Reviewerly/exHarmony huggingface dataset:
Using artificial neural networks with joint muon-gravity datasets for shallow subsurface density prediction at volcanoes

&amp;lt;p&amp;gt;The ability to accurately and reliably obtain images of shallow subsurface anomalies within the Earth is important for hazard monitoring at many geologic structures, such as volcanic edifices. In recent years, the use of machine learning as a novel, data-driven approach to addressing complex inverse problems in the geosciences has gained increasing attention, particularly in the field of seismology. Here we present a physics-based, machine learning method to integrate disparate geophysical datasets for shallow subsurface imaging. We develop a methodology for imaging static density variations at a volcano with well-characterized topography by pairing synthetic cosmic-ray muon and gravity datasets. We use an artificial neural network (ANN) to interpret noisy synthetic datasets generated using theoretical knowledge of the forward kernels that relate these datasets to density. The deep learning model is trained with synthetic data from a suite of possible anomalous density structures and its accuracy is determined by comparing against the known forward calculation.&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In essence, we have converted a traditional inversion problem into a pattern recognition tool, where the ANN learns to predict discrete anomalous patterns within a target structure. Given a comprehensive suite of possible patterns and an appropriate amount of added noise to the synthetic data, the ANN can then interpolate the best-fit anomalous pattern given data it has never seen before, such as those obtained from field measurements. The power of this approach is its generality, and our methodology may be applied to a range of observables, such as seismic travel times and electrical conductivity. Our method relies on physics-based forward kernels that connect observations to physical parameters, such as density, temperature, composition, porosity, and saturation. The key benefit in using a physics-based approach as opposed to a data-driven one is the ability to get accurate predictions in cases where the amount of data may be too sparse or difficult to obtain to reliably train a neural network. We compare our approach to a traditional inversion, where appropriate, and highlight the (dis)advantages of the deep learning model.&amp;lt;/p&amp;gt;
Τεχνολογία Blockchain και ανάπτυξη έξυπνων συμβολαίων για την αγοραπωλησία μοναδικών τεκμηρίων

Blockchain technology was first used by Shatoshi Nakamoto in 2009 to create the bitcoin platform, a digital cryptocurrency that is still considered the most successful cryptocurrency in the world today. The bitcoin platform is mainly used for the execution of digital financial transactions between strangers without the existence of an entity that mediates for their valid completion. The creation of bitcoin has radically changed the way online transactions are done. Many other forms of cryptocurrencies developed after bitcoin but most followed a similar principle of operation to bitcoin without offering anything different. But blockchain technology has continued to evolve and as a result the range of applications in which it is used is constantly expanding. Now this technology is used in applications of industry, economy and simple everyday life of a person because of the security and validity it provides. In 2013, VitalikButerin created the Ethereum blockchain, a platform that supports the creation of distributed applications known as DApps (decentralized applications). Distributed applications developed in the ethereum blockchain are not limited to financial transactions such as bitcoin because the ethereum platform allows the creation of various types of applications using logic stored within the network, the well-known smart contracts. Smart contracts are code files that are executed with absoluteness and determinism, with the result that their functionality is universal. Possible applications that are based on the functionality of a smart contract and do not involve financial transactions are the securing of rights, logistics or even applications that provide a form of hierarchy. This thesis was created in order to highlight the possibilities and limits of smart contracts as well as problems that may exist during their development and after migration. The application is developed in a local ethereum blockchain network and deals with the creation of digital tokens. Digital tokens are known in the ethereum community as intangible assets that can take the form of cryptocurrencies or have the property of uniqueness, resulting in them being distinct and acquiring specific characteristics. In this application, both tokens are developed, resulting in the creation of a digital market with unique tokens that can be created, bought and sold in exchange for tokens that resemble a cryptocurrency. In any case, the common factor for the creation of tokens is ownability, which means that a token, whether it is unique or serves as a cryptocurrency, belongs to a specific user where he has all the advantages that this item provides him and no other user has them.
Using trace element chemistry of magnetite as an indicator mineral at the Starra iron-oxide copper gold deposits, Northwest Queensland

