Reviewerly/exHarmony huggingface dataset:
Differences in values of high school students formed in different social environments (village and city)

The article is devoted to a relevant problem of the differences in the value systems of rural and urban high school students. The analysis of the scientific sources reveals insufficient research on this problem. We conducted an empirical study of the values of rural high school students in comparison to urban high school students. For the purpose of the psychodiagnostics within this research, we have translated and adapted for Ukrainian students the the Rokeach Value Survey and Schwartz's Value Survey methods. Two groups of 30 high school students were involved in the study. The main group represented the rural environment, the second group represented the urban environment. The groups were similar in terms of gender and age structure. In the course of the study, statistically significant differences in the results of the high school students from two different types of settlements were found. The most significant differences were defined in the instrumental values and Schwartz values, which are mainly instrumental. The rural high school students benefit with a stronger development of the politeness, universalism, and kindness values. Instead, urban high school students demonstrate a stronger presence of the independence, self-confidence, courage, and responsibility values. Defined during the research complex of differences indicated the higher level of the mental dependence of rural high school students compared to reference persons and groups. The hypothesis is that the reason defining such difference is a much higher level of parental control caused by the rural life. A mechanism of the mental dependence is formed in rural high school students. Further on, it extends to their relationships with teachers and the student groups.
Cloud and Cloud-Native Traffic Capture

The term “cloud computing” in the Information Technology (IT) world refers to the ability to run virtual instances of compute resources in a centralized data center with racks of physical servers and machines managed and offered as multitenant services by third parties. This new endowment to spin up on-demand compute resources in the cloud powered many businesses to host applications in a matter of minutes (if not seconds) at scale with the minimal skill set required to manage the resources yet without compromising resiliency. The term “cloud native” refers to the framework and architecture that introduces the ability of the applications to be hosted in such cloud environments to realize the maximum benefits offered by cloud computing.
A LSTM Forecasting Model Based on ASSOMA

Long short-term memory (LSTM) network model is widely used in time series prediction because of its outstanding performance on the time-series problems. However, in engineering application, LSTM is faced with the problem of network structure and hyperparameters being difficult to determine. In this paper, a self organizing migration algorithm with adaptive migration step size (ASSOMA) is proposed to optimize the LSTM structure and hyperparameters. Firstly, based on the original SOMA, a scheme based on logistic chaos mapping and adaptive step size is proposed, named ASSOMA. Then the prediction model was built based on household power usage, and its structure and hyperparameters were optimized by ASSOMA. The experimental results show that ASSOMA has better predictive performance than SOMA and related variants.
Explainable Prediction of Medical Codes through Automated Knowledge Graph Curation Framework

Medical Coding (MC) converts medical diagnoses, procedures, equipment, and services into alphanumeric codes. Automated Medical Code prediction systems use Machine Learning and Deep Learning techniques. They could save insurance companies, Government agencies, and medical staff from the hassle of reading lengthy summaries to prepare insurance claims for reimbursement of expenses and fees. If not wholly correct, these machine-understandable codes can still help the medical coders to ease their task of predicting accurate medical codes. Despite being helpful, labor-saving, and efficient, these decision support systems fizzle out due to scarcity of domain knowledge. Knowledge Graphs containing a network of concepts, their meta-data, and a hierarchy of relationships in specific domains can help build the applications of Computer Assisted Coding (CAC) with much more accurate and precise results glued with Explainability. Unfortunately, domain-specific Knowledge Graphs (KG) creation is a grueling task, as it requires healthcare experts’ intervention to annotate the medical concepts. Our proposed approach is to create a framework for the Automated Generation of Knowledge Graphs, which is yet a manual and time-consuming process, with the help of Natural Language Processing, Ontology-based information retrieval, and a semantic enrichment process. The created domain-specific Knowledge graph is then fused with a Deep Learning model. Predictions made by the Deep Learning model were compared before and after the consolidation of the domain-specific Knowledge Graph. We created a web-based application to save users from the complexity of the Deep learning model and knowledge graphs. Results proved the significance of the combination of Knowledge graphs and Artificial Intelligence. Produced results yield increased precision and accuracy with fewer false-positive results.
Secure Distributed Processing of NG with Updatable Decomposition Data and Parameters

