Reviewerly/exHarmony huggingface dataset:
Chinese Master Students In Russian University: Adaptation And Education Issues

The study examines issues concerning education of Chinese master students in Russian universities.This topic is gaining great relevance today both in connection with the transition of Russia's higher education to a multilevel system, and the tasks of increasing the internationalization of educational services by Russian universities.The presence of master programs has been considered lately one of the main criteria of the university innovative potential for it meets the objectives of the education continuity.As an example, the implementation of master programs for international students at the Vladivostok State University of Economics and Service (VSUES) is considered.Particular attention is paid to Chinese students who represent the most massive contingent of foreign citizens studying at universities of the Russian Far East in general and at VSUES in particular.The goal of the research, carried out empirically (questionnaires, personal interviews, and observations), is to identify the factors influencing the adaptation of Chinese master students in the Russian university educational environment and the associated effectiveness of their cognitive process.Some measures to improve the master training and increase the efficiency of master courses are proposed.The research results can be of practical value for university faculty engaged in international master students teaching.
Estimation of Average of Global Solar Radiation Depending on Sunshine Duration Hours for Iraqi Metrological Stations

In this study, the global solar radiation for the locations of fourteen Iraqi metrological stations was studied and calculated. This was performed because most of the Iraqi stations lack solar radiation measuring devices. The equation postulated by AngstrÃ¶m (1924) and modified by Prescott (1940) was utilized for the estimation of the solar radiation for the fourteen Iraqi metrological stations depending on sunshine duration measurements of these stations. Empirical constants of Angstrom-Prescott equation that are adopted by the Food and Agriculture Organization (FAO) were used for obtaining the results. The utilized data reported in this study were taken from the Republic of Iraq Meteorological Office (RIMO). The calculations and diagrams were carried out and the results were obtained by making advantage of Excel's program capabilities.
LISTENING COMPREHENSION DIFFICULTIES; A CASE STUDY OF EFL STUDENTS IN LISTENING CLASS

Listening skill in English is one of the most important skills in communication and educational process. However, listening skill is a skill that is not easily mastered by all non-native speaker students. The students need to know and understand the difficulties in listening process. Therefore, the current study aims to investigate the difficulties faced by the students dealing with listening comprehension. This research is a descriptive qualitative research with case study approach that examines six students in third semester of the English education study program in Universitas Muhammadiyah Kotabumi with three different proficiency levels: low, moderate, and high level. The results of this study showed that students with low level faced difficulties with unfamiliar words (message content), speed of speech (speaker), and lack of vocabulary (listener). Then in moderate level students felt difficult in the speed of speech (speaker) and noises (physical settings). Meanwhile, students with high level faced difficulty with the long spoken text (message content), variety of accents (speaker) and noises (physical settings). It can be concluded that the listening comprehension difficulties encountered by the students at tertiary level are different depending on their proficiency in English. Therefore, the implication of the current study is that the lecturer need to consider the information revealed in this study in order to be able to overcome the difficulties faced by the students.
Asymptotic elimination of partially continuous aggregation functions in directed graphical models

For a finite and relational signature σ and finite domain D we consider the set WD of all σ-structures with domain D. On WD a probability distribution is determined by a so-called parametrized probabilistic graphical model, a concept studied in statistical relational artificial intelligence. We also consider a many valued logic, denoted PLA, with truth values in the unit interval for expressing queries. PLA uses aggregation functions, for example the arithmetic mean, geometric mean, maximum and minimum, instead of quantifiers. In this setting we prove that every formula of PLA with only admissible aggregation functions is asymptotically equivalent to a formula without aggregation functions, as the domain size tends to infinity. A corollary of this is a probabilistic convergence law for PLA-formulas with only admissible aggregation functions.
Secure Cloud Key Management based on Robust Secret Sharing

