Reviewerly/exHarmony huggingface dataset:
Joint Framework for Access Control and Authentication using Blockchain over Cloud Environment

There is an increasing adoption of blockchain technology overdifferent environment for leveraging security features. After reviewingexisting blockchain-based approaches, it is noticed that there is still alarge scope of improvement towards data security using blockchain.Therefore, this paper considers a case study of healthcare facilities,where the sensitive information of patient are required to be accessed aswell as stored using a novel blockchain-based approach. The proposedsystem contributes towards developing a novel access control policy aswell as a cost-effective authentication policy using a simplified hashingscheme. The prime contribution of the study is to prove that without usingsophisticated encryption, it is feasible to integrate blockchain withhashing method for offering higher degree of threat mitigation measuresby proposed system.
Determinants of Campital Flight and its Impact on Economic Performance: A Study of Developming Economies

Capital flight imposes a great danger to the developing economies by lowering their economic activity, devaluing the currency, and hindering economic growth. This paper contributes to the literature by investigating the determinants of capital flight of developing economies. Determinants are studied as indices, and these indices are calculated by using the Arithmetic Mean approach. There is a two-steps model; in the first part, determinants are discussed, and in the second part, capital flight is used as an independent variable, and its impact on economic performance is measured in 59 Developing Economies. GMM (Generalized Method of Moments) is used for the determinants of capital flight, and panel GLS (Generalized least square) is used to check the impact of capital flight on economic performance. Macroeconomic variables (external debt, inflation, exports, interest rate, and exchange rate) are also part of the study with four indices as determinants (Country Risk Index, Economic Freedom Index, financial inclusion index, and Governance institution and corruption index). Results suggest that all included indices were significant along with all macroeconomic variables. Country risk, external debt, and exports have a negative coefficient, while others have a positive coefficient value. Capital investment, foreign direct investment, and Gross domestic product were used as proxies of economic performance. Results of GLS have shown there is a significant and negative impact of capital flight on economic performance. Capital flight is measured by using the residual methodology.
Jyh-An Lee/Reto M. Hilty/Kung-Chung Liu (eds.) Artificial Intelligence and Intellectual Property

Artificial intelligence (AI) is one of the most researched emerging technologies, which is understandable given the multidimensional nature of this topic. Its imminent economic relevance and the enormous pace of development in this field make speedy and thorough answers to many challenges – including legal ones – essential. Even more importantly, AI – unlike many other emerging technologies – is clearly capable of triggering a paradigm shift in all aspects of society, including the legal system. Personality, liability and the protectability of AI and its outputs are just the tip of the iceberg. Indeed, none of these issues seem solely theoretical anymore. Most recently, the protectability of AI outputs (something that can have the most immediate economic consequences) was in the forefront of case law in multiple jurisdictions. In the last two years, for example, we have seen court rulings and other decisions (issued mainly by intellectual property offices) related...
Literary Embodiment on the Levels of Comprehension in the “Balak”

This study claimed that the local poem, known as “Balak” contained aspects that could be used in teaching reading and in assessing the reading comprehension level of students. This study used a descriptive-interpretive type of research. It is anchored on Reader-Response Criticism facilitated through focus-group discussion, and duly validated through investigator triangulation. Ten collected local poems (“Balak”) were analyzed using Barrett’s five levels of comprehension: literal, reorganization, inference, evaluation, and appreciation. It was found out that the ten “Balak” could be used as a tool for reading and assessing reading comprehension because it contained embodiments of the different levels of reading comprehension. Each “Balak” comprised contents fitted to the level and experience of the learners as manifested in how they connected and related with the ideas, emotions as well as situations portrayed in the text. It was also found out that in terms of reading comprehension level, the respondents were proficient in the literal level, beginning level in both the reorganization and inference levels, developing in the evaluation level, and proficient in the appreciation level. The ten “Balak” contained embodiments on the levels of reading comprehension which could be used as teaching tools in reading, more activities in the reorganization and inferential levels were to be emphasized. Based on the findings of the study, the following recommendations were formulated. Teachers should apply contextualization and localization in the teaching-learning process. They should also be aware that local literary pieces were good sources to develop students' reading abilities. It could be used as springboards for teaching strategies, reading comprehension assessment, and activities that encourage discussions and stimulate imaginative responses leading to well-rounded and interesting student interactions.
Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi

Selecting a major can be quite difficult for prospective college students. The choice may have an effect not only on their academic life, but also on their career path. Due to some restrictions as the impact of the COVID-19 pandemic, universities must find novel ways to reach prospective students and assist them in choosing their majors, one of which is a college major recommendation system. This system can assist prospective students in determining the most appropriate majors for them based on data from the current students. Unlike other existing systems that employ either a rule-based or fuzzy model, this study employs a machine learning approach using data from undergraduate students at Universitas Islam Indonesia. This paper aims to compare several clustering models (i.e., K-means, Agglomerative, Birch, and DBSCAN) for the purpose of categorizing current students, to which the results will be used for classification purposes using various approaches (i.e., single stage vs. multistage), algorithms (i.e., multinomial logistic regression, random forest, and support vector machine), and scenarios (i.e., with or without GPA-based label). Our findings indicate that the K-means model outperformed all other clustering models and that the single stage with random forest classification model performed the best across all scenarios. &#x0D;
The New Heresy and the Modern Inquisition

In this paper, I look critically at a new trend on college campuses regarding the banning of certain words, especially the biggest racial taboo word in the USA. I contend that these new bans impede the rise of a dialogic, democratic, and pluralistic temperament, ultimately promoting and legitimizing violence as good and necessary.
Sentiment Classification using Neural Network and Ensemble Model based on Genetic Algorithm

The fast development of web sites and the number of product on these websites are available. The purpose of classification of sentiment is to efficiently identify opinion expressed in text. This paper compares three different optimized models including genetic optimized feature selection method, Genetic Algorithm (GA), ensemble approach that uses information gain and genetic algorithm as feature selection methods incorporated SVM model, Genetic Bagging (GB) and the next method uses optimized feature selection as feature selection technique incorporated back propagation model, Genetic Neural Network (GNN) models are compared. We are tested in sentiment analysis using sample multi-domain review datasets and movie review dataset.. These approaches are tested using various quality metrics and the results show that the Genetic Bagging (GB) technique outperforms in classifying the sentiment of the multi domain reviews and movie reviews. An empirical analysis is performed to compare the level of importance of the classifiers GB, GNN methods with McNemar’s statistical method.
Transitivity and Bidirectional Framing in Telecommunication Advertisements

Advertisements are onerous ways of representing the identity of producers and consumers who are both agents of social life, and mobile telecommunication advertisement is not an exception. However, this has not attracted sufficient attention in linguistic studies. This study investigates the bidirectional framing of the identities of telecommunication service providers and the subscribers in twelve MTN and GLO advertisements, aired on popular mainstream television stations between 2014 and 2017. These were downloaded from youtube, where they are trended as unsolicited adverts to reach millions of users. The qualitative analytical approach was adopted, using transitivity, in the systemic functional linguistic (SFL) framework, as theory, to analyse transcribed texts from the advertisements. The findings show that copywriters use material, mental, verbal, relational and existential processes to frame service providers as welfarists, supremacists and benefactors/beneficiaries, and the subscribers as consumerists, opportunists and beneficiaries/benefactors. The supremacist’ and welfarist’ identities are used as indexes to establish the stiff competition between and among service providers to gain more subscribers. The results also show that welfare services are initiated as basis for out-doing self and other-self services. The enthusiasm of the subscribers to use services account for their framing as ‘consumerists’ and ‘opportunists.’ The beneficiary and benefactor identities are bidirectional, as both service providers, and subscribers mutually benefit from each other in a one-to-one relation. The study concludes that transitivity processes are indelible linguistic resources used in telecommunication advertisement to bidirectionally frame the identities of service providers and subscribers, with a view to enhancing consumerism.
Estimation of radioelements concentration in Sokoto Basin for geothermal energy studies using airborne radiometric data

High resolution aero-radiometric data of Sokoto basin Northwestern, Nigeria were used in this research work. The location of the basin is between latitude 11o30I to 13o30IE and longitude 4o30I to 6o30IN. Sixteen (16) sheets of high resolution of airborne radiometric data were used to obtain the concentration of Potassium (K), Uranium (U) and Thorium (Th) respectively in the area of interest. The data were gridded and analyzed using Geosoft software. The result of the analysis revealed that potassium concentration is between -0.577935% to 7.34347% with an average of 3.3827%. The Thorium concentration values ranged from -3.81541ppm to 91.9404ppm with an average of 44.0624ppm. The Uranium concentration revealed values from -4.44524ppm to 46.1767ppm with an average of 20.86572ppm. The ratio of potassium and equivalent thorium (K/eTh) indicated values between -8.69847%/ppm to 39.567%/ppm and ratio of equivalent uranium and equivalent thorium (eU/eTh) revealed values between -412.181ppm to 99.4613ppm. The ternary diagram of the radiometric data showed that the south and southeastern regions of the study area have high radioactive mineral concentration above the average normal value of radioactive minerals in a natural environment and radioactive minerals are good source of geothermal energy. The results obtained has shown that the basin has geothermal potential which can cause non productivity of hydrocarbon in the area of interest and the research area can be recommended for geothermal energy resource to compliment the electric power supply in the nation.
Kombinasi 3D Convolutional Neural Network Dalam Klasifikasi Citra Hiperspektral Pada Tutupan Lahan

