Reviewerly/exHarmony huggingface dataset:
FSC: File Storage in Coded Blockchain with C-PBFT Consensus Protocol

Secure file storage and distribution is a key challenge in the information era. It is important to ensure the files are not tampered or eavesdropped during storage and transmission. A promising solution is to store the files in blockchain. However, the massive blockchain data makes it hard to operate a file blockchain in resource restricted end devices. To this end, this paper proposes a File Storage in Coded Blockchain (FSC) solution that leverage the advantage of error correction code. In particular, the block data can be encoded and distributed stored at different nodes to reduce the storage requirement. In addition, this paper propose a Coding Practical Byzantine Fault Tolerant (C-PBFT) consensus protocol to distribute the coded information with reduced communication overhead. Simulation results show that the proposed FSC significantly reduces the storage requirement as compared with traditional blockchain technologies.
A Theoretical Formulism for Evidential Reasoning and Logic Based Bias Reduction in Geo-Intelligence Processing

Geo-intelligence processing is strongly based on the need to bring together analytical viewpoints from multiple members comprising a geo-intelligence team so that unified answers to problems can be provided to leadership responsible for decision making.A three-tier evidential reasoning formulism is proposed and explained embodying a guide for the statistical/cognitive processing of geointelligence sensor information to facilitate this aim.The first tier comprises computational modeling used in conjunction with informal logic-based bias reduction by a multiple analyst team to interpret geo-intelligence information and create geo-intelligence reports.In the second tier, Bayesian belief networks over distinct provinces under geo-intelligence analytical investigation are created by each analyst through the amalgamation of statistical information provided by geo-intelligence reports.Bayesian belief network (BBN) results coupled with ancillary intelligence and analyst beliefs provide a set of propositions and probability masses summarizing the state of each province analyzed by each team member.The BBN state levels denote the three conditions of lack of nefarious substance presence, probable nefarious substance presence, and definite nefarious substance presence and are taken to be related, via a one-to-one mapping, directly to a new set of decision-based propositionslack of adversary attack, probable adversary attack, and definite adversary attack.In the third tier, team member probability masses associated with these propositions, along with conjunctive and disjunctive combinations, are gradually amalgamated using Dezert-Smarandache (DS) evidential theory.A numerical example demonstrates the mechanics of the thirdtier information fusion process which takes into account logical paradoxes and results in a single virtual analyst probability mass distribution associated with the geo-intelligence information amalgamation problem.
The Influence of Leisure Engagement on International Students Adaptation to College Life : Focusing on the Mediation Effect of Acculturation

Leisure engagement and leisure benefits have been widely investigated in various contexts. However, prior leisure studies have provided limited information on the impact of leisure engagement on international students’ school life in Korea. Therefore, this study examined the relationship between leisure engagement and international students’ school adaptation, and the mediating role of acculturation between leisure engagement and school adaptation. The structural equation modeling was adopted for data analysis, using JAMOVI 2.2.5. The findings described that leisure engagement positively affected the acculturation level, and the acculturation level also positively affected international students’ school adaptation. However, there was no significant direct path effect observed to school adaptation from leisure engagement. The result also demonstrated the significant mediation effect of acculturation between leisure engagement and school adaptation.
Gaussian theory for estimating fluctuating perturbations with backaction-evading oscillator variables

We apply a Gaussian state formalism to track fluctuating perturbations that act on the position and momentum quadrature variables of a harmonic oscillator. Following a seminal proposal by Tsang and Caves [Phys. Rev. Lett. 105, 123601 (2010)], Einstein-Podolsky-Rosen correlations with the quadrature variables of an ancillary harmonic oscillator are leveraged to significantly improve the estimates as relevant sensor variables can be arbitrarily squeezed while evading adverse effects from the conjugate, antisqueezed variables. Our real-time analysis of the continuous monitoring of the system employs a hybrid quantum-classical description of the quantum probe and the unknown classical perturbations, and it provides a general formalism to establish the achievements of the sensing scheme and how they depend on different parameters.
Efficacy of Sine Cosine Algorithm With Different Remedies for Search Space Violation

This contribution compares the performance and efficacy of the sine cosine optimization technique for two alternative initialization procedures. These initialization of procedures are utilized when an dimension of any solution during the whole iterative course violates search-space. In the first approach, the dimension violation is set at search-space&#x2019;s boundary. The dimension violation is randomly initialized inside the search-space in the second approach. The modified sine cosine technique is used to reduce ten different unimodal benchmark-functions for performance comparison. To evaluate efficacy and performance, the findings obtained from the value of the objective-function i.e. Figure of merit (FOM), minimum, mean, standard deviation, and maximum values of FOM are utilized.
New Dataset for Software Defect Prediction Model