The aim of this paper is to propose a model to strengthen the security of key management in cloud computing, where the model is shared or entirely controlled by a non-trusted third party provider. Key management is not a straightforward matter for IT-teams, in addition to critical issues related to properly managing and securing the keys on providers’ infrastructures, they have to deal with concerns specific to multi-cloud key management. Hardware Security Module (HSM) solution that offers a secure on-premise encryption key management turned out be impracticable for widespread cloud deployment. HSM as a Service seems to be the best approach for key management in multi-cloud, but the service is wholly owned and managed by another cloud provider. In This paper, we present an efficient and secure cloud key management that fulfills the requirements of multi-cloud deployment. The proposed design splits the key into a blinded version of n shares that will be stored in encrypted format at the cloud provider side. To demonstrate the efficiency of the proposed design, we implement a fully featured prototype and evaluate its performance. Results analysis shows that the proposed design is highly efficient and can serve as a groundwork for using secret share as a way to protect keys in a multi-cloud environment.
Pengembangan Aplikasi E-Book Elektrokimia Berbasis Android Untuk Menumbuhkan Self-Directed Learning Mahasiswa

Students' weak ability to understand electrochemical concepts is due to the lack of self-directed learning (SDL) in finding their own understanding outside the classroom and not used to evaluate the learning difficulties they experienced. The android-based e-books could be used to support students’ independent learning, to evaluate learning outcomes through practice questions and direct feedback, and to exchange opinions between students and lecturers. The aim of this study is to produce an android-based electrochemical e-book to emerge students’ SDL. To achieve the aim, this study employs ADDIE development models which consist of five steps; analyze, design, develop, implement and evaluate. Data were collected using content validity, face validity assessment, response questionnaires and SDL. The result of content validity is 0.94 and categorized as valid. Based on face validity, vast majority of users stated that the e-book application is attractive and easy to use. Moreover, the explanation, graphics, videos, quiz questions and discussion in the e-book application are clear. The results of the implementation of application as additional learning resource for electrochemical concepts can foster students’ SDL in the high category with an average percentage of confidence at 87.20%. Students show a positive response to the use of the application in helping them understand the concepts, working on assignments, discussions between friends and lecturers, digging more in-depth information, and do practice questions in a short time, also easy to use. Thus, it can be concluded that the android-based e-book application could help students develop their SDL
Exploring Distantly-Labeled Rationales in Neural Network Models

Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales.Most existing methods distribute their models' focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words.In this paper, we propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationales (PINs) and alleviate redundant training on non-helpful rationales (NoIRs).Experiments on two representative classification tasks show that our proposed methods can push a classification model to effectively learn crucial clues from nonperfect rationales while maintaining the ability to spread its focus to other unlabeled important words, thus significantly outperform existing methods.
Thinking Fast and Slow in AI.

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
Correction to: Energy Consumption and Bitcoin Market

In the original publication of the article, the acknowledgement section has been missed to update.
PENGARUH KEPERCAYAAN DIRI TERHADAP KEMAMPUAN PUBLIC SPEAKING PADA MAHASISWA PENDIDIKAN SEJARAH UNIVERSITAS JAMBI

&#x0D; The aim of this study investigated is to describe the influence of self confidence on public speaking skills at History Education Students of Jambi University. Data obtained by distributing questionnaires and then analyzed quantitatively with a correlational research design. The results showed: there was an influence of self confidence on public speaking skills at History Education student of Jambi University because the value of thitung (16,960) &gt; ttabel (1,65) and the significance probability value is 0,000 &lt; 0,05. It means that the higher self confidence of history education students also makes the higher public speaking skills. The magnitude of the influence of self confidence on public speaking skills at History Education Students of Jambi University is 66,5% and the remaining 33,5% is influenced by other variables outside of this study. From the results of this study, it's recommended that lecturers no longer only focus on models, media, or learning methods but also have to pay attention to the psychology and mentality of students who can influence their attitude during class, so there’s no assumption that students who are less active when appearing in front of the class are students who are not good at learning
Descriptive Image Captioning with Salient Retrieval Priors

Captions are often expected to carry detailed, essential information of images, but current image captioning models tend to play safe and generate generic captions that is less informative.Cross-modal retrieval is a promising solution as texts with more details has better performance in retrieval.In this work, we first explore two types of salient n-grams, i.e., Support N-grams (SN) and Deletion N-grams (DN), in captions which significantly affect the performance of typical cross-modal retrieval models.We further exploit these n-grams to enhance the original learning objectives for generating descriptive captions with more details.The experiments on two benchmark datasets show that our proposed model outperforms baselines significantly when evaluated with a wide range of metrics.
Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings

In Bayesian Network Structure Learning (BNSL), we are given a variable set and parent scores for each variable and aim to compute a DAG, called Bayesian network, that maximizes the sum of parent scores, possibly under some structural constraints. Even very restricted special cases of BNSL are computationally hard, and, thus, in practice heuristics such as local search are used. In a typical local search algorithm, we are given some BNSL solution and ask whether there is a better solution within some pre-defined neighborhood of the solution. We study ordering-based local search, where a solution is described via a topological ordering of the variables. We show that given such a topological ordering, we can compute an optimal DAG whose ordering is within inversion distance r in subexponential FPT time; the parameter r allows to balance between solution quality and running time of the local search algorithm. This running time bound can be achieved for BNSL without any structural constraints and for all structural constraints that can be expressed via a sum of weights that are associated with each parent set. We show that for other modification operations on the variable orderings, algorithms with an FPT time for r are unlikely. We also outline the limits of ordering-based local search by showing that it cannot be used for common structural constraints on the moralized graph of the network.
The COVID-19 Pandemic as a Stress Test on the Right to Protection of Personal Data : The Case of Greece

The news items contain Internet links referring to more detailed information.These links are embedded into the news text.They can be easily accessed by clicking on the underlined text in the online version of the journal.If an external website features multiple languages, the Internet links generally refer to the English version.For other language versions, please navigate using the external website. Legal effects of COVID-19The Associations for European Criminal Law and the Protection of Financial Interests of the EU is a network of academics and practitioners.The aim of this cooperation is to develop a European criminal law which both respects civil liberties and at the same time protects European citizens and the European institutions effectively.Joint seminars, joint research projects and annual meetings of the associations' presidents are organised to achieve this aim.
Analysis of the Accounting Learning Digital Disruptive in Industrial Revolution 4.0 and Society 5.0

The Industrial Revolution 4.0 is a fundamental work processes and change in the way of human life.The advancement of information technology integrates the world with digital which can have an impact on scientific disciplines.The emergence of the Industrial Revolution 4.0 made a new face in the phase of technological progress.The study of accounting learning in the era of the Industrial Revolution 4.0 was carried out.There is a variety of very fast transformations in design, operation, manufacturing, and production systems that cause digital disruptive.The existence of disruptive and degradation of human role then makes a change as Society 5.0, a human-centered society developed by Japan.Thus, the purpose of this study is to evaluate the things that have and need to be done in accounting learning in the context of Society 5.0.The study was conducted with descriptive, analytic and qualitative methods which were supplemented by literature studies and observations of the evolution of accounting learning.This study concludes that some have anticipated accounting learning in Society 5.0, but there are still many opportunities for improvement.
Flora and Fauna in Danau Singkarak (Ecolinguistic Study)

This article is focused on the ecolinguistics which discusses about the ecology study and linguistic study. This study discusses about the flora and fauna around Dana Singkarak area. It aims to give the name of plantations and animals which live so long near the lake. The animals are also so a little and nearly endanger because many people search the animals and plantation. They find out some animals to sell in the market because the cost is expensive and the animals meats are so delicious and full of vitamins so that there are some animals that have endanger and difficult to find. Then the plantation also nearly endanger because there are so many pollutions either air pollution or water pollution that have made the humans as the predators of them. This search has found some the unique plantations. So in the context of preserving the habitat of flora and fauna around Danau Singkarak. Because of that, ecolinguistics is to rename the flora and fauna to write in the dokumen or library and it will be useful for the next generations.
Teaching Methods of Computer Education under the Background of Big Data