Land cover information is one of the basic variables in determining phenomena on the earth's surface as well as a measuring variable for the development of an area. Land cover with hyperspectral imagery can provide accurate information on the earth's surface. Limited labeled samples and imbalances data are common in land cover hyperspectral image classification. However, the data that available publicly is very limited makes it difficult to evaluate the performance of existing algorithms. Various methods were developed with several public datasets using as few training samples as possible to produce a method that is robust against sample limitations and imbalances data, but these studies have complicated parameters and the accuracy results are not optimal. In this study, several combination of Multiple Spectral Resolution (MSR), 3D Spectral Dilated Convolution (SDC) and Hybrid Convolutional Neural Network (HybridSN) were carried out to increase accuracy in hyperspectral image classification with very small training samples and simpler parameters and computations. This method was tested on two public datasets Indian Pines (IP) and Salinas (SA) with 5% and 1% training data respectively (only 500 training samples) and showed high accuracy results in land cover hyperspectral image classification. The best results were obtained with a combination of SDC and HybridSN methods which were optimized with Adam with 96.58% % Overal Accuracy (OA), 87.83% Average Accuracy (AA) and 96.09% kappa on the IP dataset, and 99.08% Average Accuracy (AA), 99.00% kappa on the SA dataset
Investigation of geochemistry and determination of tectonomagmatic setting of igneous rocks in North Oshnavieh, Western Azerbaijan

Investigation of geochemistry and determination of tectonomagmatic setting of igneous rocks in North Oshnavieh, Western Azerbaijan
A meta-feature selection method based on the Auto-sklearn framework

In recent years, the task of selecting and tuning machine learning algorithms has been increasingly solved using automated frameworks.This is motivated by the fact that when dealing with large amounts of data, classical methods are not efficient in terms of time and quality.This paper discusses the Auto-sklearn framework as one of the best solutions for automated selection and tuning machine learning algorithms.The problem of Auto-sklearn 1.0 solution based on Bayesian optimization and meta-learning is investigated.A solution to this problem is presented.A new method of operation based on meta-database optimization is proposed.The essence of the method is to use the BIRCH clustering algorithm to separate datasets into different groups.The selection criteria are the silhouette measure and the minimum number of initial Bayesian optimization configurations.The next step uses a random forest model, which is trained on a set of meta-features and the resulting labels.Important meta-features are selected from the entire set.As a result, an optimal set of important meta-features is obtained, which is used to find the initial Bayesian optimization configurations.The described method significantly speeds up the search for the best machine learning algorithm for classification tasks.The experiments were conducted with datasets from OpenML to compare Auto-sklearn 1.0, 2.0 and a new version that uses the proposed method.According to the results of the experiment and statistical Wilcoxon T-criterion tests, the new method was able to outperform the original versions in terms of time, outperforms Auto-sklearn 1.0 and competes with Auto-sklearn 2.0.The proposed method will help to speed up the time to find the best solution for machine learning tasks.Optimization of such frameworks is reasonable in terms of saving time and other resources, especially when working with large amounts of data.
Crusher Design for Plastic Waste in 3D Printing

3D printing is a manufacturing process widely used in the 4.0 Industry era. One of the concepts of 3D printing is to make prototypes, whether it is for industrial purposes or studying purposes. Knowing that a prototype is only needed in the early stages of any process, it is bound to be redundant in a short time; thus, the plastics used to prototype using the 3D printing technology are now labeled as waste. Since most plastics used for 3D printing are thermoplastics, they can be recycled to live another purpose. This research aims to design a crusher dedicated to processing 3D printing waste as the first step towards recycling these wasted materials. This step encourages students to comprehend the process of designing and manufacturing such equipment while also opening doors for the faculty towards recycling 3D printed plastics. The designing process consists of surveying the materials commonly used for 3D printing, calculating the machine elements, choosing the mechanism needed to destroy the material, and designing a model from the calculations. The outcome of the design is a device that can crush and destroy PLA (Polylactic Acid) and ABS (Acrylonitrile Butadiene Styrene) plastics into pieces ready for recycling
The Levels Of Questions Used In The English Textbook Entitled “Stop Bullying Now”For The Xi Grade Of Senior High School

