Reviewerly/exHarmony huggingface dataset:
Automated Learning of Hungarian Morphology for Inflection Generation and Morphological Analysis

The automated learning of morphological features of highly agglutinative languages is an important research area for both machine learning and computational linguistics. In this paper we present a novel morphology model that can solve the inflection generation and morphological analysis problems, managing all the affix types of the target language. The proposed model can be taught using (word, lemma, morphosyntactic tags) triples. From this training data, it can deduce word pairs for each affix type of the target language, and learn the transformation rules of these affix types using our previously published, lower-level morphology model called ASTRA. Since ASTRA can only handle a single affix type, a separate model instance is built for every affix type of the target language. Besides learning the transformation rules of all the necessary affix types, the proposed model also calculates the conditional probabilities of the affix type chains using relative frequencies, and stores the valid lemmas and their parts of speech. With these pieces of information, it can generate the inflected form of input lemmas based on a set of affix types, and analyze input inflected word forms. For evaluation, we use Hungarian data sets and compare the accuracy of the proposed model with that of state of the art morphology models published by SIGMORPHON, including the Helsinki (2016), UF and UTNII (2017), Hamburg, IITBHU and MSU (2018) models. The test results show that using a training data set consisting of up to 100 thousand random training items, our proposed model outperforms all the other examined models, reaching an accuracy of 98% in case of random input words that were not part of the training data. Using the high-resource data sets for the Hungarian language published by SIGMORPHON, the proposed model achieves an accuracy of about 95-98%.
An Efficient Data Segmentation and Replication Technique for Cloud using Fruit Fly Optimized Centrality Measures

Cloud computing is a creating worldview to give dependable and versatile framework permitting the clients (data proprietors) to store their data and the data customers (clients) can get to the data from cloud servers. This paradigm decreases stockpiling and upkeep cost of the data proprietor. However, cloud data storage still gives rise to security related problems. In case of shared data, the data face both cloud-specific and insider threats. In this work, we propose FOA( fruit fly optimization algorithm ) optimized centrality measure fragmentation and replication of information in the cloud for optimum performance and security that consider both security and performance issues. FOA is a technique for deducing global optimization based on the foraging character of the fruit fly. The sensory perception of the fruit fly is superior than that of other species, particularly the sense of smell and vision . In our methodology, we divide a data files and replicate the fragmented data over the cloud nodes using FOA centrality measures. Every one of the cloud node just store a single information data fragment that ensures even if there arise an occurrence of a successful attack ,no important information is shown to the attacker .We also compare the performance of the our methodology with other standard replication schemes. Observed results shows higher level of security and performance imrpovements.
Conclusions and future work

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
Efficient coding for secure computing with additively-homomorphic encrypted data

A framework is introduced for efficiently computing with encrypted data. We assume a semi-honest security model with two computing parties. Two different coding techniques are used with additively homomorphic encryption, such that many values can be put into one large encryption, and additions and multiplications can be performed on all values simultaneously. For more complicated operations such as comparisons and equality tests, bit-wise secret sharing is proposed as an additional technique that has a low computational and communication complexity, and which allows for precomputing. The framework is shown to significantly improve the computational complexity of state-of-the-art solutions on generic operations such as secure comparisons and secure set intersection.
Diagnostics of the ability to understand mathematical texts by school graduates

The ability to understand the text plays a leading role among the basic educational competencies of students. In this paper the technology for diagnosing the ability to understand mathematical texts by school graduates is presented. The discussed technology is built in accordance with the requirements of ontological diagnostics, which makes the diagnostic result unambiguously interpreted, that is, the tools used in this diagnosis meet the validity requirement. Ontological diagnostics is based on the method of “dialectical deduction” (Hegel’s method), which assumes the choice of an initial predicate for its sequential morphologization with a specific content. In the work, the concept of ontological diagnostics is being clarified as a methodological basis for constructing a conceptual and technological scheme for diagnosing the ability to understand mathematical texts. The construction of a system of postulates that provide the formation of a conceptual and technological scheme of the considered diagnostics is discussed. On the basis of this study, a list of abilities was obtained that provide an adequate understanding of mathematical texts by respondents. As a result, the proposed diagnostics makes it possible to reveal the presence or absence of the certain abilities to understand mathematical texts from this list: the ability to choose a basic predicate corresponding to the proposed subject of thought, i. e. ego; the ability to construct complex predicates by the addition and refinement method based on the selected basic predicates; ability to construct the content of the predicate; the ability to read the content of the predicate and the ability to compare the content of the predicate with ego for their identity. A brief description of the conceptual-technological scheme of the regarded diagnostics is given, and a specific example of the constructed technology application is considered for diagnosing the ability to understand mathematical texts by school graduates.
The Standardization Design and Implementation of Voice Communication System of Large Enterprise Group

