Reviewerly/exHarmony huggingface dataset:
URGENT ISSUES AND MODERN CHALLENGES OF HIGHER EDUCATION

Modern society, which is experiencing dynamic changes due to the new global challenges, is definitely looking for solutions in science and education. The life of many generations has proved that no matter what problems humanity faces, their solution requires intellectual effort, a scientific approach and a quality education system as one of the most effective means of disseminating ideas, knowledge and values. At the same time, such a situation always leads to the thorough attention to the phenomenon of education, in particular higher education. Great expectations and hopes cause significant demands and fundamental tasks that the society puts forward to the academic community. Satisfaction of these requirements and the solution of such tasks is probably not possible without self-analysis and self-reflection, without identifying those problems that inhibit the development of education itself, decrease its functionality, and discredit its status as the spiritual basis of social life. Therefore, we will further try to formulate our vision of current issues and current challenges in higher education.
Determination of Data Mining Application Design Patterns Booking Raw Food In Restaurant Fountain With Apriori Algorithm

Booking is an activity carried out by certain parties to ensure availability, in carrying out certain activities the company has a supply of material in quantities that exceed the needs. As a result, in the warehouse there is a buildup of raw materials or it can happen otherwise. Inventories of materials that are too small can hinder the company's operations in the form of unavailability of materials when needed. The role of inventory will determine the operation of the company because the inventory will run well if supported by good management. Therefore, the concept of inventory management that affects ordering is very important to be applied by companies so that the goals of effectiveness and efficiency are achieved. So we need a Data Mining that can quickly to determine the Determination of Food Raw Material Ordering Patterns in Restaurant Fountain Using Apriori. Data Mining is the extraction of new information taken from large chunks of data that helps in making decisions. One of the applications of data mining for Determining the Pattern of Ordering Food Raw Materials in Restaurant Fountain Using Apriori. Apriori method is a method for determining frequent itemsets for boolean association rules. The research aims to build the application of Determining the Pattern of Ordering Food Raw Materials in Restaurant Fountain with a web-based application and as a tool for designing applications using the Mysql Database. This data mining is able to determine the ordering of food items in the Restaurant Fountain with the required amount.&#x0D;
Looking with the (computer) mouse: How to unveil problem-solving strategies in matrix reasoning without eye-tracking

Problem-solving strategies in visual reasoning tasks are often studied based on the analysis of eye movements, which yields high-quality data but is costly and difficult to implement on a large scale. We devised a new graphical user interface for matrix reasoning tasks where the analysis of computer mouse movements makes it possible to investigate item exploration and, in turn, problem-solving strategies. While relying on the same active perception principles underlying eye-tracking (ET) research, this approach has the additional advantages of being user-friendly and easy to implement in real-world testing conditions, and records only voluntary decisions. A pilot study confirmed that embedding items of Raven's Advanced Progressive Matrices (APM) in the interface did not significantly alter its psychometric properties. Experiment 1 indicated that mouse-based exploration indices, when used to assess two major problem-solving strategies in the APM, are related to final performance—as has been found in past ET research. Experiment 2 suggested that constraining some features of the interface favored the adoption of the more efficient solving strategy for some participants. Overall, the findings support the relevance of the present methodology for accessing and manipulating problem-solving strategies.
Modern Opportunities of Using Blockchain Technology in the Education System

The article is devoted to the blockchain technology that that has been booming in recent years. The purpose of the article is to review the literature on the modern possibilities of using blockchain technology in the education system. A brief description of blockchain technology is given, modern directions for its development are put forward. The article analyzes the possibilities and prospects of using blockchain to solve some problems in the education system, describes the features and advantages of using blockchain technology in the field of education that are known today. The positive changes in the educational process, which may result from the use of this technology are defined. The article discusses the shortcomings and problems associated with the use of blockchain technology in education. Methods and materials: a systematic search of literature on Google Scholar databases was performed, works on the use of BT in education were reviewed, and materials on blockchain technology in education that are available on Research Gate were analyzed. The authors outline that most projects using the blockchain in education are under development. Currently, there is already data on the implementation of blockchain technology in the form of confirmation of the authenticity of documents on education, a student’s personal card, confirmation of accreditation of an educational organization and intellectual property, student identification. Results. 67 papers were identified in the Google Scholar database and 80 publications in the eLibrary database. A large number of online conversations on the topic under study were revealed, and 25 publications turned out to be full-text journal articles. The authors noted that every day there are new articles, reports about the organization of startups, the formation of research teams working to study the possibilities of using BT in the education system. Conclusions. The authors summarize the possibilities of using blockchain technologies in the field of education. The interdisciplinary approach and cooperation of educators with blockchain specialists will keep up with the booming digital technologies.
THE USE OF “KAHOOT” (ONLINE QUIZ APPLICATION) IN TEACHING GRAMMAR FOR SECOND YEAR STUDENTS AT SMA PEMBANGUNAN LABORATORIUM UNP

