Reviewerly/exHarmony huggingface dataset:
Comparison between Linear Regression, Support Vector Machines, and Neural Networks in Forecasting the Number of Foreign Tourist Visits to Indonesia

The tourism sector is one of the largest foreign exchange contributors to the country's economic growth.One of the determining factors for tourism development is the number of foreign tourist arrivals.It is necessary to carry out development, planning strategies that appropriate, effective, and efficient to increase foreign tourist arrivals.Business Intelligence is one of the digital trends and the main digital marketing strategy for Digital Tourism by implementing data mining for data analysis, including forecasting.There are various kinds of forecasting methods, but the problem is in choosing the best method for the problem and the desired output.This study aims to compare the accuracy between Linear Regression, Support Vector Machine (SVM), and Neural Network methods to forecast the number of foreign tourist arrivals to Indonesia using RapidMiner Studio and parameter of Root Mean Square Error (RMSE) to compare the accuracy of each method.The results of this study indicate the RMSE value of the SVM has the smallest RMSE value compared to the other two methods.SVM is used to make a forecast for the next period which can be used as a basis for the government and the tourism industry in making decisions.
A unified framework for automated person re-identification

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.
Security of IoT System using Blockchain

There is exponential growth in the in the research industry of Internet of Things (IoT), but it is still vulnerable to privacy and security. Devices in the IoT having resource-constraints and decentralized topology are not capable of conventional privacy and security approaches. The paper predominantly focuses on the survey in the IoT system. Where we can see the various issues in IoT. To overcome these issues researchers, use blockchain mechanism. Due to blockchain’s popularity and success this technology is incorporated into many other industries. Blockchain is a technology that is booming since a decade. Despite various advancements in blockchain there are issues that are still present in IoT that needs to undergo improvisation. Before going to design and implement a model based on blockchain it is essential to understand the issues and challenges met by earlier research works. The paper presents study on the previous challenges and the solutions which are presented to get rid of the problems. Some of the existing problems are as follows- When the data is collected from the IoT devices and stored using blockchain still the data is under constant threat due to lack of security mechanisms relating to authentication and data privacy. Hence, providing security to blockchain becomes of utmost importance. The goal of this paper is presenting a detailed survey of security mechanisms for IoT systems. Also, this survey examines and explores why providing security to IoT devices is important. Readers can ultimately get aware of the issues that are present in the IoT systems and how they can be overcome. The study is beneficial for those who are looking forward to deploying IoT applications. They can get to know what the prerequisites are to be met for a secure IoT system.
Management of Teacher Certification in Educational Institution

Modernization of vocational education entails the need to find effective methods for managing an educational institution and teaching staff in particular. The relevance of the study is due to increasing role of teaching staff management, characteristics of comprehensive assessment of professional competence and performance of teachers at college. The purpose of the article is to improve management of teacher certification. For this, the authors developed a plan that reflects teachers’ professional competence development stages, the basis of which is the development and implementation of management model for teacher certification at college in the activities of Gorodetsky provincial college. The introduction of the model is the third stage of the plan which will increase the level of certification management, and hence teachers’ competence. Verification of effective implementation of the model was carried out using analysis of consolidated statements for 2016 and 2018. The results showed that after the model introduction the level of students’ training at college has increased. The model developed by the authors allowed increasing the level of teacher certification management which had a positive effect on students training.
An Extensive Review of Face Powder Formulation Considerations

The face powder was demanded by many nations in the world in the beginning AD and in Asia white skin was believed to be the sign of aristocratism, membership of the elite, and yet, white color is the pure symbol of the internal beauty and nobility. In addition, some face powders are sold in varying specialty shades to suit different skin needs; for example, a face powder with a greenish tinge will minimize the appearance of redness, while a purple-tinted powder may help the appearance of sallow or yellow skin. There is a legitimate reason to use face powder, and the pharmacopeias prescribe them in the treatment of many skin affections. At all events the proper use of powder is beneficial, it lightly covers and unifies a complexion, hiding the ravages of time, improving even the beautiful face. Face powder comes in different shades to match varying skin tones, and it is a good idea to choose the skin tone that most closely matches the natural skin. This will help the makeup appear more natural; it should be virtually unnoticeable. It may be necessary to use different face powders for summer and winter, as the skin may become tanner in the summer, or drier and in need of extra moisture in the winter. They are of benefit in acne, freckles, sunburn and red nose. Beneath their attractive aspect and odor, face powders should be made by the perfumer to combine the qualities of an elegant cosmetic and therapeutic agent; they must primarily possess adherence, lightness and be transparent; secondly, they should be detergent and delicately absorbent in order to aid the natural functions of the skin, taking up the fatty matters not easily dislodged by water; they should also tend to increase the natural elasticity and regular functions of the skin.
Comparison of Discrete and Continuous State Estimation with Focus on Active Flux Scheme