Reading comprehension in Indonesian students is very weak. It is proven by Pisa 2018, a result which states that from a comparison between science, mathematics, and reading comprehension, Indonesian students are ranked 74th in the sixth from bottom. This article aims to analyze the level of questions used in an English textbook entitled "Stop Bullying Now" for class XI SMA published by the Ministry of Education and Culture of the Republic of Indonesia in 2017. This analysis uses Barrett's taxonomy to test reading comprehension. Questions in the book with content analysis of qualitative research. Based on Barrett's taxonomy, there are five levels of reading comprehension questions: literal comprehension, reorganization, inferential, evaluation, and appreciation. The results showed that the level of literal understanding (7.45%), reorganization (0 %), inferential (39.36%), evaluation (36.17%), and appreciation (17.02%). This shows that the higher-level comprehension is dominant over lower-level comprehension, but the level is not evenly distributed in all texts. The results of this study can provide an overview of the level of reading comprehension questions used in the English textbook entitled "Stop Bullying Now," which is helpful for teachers as a guide in learning English reading comprehension.
Collaboration patterns in students' teams working on business cases

In recent years, computer science education teachers needed to incorporate challenge-based and teambased collaboration projects to enhance learning and prepare students for their future careers. The successful use of team-based work structures depends on the ability of members to work together with each developing its own pattern of work. Using affinity propagation as an algorithm, we study the patterns of collaborative behavior from Trello data obtained from 16 teams collaborating on business cases. All the teams were given the same instructions and were asked to use Trello to organize and monitor tasks. Actions of the group members in Trello were categorized as different contribution types, namely activities involving planning, coordination, further input, deletion, or updates on tasks. Sequences of those actions were first created for each group and later used to explore the differences in the working process between the groups. We analyze data and interpret patterns of collaborative work during the entire collaboration on the project, as well as patterns of work that emerged in the first 50 actions in teamwork, since literature indicates that the first actions in teamwork are important for the creation of a team memory. We present both the initiating sequences along with entire sequences for all teams, and some first results for the different collaboration patterns we observed. This study is the very first exploratory research covering collaboration patterns with Trello data. This kind of pattern analysis could provide teachers with a means to identify teams that need further support in their teamwork. Our data suggest that future studies could complement the analysis with data also coming from other channels used by students for communication and organization of teamwork triangulating data with them.
A Note to Introduce Joanna Walsh’s 9 1/2 EXEMPLARY THOUGHT EXPERIMENTS

Sarah Cook introduces Joanna Walsh's experimental text, 9 1/2 EXEMPLARY THOUGHT EXPERIMENTS.
Research and Practice on the Quality Construction of Free Elective Courses in Colleges and Universities

Curriculum construction is the key to the quality of undergraduate teaching, and the free elective course is the expansion of professional knowledge. Free elective courses in colleges and universities are courses that can be independently selected by students according to their individual differences outside the professional courses. It is a necessary supplement and expansion of professional knowledge. Gold course construction refers to the first-class curriculum construction implemented by the Ministry of education, which aims to build a high-level curriculum with high-level, innovative, challenging and other attributes. The construction of golden course plays an important role in the curriculum construction of colleges and universities. While striving to build national first-class courses and provincial first-class courses established by the Ministry of education, colleges and universities are also actively building school-class golden courses to comprehensively improve the quality of curriculum construction. However, at present, gold class construction usually emphasizes professional courses curriculum construction. Based on the author's teaching practice and management experience, this paper expounds the importance of the construction of gold free elective courses and the way of construction. This paper emphasizes that the gold free elective course has the characteristics of flexible content selection, close connection with practice, close combination with the second classroom, innovation and entrepreneurship, etc. To create an elective free gold course, one can start from the aspects of teacher selection, classroom organization, teaching evaluation, free elective course management, etc, and strive to improve the quality of free elective courses and the participation of students, so as to make it an important link in the cultivation of high-quality talents.
Sistem Rekomendasi Jurusan pada Sekolah Menengah Kejuruan (SMK) dengan Algoritma K-Means

The system of determining majors in SMK Negeri 02 Moswaren South Sorong Regency, is very important, because in SMK there is no good majoring system so students are often wrong in choosing a major based on their abilities. The purpose of this study is to use the recommendation system of majors in vocational high schools (SMK) using the K-Means Algorithm. The research method conducted is to conduct interviews with the school or those responsible at smk to get the data they need. The results of the classification pattern of major determination can be used by the school in determining policies in determining prospective student majors in the admission process of new learners. After conducting this research, it was found that the system had successfully implemented the K-means algorithm to determine the priority of determining vocational expertise programs based on raport junior high school value criteria, interests, student talent