This research is motivated by the lack of mastery of Grammar in students. This is because the practice in the learning process is still not varied, one example of an exercise that can be used is Kahoot. This study aims to determine the effectiveness of the use of Kahoot on the teaching of Grammar students in the second year of the SMA Pembangunan Laboratorium UNP. This research uses a quantitative with quasi-experimental methods “pre-test and post-test control group design”. The population in this study were second year students of SMA Pembangunan Laboratorium UNP who were registered in the June-December period of the 2019/2020 school year. The research sample was 20 students of class XI IBB as the experimental class and 22 students of class XI MIPA 1 as the control class. The data of this study are the scores of the final test results of Grammar teaching without and using Kahoot second year students of SMA Pembangunan Laboratorium UNP. The test scores were compared using the t-test formula to determine the effectiveness of using Kahoot in Grammar teaching. Based on the results of the t-test, it was concluded that learning using Kahoot was effective in the teaching of Grammar in the second year students of SMA Pembangunan Laboratorium UNP because tcount&gt; ttable (4.75&gt; 1.68) at a significant level of 0.05. So, it was concluded that the teaching of Grammar in the second year students of SMA Pembangunan Laboratorium UNP by using Kahoot was better than without using Kahoot.
Blockchain: Future of Accounting Education

Blockchain will be the future of accounting education. Triple entry accounting system is here, and shared ledger has been considered. From the shared ledger different parties can access transactions. As our discussion reveals that distributed ledger, Smart contract and Blockchain are three important elements in the triple entry accounting system. As a result, blockchain technology is helping the upgrading the process of education system. Blockchain technology is a peer to peer communication that allows participants to secure the settlement of transactions, achieve the transactions and transfer of assets at low cost. With certain advantages there are disadvantages too. Based on performance and acceptance, it is clear that in future the implication of blockchain technology would be developed. The concept of triple accounting has introduced the new way of accounting work replacing accounting standard formula. The blockchain technology eliminates the involvement of third party, maintain transparency and charges low transaction cost. It will save money and time of people as it is secure and due to decentralization, it is not controlled by one single entity. Due to the decentralization, every user of the network can see the file. So, blockchain should the part the Accounting Education in future.
Multi-valued Verification of Strategic Ability

Some multi-agent scenarios call for the possibility of evaluating specifications in a richer domain of truth values. Examples include runtime monitoring of a temporal property over a growing prefix of an infinite path, inconsistency analysis in distributed databases, and verification methods that use incomplete anytime algorithms, such as bounded model checking. In this paper, we present multi-valued alternating-time temporal logic ( mv-ATL → ∗ ), an expressive logic to specify strategic abilities in multi-agent systems. It is well known that, for branchingtime logics, a general method for model-independent translation from multi-valued to two-valued model checking exists. We show that the method cannot be directly extended to mv-ATL → ∗ . We also propose two ways of overcoming the problem. Firstly, we identify constraints on formulas for which the model-independent translation can be suitably adapted. Secondly, we present a model-dependent reduction that can be applied to all formulas of mv-ATL → ∗ . We show that, in all cases, the complexity of verification increases only linearly when new truth values are added to the evaluation domain. We also consider several examples that show possible applications of mv-ATL → ∗ and motivate its use for model checking multi-agent systems.
Multi-Dimensional Explanation of Target Variables from Documents

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or a drop in accuracy. Furthermore, rationale methods cannot capture the multi-faceted nature of justifications for multiple targets, because of the non-probabilistic nature of the mask. In this paper, we propose the Multi-Target Masker (MTM) to address these shortcomings. The novelty lies in the soft multi-dimensional mask that models a relevance probability distribution over the set of target variables to handle ambiguities. Additionally, two regularizers guide MTM to induce long, meaningful explanations. We evaluate MTM on two datasets and show, using standard metrics and human annotations, that the resulting masks are more accurate and coherent than those generated by the state-of-the-art methods. Moreover, MTM is the first to also achieve the highest F1 scores for all the target variables simultaneously.
Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare

Introduction: Dementia is a progressive disorder associated with age, which is characterized by deterioration of individuals' cognitive functions such as the ability to perform routine tasks.With the increase of human life expectancy, the prevalence of dementia patients will reach 152 million in 2050.Unfortunately, there is no treatment available to cure dementia or alter the course of its progression.However, there is an area of support for patients and caregivers to assist daily living.Technological devices and applications are increasingly advancing, exploiting sensory data for dementia patients and homecare using smartphones to permit monitoring of their activities.Aim: This paper uses the labeled dataset besides comparing the 3-classification algorithm to evaluate whether or not the algorithms deployed can classify the activities with high accuracy.Results:A public data is used to classify human activities into one of the six activities, BigML platform is used to build machine learning models.Results show that machine learning algorithms can achieve high accuracy.The activity recognition algorithms are highly accurate using ridged regression and deep neural networks, with almost all activities being recognized correctly over 98% of the time.Conclusion: An application of smartphones can be utilized for human activities monitoring by proposing a high level for dementia patients and homecare monitoring services.Using this service, the patients only need to carry the smartphone, and their caregivers simply need to use the application that monitors their patients' activities.
The effect of uniqueness and student's experience in improving university image: Empirical study at private universities in Indonesia

