Reviewerly/exHarmony huggingface dataset:
Privacy Enhancing for Fog Computing based - IoT

With the massive inflation of newly developed technologies, recourse to data has become a necessity in light of the current inflation and excessive need dominating the world and developed societies.According to the control of millions of smart devices and sensors connected to an interconnected and controlled automated system within installed scales due to the services provided by IOT devices through the created fog layer that connects the cloud centers and those devices, in addition, very large amounts of that data including public and private are passed through the connection of Internet of Things devices to each other.Smart and advanced networks as one of the fog computing applications play a prominent and accurate role in the infrastructure for reliable and sound data transmission.Accordingly, the process of data aggregation is an important and common matter in the world of fog-enhancing Internet of Things, so preserving the privacy of that data is a matter of concern, and based on this principle, we propose in this paper a model for data aggregation that maintains privacy using a foggy computing environment called PPFDA (privacy preserving based-fog computing data aggregation).We use in our scheme DF homomorphic cryptosystem as it consider one of the aggregation models that ensures the privacy purpose.The theoretical results and analyzes show that our design is ensuring the privacy of data during collection using an algorithm of DF.The results confirm that the proposed scheme achieves security and privacy purposes in modern network systems for the Internet of things based in fog computing.In addition, it contributes significantly to the efficient performance of storage operations.
A Case Study of Developing a Blended Reading Program Using Process Drama for Korean EFL Secondary Students

This paper presents a case study of developing a blended reading program for Korean EFL middle school students using a process drama methodology to generate intrinsic motivation to read, improve reading comprehension and language fluency, and foster twenty-first-century skills such as creativity, communication, and cooperation. For the development of the reading program, a needs analysis was conducted using student questionnaires and diagnostic tests. From results of the needs analysis, sample lessons were designed based on the process drama methodology following its planning principles and strategies. Based on feedback from teachers and students after piloting the sample lessons, the blended reading program was finalized. The proposed blended reading program is expected to help Korean EFL middle school students build a positive attitude toward reading English books and develop reading comprehension, language fluency, and 21st-century core competencies. It will also encourage English teachers to use innovative teaching methods in English education.
Instructional Activities and Study Strategies in Learning Less Commonly Taught Languages

The purpose of this study is to investigate the effective instructional activities as well as the effective study strategies for students to use related to their academic performance in less commonly taught language, in the case of learning Japanese. The sample was college students who studied Japanese from 2017 to 2018 at the beginning level. Factor analysis was used to group items into meaningful factors. Independent t tests were used to investigate correlations with Test Scores. The test scores were gathered from the classroom tests (listening and written). Instructions showed that Cooperative and Challenging Instructions revealed statistically positive relation with Test Scores. Communicative Instruction did not show a statistically positive relation with Test Scores. Learning strategies revealed statistically positive relation with Cognitive Strategy, Memory Practice, Study Skills, and Resource Management. Romaji (romanization of the Japanese written language) Use showed negative correlation. The pedagogical implications were presented. Further investigation was suggested using both quantitative and qualitative approaches to together to investigate more in depth.
Demo: SLASH: Serverless Apache Spark Hub

Application needs for big data processing are shifting from planned batch processing to emergent scenarios involving high elasticity. Consequently, for many organisations managing private or public cloud resources it is no longer wise to pre-provision big data frameworks over large fixed-size clusters. Instead, they are looking forward to on-demand provisioning of those frameworks in the same way that the underlying compute resources such as virtual machines or containers can already be instantiated on demand today. Yet many big data frameworks, including the widely used Apache Spark, do not sandwich well in between underlying resource managers and user requests. With SLASH, we introduce a light-weight serverless provisioning model for worker nodes in standalone Spark clusters that help organisations slashing operating costs while providing greater flexibility and comfort to their users and more sustainable operations based on a unique triple scaling method.
Image segmentation with texture-oriented superpixel

Superpixel segmentation is an image preprocessing technique that uses pixel blocks instead of pixels to improve the efficiency of subsequent image tasks. Existing methods are not sensitive to image texture. To solve this problem, a texture-oriented superpixel (TOS) segmentation method is proposed. Firstly, an adaptive parameter function based on pixel boundary probability is used to calculate the distance. Secondly, a gradual merging strategy is used for merging. And finally, a loop iterative framework is used to optimize the seeds. The experimental results show that TOS can effectively preserve the smaller texture information, generate seeds that are more consistent with image texture, and better detect image texture.
Analysis of Blockchain Platforms for Generation and Verification of Diplomas