Iron Oxide-Copper-Gold (IOCG) deposits are hydrothermal ore deposits that occur worldwide and globally account for significant amounts of copper, gold, and uranium. They are defined by large scale potassic, sodic and iron alteration that often extend several kilometres from the mineralised centre, making it difficult to differentiate between fertile and barren alteration systems. In recent years, it has been suggested that trace element chemistry of alteration minerals may be used to discriminate fertile from barren hydrothermal systems. In this aims to test the discrimination potential of magnetite at the Starra Au-Cu system which is hosted in the Eastern Fold Belt of the Mount Isa Inlier in Northwest Queensland (Australia). Five deposits occur along a circa 6 kilometres long interval. The mineralisation is spatially associated with magnetite-hematite dominated ironstones along the Starra shear, which are focused at the contact between the Answer Slate to the west and the Staveley Formation to the east. Magnetite is the dominant iron oxide mineral in the ironstones and define a strong magnetic anomaly that can be detected for more than 20 kilometres. Incorporation of trace elements in magnetite is influenced by physicochemical parameters at time of precipitation such as temperature and redox, making it ideal to record process-based information. In addition, the extensive nature of the ironstones at Starra makes it an ideal location to study the trace and major elements variations in magnetite with increasing distance to the ore bodies. At Starra, mineralisation zones are associated with variable magnetite overprinted by hematite. New laser ablation ICP-MS of magnetites from distal, proximal, and mineralised setting reveals that magnetite spatially associated with the mineralisation contain lower V content compared to distal magnetite, suggesting higher fO2 conditions in mineralised areas. Hematite within in ironstones replaced the pre-existing sedimentary lithology retains the original texture as shown by different crystal orientation and distinct trace element concentrations in hematite. Scheelite inclusions in magnetite support reduction of hematite to magnetite due to relative incompatibility of W in magnetite compared to hematite. Together, these indicate a complex evolution of prevailing redox conditions during formation of the IOCG system. Our findings support previous formation models that suggest the main controlling factor on the Cu-Au precipitation in IOCG systems is the oxygen fugacity fO2 of the hydrothermal fluid. At Starra, previous genetic model suggested that oxidized fluids interacted with magnetite and were reduced, leading to reduction of sulphates to bisulphides and precipitating Cu.
Efektivitas Penggunaan Media Pembelajaran Visual pada Mata Pelajaran Matematika Terhadap Prestasi Belajar Siswa

The purpose of this research was to see the effectiveness of learning with visual learning media using a digital projector on student achievement at SMP Negeri 1 Tomia. This research used quantitative approach with quasi experiement. Population of this research was all student of eighth grade a SMP Negeri 1 Tomia that consisted of 3 parallel classes. Because the population hand homogeneous learning achievement, the sample was taken from two classes, namely class VIIIA and class VIIIB. Data collection technique in research used test. The results of research were the significant value was 0.001, the N-gain value obtained a difference in the average value of students’ mathematics learning achievement of the experimental class by 65.07 and the control class by 44.46. Then the effectiveness criterion was based on the gain score obtauned by the percentage of the experimental class of 65.07% which was categorized as quite effective because it was between 56% and 75%, so that Ho was rejected and H1 was accedted based on the hypothesis acceptance criteria. This meant that the application of visual learning media using a digital projector was quite effective on the mathematics learning achievement of grade VIII students of SMP Negeri 1 Tomia on the subject of number patterns.
ENHANCING THE EDUCATIONAL EFFECTIVENESS OF SOCIAL SCIENCES FOR FUTURE PROFESSIONS IN HIGHER EDUCATION INSTITUTIONS

The article emphasizes the importance of enhancing the educational effectiveness of the social sciences in the process of mobilizing students in higher education to realize the social significance of their profession, constantly improve their skills and achieve goals in the public interest. The essence of our reforms, ensuring the effectiveness of the implementation of current tasks in the field of higher education, is directly related to the development of higher education, related sciences and industries, bringing it to world standards, as well as increasing the spiritual and moral content of higher education. attention is paid to strengthening the position and status of the sciences, increasing their responsibility, educating a harmoniously developed generation capable of withstanding the intensity of world events thanks to their knowledge and ideological convictions.
GoBaby CarSeat / Nur Syahirah Mohd Isa