Software defect prediction is one of the critical fields that is related to software quality. The accuracy of prediction models relies on how the features are related to class value. However, this field has limitations of the lack of robust dataset in previous studies. Therefore, this research paper aims to develop new dataset to fill the gap of research area. So, we train the J48, NB, and MLP algorithms on our new dataset, JM1and KC1. The results indicated that our proposed new dataset performed better in terms of accuracy, recall, and precision.
The impact of knowledge sharing on employee performance at Palangka Raya's health college

The goal of human resource management research is to establish the impact of LMX on employee performance in health education institutions through job satisfaction and innovation moderated by knowledge exchange. It is critical for an organization's success to be able to apply LMX theory to work satisfaction, innovation, and knowledge exchange. Knowledge Sharing was used as a moderating variable in this study. Employees' inventive behavior is encouraged by the act of exchanging knowledge and collectively developing new knowledge, which involves synergistic teamwork between individuals. Quantitative research was used in this study. Questionnaires were distributed as part of the data collection process. The author enlisted the help of three Palangka Raya-based health education institutions as correspondents. On 75 respondents, data was collected using simple random sampling, and data was analyzed using descriptive statistical analysis and inferential statistical analysis. The findings revealed a link between LMX, innovation, and the moderating of knowledge sharing on performance. As a result of the findings, it is critical to use LMX and innovation in order to boost employee performance.
SELF-MANAGEMENT AS A MEANS OF PROFESSIONAL PERSONALITY DEVELOPMENT OF FUTURE TEACHERS

In this article is spoken about necessity of formation of students skills to self-management fo r their professional development in the pedagogical sphere.
PROBLEM SITUATION AND CONDITIONS AND WAYS CREATING IT IN LANGUAGE LESSONS IN 2-3 CLASSES

The article is devoted to the using o f problem tasks at the lessons o f Ukrainian (native) language in the second and third forms as effective method o f development o f junior pupils cognition.
Common Errors in Vietnamese – English Translation of English Majored Juniors at Tay Do University, In Viet Nam

Translation plays a vital role in transferring messages across languages. However, it is not easy for students to master. In the process of translation, students could encounter common errors. When translating a sentence, it is a far more problematic one for English majored students regardless of their learning years. For this reason, the research was carried out to point out potential traps that English majored juniors could get when translating a sentence from Vietnamese into English. The participants were 100 juniors from Bachelor of English course 13 (2018 – 2022) at Tay Do University. Questionnaires and interview were delivered to collect data and get more information. The collected data from the instruments mentioned above were all analyzed afterwards. The findings revealed that juniors could meet errors in vocabulary, grammatical structure and culture items.
Application of Machine Learning for Heart Disease Classification Using Naive Bayes

The Naive Bayes classifier uses an approximation of a Bayes theorem by combining previous knowledge with new ones. The purpose of this research is to develop machine learning using Naive Bayes classification techniques and as a decision system in producing fast and accurate classification accuracy in diagnosing cardiovascular diseases such as heart disease. Cardiovascular disease is the leading cause of death, 32% of all global deaths, of which 85% are caused by stroke and heart disease. Based on the results of the analysis, it was found that the accuracy of classification accuracy in the training data on patient data was classified as having and not having heart disease, respectively 83,21% and 83,1%. In data testing, the percentage of patient data classified as having and not having heart disease was 83,78% and 87,50%, respectively. Based on the AUC values ​​in the training data and testing data, they are 83,15% and 85,24%, respectively. So, from these results, it can be concluded that the Naive Bayes method is good for classifying heart disease patient data.
Two-Stream Spatial-Temporal Fusion Graph Convolutional Network for Dynamic Gesture Recognition

