한, 아동복지교사의 역할 인식 및 전문성이 직무만족도에 영향을 확인한 결과, 아동복지교사의 역할 및 전문성은 직무만족도에 정적 영향력을 나타났다. 즉, 아동복지교사의 직무만족도를 향상하기 위해서는 역할 인식과 전문성에 대한 인식을 높아야 한다. 이러한 결과를 토대로 아동복지교사의 역할 인식과 전문성 향상을 위한 교육 및 훈련이 필요함을 제시하고자 한다. The purpose of this study was to establish the level of awareness of child welfare teachers" roles and to identify differences in professionalism and job satisfaction based on role recognition. A nationwide online survey was conducted on role perception, professionalism, and job satisfaction among child welfare teachers. A number of 825 child welfare teachers responded and completed the online questionnaire. The data obtained were subjected to descriptive analysis, ANOVA, and regression analysis. We found that the rate of child welfare teachers who perceived themselves as educators and child welfare workers was the highest (87.6%). The group that recognized the role of child welfare teacher as an educator, child welfare worker, and educator recognized higher professionalism and job satisfaction than the group that recognized child welfare teacher alone. The role and expertise of child welfare teachers showed a positive influence on job satisfaction. Therefore, we confirmed that the job satisfaction of child welfare teachers could be improved by promoting role recognition and professionalism. These findings indicate the need for education and training that can improve the role recognition and professionalism of child welfare teachers.
Facial Expression Recognition Using Mask Region Segmentation

얼굴 표정인식(facial expression recognition)은 인간 중심의 사람-기계 인터페이스에서 가장 중요한 요소기술 중 하나이다. 마스크 착용 얼굴의 경우 얼굴 하관이 대부분 가려져 충분한 양의 분별 특징정보(discriminative feature)를 추출하기 어려우며, 마스크 착용 여부 및 마스크 패턴(형태, 크기, 무늬 등)의 다양성은 인식 정확도를 저해시키는 방해요소로 작용할 수 있다. 본 논문에서는 마스크 착용으로 인한 영향을 감소시키기 위한 얼굴 표정인식 방법을 제안한다. 먼저 마스크 착용 여부에 의한 불일치를 감소시키기 위해 마스크를 착용한 테스트 얼굴에 대해 표정인식을 수행할 때 학습용 얼굴 이미지들에 마스크를 합성한다. 그리고 마스크 패턴의 다양성에 의한 불일치를 최소화하기 위해 테스트 및 학습용 얼굴 이미지들에서 이미지 분할을 적용하여 추출된 마스크 영역을 제거하여 검정색으로 표시한다. 마스크 영역이 제거된 테스트 얼굴 이미지를 동일한 방식으로 전처리 된 학습용 얼굴 이미지들의 최적 조합으로 복원(reconstruction)한 뒤 복원 기여도가 가장 높은 클래스를 찾음으로써 테스트 얼굴 이미지의 감정 클래스를 결정한다. 제안하는 방법은 6가지의 대표 감정에 대해 70% 이상의 마스크 얼굴 표정인식 정확도를 달성하였으며 이는 학습용 얼굴 이미지들이 마스크 미착용인 경우에 비해 46% 정도 높은 수치이다. Facial expression recognition is one of the most important technologies in human-machine interface. For the case of masked face image, it is difficult to extract discriminating features as a large facial part is occluded. Moreover, a variety of mask patterns could degrade the recognition rate. In this paper, a facial expression recogition method is proposed aiming to reduce the effect of wearing masks. In the proposed method, to deal with the mismatch due to masked test faces, mask images are synthesized with the training face images. After that, for each of the test and training face images, the mask region is segmented and eliminated. A proprecessed test face image is recontructed with an optimal combination of the preprocessed training face images, and categorized into an emotion class by finding the most contributing class during the reconstruction. The proposed method achieves 70% in recognition rate where about 46% improvement can be achieved compared to the case of unmasked training faces.
Predictive Modeling of Photovoltaic Solar Power Generation

Photovoltaic solar power referred to as solar power using photovoltaic cells, is a renewable energy source. The solar cells' electricity may be utilized to power buildings, neighborhoods, and even entire cities. A stable and low-maintenance technology, photovoltaic solar power is an appealing alternative for generating energy since it emits no greenhouse gases and has no moving components. This paper aimed to provide a photovoltaic solar power generation forecasting model developed with machine learning approaches and historical data. In conclusion, this type of predictive model enables the evaluation of additional non-traditional sources of renewable energy, in this case, photovoltaic solar power, which facilitates the planning process for the diversification of the energy matrix. Random Forests obtain the highest performance, with this knowledge power systems operators may forecast outcomes more precisely, this is the main contribution of this work.
Development of supplementary material using Wordwall for 12th-grade students in learning relative clauses

The study aimed to develop supplementary material using Wordwall-based learning media for learning relative clauses and examined the experiences of 12th-grade students in using Wordwall. The study employed Design and Development (DnD) research method which conducted design, development, and evaluation steps. This study used the topic of the relative clause as a result of pre-observation in tackling an existing problem in learning relative clauses. This research used two instruments: a blueprint of supplementary material and a User Evaluation Questionnaire (UEQ). The UEQ examined 5 dimensions of users’ experiences in using a digital product: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The data were collected by analyzing the syllabus, preparing the Wordwall platform, distributing the questionnaire, and analyzing the questionnaire’s results. The questionnaire was filled with 32 students and 1 teacher who are taking English Language and Literature (Concentration) subjects. The data collected was analyzed and explained qualitatively and quantitatively. The results of the user experiences were attractiveness gained excellent (2.1), perspicuity gained above average (1.68), the efficiency gained excellent (1.88), dependability gained excellent (2.25), stimulation gained good quality (1.55), and novelty gained excellent quality (2.13). These results indicate that the developed supplementary material brings students attractive, fun, motivating, efficient, and fresh experiences in using Wordwall for learning relative clauses. Therefore, the developed Wordwall-based supplementary material for learning relative clauses material is very good to be implemented for the learning and teaching process for 12th-grade students.