This paper deals with the state estimation of non-linear stochastic dynamic systems, both continuous and discrete in time, with an emphasis on a numerical solution to the Bayesian relations by the point-mass filters. The filters for discrete-discrete and continuous-discrete state-space models are reviewed and a new highly accurate and fast active flux method is introduced and adapted for a continuous filter design. A wide set of the point-mass filters is compared in a numerical study together with a set of particle filters.
Improving Suspicious URL Detection through Ensemble Machine Learning Techniques

In this chapter, we design a framework for detecting suspicious URLs by considering the URL features without requiring the content of the web page. We distinguish malignant web pages based on different discriminative and effective URL features, including lexical features, HTTP header information–based features, host-based features, geographical features and network features whose predictive power is high and improves performance significantly. Moreover, our approach uses both batch machine learning (ML) algorithms and ensemble machine learning classifiers (EMLCs) to identify the suspicious URL. EMLCs use multiple weak learners that are trained on different training examples to enhance the model performance effectively (TRAGHA 2019). We have compared the batch ML algorithms with ensemble models experimentally and ascertained that the ensemble approach outperforms the batch ML classifiers. We have extended our previous approach (Mohanty et al. 2020), where we used only some batch learning classifiers and a few URL features to identify URLs as either malignant or safe and obtained the classifier; the random forest (RF) model achieved the highest accuracy at 95%. Our proposed approach is evaluated against a training dataset that contains some safe and some malicious URLs and shows that the ensemble techniques obtain a TPR (true positive rate) of 0.98, FPR (false positive rate) of 0.01 and accuracy of 98.66%, precision of 0.95, recall of 100%, F1 score of 96% and AUC of 0.982.
A Hybrid Framework based on IoT and Blockchain Network to Store the Patient Health Data

Healthcare industry constantly faces many issues such as patient data access, drug storage log, medical records, or logs these are only a few of the many problems hospitals and other medical institutions must deal with. Healthcare industry must balance patient care with information privacy, security. The healthcare industry faces major issues like, putting the patient at the center, privacy and access, completeness of medical information, cost, supply chain management, drug records. Medical record keeping has evolved into a science of itself, even though the traditional method of storing data over a centralized database can be harmful as mentioned in the sections above, it can be prone to hacking or even a single point failure. Another issue with storing medical data in these databases is that, when these databases need an update in software all the servers are temporally down until the updates are finished. This small window can lead to be very lethal as healthcare is a 24/7 work. Blockchain technology and cryptocurrencies are being touted as the “solution” to problems in many different, disparate sectors throughout multiple industries. In this paper the goal is to identify blockchain technology applications in healthcare industry by conduction a systematic literature based on which a framework will be proposed. The promise of blockchain extends into the healthcare industry allowing a transformation of the current system and its use of information technology.
Automatic Recognition Method of Machine English Translation Errors Based on Multisignal Feature Fusion

The current automatic recognition method of machine English translation errors has poor semantic analysis ability, resulting in low accuracy of recognition results. Therefore, this paper designs an automatic recognition method for machine English translation errors based on multifeature fusion. Manually classify and summarize the real error sentence pairs, falsify a large amount of data by means of data enhancement, enhance the effect and robustness of the machine translation error detection model, and add the source text to translation length ratio information and the translation language model PPL into the model input. The score feature information can further improve the classification accuracy of the error detection model. Based on this error detection scheme, the detection results can be used for subsequent error correction and can also be used for error prompts to provide translation user experience; it can also be used for evaluation indicators of machine translation effects. The experimental results show that the word posterior probability features calculated by different methods have a significant impact on the classification error rate, and adding source word features based on the combination of word posterior probability and linguistic features can significantly reduce the classification error rate, to improve the translation error detection ability.
Formation of the subject digital competence of future foreign language teacher