As a compelling field of computer vision, dynamic gesture recognition lays the foundation for interactive interactions of virtual reality (VR) and augmented reality (AR). Compared with other body joints, hand joints feature a smaller range of movement, faster movement speed, and more movement details. It is necessary to further explore the local spatial-temporal information and global dependencies in the process of action execution. On that basis, we propose a two-stream spatial-temporal fusion graph convolutional network, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2s$</tex> -STFGCN, for dynamic gesture recognition. In order to enrich detailed joint features, the second-order bone information is introduced to the model. The local spatial-temporal information is fused in the one graph to capture the complex spatial-temporal relationship. At the same time, the gated dilated convolution is employed to ensure the correlation of long sequence to be better noticed. Additionally, by simulating actions in the VR interactive applications, we collect and make the dynamic gesture skeleton data set, VR-DHG, based on different grain sizes. Experimental results suggest that the model proposed by us can achieve better recognition effects of the public data set, DHG-14/28. Compared with the DeepGRU algorithm, the recognition rate of our algorithm, when being used to recognize 14 kinds and 28 kinds of gestures, can be improved by 1.21% and 2.64%, respectively. Our algorithm also outperforms in fine-grained gesture recognition. All this provides solid evidence for the effectiveness of our algorithm.
Social Network Analysis of Cryptocurrency using Business Intelligence Dashboard

There are currently more than 10.000 cryptocurrencies available to buy from the online market, with a vast range of prices for each coin it sells. The fluctuation of each coin is affected by any social events or by several important companies or people behind it. The aim of this research is to compare three cryptocurrencies, which are Bitcoin, Ethereum, and Binance Coin, using Social Network Analysis (SNA) by visualizing them using Business Intelligence (BI Dashboard). This study uses the SNA parameters of degree, diameter, modularity, centrality, and path length for each network and its actors and their actual market price by crawling(data collecting process) from Twitter as one of the social media platforms. From the research conducted, the popularity of cryptocurrencies is affected by their market price and the activeness of their actors on social media. These results are important because they could help in the decision-making to buy cryptocurrencies with high popularity on social media because they tend to retain their value over time and could benefit from price spikes from influential people. Doi: 10.28991/HIJ-2022-03-02-09 Full Text: PDF
Neuro-dynamics based Intelligent Control of Various Autonomous Robotic Systems

Research on biologically inspired intelligence has made significant progress in both understanding the biological systems and developing bionic engineering applications to robotics and control systems.In this talk, I will start with a very brief introduction to biologically inspired computational neural dynamics algorithms and their applications to early vision and sensory motion in biological systems.After that, several neuro-dynamics intelligent control of various autonomous robotic systems will be presented, such as real-time path planning, tracking, and control of autonomous ground, aerial, water surface and underwater robotic systems; and intelligent navigation and cooperation of multi-robot systems.
Implementation of the Least Square Method for The Application of Population Growth Rate Prediction in Air Sugihan District

Air Sugihan is one of the 18 sub-districts in Ogan Komering Ilir Regency, South Sumatra Province which has 19 villages. After observation, in the annual program policy planning carried out by the Air Sugihan District, almost all development plans need to have an information base for future time estimates, namely predictions of population growth rates. Therefore, this study aims to contribute to the Air Sugihan sub-district by conducting a predictive analysis of the population growth rate with the least square method and implementing it into an application. The use of the least square method is a suitable method for predicting the rate of population growth. From the results of the analysis of prediction calculations for 2021, the same results were obtained with the details of the birth value of 762 with MAD errors (77.04) and MAPE (11.78 %), the death value of 460 with MAD errors (65.41) and MAPE (20.41 %), the Migration-coming value of 637 with MAD errors (190.67) and MAPE (81.55 %) and the Migration-away value of 877 with errors MAD (169.99) and MAPE (45.35 %). With the implementation into the application, it facilitates the process of managing population growth rate data in determining the results of predictions or forecasting and conclusions can be drawn from the prediction results for which factors or variables are more specific to affect the rate of population growth in the future.
Estimated Economic Growth Rate Based on Efek Decrease in PPKM Level Using Support Vector Regression Method

Economic growth is a change in economic conditions towards a better level. An increase in the value of income and production will affect the condition of a country. It can be known that the impact of Covid-19 is enough to affect economic conditions in Indonesia. Economic conditions in Indonesia are also affected by the PPKM program from the government. The bill of decreasing the PPKM level that is applied is considered to provide an increase in the economy towards normal and better. The purpose of this study is to provide predictions and analysis of the value of future economic growth. The method used is SVR (Support Vector Regression). This method is processed using a Polynomial kernel and using MAPE (Mean Absolute Percentage Error) error accuracy. Based on research that has been carried out, the results of the value of each economic nit in the fourth quarter of 2021 and the first to fourth quarters of 2022 with a MAPE value of 3.6% which is included in the very good category. In this study, an analysis was also obtained that there will be an economic increase of 1.14% along with a decrease in the PPKM level.
Iannis Xenakis' drawing expands the morphological intuition-thought inside musical composition