GoBaby Enterprise produced a technology named GoBaby CarSeat. This baby car seat has an airbag system that will protect the children from throw forward. The airbag will inflate if the car seat has detected a collision. Besides, GoBaby CarSeat also comes with Sensor Safe alarm system where it will notify the parents if there is an uncomfortable thing happen around the children. This baby car seat also produces a lightweight to easier the parents. Moreover, the parents can get a cover bag for the car seat in every purchasing. The most important is GoBaby CarSeat can be trusted because the company had applied SIRIM certificate. This baby car seat is an innovative product since there is no similar function of car seat exist yet. The target market for this baby car seat is the parents in M40 and T20 categories. Further explanation about the potential buyer will be explained in the marketing section. The competitors for GoBaby Enterprise are Jarrons & Co. and Chicco since they also produce the same product which is the baby car seat. Therefore, GoBaby Enterprise must achieve the business goal to compete with great competitors.
Mining Collection of Documents: Clustering and Categorization

Technical Documents from Web pages and News Articles, Transcripts of phone calls with customers, Customer complaint letters through E-mails, Insurance Claims is Corporate Knowledge “Ore”. Information is highly unstructured form not readily useful for statistical analysis; huge collection of documents (Big Data) is challenges in Text Mining. The Sentiment Analysis (SA) is an active subject of a research in the text mining industry. SA is the new computational handling of views, feelings, and the subjectivity of text. The paper's key contributions include sophisticated clustering and the categorizations of a large number of recent articles, as well as an example of the current research pattern in sentiment analysis and related topics. The corpus-based technique starts with a seed list of opinion words and then searches a huge corpus for additional opinion words to aid in the discovery of opinion words with the context specific orientations. This might be accomplished by employing a statistical two phase text mining approach: feature extraction and information distillation. In Text Categorization, a document collection is processed and categorised into specified categories based on a user-supplied taxonomy. Clustering involves processing and grouping documents into clusters that are dynamically created by an algorithm. Text mining makes extensive use of information can from blogs, microblogs, and forums, as well as a news sources. This media information is extremely important for communicating people's sentiments or ideas about the certain issue or product. The use of social networking and microblogging sites as data sources still need further investigation. There are some real world data sets movie-pang02, chicago-affnia especially in reviews which are used for Machine Learning Classification and Clustering Algorithms evaluation using R tool.Keywords—Text Mining, corpus, tf-idf, Partition based clustering. Naive Bayes classification, movie-pang02, chicago-affnia
Extraction of Important Information from Handwritten Articles

The main objective of is to allowing user to upload an image of a article (set of lines) and storing the user’s file to AWS database. And allowing user to download the summary and detected text from the article in .txt and .MP3 format. So that they can have a short file to listen rather to read the whole page and to get the useful information contained in the article downloadable in .txt file.
Development of Make a Match Card Application Learning Media on Learning Outcomes in The Topic Growth and Economic Development

The COVID-19 outbreak has caused the government to require students to learn from home, but the facilities owned by schools to support the online learning process are very minimal, therefore researchers are innovating to develop a computer-based learning media in economic learning in high schools called Make A Match Card which can be done online and aims to improve student learning outcomes. The 4 stages of define, design, de­velop, and dissaminate are the stages of the 4D model from Thiagarajan and co. used in this study. The data collection technique was done in 2 ways, namely pretest using google form and posttest using Make A Match Card media. Data collection was carried out in class XI IIS 1 SMA Negeri 8 Surabaya. Learning outcomes obtained before the application of the media, 35% of students obtained scores above the Minimum Completeness Criteria (KKM), but after the media was applied it increased by 53% to 88% of students who obtained scores above the Minimum Completeness Criteria (KKM).
Modular education framework supported by IT project management methods

Information systems are widely used in various areas of our daily lives. Enterprises source planning systems such as SAP, Oracle or Microsoft Dynamics, etc. support business processes effectively. Management execution systems focus on specific industrial solutions, mostly used in manufacturing. Similar methodologies, technologies, and toolsets can be easily identified in learning management systems, but in practice, they are not as standardized as business and manufacturing solutions. There is a lack of an appropriate education framework that can be elastically extended and modified according to the actual educational needs. Our goal is to develop a novel educational framework assisted by well-known project management methods of business applications. The implementation of modular studies is introduced through the educational framework. Business methodologies and procedures demonstrated effectively helped students in their studies.
The Effectiveness Of English Club As Free Voluntary Speaking Activity Strategy In Fostering Speaking Skill In Saudi Arabia Context

