Reviewerly/exHarmony huggingface dataset:
<i>Canva</i> as a Media for Exploration of Indonesian Tourism Knowledge in BIPA Learning

This article discusses characteristics, cultural and tourism insights, and creativity of BIPA students in 6th-grade elementary school at Surabaya European School through infographics or media Canva.BIPA learning requires creative selection, media usage, and strategies oriented to the learning process.Media Canva is a solution to increase student creativity by combining IT skills and Indonesian insights comprehensively.This research uses qualitative descriptive by applying the project-based learning method.The data results from a project assignment using the Canva application to present Indonesian tourism infographics.The data was collected by observation, field notes, and documentation.This study obtained three main results: (1) the characteristics of Year 7 students at SES have independence in operating IT and good communication skills; the language level is averaged at B1 and B2, (2) tourism and cultural insights at SES are obtained through academic tourism activities, field studies in cultural places, historical tours, and social activities, and (3) student creativity in expressing travel knowledge to Canva media infographics is carried out to hone Indonesian, IT, and student creativity competencies.The use of infographic presentations has the complexity of student competencies regarding grammar, graphic design, and mastery of language skills.
EFFECTIVE TEACHING METHODOLOGY FOR TEACHING ENGLISH AS A SECOND LANGUAGE AT TERTIARY LEVEL

English language is a global language and it is essential for a second language learner to be proficient in it. It is the second language and has become the language of television, newspaper, law, mass media, radio, social circles, documentation, technology and economy in our country. So, it should be taught in a way that it will fulfill all the requirements properly. The day by day increasing demand of this language has also increased the demand of competent students. But most of the students cannot use English properly even after graduating. So, this research has investigated the effective teaching methodology for teaching English as second language at tertiary level. The research has been based on quantitative paradigm for the collection of data through the tools of close-ended questionnaires and structured observation. And, the data has been analyzed through SPSS software. Moreover, survey and simple random sampling has been used in this research, and teachers of English Department are included in the sample. This research has covered four universities including government and private universities of Karachi though simple random sampling. It has been analyzed that GTM and Direct Method are mostly used by the teachers, and other methods are ignored. And it is concluded that the teachers should use different methods and techniques for teaching according to needs of the learners, so eclectic approach should be used.
Design of Oral English Teaching Based on Embedded Microprocessor

Under the background of internationalization, China has put forward the strategy of “one belt and one road”; this puts forward higher requirements for the cultivation of internationalized and compound talents for English majors in China. However, through the investigation of the present situation of College Students’ oral English teaching and their oral competence, it is found that college students’ oral English competence and intercultural communication competence are generally low. The teaching concept is backward, the teaching mode is outdated, there are few opportunities for oral practice, and there is a lack of real communication environment. The level of teachers needs to be improved. The purpose of this paper was to design oral English teaching based on embedded microprocessor, aiming at the empirical research on the cultivation of College Students’ intercultural communicative competence. This paper puts forward a teaching experiment scheme, which is guided by processor-based teaching method and guided by cultural teaching skills. Teaching skills refer to the stable and complex teaching behavior system formed by teachers using the existing teaching theoretical knowledge and practice. It includes both skills based on teaching theory and experience in practical use. In order to improve students’ intercultural communicative competence through oral class and to verify the applicability of processor-based approach in oral English class of College English to improve students’ intercultural communicative competence, this paper conducts a one-semester teaching experiment. Processor-based teaching method and traditional teaching method are used in the experiment. Through these two teaching methods, the standard deviations of test scores are 3.2 and 3.0, respectively. Embedded has become the most common application method for the intelligentization of electronic systems. In addition to setting up separate courses, embedded systems are the most widely used means in many practical aspects involved, such as course design, graduation design, graduate thesis, and other topics.
Resources Factors in the Implementation of Sinabung Eruption Disaster Mitigation in Gung Pinto Village, Karo Regency

