Reviewerly/exHarmony huggingface dataset:
Prediction of Student Graduation Time Using the Best Algorithm

Data mining has a very important role in the world of education can help education institutions in predicting and making decisions related to student academic status. We use the NN, SVM and DT algorithms to predict the graduation time of academic students at one of the private universities in Indonesia. The results of this study indicate that the three models produce the accuracy of more than 80%, and the SVM model has an accuracy of 85.18% higher than the other two models. The results arising from this study provide important reference material for planning the future success of students and faculty in early warning to students in the future.
Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization

Summarizing social media comments automatically can help users to capture important information without reading the whole comments.On the other hand, automatic text summarization is considered as a Multi-Objective Optimization (MOO) problem for satisfying two conflicting objectives.Retaining the information from the source of text as much as possible and producing the summary length as short as possible.To solve that problem, an undirected graph is created to construct the relation between social media comments.Then, the Multi-Objective Ant Colony Optimization (MOACO) algorithm is applied to generate summaries by selecting concise and important comments from the graph based on the desired summary size.The quality of generated summaries is compared to other text summarization algorithms such as TextRank, LexRank, SumBasic, Latent Semantic Analysis, and KL-Sum.The result showed that MOACO can produce informative and concise summaries which have small cosine distance to the source text and fewer number of words compared to the other algorithms.
Methodological Techniques for Evaluating the Efficiency of Private Investment in Education

The article discusses the methodological approaches to evaluating the efficiency of private investment in human capital as a self-expanding value driver.I spotlight education as a crucial component of human capital, which is considered as the self-expanding value driven by an increase in the staff labor productivity, salaries.I also treat it as the property represented with professional qualities.Objectives The research aims to forge a technique for evaluating the cost effectiveness of investment in education as part of human capital investment by comparing income (average salaries at different levels of education) and training expenditures.Methods I determined the required rate of return on investment in education, considering the use of alternative manpower and finance. ResultsThe rate of return is taken as equal tranches of additional income (annuity) throughout the entire employment period, including investment in education, and income lost for the period of training as alternative costs.Based on real indicators, I precisely measured the net return on investment in education at different levels. Conclusions and RelevanceThe technique allows to determine whether a person effectively utilizes labor and financial resources as investment in education.The technique can be applied to evaluate the efficiency of private investment and require rate of return on investment in education.
DIDACTIC POTENTIAL OF CROSS-DISCIPLINARY EDUCATIONAL MODULES FOR DEVELOPING PROFESSIONAL COMPETENCES OF FUTURE MASTER-TEACHERS

Statement of the problem. The pedagogical essence of competence characterized by the ability to use subject knowledge for solving problems from the area outside the subject sphere points out inadequacy of this disciplinary (subject-knowledge) approach to solving problems of this competence formation. This fact determines the problem of searching for new content and form of education aimed at the formation of students’ competences. The purpose of the article consists in the conceptual development of the model of the cross-disciplinary educational module within the major educational program of training a master’s degree-pedagogue oriented towards the formation of his professional competences. The methodology of the research is founded on major provisions of competence - based, activity and cross-disciplinary approaches, contextual training, normative requirements of the Federal State Educational Standard for Higher Education (program track 44.04.01 Pedagogical education) and the analysis of the innovative experience. Results. The methodical model of the cross-disciplinary educational module as part of the main educational program of training the master’s degree-teacher was developed, and its didactic potential for the formation of professional competences was substantiated. Conclusion. The developed methodical model of the cross-disciplinary educational module as part of the main educational program of training the master’s degree-teacher has the didactic potential for the formation of students’ professional competences which can be implemented in the conditions of contextual education approach.
Digitalization of the university: construction of the integrated informational environment

The review article aims at formulating a systematic approach to the formation of an integrated information environment focused on the innovative development of educational services in the context of university digitalization. The methodological basis is the review of scientific publications on the problem of building an integrated information environment and knowledge management within the university, management technologies, and federal legislation on these issues. The article is practically significant due to its describing the tools and processes of knowledge management within the university, in formulating the tasks of the corporate information system, in developing recommendations on the structure of the integrated information environment of the university, focused on the target orientation in business processes management, in individualization of training, in the use of online courses and in network collaboration. The areas of using the managerial design model of the innovative educational services development are the knowledge management technologies linking with internal business processes and the external environment in the university's corporate information system, as well as the possibility of networking with consumers of educational services. The concept of the managerial design model formed the basis for improving, mostly in terms of academic knowledge, the network environment of theKazanNationalResearchTechnicalUniversitynamed after A. N. Tupolev. The article gives the strategic understanding of the role of the university's integrated information environment in online learning and research, in pro- viding training within the real economy, and in participating in network programs. The article is important because of the author's approach to the organization of an integrated information environment of the university, wherein academic and managerial knowledge are combined, as it is aimed at strengthening the competitive advantages of educational services and at achieving the goal of quality education. The article might be interesting for researchers in the field of knowledge management in higher education and for the practitioners of university business processes informatization. In the future, researchers will be able to more detailedly develop the information modules of the corporate information system and management tools of any university's organizational (academic and managerial) knowledge.
IS-IS Optimal Distributed Flooding for Dense Topologies

Dense topologies, such as data center fabrics based on the Clos andbutterfly fabric topologies. Flooding mechanisms designed for sparsetopologies, when used in these dense topologies, can result in slowerconvergence times and higher resource utilization. The modificationsto the flooding mechanism in the Intermediate System to IntermediateSystem (IS-IS) link state protocol described in this document reduceresource utilization to a minimum, while increaseing convergenceperformance in dense topologies. Note that a Clos fabric is used asthe primary example of a desne flooding topology throughout thisdocument. However, the flooding optimizations described in thisdocument apply to any dense topology.
Fear Appeals and Information Security Behaviors: An Empirical Study on Mechanical Turk

This study aims to conduct a methodological replication of the information security study conducted by Johnston and Warkentin (2010).This study leveraged the use of the fear appeals model (FAM) in the context of information security as they pertain to the individual use of anti-spyware software.We adopt all measures, instruments, statistical tests, theory, and models from the original study, but apply them to the Amazon Mechanical Turk population.The results from this replication study are not consistent with the original study, in that two of the five posited hypotheses have opposite effects than those originally found; threat severity is shown to have a positive effect on both response efficacy and selfefficacy, where in the original study, this is shown to have a negative effect on both.The results imply that there may be differences in which populations the study was conducted, thus requiring additional samples and statistical tests.
Efficient and Secure Cloud Storage Auditing Based on the Diffie-Hellman Key Exchange

Cloud storage auditing technique helps to verify the data integrity in the cloud on behalf of the user.Earlier researcher proposed many techniques on the cloud auditing, but it still lags in computational overhead for the resource constraint users.In this research, the Diffie-Hellman method is used to exchange the key in the Third Party Auditor (TPA) to improve the performance of the auditing method.The key is generated in the third party auditor and this is shared with the user as an encrypted key.The Diffie-Hellman creates the shared encryption key between the two parties and can be communicate in the insecure channel.The Diffie-Hellman generates ephemeral keys and this is extremely fast in generating new key pairs.This method is suitable for the big server and have more efficiency over the state-ofart method, which doesn't involve in ephemeral key generation.The proposed method is evaluated in the simulated environment and compared with the other state-of the art methods.The verification cost of the proposed auditing method has achieved 1.1 s, while the existing method has 1.19s.
FROM ARTIFICIAL INTELLIGENCE TO ARTIFICIAL CONSCIOUSNESS: POSSIBLE LEGAL BASES FOR THE HUMAN-ROBOT RELATIONSHIPS IN THE FUTURE.

