Reviewerly/exHarmony huggingface dataset:
Mobile Web-Based Learning Application

Teaching and Learning activities in gotong royong junior high schools are currently conventional only limited to meetings in schools. The time available for teachers and students to meet face to face in the classroom is very limited, in addition, the process of delivering the material is almost completely carried out in the classroom which causes the delivery of the material to be late, when a student does not enter school, the student will later find it difficult to follow the learning process. therefore, a Mobile Web-Based Learning Application was created at Gotong Royong Junior High School, using the Prototype model system development method, the results of this research are a mobile webbased learning application, it can be concluded that the learning application has been designed and implemented with facilities including downloading materials, assignments or online quiz and student scores.
Adaptive Chinese Pinyin IME for Most Similar Representation

Many neural-network approaches are used for Pinyin-to-character (P2C) conversion in Chinese input method engines (IMEs).However, in previous research, the conversion efficiency of neuralnetwork P2C models depends on the training of the data.Unfortunately, neural networks cannot maintain high performance with conversions across users and domains.In this study, we propose a method for improving the efficiency of model conversion and tracking user behavior based on dynamic storage and representations that can be updated using historical information from user input.Our experimental results show that our technique tracks user behavior and has strong domain adaptability without requiring additional training.For the cross-domain datasets Touchpal, cMedQA1.0,CAIL2019, compared with the direct use of neural network, its indicators, Top-1 MIU-Acc, CA and KySS, are improved by at least 20.0 8.1 18.3%, respectively, and the results are close to the in-domain training of the model.Furthermore, compared with the traditional methods On-OMWA and Google IME, this method improves at least 7.8 2.0 11.9% and 3.2 0.7 13.9% in Top-1 MIU-Acc, CA and Kyss, respectively.This demonstrates that the proposed method is superior to existing models in terms of conversion accuracy and generality, and can point a new path for P2C platforms.
Influencing factors in perceived learning mediated by satisfaction in university students in times of Covid-19

There are different factors that directly influence student satisfaction and their perceived learning, in times of pandemic, educational models in their implementation and practice have been reoriented to an eminently online education. In the present investigation, arising from a collaboration between higher education teachers, it was detected that such factors as the use of ICT, productivity and creativity in learning, openness to novelty and especially technological stress, would reasonably influence in the endogenous variables of the proposed model. Under a nonexperimental research design, an electronic questionnaire (n=25; α=.952; ω=.953) is applied to a sample of 300 university stud ents (Arequipa, Peru). The results, in agreement with previous studies, show that factors such as the use of ICT, student satisfaction, exert a positive influence on perceived learning, unlike productivity, creativity in learning and technological stress would not exert favorably on perceived learning. Regarding student satisfaction, factors such as openness to the novelty of ICT, creativity, and the use of ICT, would exert a positive influence on satisfaction, unlike productivity in learning. In conclusion, the idea of inquiry and search about the key influencing factors in dependent variables for educational development in times of Covid-19, such as perceived learning and student satisfaction in university education, is highlighted.
<i>MBMT-Net</i>: A Multi-Task Learning Based Convolutional Neural Network Architecture for Dense Prediction Tasks

Recently proposed improvements in the field of Computer Vision refer to enhancing the feature processing capabilities of Single-Task Convolutional Neural Networks. A typical Single-Task network consists of a backbone and a head, where the feature extractor is usually optimised using the gradient provided by the head. Inevitably, the backbone specialises for the given task. This sort of approach does not scale well for learning multiple tasks at once while having the same input. As a response, there is an increasing interest in Multi-Task formulations. Since most Multi-Task architectures employ a single shared backbone, when gradients from different tasks are propagated back to it, it can result in its oversaturation. Thus, this problem may be solved using Multi-Backbone feature extractors. Hence, as a strategy proposed to compensate for these shortcomings, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MBMT-Net</i> , a Multi-Backbone-Multi-Task-Network architecture based on a development strategy that infuses backbones with more diverse and specialised processing capabilities. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MBMT-Net</i> consists of parallel pre-trained backbones whose outputs are concatenated and offered to the Multi-Task heads that shall benefit from richer and more diverse features with decreased number of network parameters when compared to traditional Multi-Task architectures. Our strategy is architecture independent, and it can be applied to different types of backbones and parsing heads, which greatly extends the domain of configurable features, finally enhancing existing Single- and Multi-Task model building strategies and outperforming them when using the Multi-Backbone design. As a result, while having a deficit of 12.16M parameters, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MBMT-Net</i> reaches state-of-the-art performances and surpasses the previously best semantic segmentation Multi-Task model in terms of Mean Intersection over Union when evaluated on NYUv2 data set.
Exploring the Magnitude and Effects of Media Influence on Reddit Moderation