Machine learning using distributed data, such as federative learning (FL) and secure multiparty computation (SMC), is demanded to achieve both utility and confidentiality when using confidential data. There is a trade-off between utility and confidentiality, and in general, SMC can offer better confidentiality than FL and better utility than homomorphic encryption. In machine learning with SMC, confidentiality is improved by decomposing individual data and parameters into multiple pieces, storing each piece on each server, and learning without restoring the data or parameters themselves. However, once the conventional methods randomly decompose data and parameters, the decomposition remains permanently fixed. The fixed decomposition is considered undesirable because it gives malicious attackers more opportunities to attack the data and model. In this paper, we propose a secure distributed processing of neural gas (NG), which is one of unsupervised machine learning. In addition to the decomposition of data, the proposed method can update the decomposition of parameters during learning. Each server can independently update the decomposition of both data and parameters in the proposed method, and the data and parameters are never restored during learning. Our simulation result shows that it can achieve the same level of learning accuracy as the conventional methods.
Comparison and Improvement of Local Planners on ROS for Narrow Passages

Path planning is challenging in various extreme and complex environments due to the explosive growth of autonomous mobile robots. The widely used Dynamic Window Approach (DWA) and Time Elastic Band (TEB) local planners are integrated into Robot Operating System (ROS). A great quantity of their parameters has a significant impact on the actual performance of planning. Aiming at the scenario of robots passing through narrow passages, this work compared and optimized the typical DWA and TEB. The results of simulations showed that the improved DWA and TEB local path planners both have better performance with gentler fluctuations of velocity and smoother paths of travel in continuous narrow passages. In addition, the number of collisions was reduced to zero and the time consumption of completing navigation was decreased by an average of 15% and 7%, respectively. Moreover, the general strategies and suggestions for adjusting related parameters of DWA and TEB through narrow passages were given in this paper. Finally, the adjusted TEB local planner was applied to the customized robot in a real similar environment, and it accomplished the navigation stably with a smooth path. The results of real experiments proved the validity and reliability of our work further.
Exploiting Multi-granularity Features for Unsupervised Domain Adaptation Person Re-identification

Recently, unsupervised person re-identification (re-ID) methods based on pseudo-label generation have achieved impressive performance. Due to the lack of pedestrian labels and the problem of label noise in clustering, huge discrepancies across cameras cause the model to fail to learn discriminative features. In order to suppress label noise, many schemes try to correct the feature learning of the target domain with the help of labeled source domain data. However, because of domain gap, the discrimination of source domain is difficult to be transferred directly to the target domain. To overcome this problem, we propose a Sub Domain Specified Batch Normalization (SDSBN) to force the images of all cameras coming from two domains to fall onto the same subspace, which effectively align the distribution between all camera-level data of the two domains, thus person classifiers learned in the source domain can transfer well to the target domain. We further propose a Reference Space (RS) in which the source domain person category features act as experts to determine whether the target domain person images have certain specified visual semantic characteristics by which to correct the labelling errors caused by the target domain clustering. In addition, because of the coarse-grained holistic appearance of pedestrians is not conducive for difficult sample mining, we propose to utilize offline pose detector to crop the person images into different body parts, and then do similarity learning to pull neighbours together respectively, according to them to implement fine-grained feature learning. Thus establish our multi-granularity unsupervised re-identification framework with two branches for global feature representations and local feature representations respectively. Extensive experiments validate the superiority of our method for unsupervised person reidentification.
Research on Blockchain Usage for 5G Message Service

5G message service faces security challenges and requirements like bad message control, access authentication, and trust usage which need cooperation and trust among stakeholders for 5G message service. Blockchain can be used for uniformity, data sharing, trust data, trust interactive among multi-parties. This paper studies the security requirements of 5G message service which can be solved by blockchain and provides potential framework and procedures.
A Proposal for Chinese Pedagogy on the Metaverse Platform: Focused on the ‘Chinese Vocabulary and Culture’ course at D University