Problem and purpose. The relevance of the research problem is due to the need to make adjustments to the content of the professional training of foreign language teacher. The purpose of the study is to test practical tasks for the formation of subject digital competence as an important component of the digital competence of a teacher of foreign languages. Materials and methods. The study involved 112 third-year students of the training specialty 44.03.05 “Pedagogical education” of the training profile “Foreign language (English, German)” of the Mordovian State Pedagogical Institute named after M.E. Evseviev (Russian Federation). Methods of mathematical statistics: Fisher’s angular transformations. Results. As part of the study, special tasks aimed at developing foreign language skills and speech skills based on digital resources that contribute to the formation of subject digital competence of the future teacher of foreign languages were developed. The developed tasks were introduced in the course of experimental training in practical classes in a foreign language in full-time and distance formats, as well as during the training practice. Statistical analysis of the results obtained after the experiment showed the reliability of differences in the level of formation of the subject digital competence of students in the reference and experimental groups (φemp = 2,13; p &lt; 0,05), which indicates the effectiveness of the developed practical tasks using digital technologies and implemented organizational and pedagogical conditions in the process of professional training of the future teacher of foreign languages. Conclusion. The organization and integration of purposeful work of students with digital technologies into the educational process, taking into account the conditions identified in the study (comprehensive study and analysis of digital technologies; the use of didactic capabilities of digital technologies in the system of foreign language education; the use of digital tools along with traditional means of teaching a foreign language) contributes to raising the level the formation of the digital competence of the future teacher of foreign languages in general, and subject digital competence, in particular.
Recipe Generator using Deep Learning

Abstract: Finding new recipes daily with the ingredients in hand is a difficult task that leads to people looking at different recipe websites and cookbooks. But it is often not possible to find the recipe one is looking for with the constraint of ingredients. The objective of this paper is to build a web application so asto cater to this specific problem faced by people. The proposed system takes ingredients as an input and gives machine generatedrecipes with the help of deep learning to the user. The recipe generated model will consist of title, ingredients and instruction reflecting the dataset used to prepare the model for deep learning. The system also consists of interactive features in the user interface enabling the user to save and access the generated recipelater on. Index Terms: Recipe generator system, web application, cook-ing, deep learning , recipes, ingredients
Effectiveness of Sci-Math Board Game for Elementary Science and Mathematics

This study aimed to see how effective the Sci-Math Board Game is, at improving elementary learners’ academic performance in Science and Mathematics. Twenty Grade VI pupils from Tuyangan Elementary School in Tuyangan, Kabugao, Apayao participated. At the start of the academic year 2021-2022, the researcher used quasi-experimental research to conduct this study. The researcher employed quasi-experimental research to conduct this study at the start of the school year 2021-2022. The results showed that utilizing the Sci-Math Board Game in teaching and learning processes is successful, especially when teaching science and math. While playing board games, players can engage in computational thinking, teamwork, and creativity. Furthermore, there was a significant change in the participants' Science and Mathematics performance before and after using the Sci-Math Board Game as an instructional tool.
Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation

A knowledge graph is introduced into the personalized recommendation algorithm due to its strong ability to express structural information and exploit side information. However, there is a long tail phenomenon and data sparsity in real knowledge graphs, and most items are related to only a few triples. This results in a significant reduction in the amount of data available for training, and makes it difficult to make accurate recommendations. Motivated by these limitations, the Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation (KGET) is proposed to improve recommendation quality. To be specific, the method first learns the embedding of users and items by knowledge propagation combined with collaborative signal to obtain high-order structural information, and the attention mechanism is used to distinguish the contributions of different neighbor nodes in propagation. In order to better solve with data sparsity and long tail phenomenon, transductive learning is designed to model links between unknown items to enrich feature representation to further extrapolate the knowledge graph. We conduct experiments with two datasets about music and books, the experiment results reveal that our proposed method outperforms state-of-the-art recommendation methods. KGET also achieves strong and stable performance in sparse data scenarios where items have merely a few triples.
Pengembangan Pembelajaran Berbasis Web Dengan Pendekatan Guided Discovery Berbantuan Hypermedia Untuk Siswa SMP