In the future, robots will increasingly resemble human beings and people will engage in social interaction with them.Accordingly, this paper aims to pave the way for analyse the research problem in the case of social robots, the probable legal status of artificial intelligence in the future.The article will discuss the differences between artificial intelligence versus artificial consciousness because AI poses societal challenges so it is currently undergoing a number of important developments and the law must be rapidly changed in society so that firstly, the difference between artificial intelligence and artificial consciousness is attempted to be demystified.Subsequently, the analysis will be subjected to a current legal status of Artificial Intelligence in EU with particular emphasis on case-law in matters of intellectual property.Also possible future scenarios will be discussed in this article The starting point of the research were source queries and literature studies aimed at jointly defining competence profiles of robot-human and key from the point of view of cybersecurity challenges 4.0.Next, the most important legal and political EU programming documents were analysed and assessed in terms of their vision of society 4.0.Next, a decision-making method was used to see the impact of particular instruments applied by the union in the framework of the policy of cyberspace protection and the phenomenon of robot-human relations.In connection with this, the following basic questions arise: firstly, the direction in which contemporary policy of combating cyber-terrorism should be aimed at institutional and legal matters, and secondly, to what extent well-guided cyber-security policy is able to influence the security of relations robot-human?
Less Classroom Hours of EFL Instruction to Non-English Majors in Chinese Universities Is It a Reason-Based Policy that Provokes No Response?

This paper analyzes the phenomenon that reducing hours of EFL instruction to non-English majors in Chinese universities gets no response. It first depicts the phenomenon, pointing out that this phenomenon differs greatly from people&amp;rsquo;s response to similar events that happened in the past. It then analyzes the complicated underlying factors from perspectives of main stakeholders including university authorities, school deans and teachers, and those from the perspective of students, revealing their diversified thoughts and feelings towards the reduction of EFL instruction hours. Based on the analysis, this paper thinks that it&amp;rsquo;s not a thoroughly rational policy. In hope of minimizing the possible negative impact of the widely implemented policy, this paper proposes three suggestions for EFL instruction practice: Stratified instruction based on university-designed proficiency tests, communication oriented small-class instruction and teacher-guided autonomous learning.
Optimizing QoE of Multiple Users over DASH: A Meta-learning Approach

Dynamic adaptive video streaming over HTTP (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches concentrate on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this paper, we formulate the QoE optimization of multi-user preferences as a multi-task deep reinforcement learning problem, in which QoE refers to the metrics of visual quality, fluctuation and rebuffing events. Then, we propose a meta-learning framework for multi-user preferences (MLMP) as a new DASH adaptation approach. Finally, the simulation results show that the proposed approach outperforms state-of-the-art DASH adaptation approaches in satisfying the different users' QoE preferences regarding the three metrics.
A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

Purpose . Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD. Methods . From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation. Results . Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively. Conclusion . The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.
PENINGKATAN HASIL BELAJAR MATEMATIKA MELALUI MODEL PEMBELAJARAN DISCOVERY LEARNING BERBANTUAN MEDIA KELAS V SEKOLAH DASAR

This classroom action research aims to improve the learning outcomes of Mathematics in fifth grade students of Cebongan 03 Elementary School through the three-dimensional model of Discovery Learning. This research was conducted in two cycles through the stages of planning, implementation, observation, and reflection. Data collection is done using observation sheets for the learning process and test questions for learning outcomes. Data analysis was carried out with comparative descriptive analysis, namely comparing student learning outcomes in the pre cycle, cycle I and cycle II. From this study the following results were obtained; in the pre-cycle of 21 students only 6 students (28.57%) were completed and 15 students (71.43%) did not complete with an average grade of 51.71. Furthermore, in the first cycle, after taking action by applying the Discovery Learning learning model, a number of 9 students (42.85%) were declared complete and 12 students (57.15%) were not completed with a class average of 63.80. Then in the second cycle results were obtained that a number of 19 students (90.5%) were declared complete and 2 students (9.5%) were not completed with a class average of 85.95. This shows that the Discovery Learning learning model assisted by the media is able to improve student Mathematics learning outcomes.
Sistem Informasi Akademik Serta Penentuan Kelas Unggulan Dengan Metode Clusttering Dengan Algoritama K-Means Di Smp Negeri 3 Ubud