Drawing can reinvent musical issues.This practice of drawing was named graphic score, and various composers and artists developed it during the XX century, such as Cornelius Cardew or John Cage.However, Iannis Xenakis' drawing can be distinguished from a widespread idea of graphic score: visual notation to be creatively interpreted or final notation for writing music.Then, the investigation aims to indicate that Xenakis' drawing is a morphologic key moment inside the com position process to find musical creation.When Xenakis expanded his musical issues in the morpho logic field, it was possible to sketch sonic forms and interactions that conventional score did not allow to envision.Afterward, as drawing evolved in an idiosyncratic manner, the musical discoveries were transcribed for staves, becoming legible for the orchestra.Therefore, to demonstrate how drawing can unravel musical issues within the composition process, the investigation analyses Xenakis' creative mind in two moments: Metastasis and Terraktorch.The research recognizes drawing as a practice that unfolds a morphologic intuition-thought, in which the composer can discover new gestures, forms, and dynamics.Thus, the article reminds us that drawing can be a relevant practice within the musical com position, in which there is no choice between graphic score or conventional notation, but both can be complementary in musical creation.
Data Integrity Verification Schemes in Cloud Computing Environment: A Survey

The popularity of cloud computing is increasing day by day. Everyone, knowingly or unknowingly, is using the services of cloud computing. Users are having very limited capability devices in terms of storage or processing, etc. To solve the storage issues, they use remote cloud storage for outsourcing their data that results in various benefits to users or reduces the burden of users. However, with these benefits, some security concerns need to be addressed that are confidentiality, integrity, and availability. Cloud data integrity means whether the user’s outsourced data is intact or not. Many researchers have proposed various schemes to verify cloud data integrity. Starting with some initial work in this area, this paper reviews the most recent existing data integrity schemes based on some identified characteristics. It also provides the future research directions that can be used as a guidemap for various researchers in cloud data integrity verification area.
Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion

This article proposes the usage of the d-separation criterion in Markov Boundary Discovery algorithms, instead of or alongside the statistical tests of conditional independence these algorithms usually rely on. This is a methodological improvement applicable when designing, studying or improving such algorithms, but it is not applicable for productive use, because computing the d-separation criterion requires complete knowledge of a Bayesian network. Yet Bayesian networks can be made available to the algorithms when studied in controlled conditions. This approach has the effect of removing sources of suboptimal behavior, allowing the algorithms to perform at their theoretical best and providing insights about their properties. The article also discusses an extension of this approach, namely to use d-separation as a complement to the usual statistical tests performed on synthetic datasets in order to ascertain the overall accuracy of the tests chosen by the algorithms, for further insights into their behavior. To exemplify these two approaches, two Markov Boundary Discovery algorithms were used, namely the Incremental Association Markov Blanket algorithm and the Iterative Parent–Child-Based Search of Markov Blanket algorithm. Firstly, these algorithms were configured to use d-separation alone as their conditional independence test, computed on known Bayesian networks. Subsequently, the algorithms were configured to use the statistical G-test complemented by d-separation to evaluate their behavior on synthetic data.
Contemporary Issues of Machine Translation in the Field of Law – A Contrastive Analysis

We live in the age of constant changes and tremendous development of information technologies in all spheres of human activity.The field of translation is not exempted from these changes and developments.In this paper we shall explore the achievements of computer techologies in the field of machine translation.The research is conducted on the machine translations of four legal text in the field of EU legislation from German into Croatian language.The method used is a contrastive analysis of translations performed by two online translators: Google Translate and Microsoft Bing Translator.In the introductory part, specific features of the translation in the field of law are presented.In the theoretical introduction to the research, approaches to machine translations are discussed with specific reference to the editing and post-editing phases of translation procedure.Based on a number of features that represent challenges in translating legal texts, our hypothesis is that in translating legal texts these specific features have to be considered and that an intervention of a skilled human translator mastering both the linguistic and extra-linguistic knowledge is necessary, at least in a post-editing phase.In the empirical part, four different texts from the field of EU legislations are translated by using Google Translate and Microsoft Bing Translator.The translations are then contrastively analysed against the error analysis criteria developed by Costa and Popović Popović.In the final part of the paper, qualitative and quantitative analysis of findings are conducted, types of errors ocurring in both machine translations discussed, and conclusions drawn.
Survey on Quantum Noise-aware Machine Learning

Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.