Most platforms, including Reddit, face a dilemma when applying interventions such as subreddit bans to toxic communities — do they risk angering their user base by proactively enforcing stricter controls on discourse or do they defer interventions at the risk of eventually triggering negative media reactions which might impact their advertising revenue? In this paper, we analyze Reddit’s previous administrative interventions to understand one aspect of this dilemma: the relationship between the media and administrative interventions. More specifically, we make two primary contributions. First, using a mediation analysis framework, we find evidence that Reddit’s interventions for violating their content policy for toxic content occur because of media pressure. Second, using interrupted time series analysis, we show that media attention on communities with toxic content only increases the problematic behavior associated with that community (both within the community itself and across the platform). However, we find no significant difference in the impact of administrative interventions on subreddits with and without media pressure. Taken all together, this study provides evidence of a media-driven moderation strategy at Reddit and also suggests that such a strategy may not have a significantly different impact than a more proactive strategy.
Data Visualization of COVID-19 Vaccination Progress and Prediction Using Linear Regression

This paper provides a data visualization and analysis of the COVID-19 vaccination program. Important information such as which countries have the highest vaccination rates and numbers. In addition to the types of vaccines used and used by countries in the world, an infographic on the geographic distribution of vaccine use is also shown. To model the obtained data, daily vaccination rates were modeled by linear regression in which five sample countries with different vaccination ranges were processed using data science approach, namely, linear regression. The modeling results show a gradient coefficient that represents an increase in vaccine rates. The prediction results showed that the highest rate of increase in daily vaccination was 1,826,126 additional vaccines per day.
Preface

The Musical Metacreation workshop aims to bring together artists, practitioners and researchers interested in developing software and systems that autonomously (or interactively) recognize, learn, represent, complete, accompany, compose or interpret music.
The Role of Transformer-based Image Captioning for Indoor Environment Visual Understanding

Image captioning has attracted extensive attention in the field of image understanding.Image captioning has two natural parts; image and language expressions that combines computer vision and NLP to generate caption.Image captioning focuses on making the model to be able to get the description of the image as accurate as the ground-truth captions delivered by humans.Image captioning can be applied into different scenarios, such as helping the visually impaired people to get a better visual understanding of their surroundings environment through generated image caption that can be translated to speech.In this paper, we present a novel image captioning approach in Bahasa Indonesia, using Transformer, to enable visual understanding of indoor environments.We use our own modified MSCOCO dataset.Here, we used ten different indoor objects from MSCOCO datasets namely, beds, sinks, chairs, couches, tables, televisions, refrigerators, house plants, ovens, and cellphones.We modified the captions by creating three new captions in Bahasa Indonesia that includes the objects name, color, position, size, characteristics, and its close surrounding.We use Transformer architecture, which is then compared with merged encoder-decoder architecture model with different hyperparameter tunings.Both model architectures used InceptionV3 in extracting image features.The result of our experiment shows that the Transformer model with a batch size of 64, number of attention heads of 4, and a dropout of 0.2 outperforms other models with a BLEU-1 score of 0.527565, BLEU-2 score of 0.353696, BLEU-3 score of 0.227728, BLEU-4 score of 0.146192, METEOR score of 0.184714, ROUGE-L score of 0.377379, and CIDEr score of 0.393117.Finally, the inference result shows that the generated captions could give indoor environment understanding.
Complexity metrics of Russian legal texts: selection, use, initial efficiency evaluation

The paper describes the metrics-based model for assessing complexity of Russian legal texts, implying the use of 130 metrics divided into following categories: “basic metrics”, “readability formulas”, “words of different part-ofspeech classes”, “n-grams of part-of-speech tags”, “frequency of lemmas”, “word-building patterns”, “grammes”, “lexical and semantic features, multi-word expressions, hypertext links”, “syntactic features”, “cohesion assessments”. The paper illustrates the reasons for choosing metrics, taking into account the experience of studies on linguistic complexity, stylometric studies, as well as experimental studies of legal texts perception. The authors present the results of testing the model in an experiment on the classification of texts by complexity level using metrics as parameters. These results are compared with the results of classification using USE (Universal Sentence Encoder) language model vectors as parameters. The authors come to the conclusion that the use of metrics makes it possible to assess text complexity more precisely than in an experiment using a language model.
An Overview on Cyber Crime and Cyber Security

Loss of information through online services is called a “footprint of cybercrime”. So the topic that we will discuss is Cyber Crime and Cyber Security. It is an important part of the information technology field. We all know that cyber crime is increasing day by day, and the government also takes measures to stop this, but it’s not dying from it’s root. In this research paper, we will discuss about the different phases of cyber crime in today’s time and the negative effects tolerated by people due to cybercrime. There are so many financial loses hanging over from heavy engagement in cyber crimes that have been discussed below. This paper also consists of topics related to cyber security, which provides safety to users. Overall, this article gives an intensive overview regarding cyber crime, the work of criminals and the cyber security and because of it how to prevent the cybercrime and make it secure, as new technology is there that comes up with new ideas to protect human life.
Augmented Reality in Educational Environments: A Systematic Review

Abstract : This study focuses on the emerging technologies used in Augmented Reality (AR) and their role in the field of education. With this aim, 70 articles gathered from Scopus, Science Direct, Springer, Taylor Francis, and Web of science databases were examined. Analysis showed that the number of AR articles in educational environments have gradually increased over the years. AR is being most commonly used in science education (biology, physics and chemistry). The literature also highlights various advantages of AR when used in learning environments. It has been understood that the AR is an imminent learning tool and is lately gaining recognition and acceptance in the educational field. While marker-based AR was the key in most of the articles, it was implemented using mobile devices. The literature revealed that most articles examined their results using Quantitative methods. Our article emphasizes on the impact of AR in education, current technologies and its future scope. This paper also provides detailed analysis of the use of AR in engineering education highlighting that the most of the articles highlighted the use of marker-based AR, used non-probability sampling strategy and appliedmixed method for analysis. The findings of this study would help other researchers to identify potential direction for future research. Keywords : augmented reality; education; interactive learning environment; technologies of augmented reality; augmented reality integration.
Topic Modeling in the Context of Medical Texts

Text analysis is an important area of research that includes several areas such as information retrieval, information extraction, and text categorization. Text analysis is widely used in the field of medical research because of the number of studies published daily, which can be processed at such a speed only with the help of computational resources. This paper presents the results of an experiment to thematically model a corpus of articles from the PubMed database from 2000 to 2020.
Problems and prospects of implementation of blockchain technologies in the tourist industry of Ukraine

Due to the emergence of new digital technologies, the tourism industry is at the stage of new transformations. The new technologies that are being created allow the tourist to work directly with the manufacturer of the tourist service, which helps to provide the consumer with a higher quality and less expensive product, increasing the responsibility of the manufacturer. The subject of research is the use of blockchain technology in the tourism industry. The purpose of the article is to study the problems and prospects of using blockchain technology in the tourism industry of Ukraine. To achieve this goal, the following tasks were set and solved: foreign experience in the implementation of blockchain technologies in the tourism industry; an analysis of the current state of use of blockchain technologies in the tourism industry of Ukraine; problems and prospects of introduction of blockchain technologies in tourism are revealed. The research was conducted on the basis of system-functional, dynamic, complex scientific approaches, using scientific methods: analysis and synthesis, induction and deduction, modeling, historical-logical method, methods of statistical analysis. The following results were obtained: considering Ukraine in the context of the use of distributed registry technology, it becomes obvious that the legalization and granting of the necessary legal status to this technology should be completed first. It is necessary to assess the effectiveness and regulation of the blockchain institution in our country, as well as to identify and eliminate shortcomings in its use. Conclusions: tourism is an area that requires the introduction of blockchain technologies. With the advent of technology, tourists will get better conditions for planning and making trips. Optimization through the blockchain of various areas of activity involved in the tourism industry will increase the profits of travel companies and reduce the cost of the final product.
The Exploration for Acceptance Level of NFT Purchasing in Thailand

This movement came from the NFT purchasing Metaverse in worldwide countries.The metaverse is adopted in Thailand.Thai citizens access the virtual world to achieve plentiful activities such as joining the game and buying a digital product called NFT.Thus, people use actual payment and exchange an NFT on the metaverse.However, the level of acceptance regarding the NFT purchasing of Thai customers is an important issue that should be concerned.This study explores concerning to Thai perception related to NFT purchasing on the metaverse from 461 target respondents who purchased an NFT on the Metaverse.The exploration result covers 9 factors: personal innovativeness, perceived enjoyment, ease of use, visual attractiveness, perceived risk, social influence, facilitating condition, price value and intention to purchase.
An Effective Ensemble-based Framework for Outlier Detection in Evolving Data Streams