SMP Negeri 3 Ubud is educational establishments located at Silungan Lotunduh Ubud. SMP Negeri 3 Ubud has excellent class, to define the student entry into superior class using a manual system that is using Microsoft Excel. This system is inefficient because it requires a lot of time when creating. In addition to the data processing of academic, especially a student's scores are still manually so difficult when creating repot. Based on the problems it created an Academic Information System as well as the determination of class excellent using clustering method with K-Means algorithm. with the academic information system and determination superior class computerized then administrative staff easier and faster in processing student data, teacher data and employee data. The method used to determine which class superior that is Clustering K-Means algorithm. With the K-Means algorithm will process the value system and grouping students according to the value closest to the cluster center point. With this system superior class determination more quickly and efficiently
USING SOCIAL NETWORK COMMUNITIES AS A TOOL FOR ORGANIZING IT EDUCATION

1 Sholom-Aleichem Priamursky State University (RUSSIAN FEDERATION)2 Moscow State University of Civil Engineering (RUSSIAN FEDERATION)3 Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi (UZBEKISTAN)4 Saint Petersburg Humanitarian University and Social Sciences (RUSSIAN FEDERATION)5 Blagoveschensk State Pedagogical University (RUSSIAN FEDERATION)
A Hybrid Network Model for Tibetan Question Answering

Currently, research on question answering (QA) with deep learning methods is a hotspot in natural language processing. In addition, most of the research mainly focused on English or Chinese since there are large-scale open corpora, such as WikiQA or DoubanQA. However, how to use deep learning methods to QA of the low resource languages, like Tibetan becomes a challenge. In this paper, we propose a hybrid network model for the Tibetan QA, which combines the convolutional neural network and long short memory network (LSTM) to extract effective features from small-scale corpora. Meanwhile, since the strong grammar rules of Tibetan, we use the language model to decode the output of the LSTM layer which makes the answer more accurate and smoother. In addition, we add the batch normalization to accelerate deep network training and prevent overfitting. Finally, the experiments show that the ACC@1 value of the proposed model in Tibetan QA is 126.2% higher than the baseline model.
Improved Online Security Framework for e-Banking Services in Nigeria: A Real World Perspective

Internet technology has given Banks the opportunity to provide customers robust, convenient and flexible banking services including, but not limited to fund transfers, account checking and payment of bills. Despite these huge benefits, e-banking has given rise to so many security concerns arising from countless threats. The rise in security threats against e-banking has caused a decline in the use of online banking and has negatively affected customer confidence in the ability of banks to protect their money and information and are looking up to the banks to fix the problems. This research, Improved Online Security Framework for e-banking services is geared towards developing an improved security framework that solves the issues of authentication, confidentiality, integrity and non-repudiation as it pertains to online banking attacks. Data was collected from primary and secondary sources ranging from interviewing relevant stakeholders that use internet banking to consultations of related journals articles and technical reports. Design and modeling tools such as UML usecases, Entity relationship (E-R) diagrams, process flow modeling and MySQL for a robust database design were used to capture basic system functionalities and artifacts required. The entire design was implemented on Visual studio platform. Upon running and testing on a XAMPP server, the system was found to meet all design objectives and operationally effective.
Effect of Educational Facilities and Infrastructure in Primary Schools on Students’ Learning Outcomes

This research aims at revealing the effect of educational facilities and infrastructure in primary schools on students’ learning outcomes. This was based on the Education Data Centre (Dapodik – Data Pokok Pendidikan ) of 2017/2018 Academic Year stating that high rates of damage to educational facilities and infrastructures, particularly classrooms, and less optimal students’ learning outcomes indicated higher numbers of students who had repeated a grade (failing a grade). This research employed a quantitative method using the logistic regression as the data analysis technique. Dependent variable in this research was learning outcomes, while independent variables were classrooms, teacher toilets, student toilets, libraries, school based-management (SBM), and double shift. The research population were 599 primary schools in Sumedang District based on Dapodik 2017. The results indicated that there was an effect of educational facilities and infrastructure on students’ learning outcomes, namely classrooms, MBS and double shift, while there was no effect of educational facilities and infrastructure on students’ learning outcomes, namely libraries, teacher toilets, and student toilets.