With the advancement of society and the increase of computer, network and digital media technology, big data technology has become an indispensable part of modern service industry. Big data technology has been developed in all aspects of the education industry, and the value of products and services created has gradually increased. Big data plays an important role in technology in computer education and other aspects. This article mainly introduces the teaching method research of computer education under the background of big data. This article uses the research of the teaching method of computer education under the background of big data, starting from the two aspects of computer education and basic courses, rationally analyzing the feasibility of the teaching method of computer education. From the labor market in our country, the demand for computer talents is concentrated on application-oriented talents, but the purpose of computer education in many schools mostly stays in traditional research-based teaching, resulting in a decline in the quality of education, and it is difficult to meet the market’s demand for computer application-oriented talents. It is the main body that determines the course content and teaching mode. The experimental results of this paper show that the research on the teaching methods of computer education under the background of big data has increased the efficiency of computer education by 18%. The limitations of the research on the teaching methods of computer education under the background of big data are analyzed, and the methods and ways of computer ability training are analyzed. Discuss and summarize, so as to enrich the academic research results.
Web Harvesting for Data Retrieval on Scientific Journal Sites

Publishing scientific articles online in journals is a must for researchers or academics. In choosing the journal of purpose, the researcher must look at important information on the journal's web, such as indexing, scope, fee, quarter and other information. This information is generally not collected in one page, but spread over several pages in a web journal. This will be complicated when researchers have to look at information in several journals, moreover, the information in these journals may change at any time. In this research, web harvesting design is conducted to retrieve information on web journals. With web harvesting, information that is spread across several pages can be collected into one, and researchers do not need to worry if the information has changed, because the information collected is the last or updated information. Harvesting technique is done by taking the page URL of the page, starting the source code from where the information is retrieved and end source code until the information stops being retrieved. Harvesting technique was successfully developed based on the web bootstrap framework. The test data is taken from several scientific journal webs. The information collected includes name, description, accreditation, indexing, scope, publication rate, publication charge, template and quarter. Based on tests carried out using black box testing, it is known that all the features made are as expected.
Implementation of Dijkstra Algorithm and Welch-Powell Algorithm for Optimal Solution of Campus Bus Transportation

This research deals with applying the Dijkstra algorithm and Welch-Powell algorithm to on-campus bus transportation problems. This research aims to determine the optimal solution of campus bus transportation routes in the shortest routes and schedules. In determining the fastest way, each intersection represented as a node, and the path described as the sides. The shortest path obtained V1 - V2 - V5 - V8 - V9 - V10 - V13 - V16. In determining the optimal schedule, the number of buses represents the vertices, and the time expresses the side that connects each node. The optimal program of the bus starts from 06.30 am to 5.00 pm. Every bus gets four sessions of departure and four sessions return with travel time each session is 60 minutes.
Encryption of Data based on Triple encryption and Affine Algorithm

In recent years in view of the recent scientific and technological development, Encryption has emerged as a solution and is critical in the information security system.To protect the shared data, a variety of techniques are needed.To begin, data must be encrypted before being sent from sender to receiver using a cryptographic algorithm for encryption.Second, the receiver can display the original data using decryption techniques.Encryption is a well-known method of safeguarding classified information.It is a procedure for converting text or data into an unreadable format for the purpose of hiding this data or is converting plaintext to cipher text is an operation.In conjunction with an algorithm or a key.The current research focuses on using cryptography to protect data as it is being transmitted, we implemented three encrypt techniques (Caesar-Reversing-zigzag) by combining the three codes called triple encryption for encrypting the message, re-encoding the encrypted text using the affine cipher.The present work is implemented by using Visual Basic 6.
Sensitive Keyword Detection on Textual Product Data: An Approximate Dictionary Matching and Context-score Approach

In this paper, we define and study a new problem in the field of natural language processing and data science called Sensitive Keyword Detection, which aims at detecting variants of keyword in each textual product.We propose a method using approximate dictionary matching algorithm and contextscore to solve this new text mining problem in a general way.Given a product data set, for each document will be dectected keyword-variant pairs, then to determine if they are similar in semantic, we compare the context score between them.We conduct experiments on 1.189.690textual sentences extracted from descriptions and titles of 300.000 products, each textual sentence contains an original keyword or a keyword variant.Experimental results show that important role of the context-score approach and the proposed method is effective when using context-score based on character and word embedding.