Communication means each order the necessary analysis of nuclear power plant, a stage through side regret, for nuclear power and multi pass liquid, means integration for more business, the cost of safety and convenience of system test, with the help of the multi Taiwan card speed to introduce multi communication system integration to solve the purple construction requirements and fusion methods, combined with the nuclear power plant on the point, from the value node angle in operation and operation in order dispatch station communication means fusion provides a new way with words hung off wrinkles.
Mendeteksi Tepi Citra Penyakit Hemokromatosis Dengan Menggunakan Metode Log (Laplacian Of Gaussian)

Hemochromatosis is a genetic or hereditary disease. Abnormalities of iron metabolism characterized by excessive deposition of iron in the tissues. Derivative conditions that cause the body to absorb too much iron from the food eaten. Excess iron is stored in organs such as the liver, heart and pancreas. Excess iron can cause toxicity to these organs, and is life threatening because it can cause diseases such as cancer, cardiac arrhythmias, and cirrhosis. LOG method (Laplacian of Gaussian) is a second-order edge detection operator or has a derivative filter whose function can detect areas that have rapid changes (rapit change) such as edges (edges) in the image. But this laplacian is very sensitive or low to the presence of noise. For that, the image will be smoothed first by using Gaussian. Thus a new derivative function is known, namely LoG or Laplacian of Gaussian. Many methods are used in solving edge detection problems, including Prewitt Operators, Sobel Operators, Canny Operators, but among all these methods, the Laplacian of Gaussian method is the method most often used in detecting edges. For this reason, it is hoped that the LoG (Laplacian of Gaussian) method can help to detect hemochromatosis. Can help how severe the disease has developed, and what symptoms can later be caused, so that it can help in the healing process.
Merger, Reorganization and Technology Meet Technical Services

For a variety of reasons, academic institutions across the country have had to reorganize their campuses. A number of universities have done so by merging several smaller departments with the library and consolidating similar functions. The resulting changes have had a significant impact on those who work in these departments and generate much interest in the wider library community. This workshop detailed the process and effect on Technical Services staff of merging Lehigh University’s libraries with computing, media services, and telecommunications to form one organization called Information Resources. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
To the Prospects of the Formation of a New Paradigm of Engineering Education

The article shows that the ingrained orientation of engineering education to the demands of the labor market requires rethinking and adjustment, since in modern conditions it does not guarantee the ability of an engineer to meet the challenges of the future. A real guarantee of the success of engineering education is now taking into account, along with the order of the labor market, also historical and epistemological trends in the development of knowledge, its functions and forms of existence. In this context, techno-science and the principle of “oriented intellect” are of particular relevance in the training of an engineer. It is also emphasized that the current trends in the development of knowledge bring to life a new paradigm of education - projective-personal.
Magnetism

Modern Condensed Matter Physics brings together the most important advances in the field of recent decades. It provides instructors teaching graduate-level condensed matter courses with a comprehensive and in-depth textbook that will prepare graduate students for research or further study as well as reading more advanced and specialized books and research literature in the field. This textbook covers the basics of crystalline solids as well as analogous optical lattices and photonic crystals, while discussing cutting-edge topics such as disordered systems, mesoscopic systems, many-body systems, quantum magnetism, Bose–Einstein condensates, quantum entanglement, and superconducting quantum bits. Students are provided with the appropriate mathematical background to understand the topological concepts that have been permeating the field, together with numerous physical examples ranging from the fractional quantum Hall effect to topological insulators, the toric code, and majorana fermions. Exercises, commentary boxes, and appendices afford guidance and feedback for beginners and experts alike.
Keynote Speech 2: Intelligent Video Summary Generation: Current Challenges and Future Directions

There has been an exponential growth in the amount of multimedia data daily acquired from surveillance cameras and many other resources. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Video summarization tends to detect important visual data in the surveillance stream and can help in efficient indexing and retrieval of required data from huge surveillance datasets. This presentation will focus on the mainstream video summarization strategies with their broader overview for entertainment, medical and surveillance domains. Further, it will highlight the challenges confronted by researchers in video summarization domain in terms of multimedia data computing, multi-modality information processing, among many others. Finally, the presentation will be concluded with some future research directions considering the discussed challenges.
Improvement Of English Speaking Ability Through The English Game In Class Viii Students Of SMP Negeri 6 Kecamatan Kupang Tengah

This study aims to determine the improvement of speaking skills through the Forced Cultivation Model Group Discussion method which includes: (1) a description of involving all students actively in learning activities, (2) a description of improving speaking skills through the English Games method.This research was conducted in SMP Negeri 6, Central Kupang District, with class VIII students in 2019/2020 with a total of 22 students.The Classroom Action Research (CAR) was carried out in three cycles, namely through the stages of planning, implementation, observation, and reflection.The research data was gathered through observing student activity sheets, test, student achievement learning competencies tests, interviews, and questionnaires.The results of the study were, (1) the English Games method proved to be able to actively involve all students in learning activities, (2) the English Games method proved effective in improving English speaking skills.
Analysis and optimisation of server load balancing-based multi-factor integrated algorithm

