Reviewerly/exHarmony huggingface dataset:
Increasing students’ culture literacy using virtual reality field trip model: Need analysis

The rapid exchange of information through technology has caused a cultural change in Indonesia. This causes the local culture to be lost and the penetration of foreign cultures. This article aimed to analyze the need to develop Field Trip Model Virtual Reality for elementary school students. This study used the qualitative descriptive method. The 50 students and 28 teachers from an elementary school in Jakarta became participants. The data were collected using questionnaires form and interviews. The data were analyzed using triangulation. The result showed that the learning model was needed to increase cultural literacy. From questionnaire data show that (1) students need learning media that can make it easier to recognize the culture, (2) students' awareness of culture is still low, and (3) students need technology that allows users to feel the conditions as real from the three-dimensional effects. &#x0D; &#x0D; Keywords: media; VR, cultural, literacy, school
Estimation of Reliable Proposal Quality for Temporal Action Detection

Temporal action detection (TAD) aims to locate and recognize the actions in an untrimmed video. Anchor-free methods have made remarkable progress which mainly formulate TAD into two tasks: classification and localization using two separate branches. This paper reveals the temporal misalignment between the two tasks hindering further progress. To address this, we propose a new method that gives insights into moment and region perspectives simultaneously to align the two tasks by acquiring reliable proposal quality. For the moment perspective, Boundary Evaluate Module (BEM) is designed which focuses on local appearance and motion evolvement to estimate boundary quality and adopts a multi-scale manner to deal with varied action durations. For the region perspective, we introduce Region Evaluate Module (REM) which uses a new and efficient sampling method for proposal feature representation containing more contextual information compared with point feature to refine category score and proposal boundary. The proposed B oundary Evaluate Module and R egion E valuate M odule (BREM) are generic, and they can be easily integrated with other anchor-free TAD methods to achieve superior performance. In our experiments, BREM is combined with two different frameworks and improves the performance on THUMOS14 by 3.6% and 1.0% respectively, reaching a new state-of-the-art (63.6% average mAP). Meanwhile, a competitive result of 36.2% average mAP is achieved on ActivityNet-1.3 with the consistent improvement of BREM. The codes are released at \urlhttps://github.com/Junshan233/BREM.
College English Blended Teaching Reform Based on VR Technology in the Post-Epidemic Era

This paper examines the subject of English as a case study and uses VR technology as a tool to explore the reform of blended learning college English based on VR technology in the post-epidemic era in order to provide recommendations for the reform of college English teaching.This paper elaborates on the related theoretical concepts of VR technology and blended mode teaching and their combination to propose the reform of blended learning of college English based on VR technology; then, through questionnaires and interviews, 100 freshmen of business management and accounting department of our provincial university were randomly selected as experimental subjects and divided into an experimental group (50) and a control group (50) according to the difference The experiment of blended college English teaching reform was conducted.Finally, analyzing the results of the experiment, it was found that after a semester of blended teaching, the test scores of students in the experimental group improved significantly compared to the previous ones; while the oral test scores of students in the control group improved slightly, their written test scores not only did not improve but also decreased by 0.3086 points.The study showed that the blended mode of college English teaching based on VR technology can help to improve students' English proficiency, which deserves to be promoted and improved in the university education system.
Research and Application of Fault Handling Technology of Multiple Information Knowledge Atlas

The computer technician in order to faster to solve the data mining, information processing, &#x0D; data sorting, summary such as content, by adopting multivariate information knowledge &#x0D; mapping technology, effectively improve the efficiency of the daily work of technical staff, at &#x0D; present, the multivariate information knowledge mapping technology has been applied in the &#x0D; fields of intelligent q&amp;a, intelligent search. However, in the application process, the multivariate &#x0D; information knowledge graph technology can not only quickly screen big data, but also build an &#x0D; Internet modeling platform, so that more users can understand the multivariate information &#x0D; knowledge technology, shorten the data query time, and improve the accuracy and authenticity &#x0D; of data acquisition. This paper analyzes and studies the technical methods used in the process of &#x0D; Internet information failure by the technology of multiple information knowledge graph.
Analysis of concept mapping for pre-service early childhood teachers’ perception on play in young children

본 연구는 개념도 방법을 활용하여 놀이에 대한 예비유아교사의 인식을 파악하는 것을 목적으로 한다. 예비유아교사 22명을 연구 참여자로 선정하였다. 연구 참여자는 초점 질문에 대한 자신의 생각을 진술하였고, 정리된 진술문을 범주화 후 중요도 평정을 실시하였다. 연구결과 첫째, 예비유아교사의 유아 놀이에 대한 인식으로 39개의 최종 진술문이 생성되었으며, 다차원 척도분석을 통해 「사람-사물」, 「관계적 특성-심리적 특성」의 2개 차원으로 나누었다. 또한 진술문은 군집분석을 통해 ‘희열을 느끼는 놀이’, ‘얽매이지 않는 놀이’, ‘의견을 조율하는 놀이’, ‘상호관계를 형성하는 놀이’, ‘언제 어디서나 일어나는 놀이’, ‘놀잇감을 가지고 하는 놀이’, ‘발달에 도움이 되는 놀이’등 7개의 군집으로 분류하였다. 둘째, 군집별 중요도는 ‘얽매이지 않는 놀이’에 속하는 진술문들이 높은 중요도를 나타냈으며, 진술문별 중요도에서는 <재미있는 것>, <즐거운 것> 등이 높은 중요도를 나타냈다. 본 연구결과는 유아의 놀이를 지원하기 위한 예비유아교사의 놀이지도 교육방향에 시사점을 제공할 수 있을 것이다.This study aimed to understand pre-service early childhood teachers' perceptions on the play in young children by using concept mapping method. For this purpose, 22 pre-service early childhood teachers were selected as participants. The participants stated their thoughts on the focus question, categorized and rated the importance of the prepared statements. Based on these, multidimensional scale analysis and hierarchical cluster analysis were performed. The results were as follows. Firstly, 39 final statements were generated based on the pre-service early childhood teacher's perception of play in young children, and through multidimensional scale analysis, these were divided into two dimensions: ‘human-thing’ and ‘External Characteristics- Internal Characteristics’. And the statements were divided into 7 clusters by cluster analysis: ‘Joy Play’, ‘Unregulated Play’, ‘Relationship Forming Play’, ‘Opinion Co-operating Play’, ‘Anytime, Anywhere Play’, ‘Toy Play’, ‘Developmentally Helpful Play’. Secondly, as for the importance of each group, ‘Joyful Play’ showed the highest importance in general, and for the statement, ‘an interesting thing’ and ‘funny thing’ showed high importance. The results of this study can provide implications for the direction of effective education for pre-service early childhood teachers in the children play promotion.
Mind-Set Based Signage: Applying Mind Genomics to the Shopping Experience

1. Schumann et al. [1] reported only modest effectiveness of signage in shopping cart.To summarize their results: "Findings from both studies reflect that over 60% of the 2 samples noted the presence of the signs in their carts.When Ss were questioned about their awareness of cart advertising on a specific occasion, only 3.0-6.5% recalled the product.There was no evidence that cart signage acts in a subliminal fashion that results in the
Cryptocurrencies in the Digital Era. Composite Index-Based Analysis for the Top Ten Virtual Currencies Traded

The International Conferences on Economics and Social Sciences (ICESS) organized by Bucharest University of Economic Studies provide an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. The conference takes place in Bucharest University of Economic Studies, Piata Romana 6, Bucharest, Romania.Contact: conference.icess[@]ase.ro The papers presented at the Conference are available online in the Conference Proceedings series (ISSN 2704-6524): Volume 2019 Collaborative Research for Excellence in Economics and Social Sciences, ISBN 9788366675322 Volume 2020 Innovative Models to Revive the Global Economy, ISBN 9788395815072 Volume 2021 Resilience and Economic Intelligence Through Digitalization and Big Data Analytics, ISBN 9788366675704 The past years’ uncertainties and pressures resulted from the pandemic, alongside new developments in social technology, 5G, cloud computing, augmented and virtual reality, generated the perfect setting for the metaverse to gain traction. Several companies found there’s a critical need to redefine the work environment and started exploring the metaverse concept beyond the entertainment sector. Thus, new tools for collaboration and data visualization within a digital shared space are being created, aiming for a fully immersive interaction between the virtual and the physical worlds within the next few
A Hybrid Discrete Symbiotic Organisms Search Algorithm and List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

A discrete symbiotic organisms search (DSOS) and list-based simulated annealing (LBSA) are new metaheuristic search algorithms used for solving different complex optimization problems. DSOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival, while LBSA simplify parameter tuning of the simulated annealing algorithm. In this paper, we propose a hybrid algorithm, named DSOS-LBSA, to solve the well-known traveling salesman problem (TSP) which belongs to the class of NP-hard problems. Additionally, an arbitrary insertion algorithm is introduced to produce organisms in the initial ecosystem. The proposed DSOS-LBSA is implemented in the MATLAB environment and it is tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed DSOS-LBSA offers promising results, particularly for small-size symmetric instances and large-size asymmetric instances.
Feature Ranking for Feature Sorting and Feature Selection, and Feature Sorting: FR4(FSoFS)$$\wedge $$FSo

This paper introduces the application of feature ranking with a twofold purpose: first it achieves a feature sorting which becomes into a feature selection procedure given that a threshold is defined to only retain features with a positive influence with the class label (feature sorting and feature selection: FSoFS), second the feature subset is optimised using feature ranking to get a promising order of attributes (FSo). The supporting paper introduced a single stage: Feature ranking for feature sorting and feature selection, FR4(FS) $$^2$$ with the shortcoming that for some high-dimensional classification problems the pre-processed data set did not obtain a more accurate classification model than the raw classifier. The results mean that it deserves to consider feature ranking in a couple of sequential stages with different goals: i) feature sorting, accompanied by feature selection, and ii) also alone
Programming nonlinearities inside multimode fibers for optical computing (Conference Presentation)

Utilizing light propagation and optical nonlinearities is one of the strategies to accelerate computational tasks in tandem with electrical circuits in an energy-efficient manner. Computing with multimode optical fibers has been demonstrated to be energy efficient due to the high light confinement and multidimensionality. However, these optical nonlinearities have not been programmed for a specific computational task and thus the performance is not optimal. In this study, we demonstrate that the nonlinear transformation in the fiber can be programmed to obtain improved performances on several different machine learning tasks by shaping the wavefront of the information encoding beam.
Using SinaWeibo Microblogs to Identify Complaints of Food Customers

Monitoring and responding to complaints about food by consumers are critical for enterprises and governments. Here, we design a method to identify and analyze microblogs posted on SinaWeibo. We proposed a machine learning model to classify the microblogs. Our model used multimodal features and improved a term classification weighting method. We used LDA to identify the most common topics in complaints and summarized bloggers' complaint behavior. Our model obtained an optimal weighted F value of 0.942 using only 4 features. We found that linguistic and multimedia features were more important than social features. When expressing complaints, bloggers were more inclined to mention (@) other bloggers, and most bloggers (80.08%) used images or videos. However, the communication effect of complaint microblogs was even weaker than that of noncomplaint microblogs. Our proposed methods were effective social media monitoring and analysis tools. Data mining of SinaWeibo may reduce information asymmetry and enhance the ability of governments to rapidly respond to community concerns and awareness.
Parental stress and dedication to the education of their children among parents from eastern Peru

Background: The parental stress levels increased during their children's virtual education season. This makes parents feel overwhelmed by the demands of the parental role, promoting negative feelings about themselves or their children. Therefore, the aim is to determine the relationship between parental stress and dedication to the virtual education of their children in parents from the city of Tingo Maria, Peru. Methods: It is a cross-sectional descriptive non-experimental design study with a correlational scope. The instruments used in the data collection were: The parental Stress Scale and the Questionnaire Dedication to the Virtual Education of their Children, in which 356 parents aged 18 to 59 years (Me=37; SD=7.49) from the Peruvian Amazon dedicated to agriculture participated. Results: Where it was found that there is no relationship between parental stress and dedication to the virtual education of children (rho = -0.096; p = 0.071). However, a highly significant relationship was found between the dimensions of parental rewards (rho = 0.384; p=0.000) and stress factors (rho = -0.270; p = 0.000) with the variable dedication to virtual education with moderate effect size and small respectively. Likewise, significant differences were found between parental stress according to marital status (U= 12056.0, p&lt;0.05, d= 1.703) and children's dedication to virtual education according to age (U=3360.0, p&lt;0.05, d= 1.343). Conclusions: In the parents who participated in the study, the greater the stressors, the lower the dedication to their children's virtual education, and an increase in parental reward increased the dedication to their children's virtual education. Further studies can explore the relationship of these variables in parents with other socioeconomic conditions and representative samples from other regions.
A Qualitative Study on the Difficulties and Methods of Supporting Preservice early childhood teachers in the Post-Corona Era

The purpose of this study is to examine in depth the difficulties faced by preservice early childhood teachers and the support methods they require in the post-corona era. In order to achieve this purpose, 12 preservice early childhood teachers attending a university in Gyeonggi-do were selected as research participants. Research data were collected through individual in-depth interviews with study participants, and interviews were conducted from March to July 2022. The difficulties experienced by the study participants in the study were 'difficult financial situation and time management', 'stress of forming friendships', 'school and department events that no one knows about', and 'social gaze of Corona student number'. As a support method to solve these difficulties, research participants demanded 'various programs to support companionship', 'customized education to improve the professionalism', 'expansion of various scholarship systems', and 'establishment of a flexible lecture system'. The research results derived from this study will be able to contribute to establishing a customized support system for pre-service early childhood teachers who are experiencing difficulties due to the crisis caused by the post-corona era.
A study of sentiment classification methods with dual model decision fusion

Sentiment recognition is an important part of sentiment computing. To address the problem that traditional Bert models are studied only in textual aspects and a single model has significant uncertainty, an emotion classification recognition method using CM-BERT and Bi-LSTM-DenseNet dual-model decision fusion is proposed. Using both feature-level fusion and decision-level fusion strategies, the superiority of the dual model in multimodal sentiment recognition is highlighted, and the conclusion that the dual model outperforms the single model in multimodal recognition is verified. The model implemented in this paper achieves 64.5% accuracy on a homemade multimodal dataset, a 1% improvement compared to the CM-BERT model, and a 7% improvement compared to the Bi-LSTM-DenseNet model.
Geographical Information System-Aided Landmark Recognition System Using Machine Learning

The recent advancements in computer vision-based innovations like image identification and object detection are attracting the attention of the modern research community present across the globe. Data analysis domain is interesting, particularly when it comes to image augmentation and categorization. Image recognition will operate on a variety of variables in different contexts relating to human behaviour, such as what sort of behaviour someone would display when driving a car. Activation such as network expanders is used in numerous significant neural network models, which are used in image processing domain. Learning the intricacies of features is gradually becoming more prevalent due to the fact that nearly all mobile devices are connected to the Internet and to the fact that virtually all portable devices are connected to the Internet. To achieve this, the visitor obtains a digital camera and uses it to capture the marker and upload the images to a nearby server where they may be compared to the photo archive. Some useful information can be provided during an image search. For example, learning more about significant landmarks, local trails, or nearby areas of interest, or interesting features are also given on the match to be searched. The broader and deeper form of application will be better for guiding people around the region. This paper proposes an enhanced convolution neural and deep convolution neural network-based method for identifying different landmarks by using machine learning [ML] algorithms. The system uses spatial extreme learning machine (SELM) for performing the optimistic feature recognition and enhancement.
Practical Implementation and Challenges of Artificial Intelligence-Driven Electronic Health Record Evaluation: Protected Health Information

Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR. FLAIR is open-source, it includes state-of-the-art deep learning models, and it supports straightforward development of new models for language tasks including named-entity recognition. Finally, there is a discussion of how to apply the system to structured text in a database table as well as unstructured text in clinical notes. Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR. FLAIR is open-source, it includes state-of-the-art deep learning models, and it supports straightforward development of new models for language tasks including named-entity recognition. Finally, there is a discussion of how to apply the system to structured text in a database table as well as unstructured text in clinical notes.
A Study on Content Effectiveness of Klik Dengan Bijak (KDB) Program Organized by Malaysian Communications and Multimedia Commission

This study explores the effectiveness of the Klik Dengan Bijak (KDB) program content and message delivery through the content analysis method. KDB is a government initiative program by the Malaysian Communications and Multimedia Commission (MCMC) to encourage positive use of the Internet by educating participants about Internet safety and security. In any program, it is important to study the quality of the message delivery to assess the effectiveness and quality of the communication materials. For this paper, the researchers conducted a content analysis of KDB program’s website, videos, educational materials, and social media postings; to assess the content clarity, likeability, informativeness, and attractiveness. The findings of the study reveal that KDB video is the most attractive medium and the website is the least informative and engaging platform. The results also indicate several other strengths and weaknesses before coming up with recommendations and suggestions to improve the clarity, likeability, informativeness, and attractiveness of the message delivery. This will assist Malaysian Communications and Multimedia Commission to improve the KDB content for future programs.
Development of 3D Animation on How to Fight Covid19

This paper aimed on the development of a 3d animation project as a public service announcement that highlights ways to resolve the Covid19 outbreak. The SARS-CoV-2 virus is the infectious disease known as the coronavirus disease (COVID-19). The development 3D animation's goal is to evaluate the needs reviews provided by five professionals regarding the creation of this animation. The production process for this 3D animated public service announcement, which is a non-interactive multimedia product, needs feedback and criticism in order to effectively convey the message. The pre-production, production, and post-production phases of the multimedia development life cycle were used to create this 3D animation. Overall, the public has received advice and suggestions about how to deal with COVID19 through this 3D animation.