The application of blockchain in educational institutions and in particular those of higher education, is encountering numerous obstacles towards its full application. We see blockchain as a technology applied in a lot of spheres, including business, industry, health, but we see a stagnation of its application in education. In this scientific paper, we will make an overview of the educational platforms that have been created, how many applications they have, what difficulties the authors have encountered and what suggestions they have given in this regard. It is more than necessary that blockchain technology be used in institutions of higher education, since even nowadays, alongside the many sophisticated software that these institutions possess, we still see many misuses that are made either during the evaluation process, grading up to the awarding of diplomas. We will also provide a framework on how we think blockchain technology should be applied in higher education institutions. Very important is the inclusion of blockchain technology in sensitive processes in education, especially in the evaluation of students and the generation of diplomas. At the end, we give our suggestions, how we think to overcome the problems of diploma forgery and how we can contribute in this direction.
The ODQuad: Design and Experimental Validation of a Novel Fully-actuated Quadrotor

This work experimentally validates a novel fully-actuated quadrotor-based unmanned aerial vehicle named ODQuad (OmniDirectional Quadrotor). The ODQuad is composed of three main parts arranged in a gimbal configuration. The internal mechanism is composed of two rotational joints with orthogonal and incident axes which allow to decouple the horizontal motions from the vehicle body rolling and pitching. Firstly, the physical prototype is presented and the motion controller, inherited by [1], has been tailored in such a way to integrate the servo actuators of the internal gimbal mechanism. Three trajectories have been commanded to prove the decoupling between the position and attitude motion. The results confirm the effectiveness of the proposed multirotor design.
Tamil Text Error Correction with Multi-lingual T5 Model

Due to a variety of reasons, such as misspelling, typographical errors, and illiteracy, errors to arise in writing. Additionally, errors result from the conversion image or voice to text. MT5 is a well-known pre-trained multilingual transformer model and is employed correct errors in Tamil text. To improve model's capability, transfer learning is performed with Tamil dataset. The model's ability to handle various kinds and levels of errors for Tamil text is investigated. Character Error Rate (CER) & Word Error Rate (WER) are employed as the metrics to evaluate the model's performance. The model has shown promising results with improvement of 97.7% for CER and 89.3% for WER on the employed dataset.
Application of the Simplified Aquila Optimizer with the Multiple Updating Principle in Unmanned Aerial Vehicle Path Planning

The unmanned aerial vehicle (UAV) had been popular around the world and the peasant began to use UAVs to transport bags of crop, spray pesticides or herbicides manually causing the uncertainty and difficulty in path planning. Inspired by the simplified form of expressions and better performance of the Aquila optimizer (AO), a simplified AO with multiple updating principle (SAOMUP) was proposed to improve the capability of the AO algorithm and applied in UAV path planning in this paper. Simulation experiments were carried out to test and verify the capability of the proposed algorithm. And finally, the SAOMUP was applied in UAV path planning for transportation over some mountain field with trees or stones being simulated by cylinders, and better results were achieved.
Orchestrate -A GAN Architectural-Based Pipeline for Musical Instrument Chord Conversion

Acoustic instruments produce sounds that are characterized by specific patterns and qualities, including harmonic content, attack, and decay, vibrato, resonance, and timbre. The creation and manipulation of instrumental sounds in various musical contexts are one of the most important features of acoustic instruments. Acoustic music is unamplified music that produces sound only by vibrating air and acoustic means, instead of through electronic or virtual instruments. Acoustic music emphasizes simplicity in its lyrics, harmonies, and melodies. The conversion of one musical instrumental chord to another musical instrumental chord is possible in acoustic instruments. In this paper, the Differentiable Digital Signal Processing technique is employed as a new approach to the realistic neural audio synthesis of musical instruments that combines the efficiency and interpretability of classical DSP elements such as filters, oscillators, reverberation, etc. The deep learning techniques are incorporated to train the model and produce harmonious music patterns. The generated music preserves the feature of the real play. The method also allows non-instrumentalists to process music. The model can be further developed to feed existing music. The preprocessed data is fed as input to obtain the desired instrumental chord or music.
Quasi Real-Time Distributed Search Engine Based on Massive Operation and Maintenance Data

Search engine is one of the effective tools to find the required information in the information ocean. To a certain extent, search engine is the information portal of the Internet, so it is of special significance to research and implement search engines. At the same time, search engines have important commercial value and application prospects both at home and around the world, and can facilitate the processing of massive operation and maintenance data for enterprises. Therefore, this paper studies the quasi real-time distributed search engine based on massive operation and maintenance data. This paper first explains the design goals of distributed search engines and the concepts related to real-time indexing, then designs distributed search engines, and finally evaluates and analyzes distributed search engines. The research results show that the distributed search engine has good search performance and high search accuracy.
INCREASING THE EFFECTIVENESS OF THE QUALITY OF EDUCATION AMONG STUDENTS OF HIGHER EDUCATION ON THE BASIS OF MODERN INFORMATION TECHNOLOGIES