The purpose of this study is to propose a Chinese teaching-learning method using the metaverse platform, focusing on the 'Chinese vocabulary and culture' course at D University. In this paper, first, we explore the characteristics and utilization plans of the metaverse platform. When the metaverse platform is used in education, there are advantages of playfulness, realism, creativity, connectivity, and economic feasibility. ZEP is a free service and an open metaverse that supports mobile and PC. ‘Chinese Vocabulary and Culture’ is a subject that understands Chinese society, psychology, institutions, and habits through Chinese social culture included in Chinese vocabulary. For example, the first stage is the “entry” stage on the platform and the stage of guessing and finding out the source of the idiom through the image of 不恥下問. The second stage is the another outer space of learning a related story of 不恥下問. The third stage is the internal space of learning and practicing the original meaning of 不恥下問. The fourth stage is the evaluation stage that the learning contents are briefly checked by using OX quizzes among the functions of ZEP. First of all this study is meaningful in that it is a proposal for Chinese education using ZEP, an open metaverse platform in Korea. Second, it expanded the educational content to Chinese vocabulary and culture on metaverse platform. Third, it can help learners to induce a sense of achievement and motivation for learning Chinese.
The Impact of Volatility, Liquidity, and Crude Oil Price Index on Cryptocurrency Returns (Testing the Weak-Form Market Efficiency) Period January 2019 - June 2021

Since it was first founded by Satoshi Nakamoto in 2008, cryptocurrencies have attracted the attention of investors significantly. Until now, many investors have invested their money in cryptocurrencies. This study aims to prove and analyze the effect of volatility, liquidity and world oil price indices on cryptocurrency returns. It also examines whether the occurrence of market efficiency is weak or not. This study aims to prove and analyze weak efficiency market and the impact of liquidity, volatility and the world oil price index on the return of the cryptocurrency market. This study uses quantitative methods and secondary data, so that the number of samples taken are 3 types of cryptocurrencies (Bitcoin, Ethereum and Binance Coin) which are listed on coinmarketcap.com and investing.com, as well as the oil price index taken from investing.com The analytical methods used in this study are series correlation test and runs test as well as multiple linear regression analysis. The results of this study indicate that volatility and liquidity affect the efficiency of the cryptocurrency market while the oil price index has no effect. The results of autocorrelation test on return itself prove that these coins have weak form efficiency. Keywords: Market Efficiency, Cryptocurrency, Liquidity, Oil Price Index, Volatility
Animal Classification implemented in Farm fields using CNN

The day-to-day lives of people depend on the food consumed. Even though food is required regularly, people don’t often think of the struggle the farmers face in delivering the food to the market. There are much more criteria to be considered when it comes to the problems affecting the farmers and the fields. One of the most important criteria is the protection of farm fields. Animal intruding the field leads to crop damage, and of course some severe problems that affect the regular profit. Farm fields near mountain slopes are often intruded by wild elephants and wild pigs, that destroy most of the crops and pull down the profit as well as the investment. There are several old methods to protect the field like thorn fences, but those aren’t quite beneficial. The other problem is the classification of animal entering the field. The security features can be adapted only based on the animal that is entering. If the animal intruder is anonymous, preventive measures cannot be immediately taken. The proposed model uses a setup like fence, where cameras are mounted to capture animal movements using OpenCV python. Once any movement is detected, an alert sound goes on, so that people could be aware that some intrusion has occurred. Using image processing by CNN, classification of animal is done by training and testing the dataset. Precautions along with messages to people who could provide help can be implemented as an additional feature to this proposed work. This structure is considered beneficial to be implemented in military bases to capture movements and alert the soldiers.
Retracted: Using Markov predictions for handling and allocating task to virtual machines in clouds data centres

Abstract The above article from IET Communications, published online on 16 December 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Mobile Game-Based Math Learning for Primary School

The paper discusses modern learning approaches in which mobile devices are increasingly used. It presents an experiment to apply mobile game-based learning to primary school students. A suitable learning model has been designed using adaptive, micro lesson, behavioural and game-based approaches. Based on the model, a mobile math educational game was developed to complement traditional classroom learning and be used at home. The mobile game was tested in a real learning environment. A survey was conducted also with the students and teachers, participants in the experiment, which investigated the attitude toward using mobile game – based learning in mathematics in the direction of – practical applicability, motivation, design, accessibility, support and feedback.
Restrictions on copredication: a situation theoretic approach

This paper proposes a situation-theoretic account of polysemy: polysemous nouns denote situations that witness (i.e. contain) multiple entities of different types. For instance, lunch denotes situations that contain an eating event and some food where these stand in a patient relation. A puzzle regarding more than two ways polysemous nouns such as statement is the restrictions on copredication they exhibit, namely, where multiple, potentially incompatible predicates are applied based on a single antecedent, but not all combinations of readings are possible. Such restrictions fall naturally out of the situation-theoretic account of polysemy.
Sources of Students’ Errors in EFL Writing in Indonesia