In the last few years, data streams have drawn lots of researchers' attention due to their various applications, such as healthcare monitoring systems, fraud and intrusion detection, the internet of things (IoT), and financial market applications.A data stream is an unbounded sequence of data continually generated over time and is prone to evolution.Outliers in streaming data are the elements that significantly deviate from the majority of elements and then have to be detected as they may be error values or events of interest.Detection of outliers is a challenging issue in streaming data and is one of the most crucial tasks in data stream mining.Existing outlier detection methods for static data are unsuitable for use in data stream settings due to the unique characteristics of streaming data such as unpredictability, uncertainty, high-dimensionality, and changes in data distribution.Thus, in this paper, a novel ensemble learning framework called Ensemble-based Streaming Outlier Detection (ESOD) is presented to perfectly detect outliers over streaming data using a sliding window technique that is updated in response to the incoming events from the data streaming environment to overcome the concept evolution nature of streaming data.The proposed framework has three phases, namely the training phase, testing/offline phase, and outlier detection/online phase.A detection weighted vote technique is used to determine the final decisions for potential outliers.In the extensive experimental study, which was conducted on 11 realworld benchmark datasets, the proposed framework was assessed using many accuracy metrics.The experiment results showed that the proposed framework beats many other state-ofthe-art methods.
A Scale Aggregation and Spatial-Aware Network for Multi-View Crowd Counting

Previous multi-view crowd counting methods underperform in maintaining scale consistency across views and overlook the negative effect of the complex background. To solve these problems, a scale aggregation and spatial-aware network for multi-view crowd counting (SASNet) is proposed. Firstly, we design a multi-branch adaptive scale aggregation module which aggregates the appropriate scale for each pixel in each view. Benefiting from the automatic feature learning process, it can help all camera-view features maintain the scale consistency as much as possible. Then, a crowd-centric selection module is used to reasonably assign the weight of pixels at different spatial locations, thereby selecting the region of crowd and suppressing the background information. Finally, we project each view selected features to the consistent world coordinate system and fuse them. Experimental results demonstrate that the proposed SASNet outperforms the state-of-the-art methods. Our SASNet achieves 7.44 MAE (9.46 RMSE) and 1.01 MAE(1.24 RMSE) in City Street and DukeMTMC respectively.
Internet of Behavior in Cybersecurity: Opportunities and Challenges

There is a dark side to the Internet of things, and experts believe that the integration of behavioral data may supply hackers with sensitive information about consumer behavior patterns. Cybercriminals may gather and sell compromised property access codes, delivery routes, and even bank access credentials to other thieves—the possibilities are limitless. Despite many public awareness efforts, researchers discovered that people continue to use hazardous password habits. Individual variations in cybersecurity practices are the subject of little study. This review focused on the perilous habit of password sharing. As expected, people with a low level of persistence were more inclined to disclose passwords. Passwords were more likely to be disclosed by older people and those who are skilled at self-monitoring.
PERSEPSI GURU MENGENAI PEMBELAJARAN DARING DI TK AISYIYAH BUSTANUL ATHFAL 3 KOTA PONTIANAK

ABSTRACTThe purpose of this study was to describe the teacher's perception of online learning at Aisyiyah Busnatul Athfal 3 Kindergarten in Pontianak City. This study uses a qualitative approach with qualitative descriptive methods, data collection techniques used are interviews and documentation. This research uses data. Data analysis can be done by reducing data, presenting data, and verifying and drawing conclusions. Test the validity of the data using source triangulation, technical triangulation, time triangulation and member check. The results of this study indicate that, (1) the teacher's response in online learning at TK Aisyiyah Bustanul Athfal 3 Pontianak City, by knowing whether online learning has been successful. Effective or not, how the teacher handles it, (2) Organizing and implementing online learning at Aisyiyah Kindergarten Bustanul Athfal 3 City. Pontianak uses the Emergency RPPM and ten children activities.Keywords: Teacher's Perception of Online Learning
Reasoning and state monitoring for the robust execution of robotic manipulation tasks

The execution of robotic manipulation tasks needs to be robust in front of failures or changes in the environment, and for this purpose, Behavior Trees (BT) are a good alternative to Finite State Machines, because the ability of BTs to be edited during run time and the fact that one can design reactive systems with BTs, makes the BT executor a robust execution manager. However, the good monitoring of the system state is required in order to react to errors at either geometric or symbolic level requiring, respectively, replanning at motion or at task level. This paper make a proposal in this line and, moreover, makes task planning adaptive to the actual situations encountered by knowledge-based reasoning procedures to automatically generate the Planning Domain Definition Language (PDDL) files that define the task.