This study investigates the significance of English club in fostering speaking skill in Saudi Arabia context. It hypothesizes that Saudi university students are enthusiastic to learn authentic English and English club as free voluntary speaking activity with low anxiety assists Saudi university students practice English joyfully. This study aims at identifying the advantages of activating English club in promoting speaking skill and raising university English teachersâ€™ awareness of the importance of using it. The study method used was the descriptive analytic method. The study concludes that English club assists Saudi students be free from language anxiety and traditional boring routine classroom activities , it helps them practice speaking English joyfully and English club fosters critical thinking , persuasive and argumentative skills among students. It recommends that English club should be scheduled as the essential part of a course in the semester not as academic activity, English club should conduct remedial classes concerning speaking strategies and skills which assist students communicate tactfully and the native speakers should help conduct the English club in order to create realistic language context in terms of phonological aspect
PBL을 활용한 베트남어 수업 설계와 운영 : 온라인 실시간 베트남어교육론 수업을 중심으로

For the efficient teaching and learning of Vietnamese, the researcher paid attention to Project Based Learning and tried to apply it to the class. The researcher analyzed prior studies of PBL classes, including English and other foreign languages, and applied the theory of PBL to Vietnamese language education subjects, designed PBL classes, and utilized them in classes. In addition, the process in which the learners perform tasks (student presentation, peer-faculty evaluation, evaluation opinion reflection process), the results, and the questionnaire survey on learners were analyzed. As a result, it was found that PBL methods could also be applied in Vietnamese classes. The learners reorganized the learning contents into his or her own knowledge in the relationship between the learner’s own thoughts, experiences, knowledge, and understanding by referring to the instructor’s teaching plan and lecture. In addition, it was possible to achieve more useful and viable knowledge by listening to other people’s opinions and thoughts about their own knowledge, understanding, and interpretation, and through correction and supplementation processes. Also noteworthy is that through the PBL class, the level of knowledge of each student increased rapidly.
4차 산업혁명 시대의 온라인쇼핑몰에서 물류서비스 품질이 고객만족, 재구매 의도에 미치는 영향에 관한 실증연구 – 한국과 중국비교 -

Purpose : The purpose of this study was that the quality of logistics services in online shopping malls using fourth industrial revolution technology affects customer satisfaction and repurchase intentions. Research design, data, methodology : The paper compared Korea and China using the SERVQUL model Results : Results showed that in Korea, the responsiveness in service quality, such as immediate service provision and providing spontaneous information to customers, have a direct impact on customer satisfaction. Responsivity to return and exchange requests also had a positive(+) effect on customer satisfaction. In China, however, prompt service provisions, immediate responses to customer demands, and information processing have a positive(+) effect on customer satisfaction, while responsivity to return and exchange requests have a negative(-) effect. Conclusions : The results of this study may contribute to the design of modifications in online shopping malls that attract Chinese customers and promote sales.
Sentiment Analysis on Twitter Airline Data

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.
Pengelompokan Jumlah Kasus Penyakit Aids Berdasarkan Provinsi Menggunakan Metode K-Means

Acquired Immune Deficiency Syndrome (AIDS) is a collection of symptoms due to a gradual decline in the immune system caused by infection with the Human Immunodeficiency Virus (HIV). This disease is a dangerous disease and should be watched out for where it spreads very quickly. AIDS is one of the top infectious diseases that can cause death. K-Means is an algorithm in data mining that can be used to group / cluster data. There are many approaches to creating clusters, one of which is to create rules that dictate membership in the same group based on the level of equality among its members. The purpose of this study was to classify the number of AIDS cases by province. To solve the existing problems, the authors will use the K-Means cluster method using 2 clusters to determine the province which has the highest cases of AIDS and the province with the lowest cases of AIDS by calculating the centroid / average of the data in the cluster. It is especially recommended that the government take advantage of the results of this research to pay more attention and make efforts in overcoming AIDS in provinces with high AIDS disease.
Accelerating Climate Model Computation by Neural Networks: A Comparative Study

&amp;lt;p&amp;gt;In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.&amp;amp;#160; In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon di&amp;amp;#64256;erent ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These &amp;amp;#64257;rst results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.&amp;lt;/p&amp;gt;