Disaster mitigation consists of structural mitigation and non-structural mitigation.Structural mitigation is a form of mitigation that is physically related.In the implementation of non-structural mitigation, the role of citizens is important and necessary so that the implementation of mitigation can be in accordance with the plan.Disaster mitigation programs can be implemented through increased education about disasters, socialization, simulations, and several other activities aimed at increasing community capacity.One form of mitigation carried out by the Karo Regency BPBD is through the creation of the Disaster Resilient Village (Destana) program.In its implementation, the mitigation carried out is still experiencing problems, one of which is limited resources.This study aims to describe the function of resources in the implementation of disaster mitigation in the village of Gung Pinto.The research method used in this study is a qualitative research method.Primary data obtained through observation and interviews were then analyzed using the Neuman model.Research problems related to the implementation of mitigation will be described using the resource concept according to George Edward.The research results are described as follows; Human resources owned by BPBD Kab.Karo in quantity is sufficient, while in quality it is necessary to provide training related to disasters, while the facility resources in supporting the Destana program are not sufficient, as well as the utilization of the authority possessed by BPBD has not been optimal because the work of the organization focuses on the work that has been done.planned.
Early Generation and Detection of Efficient IoT Device Fingerprints Using Machine Learning

The proliferation of Internet of Things (IoT) markets in the last decade introduces new challenges for network traffic analysis, and processing packet flows to identify IoT devices.This type of device suffers from scarcity, making them vulnerable to spoofing operations.In such circumstances, the device can be recognized by identifying its fingerprint.In this paper, a novel idea to elicit Device FingerPrint (DFP) is presented by extracting 30 features from the collected traffic packets of 19 IoT devices during setup and startup operations.Raspberry Pi 3 Model B+ is configured as an access point to collect and analyze the traffic of seven networked IoT devices using Wireshark Network Protocol Analyzer.Moreover, the rest of IoT devices traffic is taken from the publicly available network traffic dataset.Each IoT device's feature extraction process starts from getting Extensible Authentication Protocol over LAN (EAPOL) protocol, continuing with the other flowed protocols until the first session of Transmission Control Protocol (TCP) related to that device is closed.Depending on some produced variation of device traffic features, 20 fingerprints for each device are created.The probability theorem of Gaussian Naive Bayes (GNB) supervised machine learning is utilized to identify fingerprints of individual known devices and isolate the unknown ones.The performance evaluation for the proposed technique was calculated based on two measures, F1-score and identification accuracy.The average F1 score was around 0.99, while the overall identification accuracy rate was 98.35%.
An Analytical Survey on the Learning Performance of the Lecturers at the Faculty of Education

Learning Evaluation and monitoring are the soul of a learning process.They are vehicles used to improve the learning process.Institution Performance Education personnel are required to have international standards.One of the weapons that are used to compete is by applying the ISO 9001: 2015 standard.This study aims to evaluate the performance of lecturers, especially the learning process carried out by lecturers at the Faculty of Education.This study uses an analytical survey method.The instrument was developed by referring to ISO 9001: 2015.The subjects in this study were lecturers at the Faculty of Education.The study results found that the most findings were in the learning evaluation domain of 68%, 21% in learning planning, and 11% in the learning implementation domain.Several reasons led to the discovery.This data will be used as one of the reviews to follow up on learning in FIP.
A Survey of Comparison Different Cloud Database Performance: SQL and NoSQL

The scales in databases have recently increased, and more expansion is expected in the future. The storage costs gradually have slowed down and quickly expanded the storage capacity. Cloud intrusion has modified the comparisons in recent years. Database performance plays an essential part in the market. Cloud Database focuses on new, conventional databases, specific applications for scalability, dynamic devices, and ease of use. Cloud databases are primarily used for data storage, retrieval, modification, and analysis by tools like business intelligence. These tools can build new business strategies and demonstrate scalability and elasticity while managing vast amounts of data with reliable, customized, and cost-effective services in various applications. This paper provides an overview of cloud computing, Cloud database architecture and kinds, and database as a service. It also highlights the characteristics, deployment, and service model of cloud computing and the performance and functionality of the various SQL and NoSQL cloud database applications and services required to evaluate them. It focuses on the different parameters to assess their performance, such as ease of software portability, transaction capabilities, and the maximum amount of data stored. The primary purpose of this paper is to assist businesses and individuals in understanding how cloud computing may provide them with dependable, personalized, and cost-effective services in many applications.
Extremal Binary and Ternary Codes of Length 60 with an Automorphism of Order 29 and a Generalization

In this paper, all extremal Type I and Type III codes of length 60 with an automorphism of order 29 are classified up to equivalence. In both cases, it has been proven that there are three inequivalent codes. In addition, a new family of self-dual codes over non-binary fields is presented.
Improved Dynamic PPI Network Construction and Application of Data Mining in Computer Artificial Intelligence Systems

The protein interaction network is a network composed of the interactions between all proteins in living organisms. The use of computational methods to prove the functional modules in the PPI network is an important research topic in bioinformatics. With the development of technologies for detecting protein interactions, the available data on interactions has become more and more extensive, and the PPI networks created have become more and more extensive. At the same time, with the deepening of research, people have found that the real PPI network is constantly changing with time and conditions and this change is closely related to the creation and development of life activities. Therefore, it is also very important and necessary to understand and study the functional modules of the PPI dynamic network. At the same time, it focuses on analyzing the basic attributes of artificial intelligence and computer technology and analyzing the goals that can be achieved by using artificial intelligence in computer technology. The effects and directions that can be further explored can be extracted from a large amount of unrealistic, incomplete, fuzzy, and random actual data. These data are hidden from people who do not know them before , but this is a potentially useful process for obtaining information and knowledge. Applying data mining technology to diagnose computer network problems can improve reliability, stability, flexibility, troubleshooting speed, and proper network management, automatically diagnose, predict, and maintain network failures, and ensure that the network has high-quality services and reliability. Drive AI applications in computer science.
Ground Settlement-Induced Building Damage Assessment With Modified Lanczos Algorithm and Extreme Learning Machine

Construction, tunneling, and other urban anthropogenic activities strain neighboring buildings through distortion and rotation on both the surface and underground, resulting in instability of the local geological structure. This may cause devastating structural damage to buildings. Therefore, quantitative assessment of building structural damage is essential for the safety of local communities. In this study, a novel data-driven approach was applied to assess the building damage risks in urban areas. Data collected from over 50 buildings adjacent to the construction site were analyzed. The extreme learning machine (ELM) algorithm was applied to predict building structural risks. A modified Lanczos algorithm was used to regularize the ELM and improve the overall prediction performance. The computational results demonstrate the robustness and efficiency of the proposed Lanczos algorithm-regularized ELM.
Linear Differential Driven Wheel Mobile Robot Based on MPU9250 and Optical Encoder

In this paper, the operability of a custombuilt differential drive wheel mobile robot was evaluated using a self-driving algorithm in practical application. Several key parameters need to be realized by the WMR to enable succinct self-driving algorithm evaluations and parameter derivations. The main components of the WMR development were examined in this work, specifically parameter derivations, hardware implementation, steering algorithm and WMR operability. The result obtained indicate that with proper design and careful calibration of sensors, the required driving parameters could be realized. This enables the WMR to potentially serve as data collection platform for future evaluation of self-driving algorithms.
Improving spam email detection using deep recurrent neural network

&lt;span&gt;Nowadays the entire world depends on emails as a communication tool. Spammers try to exploit various vulnerabilities to attack users with spam emails. While it is difficult to prevent spam email attacks, many research studies have been developed in the last decade in an attempt to detect spam emails. These studies were conducted using machine learning techniques and various types of neural networks. However, with all their attempts the highest accuracy acquired was 94.2% by random forest classifier. Deep learning techniques have demonstrated higher accuracy performance compared to the traditional machine learning algorithms. In this paper, deep recurrent neural network was used to determine whether an email is a spam email. After investigating different configurations for this method, the best setting that generated the highest accuracy was based on using Tanh as the activation function with the dropout rate equals to 0.1 and the number of epochs achieving 100. The proposed approach attained a high accuracy of 99.7% which surpassed the best accuracy (98.7%) obtained by the hybrid gated recurrent unit recurrent neural network approach.&lt;/span&gt;
Digital Image Encryption Techniques: Article Review

In this age of the multi-media, images have an important impact on communication. When users upload images over an insecure communication network, total security is a difficult problem to address in order to maintain image confidentiality. Also, encryption is a technique for keeping images secret. This study gives a basic introduction to cryptography, as well as a concise overview regarding the many image encryption algorithms' elemental security criteria. This paper includes an overview of several image encryption approaches as well as a comparison of discrete image encoding techniques, before coming to a conclusion and recommending future research.
Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization

At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.
Indonesian Tourism Demand Forecasting using Time Series Approach to Support Decision Making Process

Tourism industry is always growing dan uphold an important role in national economy as the second largest portion of foreign exchange contributor, as well as its role in national employment. In improving tourism industry, forecasting is needed to anticipate the perishable nature of tourism. Therefore, an accurate forecasting is needed as the baseline of strategic resource planning in order to maximize the utilization dan efficiency of the available resources. The objective of this research is to build an accurate model that is able to forecast Indonesian tourism demand. This research use ARIMA algorithm to forecast the arrivals of tourist. The result of this paper is a time-series model for tourist arrivals in Indonesia Keywords: Tourism, Demand Forecasting, ARIMA, Indonesia.
Structuring a course service for developing the learning digital skills of university students in Fribourg

This paper is an analytical description of a course service that aims to improve the digital skills of university students. This innovation is part of the Digital Skills program of Swissuniversities (P8) and is currently developed in Fribourg (collaboration between the University and the HES-SO Fribourg). It is more a course service than a course because we propose: different modalities (a 3 ECTS course in Autumn and a self-learning course all year long), flexibility of contents (adaptation of the digital skills to be developed), flexibility of the course opening (students from all faculties and all BA years) and communication actions on the course in the institutions. We first analyze why a digital skills development is important today and what the main learning objectives are. A second part shows, with the help of surveys on the students’ learning needs in digital skills, why flexibility has become a key element in the design of our service. A third section describes how the construction of a detailed competence framework facilitates the definition of learning resources that are aligned with the targeted digital skills and then enables the implementation of content flexibility. Finally, we describe the course system structure itself.
Applications of internet of things for monitoring drivers-a comprehensive study

&lt;span&gt;Driving is a complex task that involves interacting adequately with the vehicle and the environmental changes simultaneously. Drivers' health is an essential factor in determining performance outcomes and enhancing road safety. It is a known reality that drivers with sudden health complications are most likely to be involved in road accidents and suffer several injuries. Besides that, drunk driving is another aspect of a significant public health issue, where drivers under the influence of alcohol show a clear vision loss and vehicle control. The internet of things (IoT) is a trendsetting advancement in which all sensor data can be collected in the cloud. In this paper, an active monitoring tool is developed to record the driver's heart rate if these readings reach vital values while on the move. Additionally, the tool monitors the driver's alcohol concentration, and if it rises beyond a certain threshold, an alarm is sent to the designated emergency contact. The tool has been tested and has been found to work satisfactorily.&lt;/span&gt;
Sports Motion Feature Extraction and Recognition Based on a Modified Histogram of Oriented Gradients with Speeded Up Robust Features

&lt;p&gt;Traditional motion recognition methods can extract global features, but ignore the local features. And the obscured motion cannot be recognized. Therefore, this paper proposes a modified Histogram of oriented gradients (HOG) combining speeded up robust features (SURF) for sports motion feature extraction and recognition. This new method can fully extract the local and global features of the sports motion recognition. The new algorithm first adopts background subtraction to obtain the motion region. Direction controllable filter can effectively describe the motion edge features. The HOG feature is improved by introducing direction controllable filter to enhance the local edge information. At the same time, the K-means clustering is performed on SURF to obtain the word bag model. Finally, the fused motion features are input to support vector machine (SVM) to classify and recognize the motion features. We make comparison with the state-of-the-art methods on KTH, UCF Sports and SBU Kinect Interaction data sets. The results show that the recognition accuracy of the proposed algorithm is greatly improved.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;