As a result of the historical and pedagogical analysis, the main trends and features of adult education in universities based on modern information technologies at various stages of the development of pedagogical theory and practice have been identified. The pedagogical characteristic of the adult education category is given. The pedagogical essence of the process of adult education in universities is substantiated on the basis of modern information technologies, taking into account their socio-psychological characteristics, the content and structure of the pedagogical process under study are disclosed. A pedagogical target program for improving the effectiveness of adult education in universities based on modern information technologies has been developed and tested. Criteria and indicators for evaluating the effectiveness of adult education in universities based on modern information technologies have been determined: meaningful, organizational and productive. The main ways to increase the effectiveness of adult education based on modern information technologies are identified: the development of a distance education system in modern universities, improving the efficiency of the corporate information system of a university, training faculty and university administration as tutors and managers of students' cognitive activity.
Influence of interpolation of the method of mixing the liquid in the container on the mixing efficiency

An anthropomorphic robot, i.e. a robot that can reproduce the movements of the human arm, is equipped with various types of interpolation. The robot can be used to mix two liquids enclosed in a shaker mixer. The problem to be solved is the selection of the optimal types of interpolation, as well as the sequence of these robot movements that is able to provide better results - higher mixing efficiency, in other words a more sustainable approach to the mixing process, using an anthropomorphic robot. The shaker-type container used - the shape of a truncated cone has a positive effect on the efficiency of the liquid mixing process. The difference in density between the two liquids contributes to a sustainable approach to the mixing process. It is preferable to use different shaker position with respect to the gravitational acceleration vector and to use different linear and arc interpolation. The use of variable position in relations to gravitional acceleration vector, due to the shape of the truncated cone of the mixer, will cause chaotic movement of particles inside the shaker, which is a desirable phenomenon, because then both mixed liquids mix quickly.
KLASIFIKASI OPINI MASYARAKAT DI TWITTER TENTANG KEBOCORAN DATA YANG TERJADI DI INDONESIA MENGGUNAKAN ALGORITMA SVM

Personal data is sensitive and private, it can only be seen by an individual. However, as time goes by, data leaks often occur on the internet, especially in Indonesia. This data leak was busy on Twitter social media and was used as a forum for opinions regarding data leaks that occurred in Indonesia. The classification method used for this research is Support Vector Machine (SVM) with TF-IDF feature extraction. The dataset was obtained through the results of scraping Twitter and getting 5000 tweets. The dataset is manually labeled as Positive, Negative, and Neutral before entering the SVM Classification stage. And based on the results of the SVM classification, SVM produces an accuracy of 83%.
Review Paper on Social Network for Problem Solving and Knowledge Sharing

This review paper presents a comprehensive analysis of 10 selected research papers that investigate the interplay between social media, gamification, and sentiment analysis. The papers cover a wide range of topics, including customer engagement, user behaviour in mobile apps, sustainable behaviour change, opinion mining, sentiment analysis techniques, and applications of sentiment analysis on social media data. The review offers valuable insights into the effects of social media and gamification on customer engagement by examining empirical studies and conceptual models In the realm of sentiment analysis, the reviewed papers shed light on opinion mining and sentiment analysis techniques applied to social media data. They explore the use of machine learning and lexicon-based approaches for sentiment analysis, along with the application of convolutional neural networks with word embedding for sentiment analysis of customer reviews. Furthermore, the review presents a comprehensive survey of techniques and applications of sentiment analysis on social media data. This comprehensive review contributes to the existing knowledge by offering a holistic perspective on the relationships between social media, gamification, and sentiment analysis. It underscores the significance of these areas in understanding customer engagement, user behaviour, and sentiment analysis on social media platforms. The findings from these papers have implications for researchers, practitioners, and professionals working in the fields of social media, gamification, and sentiment analysis, providing valuable insights for further exploration and practical implementation.
PENERAPAN METODE GRAMMAR TRANSLATION METHOD (GTM) PADA GURU BAHASA INGGRIS DI SMP NEGERI GUDANG ARANG