The purpose of this research is to develop a conceptual model to explain the relationship between the uniqueness of higher education, student experience and university image.Structural Equation Modeling (SEM) is an analytical technique used to test the model.Data collection is conducted on 268 active students from 49 private universities in Java West.Research findings report the uniqueness of tertiary institutions influences student experience; uniqueness of tertiary institutions influences the image of the University.Moreover, experience of students influences the image of the University.
Microscopic Fine-Grained Instance Classification Through Deep Attention

Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. Deep learning based vision systems mostly deal with high number of low-resolution images, whereas subtle details in biomedical images require higher resolution. To bridge this gap, we propose a simple yet effective deep network that performs two tasks simultaneously in an end-to-end manner. First, it utilises a gated attention module that can focus on multiple key instances at high resolution without extra annotations or region proposals. Second, the global structural features and local instance features are fused for final image level classification. The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results in two separate fine-grained multi-instance biomedical image classification tasks: a benchmark breast cancer histology dataset and our new fungi species mycology dataset. In addition, we demonstrate the interpretability of the proposed model by visualising the concordance of the learned features with clinically relevant features.
Educational development partnerships and practices: Helping librarians move beyond the one-shot

Given the current, widespread concern about “fake news” and information disorder, those working in post-secondary contexts have recognized a pressing need to develop students’ digital literacy (DL). Based on our experience collaboratively designing and delivering a faculty workshop on “Teaching Students about Fake News,” we see library connections to educational development as one way to address this need. Because faculty members design, develop, and deliver the requisite curriculum—and are often called upon to address the challenges that their students face in navigating, evaluating, and applying online content—they are frequently a first point of contact for help. Research examining student interactions with online news, social media, and other digital content also demonstrates how faculty play a vital part in facilitating and supporting critical digital engagement. All of this underscores the importance of faculty roles in promoting digital and information literacies. A fruitful strategy for librarians to build better connections with faculty is through educational development strategies.
Impulse noise removal by k-means clustering identified fuzzy filter: a new approach

Removal of impulse noise from corrupted digital images has been a hitch in the field of image processing. Random nature of impulse noise makes the task of noise removal more critical. Different filters have been designed for noise removal purpose and have shown formidable results mostly for low and medium level noise densities. In this paper, a new two-stage technique called k-means clustering identified fuzzy filter (KMCIFF) is proposed for de-noising gray-scale images. KMCIFF consists of a k-Means clustering-based high density impulse noise detection, followed by a fuzzy logic-oriented noise removal mechanism. In the detection process, a 5 $\times$ 5 window centering upon each pixel of the image is considered. K-Means clustering is applied on each 5 $\times$ 5 window to group the pixels into different clusters to detect whether the central pixel of each window is noisy or not. In the noise removal process, a 7 $\times$ 7 window centering upon each noisy pixel of the image, as detected by the clustering is considered. Fuzzy logic is used to find the nonnoisy pixel in each 7 $\times$ 7 window having the highest influence on the central noisy pixel of the window. Finally, that pixel is replaced by the approximated pixel intensity value calculated from the highest influencing non-noisy pixel. KMCIFF is evaluated upon seven different standard test images using peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), Percentage of actual nonnoisy pixels detected as erroneous out of the total number of pixels (PDAE) and average run time (ART). It has been observed that KMCIFF shows significantly more competitive visual and quantitative performances vis-a-vis most of the extant traditional filters at high noise densities of up to 90$\%$.
Impersonation Attack-Defense Tree

Nowadays, online learning or e-learning has become increasingly popular and evolved. Many academic institutions use the Learning Management System (LMS) as a medium for delivering e-learning. A vital feature in such a system is the electronic examination (e-exam), where verifying student’s authentic competence is a challenge. This paper aims to present countermeasures for impersonation attacks. This research was a more focused effort and a continuation of previously owned one and many others found in works of literature. The method of protection is presented in the form of an attack-defense tree model.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
An Active Learning Algorithm Based on Shannon Entropy for Constraint-Based Clustering

Pairwise constraints could enhance clustering performance in constraint-based clustering problems, especially when these pairwise constraints are informative. In this paper, a novel active learning pairwise constraint formulation algorithm would be constructed with aim to formulate informative pairwise constraints efficiently and economically. This algorithm consists of three phases: Selecting, Exploring and Consolidating. In Selecting phase, some type of unsupervised clustering algorithm is used to obtain an informative data set in terms of Shannon entropy. In Exploring phase, some type of farthest-first strategy is used to construct a series of query with aim to construct clustering skeleton set structure and informative pairwise constraints are also collected meanwhile based on the informative data set. If the number of skeleton sets equals the number of clusters, the new algorithm gets into third phase Consolidating; otherwise, it would finish. In Consolidating phase, non-skeleton points included in the informative data set are used to construct a series of query with skeleton set representative points constructed in Exploring phase. And some type of priority principle is proposed to help collect more must-link pairwise constraints. Treat the well-known MPCK-means (metric pairwise constrained K-means) as the underlying constraint-based semi-supervised clustering algorithm and data experiment comparison between this new algorithm and its counterparts would be done. Experiment outcome shows that significant improvement of this new algorithm.
ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy