Reviewerly/exHarmony huggingface dataset:
Combining Subword Representations into Word-level Representations in the Transformer Architecture

In Neural Machine Translation, using wordlevel tokens leads to degradation in translation quality.The dominant approaches use subword-level tokens, but this increases the length of the sequences and makes it difficult to profit from word-level information such as POS tags or semantic dependencies.We propose a modification to the Transformer model to combine subword-level representations into word-level ones in the first layers of the encoder, reducing the effective length of the sequences in the following layers and providing a natural point to incorporate extra word-level information.Our experiments show that this approach maintains the translation quality with respect to the normal Transformer model when no extra word-level information is injected and that it is superior to the currently dominant method for incorporating word-level source language information to models based on subword-level vocabularies.
A Genesis of a Meticulous Fusion based Color Descriptor to Analyze the Supremacy between Machine Learning and Deep learning

The tremendous advancements in digital technology pertaining to diverse application areas like medical diagnostics, crime detection, defense etc., has led to an exceptional increase in the multimedia image content.This bears an acute requirement of an effectual retrieval system to cope up with the human demands.Therefore, Content-based image retrieval (CBIR) is among the renowned retrieval systems which uses color, texture, shape, edge and other spatial information to extract the basic image features.This paper proposes an efficient and unexcelled hybrid color descriptor which is an amalgamation of color histogram, color moment and color auto-correlogram.In order to determine the predominance between machine learning and deep learning, two machine learning models, Support vector machine (SVM) and Extreme learning machine (ELM) have been tested.Whereas from deep learning category, Cascade forward back propagation neural network (CFBPNN) and Patternnet have been utilized.Finally, from these divergent tested algorithms, CFBPNN attains the highest accuracy and has been selected to enhance the retrieval accuracy of the proposed system.Numerous standard benchmark datasets namely Corel-1K, Corel-5K, Corel-10K, Oxford flower, Coil-100 and Zurich buildings have been tested here and average precision of 97.1%, 90.3%, 87.9%, 98.4%, 98.9% and 82.7% is obtained respectively which is significantly higher than many state-of-the-art related techniques.
Several new classes of (balanced) Boolean functions with few Walsh transform values

&lt;p style='text-indent:20px;'&gt;Three classes of (balanced) Boolean functions with few Walsh transform values derived from bent functions, Gold functions and the product of linearized polynomials are obtained in this paper. Further, the value distributions of their Walsh transform are also determined by virtue of the property of bent functions, the Walsh transform property of Gold functions and the &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-tuple balance property of trace functions respectively.&lt;/p&gt;
Quantum-dot Controlled Electronic Block Triggering a Quantum Computation Procedure

The works devoted to an issue of quantum computer design have been analyzed.The main problems related to creation of the quantum computer are discussed.A fundamentally new approach to solving the problem of creating a truly quantum computer based on the "up to bottom" strategy has been proposed and justified.The strategy can be implemented by preliminary visualization of the quantum states of qubits using nanotriggers formed from two-dimensional material, in particular, graphene.This refers to the visualization (materialization) of all, including entangled, states, which mainly determine the theoretically possible large mathematical resource of a quantum computer.A block-diagram of the electronic device based on "a priory" quantum states of q-bits is proposed.It is shown that for implementation of quantum computation procedure, each materialized (visualized) Shor's cell should correspond to an element of the electronic scheme.The device includes a block containing at least 10 10 nanotriggers that perform a role of q-bits of quantum computation, which are created using graphene nanoribbons and controlled by a special element.The latter represents a self-organizing quantum dot having two essentially different states in terms of magnetic properties.This quantum dot is prepared on the basis of a compound, the molecules of which are characterized by the intramolular rearrangement.The nanotriggers are employed to form reversible logic blocks or gates.Each gate contains three triggers to perform logical operations.The offered device is an additional electronic unit that is embedded in a digital computer, which makes it possible to implement the computational process in accordance with the requirements of the provisions of quantum physics.
Cybersecurity Risks and Situation Awareness: Audit Committees’ Appraisal

The issue of cybersecurity has become a challenge for companies and boards of directors. Cybersecurity is not only an IT topic, but a risk extended to all operations of the companies. Indeed, cybersecurity potentially has an impact on financial reporting quality, this attribution being one of the duties of audit committees. Using Endsley’s model, our exploratory study seeks to determine the levels of cyber situational awareness of audit committee members, how they comply with it and if this appraisal matches the steps identified within the model.
CLOUD TECHNOLOGIES IN INFORMATIONAL AND METHODOLOGICAL SUPPORT OF UNIVERSITY STUDENTS’ INDEPENDENT STUDY

Based on the analysis of scientific and methodological sources, the article looks at the state of informational and methodological support of students’ independent study. The authors conclude that insufficient attention is being currently paid in Ukraine to the implementation of informational and methodological support of students’ independent study using cloud technologies. It has been determined that independent work as the dominant component of the students’ educational activity in the framework of joining the European educational space requires a new approach to its construction, covering the whole set of innovative aspects. The general problem of education is the reduction of the number of classroom hours and transfer of a significant amount of study material to independent study. Such a situation requires further scientific analysis and the development of specific recommendations in order to improve the independent learning activities. On the basis of the content analysis of scientific articles on the professional training of university students, the directions of improving informational and methodological support of students’ independent study were singled out on the example of the discipline “Modern Information Technologies”. Studies have been conducted among the students regarding their readiness to use the informational and methodological support of independent study and it is determined that it is advisable to use Google cloud services and an educational site with educational materials for independent work. In the article the approaches to introduction of informational and methodological support of students’ independent study are theoretically grounded and a model of informational and methodological support of independent study of university students is developed. Informational and methodological support of students’ independent study is based on the integration of two classifications of independent work (in terms of the type of learning tasks and of educational and cognitive activity) that will be performed in the complex way and will facilitate the formation of university students’ ability to organize and plan and the ability to work independently. For the discipline “Modern Information Technologies” an Internet resource is developed in the form of a site that contains the necessary information for performing independent work. Separately, an electronic calendar for the organization of independent student learning was developed. The integration of calendar elements into the educational site has been carried out in order to ensure the functioning of the informational and methodological support of students’ independent study. The methodological principles of informational and methodological support of students’ independent study were developed, namely the use of cloud service Google Calendar in the course “Modern Information Technologies”.
A New Fusion Method on Machine Reading Comprehension

Machine reading comprehension, as one of the core tasks of natural language processing, has been a research focus in this field. This paper is mainly aimed at Dureader, a Chinese-oriented multi-document reading comprehension task dataset, and an end-to-end machine reading comprehension model based on neural network is proposed. In this paper, a combination of context-to-question attention and self-attention, and a new fusion method are used to fuse context and attention information. The comparison of experimental results shows that compared with the baseline model, by our methods, BLEU-4 index has been improved by 6% and ROUGE-L index has been improved by 3%.
ECharacterize: A Novel Feature Selection-Based Framework for Characterizing Entrepreneurial Influencers in Arabic Twitter

&lt;p class="Els-table-text"&gt;&lt;strong&gt;Abstract—&lt;/strong&gt; Social media are widely used as communication platforms in the world of business. Twitter, in particular, offers valuable opportunities for collaboration due to its open nature. For that, many entrepreneurs employ Twitter for different reasons, such as mobilizing financial resources, get funding, and increase their innovation capabilities. Therefore, they keep looking for local entrepreneurial accounts to help them. Messages from entrepreneurial influencers -opinion leader- increase the information diffusion to entrepreneurs, helping them to find more opportunities. Discovering the characteristics of entrepreneurial influencers in Twitter networks becomes extremely important since it reflects the way to reach entrepreneurs. In the present paper, we propose a novel framework called ECharacterize based on feature selections techniques to discover the characteristics of the entrepreneurial influencer in the Saudi context in a robust manner. The framework extracts abundant influencers’ features and then employs seven state-of-the-art ranking methods to determine the characteristics of the most relevant influencer. It robustly aggregates the lists to come out with the accurate final list using Robust Rank Aggregation. The framework examined on 233,018 real-life Arabic tweets. The results show the ability of the proposed method to distinguish between the influencers by their popularity, reliability and activity level. &lt;/p&gt;
Building online skills in off-line realities

The growth rate of new Internet users has declined over the past few years, despite billions of dollars being spent on attempting to provide access and get people connected. Yet, the focus on simply getting more people connected is — and always will be — insufficient, because lack of access is not the central problem. Skills to navigate, identify, evaluate, effectively use, and create information are what’s truly missing. Information literacy and digital proficiency must accompany connectivity, or else inequalities — digital and otherwise — continue to worsen. This article draws from the literature on digital inclusion and information literacy to make the case that empowering, Internet-ready skills will only be developed if a concerted effort is made to build these skills. We argue that Internet-ready skills, such as how to carry out research, and how to distinguish whether information is trustworthy or not, can be — and indeed, may best be — taught in an off-line environment, before the Internet reaches the as-yet-unconnected. We draw from the in-field experience of the SolarSPELL (Solar Powered Educational Learning Library) initiative. SolarSPELL is an ultra-portable, rugged, solar-powered, digital library that generates an off-line WiFi hotspot to which any WiFi-capable device can connect and freely surf the library’s expansive, localized content. The innovative, solar-powered technology means that the library can reach those in off-grid, unconnected locations. Yet, what distinguishes the SolarSPELL initiative’s approach to introducing digital technology to schools is that the libraries are matched with locally based trainers who can support the necessary development of Internet-ready skills.
Linguistic knowledge-based vocabularies for Neural Machine Translation

Abstract Neural Networks applied to Machine Translation need a finite vocabulary to express textual information as a sequence of discrete tokens. The currently dominant subword vocabularies exploit statistically-discovered common parts of words to achieve the flexibility of character-based vocabularies without delegating the whole learning of word formation to the neural network. However, they trade this for the inability to apply word-level token associations, which limits their use in semantically-rich areas and prevents some transfer learning approaches e.g. cross-lingual pretrained embeddings, and reduces their interpretability. In this work, we propose new hybrid linguistically-grounded vocabulary definition strategies that keep both the advantages of subword vocabularies and the word-level associations, enabling neural networks to profit from the derived benefits. We test the proposed approaches in both morphologically rich and poor languages, showing that, for the former, the quality in the translation of out-of-domain texts is improved with respect to a strong subword baseline.
Texture collinearity foreground segmentation for night videos

One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an N-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail.
Analysis of students’ competence in chemistry cognitive test construction based on revised bloom’s taxonomy

Abstract The test is one of the tools used to measure the cognitive competence of students. The ability to develop tests is one of the competencies teachers and prospective teachers must possess. Revised Bloom’s Taxonomy combines cognitive levels and types of knowledge in constructing cognitive tests. The subject of this study was a postgraduate student in the Chemistry Education Study Program of Makassar State University consisting of 25 people. The purpose of this study was to analyze the students competence in construction of chemistry cognitive tests based on the Revised Bloom’s Taxonomy. This research is quantitative descriptive. Analysis of student competency in developing tests is measured by the test suitability indicators with cognitive levels and types of knowledge. The results of the analysis show that the students competence percentage construction questions level C1, C2, C3, C4, C5, C6 respectively: 98%, 89%, 96%, 89%, 65%, and 60%. The percentage of students competence in tests construction according to the type of knowledge, respectively for the types of factual, conceptual, procedural, and metacognitive knowledge are 84%, 83%, 85%, and 83%. Thus it can be concluded that the student competences in constructing of Chemistry cognitive test based on Revised Bloom’s Taxonomy is in a good category.
Genetic Algorithm in Image Inserting with Modified Least Significant Bit Method to Find The Best MSE Value

Abstract Inserting data into digital media tends to change the large size of the media cover file, this is because the inserted pixel has a different size from the insertion value, i.e. with a larger or smaller value, for example a pixel value of 10 is inserted with larger or smaller data from the value 10. So that the pixel value is getting further from the value of 10, this causes the value of Mean Squared Error (MSE) of the media of the cover (cover) to become larger. In this study, the selection of pixels in the image is in accordance with the value of the insertion data as the insertion place with the Genetic algorithm (GA). Insertion of data on selected pixels using the GA algorithm is performed using the Modified Least Significant Bit (MLSB) algorithm at the position of the least significant bits. To find out the reliability of the proposed algorithm, the data insertion experiment was carried out by comparing the conventional LSB algorithm with the proposed algorithm where the results obtained a significant MSE value, i.e. the average MSE value of the LSB algorithm was 20.54 and the proposed algorithm was 7.12.
A New Similarity Measure Based on Gravitational Attraction for Improving the Accuracy of Collaborative Recommendations

Recommender Systems (RSs) work as a personal agent for individuals who are not able to make decisions from the potentially overwhelming number of alternatives available on the World Wide Web (or simply Web).Neighborhood-based algorithms are traditional approaches for collaborative recommendations and are very popular due to their simplicity and efficiency.Neighborhood-based recommender systems use numerous kinds of similarity measures between users or items in order to achieve diverse goals for designing an RS such as accuracy, novelty, diversity etc.However, the existing similarity measures cannot manage well the data sparsity problems, which results in either very few corated items or absolutely no co-rated items.Furthermore, there are also situations where only the associations between users and items, such as buying/browsing behaviors, exist in form of unary ratings, a special case of ratings.In such situations, the existing similarity measures are either undefined or provide extreme values such as either 0 or 1.Thus, there is a compelling need to define a similarity measure that can deal with data sparsity problem and/or unary rating data.This article proposes a new similarity measure for neighborhoodbased collaborative recommender systems based on Newton's law of universal gravitation.In order to achieve this, a new way of interpreting the relative mass as well as the relative distance has been taken into consideration by using the rating data from the user-item matrix.Finally, for evaluating the proposed approach against baseline approaches, several experiments have been conducted using standardized benchmark datasets such as MovieLens-100K and MovieLens-1M.Results obtained demonstrate that the proposed method provides better predictive accuracy in terms of RMSE and significantly improves the classification accuracy in terms of precision-recall.
Emotion Categorization Using Twitter

Recent interactions between regular users of net can be seen using Micro blogging.Several users share their views on various aspects of life regularly.The micro blogging webs resources are wealthy sources of information for opinion mining as well as sentiment analysis.As a result of micro blogging has appeared comparatively recently, there are some analysis works that were dedicated to this subject.Using sentiments present in twwts posted by users, organizations can get a quick check non public opinion about their business and about their whole organization structure.Sentiment classifiers for twitter datasets are worked upon in recent times and has observed a variety of results.Sentiment classification will benefit corporations by providing knowledge for analyzing client feedback for product or conducting research.Sentiment classifiers ought to be ready to handle tweets in multiple languages [9] to hide a bigger portion of the obtainable tweets.In this paper we have developed a sentiment classifier which can tell positive, negative and neutral sentiment for a tweet.Experimental evaluations show that our projected techniques are efficient and perform higher than techniques found in literature.We here propose to mine sentiments from micro blogging service, Twitter, where users blog for real time situations.They express their opinions for almost every domain.Throughout this paper, we present a hybrid approach for exploitation lexicon based technique to find out the language point of direction of the opinion words in tweets.
Historical-philosophical aspects of professional communication in education management

The study deals with the implementation of historical and philosophical aspects of communication into the management process of an educational institution.The importance of this communication is often underestimated, even though it allows the manager to reflect on his communication mistakes.These communication mistakes are frequently causing inappropriate working climate, and it causes frequent changes in the jobs of employees.That all does not lead to the prosperity of the organization.Unfortunately, managerial communication is not sufficiently accepted in the training of school managers in Slovakia.This issue is included among the main subjects of the new study program at Constantine the Philosopher University in Nitra called Management of Education and Public Administration.The main goal of the new program is to prepare future managers of schools and school facilities for effective communication depending on the specific decision-making situation.The paper aims are to generalize several research findings of national and international authors who deal with the issue of managerial communication and its impact on current practice.The study answers questions related to philosophical aspects of communication from the theory of interpersonal relationships (behavioral, neoclassical theory), especially Rogers' theory of communication, which is based on the PCA (Person-Cantered Approach).The attention of the study is focused on some forms of managerial, mostly interpersonal communication such as effective telephone communication and conducting meetings, as well as communication bad habits and mistakes.