The purpose of this review paper is to compile studies related to the sources of errors made by Indonesian students in a writing skill, to summarize some prior findings in order to provide updated literature on error analysis, to identify difficulties that Indonesian students face in an EFL writing context, and to investigate the factors that influence writing success and failure. The results showed that three major factors contribute to students' errors in EFL writing in Indonesia: the influence of the first language on writing, large class sizes, and a lack of writing practice. This is reasonable, as studies have demonstrated that despite years spent in school learning English, a significant number of errors can still be found in writing products composed by university students. Therefore, the teacher can use several strategies, including implementing a specific learning strategy that affects the target language, reducing the number of students in the class so that they have enough time to practice writing based on the writing process, and providing better instruction in the structure and grammar sections. Furthermore, teachers should spend more time and effort outside of the classroom to help students receive feedback, particularly through conferencing.
DIPLOMA VERICIFATION USING BY BLOCKCHAIN TECHNOLOGY

Blockchain technology is attracting attention from academies, industries, governments all over the world. Expectations of increased potential have led to extensive exploration of the use of this technology in various application platforms. Today, blockchain is considered to be a technological breakthrough that touches all areas of social life, and is expected to lead to very significant changes, especially in a few application areas. In the study; A certification diploma verification system has been developed using the decentralized, open-source blockchain platform. Thanks to the integration of blockchain technology with the document verification system, the centrality in the control mechanism can be eliminated and user initiative can be eliminated. In addition, the task of verifying documents in existing systems is a time-consuming process. Treatment can take up to several months, depending on the provider's response. Thanks to the blockchain, employers or institutions that want to verify documents will be able to perform their transactions without spending time and money.
Hybrid classifier for sentiment analysis in Malayalam with modified TF-IDF features

Sentiment Analysis (SA) is a computational study that examines people’s opinions, attitudes, and opinions based on their written text. Keralites’ mother tongue, Malayalam, is the most often used language to express themselves on Twitter. As there is no automatic Sentiment Analyzer in Malayalam, the SA of Twitter messages is necessary. In this research work, a Malayalam sentiment analysis model is introduced. The input raw data in the form of reviews are fed for the pre-processing stage. The pre-processing module includes sentence tokenization via Sandi Splitting and parts of speech (POS) tagging. Subsequently, Bag of Word, the Proposed weightage-based Term Frequency-Inverse Document Frequency, Unigram with the dictionary and Unigram with dictionary including negation words have been considered for feature vector formation. Finally, the review classification is undergone via the proposed hybrid classifier, which is constructed by hybridizing the Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), respectively. To enhance the classification performance, the weight of the CNN classifier will be fine-tuned via an Improved Bumble bee mating Optimization (IBBMO), which is an advanced version of standard BBMO. The performance of the proposed work is compared over other conventional models in terms of positive, negative, convergence, and other measures. The accuracy of the proposed method is 95.436, which is much better than the existing works like DBN[Formula: see text]86.25, NN[Formula: see text]87.080, SVM[Formula: see text]87.234, WOA+Hybrid Classifier[Formula: see text]88.440, LA+Hybrid Classifier[Formula: see text]89.35285, CSO+Hybrid Classifier[Formula: see text]92.021, BBMO+Hybrid Classifier[Formula: see text]93.4248, and ML techniques[Formula: see text]94.014, at the 90th training percentage.
The Impact of Tourism Management on Firm Performance: Chiang Mai Tourism Industry Perspective

Due to the important of tourism industry, the entrepreneurs in tourism supply chain should concern about information sharing in order to increase service quality and the firm performance. However, there is limited study focusing on the impact of information sharing factor toward firm performance especially in tourism sector. This research aims to examined the consistency of structural equation modelling of an impact of information sharing on firm performance for tourism industry. Questionnaires were distributed to 280 participants in tourism industry in Chiang Mai, Thailand. Data were analysed by the analysis of CFA and SEM via LISREL 8.54. The structural equation modelling in information sharing and the performance of tourism industry in Chiang Mai were consistent with empirical data (χ²/df = 2.10, GFI = 0.88, AGFI = 0.98, RMSEA = 0.03, PCLOSE = 0.98, NFI = 0.98, IFI = 0.99 and CFI = 0.97). The SEM results presents that information sharing factor is positively relationship with tourism supply chain, service quality and firm performance variables. Also, Service quality is significant positive impact on Firm performance through information sharing at 0.01. Therefore, tourism supply chain and service quality with considering on information sharing can lead to the increasing of the firm performance.&#x0D;