The implementation of this community service activity aims to make SMP Gudang Arang students able to understand and know English as a foreign language and provide an understanding of English Learning Methods that are useful for increasing students' interest, willingness and interest in learning English vocabulary and grammar. This service activity was carried out at the SMP Gudang Arang in September 2021, while the population consisted of 20 class VII students of SMP Gudang Arang. There are two methods of implementing this activity, namely training and mentoring. The training materials are divided into two, namely Exposure to Materials on tips to become successful English learners and Application of the Grammar Translation Method (GTM) in English while Mentoring is assisting teachers in implementing the GTM method so that students are interested in learning English. The results of this training showed that most of the students who were involved in English vocabulary and grammar training activities experienced an increase in their ability and motivation to learn English. In addition, an increase in students' motivation and interest in learning was also seen during this training activity. This is indicated by their active involvement in every learning activity and their enthusiasm in carrying out each task given. In mentoring in learning English vocabulary and grammar there are some students who have good English skills. Some of these students can pronounce English vocabulary and English grammar with correct pronunciation, students also tend to be active, students' English learning outcomes are also quite good
K-MEDOIDS ALGORITHM ANALYSIS IN PERMANENT WORKER GROUPING OF INDONESIAN CONSTRUCTION COMPANIES

The construction companies are both those who run the construction work, both construction administrators and construction consultants who need the manpower for their operations. There is no way to determine the existence of a policy of the workers who have a work agreement with the business owner for a period of time. The company's long-term rating of construction workers in Indonesia from 2010-2018 is based on the need to provide information and input to the local government center at the construction site in Indonesia. One of the grouping methods that can be used is k - Medoids. The advantage of this method is to overcome sensitive to outlier. This method in its horn is represented by objects close to the center and thus capable of sterilizing a more precise value. Analysis of the data grouping shows that two cluster data produced one in the low and 33 in high cluster with total cost of 2.7557.
STOCK MARKET ANALYSIS OF ELECTRONIC FINANCIAL MARKETS AND ADOPTION OF CRYPTOCURRENCIES, BLOCKCHAIN APPLICATIONS OF THE ECUADORIAN FINANCIAL SYSTEM AND ITS LEGISLATIONN

The present work of carrying out a Stock Market Analysis of the Financial Markets for the Adoption of Cryptocurrencies and Blockchain Applications in the Ecuadorian Financial System, which will allow to have an overview of the situation in which the population finds itself with respect to the use and knowledge of cryptocurrencies and the investments that can be made with them. The research was carried out through the application of surveys and interviews aimed at users of the financial system and managers of banks and cooperatives who could make a judgment based on their experience and knowledge in finance. As a result of them, it was determined that the Ecuadorian population does not have the necessary knowledge to be able to start using cryptocurrencies on a daily basis, and that users of the financial system are accustomed to traditional financial culture, so it would require a change and update at the technological level as well. The proposal contains information on the most used cryptocurrencies and the suggestion of which would be the best cryptocurrency to adopt when the society was ready for it, with the necessary reasons and justifications on the decision taken. Therefore, the present research work may serve as a foundation for possible future research to be carried out further so that this transition can be developed in a successful manner and at the right time.
Employing Graph Databases for Business Process Management and Representation

There's growing interest in leveraging the structured and formal nature of business process modeling languages to make them available not only for human-oriented visualization and analysis, but also to machine-oriented knowledge representation. Standard serialization formats are predominantly XML-based, originating in times when XML was imposed as the mainstream interoperability format. Recent research on the interplay between knowledge representation and business process modeling has involved OWL or RDF for semantic enrichment and reasoning, which suggests an evolution towards treating business process models in formats that are better fit to their native structure—e.g., directed labelled graphs. In this paper we introduce a converter that translates the standards-compliant BPMN XML format to Neo4J labelled property graphs (LPG) thus providing an alternative to traditional XML-based serialization, while ensuring conceptual alignment with the standard serialization of BPMN 2.0. A demonstrator was built to highlight the benefits of such a parser and to test the conceptual coverage of BPMN models. The proposal enables graph-driven navigation of business process models in a knowledge-intensive context where procedural knowledge available as BPMN diagrams must be exposed to LPG-driven applications.
Problems Difficulties in Recognizing Flat Figures

The paper explores classes of visual objects that have not yet received a stable solution, and private methods need artificial adjustments with subsequent difficulties with integration into the general and completely harmonious mode of the system. Flat figures act as one of these classes, and a model of particular approaches to problem classes of objects is built to the solutions of problems associated with them.
Analysis of the Information and Communication Technology in Blended Learning for Economics Students in the Context of Digitalization

The study was aimed at analyzing the effectiveness of blended education for economics students using information and communication technology (ICT).The research methods consisted of literature analysis, case method, comparative analysis, mathematical statistics, and statistical experiment.The article describes the following results.Three Russian universities (Vladivostok State University of Economics and Service [VSUES],