In distributed server application service deployment, load balancing is an important factor that affects service performance. However, the biggest factor affecting load balancing is the performance of the server and the performance ratio of the requested service execution. This paper used the application of Service-Oriented Architecture system as the embedding point, based on the analysis and comparison with traditional load balancing algorithms, such as: Random load balancing algorithm, Round-Robin algorithm, Consistent Hash algorithm, etc. This paper is aimed to design a new load factor-based algorithm to solve some defects of traditional load balancing algorithm by improving minimum concurrency number and integrating various factors that affect server performance. This paper proposes an optimisation algorithm that can more fully reflect server load capacity. The experiment results show that the load balancing effect of this algorithm has a better effect than the traditional load balancing algorithm.
Interference of First Language in Secondary School Students

First language (L1) interference is inevitable in second language (L2) acquisition. The research was carried out to investigate types of students writing approach that relates to first language interference among upper secondary students and the level of first language interference in L2 written essays among lower secondary students in Malaysia. The participants in this research were 50 students currently studying Form Four in a state-funded school, Akademi Menara Gading, Pahang. The data was collected using the Student L1 Interference Tendency Questionnaire [SLITQ], and analysis of students’ essay writing samples. After triangulating the data, the result indicated that most students positive they have under-differentiation in their essay writing, and analysis of the student's written essays showed that there is less evidence of L1 interference. In conclusion, teachers and schools should acknowledge L1 interference in students’ writing skill and promote a variety of writing strategies to support students existing writing skills.
Fisherian Inference and Maximum Likelihood Estimation

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Data-Driven Hint Generation in Intelligent Tutoring Systems for Learning Microeconomics

Scientific inquiry skills is used in all educational areas. In the context of microeconomics, it is widely known that the development of scientific inquiry skills is central to the construction of ideas that enable understanding. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. ITSs provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. The main goal of this paper is to: 1) presents ITSs used in the microeconomics education; and 2) introduce data-driven ITSs for microeconomics education.&#x0D;
Computer and Information Science and Engineering Research Initiation Initiative (NSF)

Federal Grants & ContractsVolume 43, Issue 12 p. 1-2 Grant Alerts Computer and Information Science and Engineering Research Initiation Initiative (NSF) First published: 14 May 2019 https://doi.org/10.1002/fgc.30432Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume43, Issue1216 May 2019Pages 1-2 RelatedInformation
MERN Stack: Part II

In the previous chapter, you learned more about how to structure your React functions, classes, components, and subcomponents. You were introduced to the MERN stack. You learned about Recoil and how to build an exclusive members-only area by creating a login system with React as the MERN stack’s front end. We even created a custom front-end toast message component to display messages.
Image Restoration Model using Total Variance, Bilateral and Wavelet Denoising Filter

iNoise in images are most common due to various degradation. Noises in images are random variations in images due to lighting conditions, camera electronics, surface reflectance, lens, atmospheric conditions and motions (Either camera is moving or object is moving). Image Restoration is a process which restores a degraded image into its original image which has been degraded by some degradation model which degraded the image. Images are degraded due to various reasons. The first and foremost reason for image degradation is the fault in the imaging devices during the image acquisition process. The noise is generated in the imaging devices and is propagated to the image. The second source of degradation in image is the noise added during the image transmission. This type of image degradation is most common. The third source of image degradation is due to the motion blur and atmospheric turbulence. This paper analyzes various image noise models and restoration techniques. Particularly in analyses three kind of filters namely total variance filter, bilateral filter and wavelet image denoising. The image restoration is measured using the PSNR and SSI of original and degraded images
Human Simulations of Vocabulary Learning

This paper investigates the generalization that noun learning systematically precedes verb learning. Rather than blame this observation on differences in conceptual content, the authors argue that the information requirements of verb learning crucially determine the acquisition order. Efficient verb learning requires access to structural features of the exposure language and thus cannot take place until a scaffolding of noun knowledge enables the acquisition of clause level syntax. A set of experiments with adult subjects ask about the kinds of information that support learning nouns vs. verbs. The proportion correctly identified and the type of word that is identifiable changes significantly as a function of information type.
Call for applications: CAML Review / Appel de mise en candidature : Revue de l’ACBM

CAML Review is published three times per year. It is concerned with music and music materials and strives to promote a better understanding of the cultural importance of music libraries, archives and documentation centres locally, regionally and nationally.
Threat Scenarios

Threat scenarios provide the context for security risk assessments. Their canonical structure and the relationship between its structural elements lead to a universal assessment process and drive the requirement for security controls. Therefore, threat scenarios are not abstractions without connection to the real world.
Inferences Involving The Mean Of a Single Population When σ is Known

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.