The purpose of this study was to produce web-based learning using a hypermedia assisted discovery guided approach for junior high school students. The development model applied in this study is the ASSURE model. The development stage begins with analyzing the characteristics of students, then stating the learning objectives. The next stage is selecting strategies, technology, media and materials to support the achievement of predetermined learning objectives, then utilizing technology, media and materials, ensuring student participation, and the final stage is conducting evaluation and revision. The result of the development is in the form of a learning web whose activities use the guided discovery procedure and the content is presented using the hypermedia principle. Based on the results of observations, students looked excited and they were actively involved during the learning process. Students say that learning becomes more flexible because they can adjust to the time they have. Based on the response analysis, it was found that the average student gave a positive response. So it can be concluded that web-based learning with the guided discovery approach assisted by hypermedia can improve the quality of learning in junior high schools, especially in terms of student participation in learning, enthusiasm and learning flexibility
Comparing prosody of Italian varieties and dialects: data from Neapolitan

The paper provides a preliminary, qualitative examination of the prosody of Neapolitan dialect (ND) as it relates to Neapolitan Italian variety (NI).Taking NI as baseline for comparison, ND data seem characterized by several phoneticphonological strategies to enhance prosodic prominence, suggesting that phonetic parameters have a larger and more dynamic range of variation in ND than in NI.The data also highlight the interlacement between rhythmic, metric, and intonational facts, and the importance of sociolinguistic factors in shaping prosody.In particular, the larger variability of phonetic parameters observed in ND is likely to index dialectal speech as socially marked.We identify several prosodic discrepancies between ND and NI involving gradient features and tonal organization that call for further investigation.Future studies need to examine such differences in relation to sociolinguistic factors and consider the range of prosodic variation between Italian varieties and dialects spontaneously used by less linguistically-informed speaker.To strongly support our proposal, a larger sample of speakers is required.
technical review on knowledge intensive NLP for pre-trained language development

In today’s world where data plays the very important role, we have various sources of pre-data like online books, equation analysis, encyclopedia, common-sense reasoning, common-sense knowledge, etc. The increasing capacity of pre-training language models have given knowledge intensive natural language processing (KI-NLP) a new boost for advanced functionalities for establishing a stable, flexible, robust and efficient model. Though pre-trained models have its own drawback for handling the KI-NLP tasks, we are here to discuss the challenges faced in this field. A wide variety of pre-trained language models enhanced with external knowledge sources have been proposed and are in rapid development to meet this difficulty. In this research we have also discusses the challenges in NLP in terms of generation of knowledge intensive models. We have also defined some mathematical model and its framework dependability for pre-training different language in NLP. Finally, we have also discussed about variety of literature reviews based on we intend to describe the present progress of pre-trained language model-based knowledge-enhanced models (PLMKEs) in this work by deconstructing their three key elements: information sources, knowledge-intensive NLP tasks, and knowledge fusion methods.
THE EFFECT OF VARIATION CONCENTRATION AND DEPOSITION PARAMETER TO THE OPTICAL CHARACTERISTICS AND THE CRYSTALLITE PROPERTIES OF ZINC OXIDE

The objective of the study was to obtain the ZnO thin films with variation concentration of zinc acetate dehydrate (ZAD), and variation of spin coater speed and rotation time. We observed the effect of the former parameters on the optical characteristics that comprised of the spectrum of absorption, transmittance, and ZnO crystallite size from UV-Vis spectrometer and XRD orderly. For the analysis of the effect of the variation of concentration, ZAD diluted into ethanol with the concentration 0.1M, 0.25M, 0.5M, 0.75M dan 1M, with the addition of diethanolamine (DEA). Then it was deposited above the glass substrate with spin coater, continued by the heating on the hotplate with the number of layers variation. For the analysis of deposition parameter effect on the transmittance and the size of ZnO crystal, we used 0.25M ZnO precursor solution with the addition of DEA, then deposited by speed and time of rotation of spin coater of 1230 rpm, 2500 rpm, and 3200 rpm for 10, 15 and 30 seconds of each. The smallest absorbance value 0.05 obtained for single layer 0.1M ZnO, while the maximum transmittance value obtained for three-layer 0.25M ZnO. Concentration variation did not affect the energy gap, whose value was approximately 3.2 eV for all samples. From the XRD result, we found that deposition time affected the number of diffraction lines, the size of the crystallite, and the transmittance.